Homework

Weekly homework submissions:

  • Week 01 HW: Principles and Practices

    Documentation Class Assignment — DUE BY START OF FEB 10 LECTURE 1. First, describe a biological engineering application or tool you want to develop and why. This could be inspired by an idea for your HTGAA class project and/or something for which you are already doing in your research, or something you are just curious about. I have a deep interest in Japanese fireworks culture and have incorporated fireworks into my artistic practice. In Japan, fireworks have long carried meanings of memorialization and life, making transience and cyclic time a shared embodied experience. At the same time, contemporary conditions—environmental footprint and responsible deployment—ask us to rethink what fireworks can mean today.

  • Week 02 HW: DNA Read, Write, & Edit

    ‘Week 2 — DNA Read, Write, & Edit’ Documentation Make sure to document every step of the in-silico and lab experiments. Make sketches, screenshots, notes, drawings… anything that helps you - and others - understand the experiment. Your documentation should help you - and others - to understand the topic. Don’t be afraid to add things that don’t work. Show your failures - and how you overcame them. Your Documentation should be a description of the amazing journey you are on!

  • Week 03 HW: Lab Automation

    Assignment: Python Script for Opentrons Artwork — DUE BY YOUR LAB TIME! Your task this week is to Create a Python file to run on an Opentrons liquid handling robot. 0. Review this week’s recitation and this week’s lab for details on the Opentrons and programming it. 1. Generate an artistic design using the GUI at opentrons-art.rcdonovan.com. 2. Using the coordinates from the GUI, follow the instructions in the HTGAA26 Opentrons Colab to write your own Python script which draws your design using the Opentrons. ・You may use AI assistance for this coding — Google Gemini is integrated into Colab (see the stylized star bottom center); it will do a good job writing functional Python, while you probably need to take charge of the art concept. ・If you’re a proficient programmer and you’d rather code something mathematical or algorithmic instead of using your GUI coordinates, you may do that instead. 3. If the Python component is proving too problematic even with AI and human assistance, download the full Python script from the GUI website and submit that: 4. If you use AI to help complete this homework or lab, document how you used AI and which models made contributions. 5. Sign up for a robot time slot if you are at MIT/Harvard/Wellesley or at a Node offering Opentrons automation. The Python script you created will be run on the robot to produce your work of art! ・At MIT/Harvard? Lab times are on Thursday Feb.19 between 10AM and 6PM. ・At other Nodes? Please coordinate with your Node. 6. Submit your Python file via this form. Colab Link HTGAA26 Opentrons Colab _ShimadaSayaka

  • Week 04 HW: Protein Design Part I

    ‘Week 4 HW: Protein Design Part I’ Documentation Homework: Protein Design I — DUE BY START OF MAR 3 LECTURE Part A. Conceptual Questions Answer any NINE of the following questions from Shuguang Zhang: (i.e. you can select two to skip): 1. How many molecules of amino acids do you take with a piece of 500 grams of meat? (on average an amino acid is ~100 Daltons) 2. Why do humans eat beef but do not become a cow, eat fish but do not become fish? 3. Why are there only 20 natural amino acids? 4. Can you make other non-natural amino acids? Design some new amino acids. 5. Where did amino acids come from before enzymes that make them, and before life started? 6. If you make an α-helix using D-amino acids, what handedness (right or left) would you expect? 7. Can you discover additional helices in proteins? 8. Why are most molecular helices right-handed? 9. Why do β-sheets tend to aggregate? ・What is the driving force for β-sheet aggregation? 10. Why do many amyloid diseases form β-sheets? ・Can you use amyloid β-sheets as materials? 11. Design a β-sheet motif that forms a well-ordered structure. ☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆

  • Week 05 HW: Protein Design Part II

    ‘Week 5 — Protein Design Part II’ Documentation Homework: Protein Design II Part A: SOD1 Binder Peptide Design (From Pranam) Superoxide dismutase 1 (SOD1) is a cytosolic antioxidant enzyme that converts superoxide radicals into hydrogen peroxide and oxygen. In its native state, it forms a stable homodimer and binds copper and zinc.

  • Week 06 HW -Genetic Circuits Part I: Assembly Technologies

    ‘Week 6 — Genetic Circuits Part I: Assembly Technologies’ Documentation Homework: Genetic Circuits Part I: Assembly Technologies Assignment: DNA Assembly Answer these questions about the protocol in this week’s lab:

  • Week 07 HW -Genetic Circuits Part II: Neuromorphic Circuits

    ‘Week 7 — Genetic Circuits Part II: Neuromorphic Circuits’ Documentation Homework: Genetic Circuits Part II: Neuromorphic Circuits Assignment Part 1: Intracellular Artificial Neural Networks (IANNs) Answer these questions about the protocol in this week’s lab:

  • Week 09 HW -cell-free-systems

    ‘week-09-hw-cell-free-systems’ Documentation Homework: Cell Free Systems Homework Part A: General and Lecturer-Specific Questions General homework questions

  1. Explain the main advantages of cell-free protein synthesis over traditional in vivo methods, specifically in terms of flexibility and control over experimental variables. Name at least two cases where cell-free expression is more beneficial than cell production. 従来の in vivo（生細胞内）法と比べて、cell-free protein synthesis（無細胞タンパク質合成）の主な利点を説明しなさい。 特に、柔軟性と実験条件の制御という観点から述べなさい。 また、細胞内での生産より無細胞発現のほうが有利な例を少なくとも2つ挙げなさい。 Cell-free protein synthesis https://en.wikipedia.org/wiki/Cell-free_protein_synthesis#:~:text=CFPS has many advantages over,required for such a reaction.

Subsections of Homework

Week 01 HW: Principles and Practices

Documentation

Class Assignment — DUE BY START OF FEB 10 LECTURE

1. First, describe a biological engineering application or tool you want to develop and why. This could be inspired by an idea for your HTGAA class project and/or something for which you are already doing in your research, or something you are just curious about.

I have a deep interest in Japanese fireworks culture and have incorporated fireworks into my artistic practice. In Japan, fireworks have long carried meanings of memorialization and life, making transience and cyclic time a shared embodied experience. At the same time, contemporary conditions—environmental footprint and responsible deployment—ask us to rethink what fireworks can mean today.

This project relocates the ideas that fireworks have historically held—one-time temporality, memorialization, and the bodily experience of light—into bio-art. Instead of treating an explosion as the engine, I treat a molecular-biology-inspired sensing→processing→reporting interface as the engine, producing a time-varying signal I call a “breathing” glow.

Conceptually, the work draws on gene-expression logic (signals that turn on, intensify, and shift over time) to imagine a living light interface: softly modulated patterns of light or color that evoke the pulse of living systems. The tool is intended to render biological rhythms and environmental changes as a poetic, legible experience of time.

Note: This is a conceptual art-research proposal and intentionally avoids procedural detail. Any implementation would require expert oversight, appropriate review, and clear public communication to prevent misinterpretation.

2. Next, describe one or more governance/policy goals related to ensuring that this application or tool contributes to an “ethical” future, like ensuring non-malfeasance (preventing harm). Break big goals down into two or more specific sub-goals. Below is one example framework (developed in the context of synthetic genomics) you can choose to use or adapt, or you can develop your own. The example was developed to consider policy goals of ensuring safety and security, alongside other goals, like promoting constructive uses, but you could propose other goals for example, those relating to equity or autonomy.

A：Biosafety (prevent harm to people and places)

  • Ensure robust containment and accident prevention across making, transport, storage, and exhibition.
  • Require risk assessment and independent review before any public-facing deployment, with staged scaling (small → larger) when appropriate.

B：Environmental responsibility (lifecycle accountability)

  • Define and minimize lifecycle impacts (materials, waste streams, cleaning, recovery, disposal).
  • Prepare clear response procedures for accidental release, contamination, or other environmental incidents.

C：Social integrity (prevent misinterpretation and protect trust)

  • Design communication to prevent audiences from mistaking the work for “diagnosis,” “certification,” or authoritative measurement.
  • Maintain transparency by stating limitations and uncertainties and avoiding overclaiming.

D：Misuse / repurposing prevention (biosecurity / dual-use)

  • Reduce the risk that materials, capabilities, or know-how could be repurposed for harmful uses.
  • Establish reporting, stop-work, and corrective-action pathways if safety/security concerns arise.

3. Next, describe at least three different potential governance “actions” by considering the four aspects below (Purpose, Design, Assumptions, Risks of Failure & “Success”). Try to outline a mix of actions (e.g. a new requirement/rule, incentive, or technical strategy) pursued by different “actors” (e.g. academic researchers, companies, federal regulators, law enforcement, etc). Draw upon your existing knowledge and a little additional digging, and feel free to use analogies to other domains (e.g. 3D printing, drones, financial systems, etc.).

---

1：Pre-deployment review (“safety case”) + clear accountability (actors: venue/operator + independent reviewers)

Purpose (what changes):
Currently, decisions can be ad hoc and person-dependent. I propose a standardized pre-deployment review that makes safety, environmental, and misinterpretation risks explicit before any public display.

Design (what’s needed; who acts):

  • A short “safety case” template + checklist (biosafety, disposal, signage/communication, incident plan).
  • An independent reviewer (or review panel) signs off; a named responsible person is designated.
  • Staged deployment: small-scale test → limited public pilot → broader deployment if evidence supports it.

Assumptions (what could be wrong):

  • Qualified reviewers are available and venues will actually enforce outcomes.
  • The process remains lightweight enough to be usable.

Risks of failure & “success”:

  • Failure: becomes a box-ticking exercise; inconsistent enforcement.
  • Success risk: creates gatekeeping and raises barriers for smaller/independent projects.

2：Communication standard (labels + website template) to prevent misinterpretation (actors: artist + venue)

Purpose (what changes):
Public-facing bio-art can be misread as scientific diagnosis or certification. I propose a communication standard that reduces misinformation risk while preserving artistic intent.

Design (what’s needed; who acts):

  • Required statements: what the work is/is not; what inputs/outputs mean; key limitations and uncertainties.
  • Simple FAQ and on-site signage; venues agree not to remove or rewrite essential safety/limits language.

Assumptions (what could be wrong):

  • Audiences will notice/read the information; venues will keep it intact.
  • Clear language will reduce, not amplify, confusion.

Risks of failure & “success”:

  • Failure: “warning fatigue” (people ignore it); signage is altered or minimized.
  • Success risk: over-standardization can flatten nuance and reduce poetic ambiguity.

3：Containment + disposal protocol (technical + operational) (actors: research collaborators + venue operations)

Purpose (what changes):
Containment, cleanup, and end-of-life handling are often unclear in public art contexts. I propose a containment-first design principle and an explicit disposal/recovery protocol.

Design (what’s needed; who acts):

  • Prefer sealed, recoverable display formats; define boundaries for scale and setting (e.g., indoor-only or other constraints as required).
  • Written procedures for handling, cleanup, waste streams, and documented disposal.
  • A basic incident plan (who to contact, how to pause, how to remediate).

Assumptions (what could be wrong):

  • Containment can coexist with the intended aesthetics and budget.
  • Operational staff can reliably follow the protocol.

Risks of failure & “success”:

  • Failure: higher cost and operational complexity; inconsistent compliance.
  • Success risk: responsibility can become diffuse (“everyone thought someone else handled it”), reducing accountability unless roles are explicit.

4. Next, score (from 1-3 with, 1 as the best, or n/a) each of your governance actions against your rubric of policy goals. The following is one framework but feel free to make your own:

Does the option…Safety case + accountabilityCommunication standardContainment+disposal protcol
Enhance Biosecurity
• By preventing incidents232
• By helping respond132
Foster Lab / Handling Safety
• By preventing incidents231
• By helping respond132
Protect the environment
• By preventing incidents231
• By helping respond231
Other considerations
• Minimizing costs and burdens to stakeholders213
• Feasibility ?212
• Not impede research212
• Promote constructive applications122
Prevent military / harmful repurposing232

5. Last, drawing upon this scoring, describe which governance option, or combination of options, you would prioritize, and why. Outline any trade-offs you considered as well as assumptions and uncertainties. For this, you can choose one or more relevant audiences for your recommendation, which could range from the very local (e.g. to MIT leadership or Cambridge Mayoral Office) to the national (e.g. to President Biden or the head of a Federal Agency) to the international (e.g. to the United Nations Office of the Secretary-General, or the leadership of a multinational firm or industry consortia). These could also be one of the “actor” groups in your matrix.

Recommendation:
I would prioritize Action 1 (pre-deployment “safety case” + clear accountability) and Action 3 (containment + disposal protocol) as the baseline, and require Action 2 (communication standard) for every public-facing display.

Why:
Action 1 and 3 most directly reduce biosafety and environmental risks, while Action 2 reduces misinterpretation and protects public trust.

Trade-offs:
Stronger safeguards can increase cost and slow iteration, and may raise barriers for smaller projects. Clear communication can also reduce ambiguity, which is sometimes part of the artwork.

Assumptions / uncertainties:
Technical feasibility and long-term impacts are uncertain, so governance should be staged (start small, learn, then scale) and updated based on evidence.

Audience:
Museum/festival leadership and safety officers; university review boards for art–science collaborations; relevant national regulators.

Assignment (Week 2 Lecture Prep) — DUE BY START OF FEB 10 LECTURE

Homework Questions from Professor Jacobson: [Lecture 2 slides]

1. Nature’s machinery for copying DNA is called polymerase. What is the error rate of polymerase? How does this compare to the length of the human genome. How does biology deal with that discrepancy? (polymeraseのエラー率は？人間ゲノムの長さと比べると？そのズレを生物はどう解決するか)

Reference URL:

https://pmc.ncbi.nlm.nih.gov/articles/PMC4267634

https://www.genome.gov/genetics-glossary

https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/dna-mismatch-repair

https://www.nature.com/scitable/topicpage/dna-replication-and-causes-of-mutation-409/

  • DNA polymerases replicate DNA with very high fidelity, but they are not error-free.
  • Replicative polymerases (with proofreading) are often described as making roughly one error per 106–108 nucleotide incorporations.
  • If we compare this to the human genome (~3 × 10^9 base pairs), polymerase-alone fidelity would imply on the order of tens to thousands of errors per genome replication if nothing else corrected them.

Biology resolves this discrepancy using layered fidelity mechanisms…

  • (1) intrinsic nucleotide selectivity during replication
  • (2) 3′→5′ exonucleolytic proofreading that removes misincorporated bases
  • (3) post-replication mismatch repair (MMR), which recognizes and fixes remaining mispairs

Together, these steps reduce the effective mutation rate to ~10-9–10-10 per base pair.

2. How many different ways are there to code (DNA nucleotide code) for an average human protein? In practice what are some of the reasons that all of these different codes don’t work to code for the protein of interest?

（平均的ヒトタンパク質をコードするDNA配列は何通りあるのか？なぜ全部うまくいかないのか？）

Reference URL:

  • 遺伝暗号の冗長性（61のsense codonで20アミノ酸）

https://www.sciencedirect.com/science/article/abs/pii/S1046202305000885

  • 平均的（中央値）ヒトタンパク質の長さ：375 aa（BioNumbers）

https://bionumbers.hms.harvard.edu/bionumber.aspx?id=106445&s=n&v=4

  • タンパク質長の分布（参考：ヒト含む、Brocchieri & Karlin 2005）

https://pmc.ncbi.nlm.nih.gov/articles/PMC1150220/

  • コドン最適性がmRNA安定性に強く影響する（codon optimality → mRNA stability）

https://pmc.ncbi.nlm.nih.gov/articles/PMC4359748/

  • 同義コドンがmRNA二次構造・安定性・翻訳速度・折りたたみに影響（NAR review）

https://academic.oup.com/nar/article/41/4/2073/2414416

  • コドンバイアスと翻訳効率の関係（ゲノム規模解析）

https://pmc.ncbi.nlm.nih.gov/articles/PMC2840511/

  • 翻訳効率に対するコドン選択の強い影響（Science 2013）

https://www.science.org/doi/10.1126/science.1241934

  • The genetic code is “degenerate”: many amino acids have multiple synonymous codons.

  • So the same protein can be written in DNA in an astronomically large number of ways (especially for proteins with a few hundred amino acids).

  • But many synonymous versions don’t work equally well because codon choice can change:

    • codon usage vs the host tRNA pool (translation efficiency)
    • mRNA stability and secondary structure
    • translation speed and co-translational folding (protein quality)
    • unintended sequence motifs (accidental regulatory signals, etc.)

Homework Questions from Dr. LeProust: [Lecture 2 slides]

1. What’s the most commonly used method for oligo synthesis currently?

（oligo合成で一番よく使われる方法は？）

Reference URL:

  • ATDBio（Solid-phase oligonucleotide synthesis）：

https://atdbio.com/nucleic-acids-book/Solid-phase-oligonucleotide-synthesis

The most commonly used method for oligo synthesis is solid-phase phosphoramidite synthesis.

2. Why is it difficult to make oligos longer than 200nt via direct synthesis?

（なぜ200ntを超えると直接合成が難しい？）

Reference URL:

Direct chemical oligo synthesis becomes difficult beyond ~200 nt because each nucleotide addition cycle is slightly imperfect, so the full-length yield drops exponentially as length increases. Errors and truncated byproducts accumulate (including side reactions such as depurination), making it hard to obtain high-purity, full-length oligos at longer lengths.

3. Why can’t you make a 2000bp gene via direct oligo synthesis?

（なぜ2000bpを直接合成できない？）

Homework Question from George Church: [Lecture 2 slides]

Choose ONE of the following three questions to answer; and please cite AI prompts or paper citations used, if any

[Using Google & Prof. Church’s slide #4] What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?

(すべての動物に存在する10種類の必須アミノ酸とは何か、そしてこれは「リシン・コンティンジェンシー」に対するあなたの見解にどのような影響を与えるか？)

[Given slides #2 & 4 (AA:NA and NA:NA codes)] What code would you suggest for AA:AA interactions?

[(Advanced students)] Given the one paragraph abstracts for these real 2026 grant programs sketch a response to one of them or devise one of your own:

（以下の2026年度助成プログラムの概要（各1段落）に基づき、いずれか一つへの回答を概説するか、あるいは独自の回答を考案してください：）

Lysine contingency?? https://jurassicpark.fandom.com/wiki/Lysine_contingency -lysine contingency : biocontainment idea: an organism is designed to survive only when lysine is supplied from the outside.　（リジンがある時だけ生存できる装置のような設計）

Contingency: conditional on / dependent on…lysine-dependent survival

  • Intuitively, if they escape, they should be unable to reproduce in the wild without ricin
  • lysine is widespread in real environments/foods, so lysine-dependence alone can be a weak containment strategy
  • Stronger containment typically requires layering safeguards, such as dependencies on something not found in nature (synthetic/non-natural metabolites or amino acids), rather than relying on a common nutrient alone

Week 02 HW: DNA Read, Write, & Edit

‘Week 2 — DNA Read, Write, & Edit’

Documentation

Make sure to document every step of the in-silico and lab experiments. Make sketches, screenshots, notes, drawings… anything that helps you - and others - understand the experiment.

Your documentation should help you - and others - to understand the topic. Don’t be afraid to add things that don’t work. Show your failures - and how you overcame them. Your Documentation should be a description of the amazing journey you are on!

Part 0: Basics of Gel Electrophoresis

Attend or watch all lecture and recitation videos. Optionally watch bootcamp.

DONE

Part 1: Benchling & In-silico Gel Art

See this week’s lab protocol “Gel Art: Restriction Digests and Gel Electrophoresis()” for details. Overview:

- Make a free account at benchling.com   
- Import the Lambda DNA.   
- Simulate Restriction Enzyme Digestion with the following Enzymes:   
  EcoRI   
  HindIII   
  BamHI   
  KpnI   
  EcoRV   
  SacI   
  SalI   
- Create a pattern/image in the style of Paul Vanouse’s Latent Figure Protocol artworks.   
- You might find Ronan’s website a helpful tool for quickly iterating on designs!

Part 2: Gel Art - Restriction Digests and Gel Electrophoresis

CL: Optional

Part 3: DNA Design Challenge

3.1. Choose your protein.

In recitation, we discussed that you will pick a protein for your homework that you find interesting. Which protein have you chosen and why? Using one of the tools described in recitation (NCBI, UniProt, google), obtain the protein sequence for the protein you chose. （自分が興味深いと思うタンパク質を1つ選んで、その理由を説明し、NCBI・UniProt・Googleなどのツールを使ってそのタンパク質配列を取得してください）

[Example from our group homework, you may notice the particular format — The example below came from UniProt]

sp|P03609|LYS_BPMS2 Lysis protein OS=Escherichia phage MS2 OX=12022 PE=2 SV=1 METRFPQQSQQTPASTNRRRPFKHEDYPCRRQQRSSTLYVLIFLAIFLSKFTNQLLLSLL EAVIRTVTTLQQLLT

🧬 I have chosen luciferase from Photinus pyralis for my final project. This enzyme catalyzes the bioluminescence seen in fireflies and certain squid species. Given that the use of light-emitting proteins is involved in the content described in my final project, I have decided to investigate the DNA of the bioluminescent enzyme found in fireflies.

Luciferase refers to a group of oxidative enzymes that produce bioluminescence and are composed of many protein groups that differ genetically. These enzymes are named “luciferase” and are found in many organisms, including fireflies and marine animals, where they are involved in bioluminescent phenomena. They are classified as oxidoreductases (EC 1.13.12.-) and catalyze reactions that incorporate molecular oxygen.

Basic Information about Firefly Luciferase:

Basic Characteristics:

　　　Species: Mainly studied in Photinus pyralis (common eastern firefly)

　　　Enzyme Class: Classified as oxidoreductase (EC 1.13.12.7)

　　　Function: Reacts with the substrate luciferin and oxygen to produce light

Mechanism:

  1. Luciferase binds to luciferin.
  2. Luciferin is oxidized at the enzyme’s active site, forming oxidized luciferin (oxyluciferin)
  3. Oxidized luciferin dissociates, releasing energy and producing light (luminescence)

Structure and Data:

　　　PDB: 1LCI (three-dimensional structure data of luciferase)

　　　UniProt: P08659 (protein sequence information)

https://en.wikipedia.org/wiki/Luciferase

Basic Information about Firefly Luciferase

　　　Formal Name: Photinus-luciferin 4-monooxygenase (ATP-hydrolysing)

　　　Common Name: Firefly luciferase

　　　Function: An oxidoreductase enzyme that catalyzes chemiluminescence in fireflies.

Firefly Light Control Mechanism

　　　Light Control: Firefly light flickers, and it has been proposed that this flashing is controlled by nitric oxide (NO)

　　　NO Production:

　　　　- Nitric oxide synthase (NOS), located between the nerve endings and light-emitting cells, produces NO
　　　　- The produced NO inhibits the activity of cytochrome c oxidase within the mitochondria of the light-emitting cells

Increase in Oxygen Levels:

　　　　- Inhibition of cytochrome c oxidase activity increases the oxygen concentration inside the peroxisomes where luciferase is localized
　　　　- The increased oxygen concentration in the peroxisomes promotes the light-emitting reaction and directly contributes to the flickering of the firefly’s light

Reaction Mechanism of Firefly Luciferase

First Stage:

  • The carboxyl group of luciferin attacks the α-phosphate site of ATP, forming the luciferyl AMP intermediate within the enzyme

Second Stage:

  • After the enzyme reacts with the luciferyl AMP intermediate, excited oxyluciferin is produced
  • As the excited oxyluciferin returns to the ground state, it releases energy as yellow-green light
[https://www.nibb.ac.jp/press/2018/10/16.html]

UniProt: Luciferin 4-monooxygenase (P08659)

[https://www.uniprot.org/uniprotkb/P08659/entry]

The Protein Sequence of Luciferin 4-monooxygenase

🧬>sp|P08659|LUCI_PHOPY Luciferin 4-monooxygenase OS=Photinus pyralis OX=7054 PE=1 SV=1
MEDAKNIKKGPAPFYPLEDGTAGEQLHKAMKRYALVPGTIAFTDAHIEVNITYAEYFEMS
VRLAEAMKRYGLNTNHRIVVCSENSLQFFMPVLGALFIGVAVAPANDIYNERELLNSMNI
SQPTVVFVSKKGLQKILNVQKKLPIIQKIIIMDSKTDYQGFQSMYTFVTSHLPPGFNEYD
FVPESFDRDKTIALIMNSSGSTGLPKGVALPHRTACVRFSHARDPIFGNQIIPDTAILSV
VPFHHGFGMFTTLGYLICGFRVVLMYRFEEELFLRSLQDYKIQSALLVPTLFSFFAKSTL
IDKYDLSNLHEIASGGAPLSKEVGEAVAKRFHLPGIRQGYGLTETTSAILITPEGDDKPG
AVGKVVPFFEAKVVDLDTGKTLGVNQRGELCVRGPMIMSGYVNNPEATNALIDKDGWLHS
GDIAYWDEDEHFFIVDRLKSLIKYKGYQVAPAELESILLQHPNIFDAGVAGLPDDDAGEL
PAAVVVLEHGKTMTEKEIVDYVASQVTTAKKLRGGVVFVDEVPKGLTGKLDARKIREILI
KAKKGGKSKL

[https://rest.uniprot.org/uniprotkb/P08659.fasta]

3.2. Reverse Translate: Protein (amino acid) sequence to DNA (nucleotide) sequence.

The Central Dogma discussed in class and recitation describes the process in which DNA sequence becomes transcribed and translated into protein. The Central Dogma gives us the framework to work backwards from a given protein sequence and infer the DNA sequence that the protein is derived from. Using one of the tools discussed in class, NCBI or online tools (google “reverse translation tools”), determine the nucleotide sequence that corresponds to the protein sequence you chose above.

[Example: Get to the original sequence of phage MS2 L-protein from its genome phage MS2 genome - Nucleotide - NCBI]

Lysis protein DNA sequence atggaaacccgattccctcagcaatcgcagcaaactccggcatctactaatagacgccggccattcaaacatgaggattacccatgtcgaagacaacaaagaagttcaactctttatgtattgatcttcctcgcgatctttctctcgaaatttaccaatcaattgcttctgtcgctactggaagcggtgatccgcacagtgacgactttacagcaattgcttacttaa

DNA Sequence of Luciferin 4-monooxygenase

According to the NCBI website:

https://www.ncbi.nlm.nih.gov/nuccore/NW_022170249.1?report=fasta&from=28340362&to=28342515

Photinus pyralis isolate 1611_PpyrPB1 unplaced genomic scaffold, Ppyr1.3 Ppyr1.4_LG1, whole genome shotgun sequence NCBI Reference Sequence: NW_022170249.1

GenBank Graphics

🧬 >NW_022170249.1:28340362-28342515 Photinus pyralis isolate 1611_PpyrPB1 unplaced genomic scaffold, Ppyr1.3 Ppyr1.4_LG1, whole genome shotgun sequence

ATTCCTTTGTGTTACATTCTTGAATGTCGCTCGCAGTGACATTAGCATTCCGGTACTGTTGGTAAAATGG
AAGACGCCAAAAACATAAAGAAAGGCCCGGCGCCATTCTATCCTCTAGAGGATGGAACCGCTGGAGAGCA
ACTGCATAAGGCTATGAAGAGATACGCCCTGGTTCCTGGAACAATTGCTTTTGTGAGTATTTCTGTCTGA
TTTCTTTCGAGTTAACGAAATGTTCTTAATGTTTCTTTAGACAGATGCACATATCGAGGTGAACATCACG
TACGCGGAATACTTCGAAATGTCCGTTCGGTTGGCAGAAGCTATGAAACGATATGGGCTGAATACAAATC
ACAGAATCGTCGTATGCAGTGAAAACTCTCTTCAATTCTTTATGCCGGTGTTGGGCGCGTTATTTATCGG
AGTTGCAGTTGCGCCCGCGAACGACATTTATAATGAACGTAAGCACCCTCGCCATCAGACCCAAAGGGAA
TGACGTATTTAATTTTTAAGGTGAATTGCTCAACAGTATGAACATTTCGCAGCCTACCGTAGTGTTTGTT
TCCAAAAAGGGGTTGCAAAAAATTTTGAACGTGCAAAAAAAATTACCAATAATCCAGAAAATTATTATCA
TGGATTCTAAAACGGATTACCAGGGATTTCAGTCGATGTACACGTTCGTCACATCTCATCTACCTCCCGG
TTTTAATGAATACGATTTTGTACCAGAGTCCTTTGATCGTGACAAAACAATTGCACTGATAATGAATTCC
TCTGGATCTACTGGGTTACCTAAGGGTGTGGCCCTTCCGCATAGAACTGCCTGCGTCAGATTCTCGCATG
CCAGGTATGTCGTATAACAAGAGATTAAGTAATGTTGCTACACACATTGTAGAGATCCTATTTTTGGCAA
TCAAATCATTCCGGATACTGCGATTTTAAGTGTTGTTCCATTCCATCACGGTTTTGGAATGTTTACTACA
CTCGGATATTTGATATGTGGATTTCGAGTCGTCTTAATGTATAGATTTGAAGAAGAGCTGTTTTTACGAT
CCCTTCAGGATTACAAAATTCAAAGTGCGTTGCTAGTACCAACCCTATTTTCATTCTTCGCCAAAAGCAC
TCTGATTGACAAATACGATTTATCTAATTTACACGAAATTGCTTCTGGGGGCGCACCTCTTTCGAAAGAA
GTCGGGGAAGCGGTTGCAAAACGGTGAGTTAAGCGCATTGCTAGTATTTCAAGGCTCTAAAACGGCGCGT
AGCTTCCATCTTCCAGGGATACGACAAGGATATGGGCTCACTGAGACTACATCAGCTATTCTGATTACAC
CCGAGGGGGATGATAAACCGGGCGCGGTCGGTAAAGTTGTTCCATTTTTTGAAGCGAAGGTTGTGGATCT
GGATACCGGGAAAACGCTGGGCGTTAATCAGAGAGGCGAATTATGTGTCAGAGGACCTATGATTATGTCC
GGTTATGTAAACAATCCGGAAGCGACCAACGCCTTGATTGACAAGGATGGATGGCTACATTCTGGAGACA
TAGCTTACTGGGACGAAGACGAACACTTCTTCATAGTTGACCGCTTGAAGTCTTTAATTAAATACAAAGG
ATATCAGGTAATGAAGATTTTTACATGCACACACGCTACAATACCTGTAGGTGGCCCCCGCTGAATTGGA
ATCGATATTGTTACAACACCCCAACATCTTCGACGCGGGCGTGGCAGGTCTTCCCGACGATGACGCCGGT
GAACTTCCCGCCGCCGTTGTTGTTTTGGAGCACGGAAAGACGATGACGGAAAAAGAGATCGTGGATTACG
TCGCCAGTAAATGAATTCGTTTTACGTTACTCGTACTACAATTCTTTTCATAGGTCAAGTAACAACCGCG
AAAAAGTTGCGCGGAGGAGTTGTGTTTGTGGACGAAGTACCGAAAGGTCTTACCGGAAAACTCGACGCAA
GAAAAATCAGAGAGATCCTCATAAAGGCCAAGAAGGGCGGAAAGTCCAAATTGTAAAATGTAACTGTATT
CAGCGATGACGAAATTCTTAGCTATTGTAATATTATATGCAAATTGATGAATGGTAATTTTGTAATTGTG
GGTCACTGTACTATTTTAACGAATAATAAAATCAGGTATAGGTAACTAAACGGA

3.3. Codon optimization.

Once a nucleotide sequence of your protein is determined, you need to codon optimize your sequence. You may, once again, utilize google for a “codon optimization tool”. In your own words, describe why you need to optimize codon usage. Which organism have you chosen to optimize the codon sequence for and why?

[Example from Codon Optimization Tool | Twist Bioscience while avoiding Type IIs enzyme recognition sites BsaI, BsmBI, and BbsI]

Lysis protein DNA sequence with Codon-Optimization ATGGAAACCCGCTTTCCGCAGCAGAGCCAGCAGACCCCGGCGAGCACCAACCGCCGCCGCCCGTTCAAACATGAAGATTATCCGTGCCGTCGTCAGCAGCGCAGCAGCACCCTGTATGTGCTGATTTTTCTGGCGATTTTTCTGAGCAAATTCACCAACCAGCTGCTGCTGAGCCTGCTGGAAGCGGTGATTCGCACAGTGACGACCCTGCAGCAGCTGCTGACCTAA

https://www.vectorbuilder.jp/tool/codon-optimization.html

🧬 Codon optimization     
ATTCCCCTGTGCTACATTCTGGAATGCAGATCTCAGTGACACTAACATAGCGGCACCGTGGGCAAGATGGAGGATGCCAAGAATATCAAAAAGGGACCCGCCCCATTCTATCCCCTGGAGGATGGCACCGCAGGTGAGCAGCTGCACAAAGCCATGAAGAGGTACGCTCTGGTGCCCGGCACTATCGCCTTTGTGTCCATCTCCGTGTGATTCCTGAGTTCCTGAAGAAACGTGCTGAATGTTAGCCTGGACAGGTGCACATACCGCGGCGAGCATCATGTGAGGGGCATCCTGAGAAATGTGAGGAGCGTGGGGCGGTCCTACGAGACTATCTGGGCCGAATACAAGAGTCAGAATAGGAGGATGCAGTAAAAGCTGTCATCTATCCTGTACGCCGGCGTGGGCCGCGTGATTTACCGGTCATGCTCCTGCGCCCGCGAGAGACATCTCTGATGAACCTAGGCCCCCAGCCCCTCAGATCCTAAAGGCATGACATACCTGATCTTCAAGGTGAACTGCAGCACTGTGTGAACATTTCGCAGCCTGCCATAGTGTCTCTTTCCTAAGCGGGGCTGTAAGAAGTTCTGAACCTGCAAGAAAAATTACCAGTGAAGCAGAAAGCTGCTGTCCTGGATTCTGAAACGCATCACCAGAGACTTTAGCAGATGCACCCGGTCCAGCCATCTGATTTATCTGCCTGTGCTGATGAATACCATCCTGTACCAGAGCCCCCTGATTGTGACAAAGCAGCTGCACTGATGATAGATCCCCTTAGACCTGCTGGGCTACCTGCGGGTGTGGCCATTTAGGATTGAGTTGCCCGCTTCAGACAGCCGCATGCCTGGCATGTCCTACAACAAACGGCTGAGCAACGTCGCCACACATATCGTCGAAATCCTGTTCCTGGCCATCAAGTCTTTTCGCATCCTGCGCTTCTGAGTGCTCTTTCACAGCATTACCGTTCTGGAGTGTCTGCTCCACAGCGACATCTGATACGTGGACTTCGAGAGCAGCTGATGCATCGATCTTAAGAAATCCTGTTTCTACGACCCTTTTAGAATCACAAAATTCAAGGTAAGGTGTTAATACCAGCCCTATTTCCACTCCTCCCCCAAGGCCCTGTAACTGACCAATACAATCTATCTGATTTACACAAAACTGCTGCTGGGCGCCCACCTGTTCAGGAAGAAAAGCGGCAAGCGGCTGCAGAACGGCGAGCTGAGTGCCCTGCTGGTGTTCCAGGGCTCCAAGACCGCCCGCAGCTTCCACCTGCCTGGCATCAGACAGGGATATGGGCTGACAGAAACCACCTCCGCCATCCTGATTACCCCCGAAGGAGACGACAAGCCTGGTGCCGTGGGCAAAGTGGTGCCCTTTTTCGAGGCCAAAGTGGTGGATCTGGATACTGGGAAAACCCTGGGCGTGAACCAGCGGGGCGAGCTGTGCGTGCGGGGCCCAATGATCATGAGTGGCTACGTTAACAACCCCGAGGCCACCAACGCTCTGATTGACAAGGACGGATGGCTGCACAGCGGGGACATTGCTTACTGGGATGAGGACGAGCATTTCTTCATCGTGGATAGGCTGAAGTCCCTGATCAAGTATAAGGGGTACCAGGTGATGAAGATCTTCACTTGCACCCACGCCACAATTCCCGTGGGTGGCCCTCGGTGAATCGGAATCGATATCGTGACCACACCCCAGCATCTGCGCAGGGGCCGGGGAAGATCCTCTCGGAGATGAAGAAGGTGAACCTCTAGACGGAGGTGCTGCTTTGGGGCCAGAAAAGACGACGATGGCAAGCGGGATCGGGGCCTGAGACGCCAGTGAATGAATAGCTTCTACGTGACTAGAACCACCATCCTGTTTATCGGGCAGGTGACCACTGCCAAGAAGCTGCGGGGGGGCGTGGTGTTCGTGGATGAGGTGCCCAAAGGCCTGACCGGCAAACTGGATGCCCGGAAGATTAGAGAGATTCTGATCAAGGCCAAAAAGGGAGGAAAAAGCAAGCTGTGAAATGTCACCGTGTTCTCCGATGACGAAATCCTCAGCTACTGTAACATTATTTGCAAGCTGATGAACGGTAACTTCGTGATTGTGGGCCACTGCACCATTCTGACCAACAACAAGATCAGATACCGCTAACTGAACGGA

We have optimized the codons for efficient protein expression of Luciferin 4-monooxygenase in a specific host organism. For this time, human cells were chosen as the host. Codon optimization is necessary to align with the abundance of the host’s tRNA. Optimized codons enhance the stability of mRNA and facilitate smoother translation processes.

3.4. You have a sequence! Now what?

What technologies could be used to produce this protein from your DNA? Describe in your words the DNA sequence can be transcribed and translated into your protein. You may describe either cell-dependent or cell-free methods, or both.

https://benchling.com/s/seq-wAvxKb4XH5gFiaRWyoN1?m=slm-x4jcGnqtraY7xJvhX2gz

  1. 適切なプライマーを設計する

まず、Photinus-luciferin 4-monooxygenase の先端にあるBtsⅠを制限酵素を用いたPrimerとして新規で設計する

https://rebase.neb.com/rebase/enz/BtsI.html

BtsⅠのカットサイト「gcagtg」だが、長さを18~25bpに設計する必要があるため、「gcagtg」の前後10塩基づつ長くする

「gcagtg」の頭「g」が31にあるため、13~43の塩基を使用する

これでプライマーのフォワードプライマーができたので同様にリバースプライマーを設計する。リバースプライマーは2108にいるTatⅠを使用する。

リバースにして、3’ Locationの場所を最後の2113にしたが

ずれた。そこで、3’ Locationを想定の頭のサイト(2093)に変更したところ…

成功した。

  1. プライマーを使用してPCRを行い、増幅したDNAの断片を生成する。特定のDNAシーケンスを大量に複製する　　　　　
  2. ベクターに挿入する。PCRによって増幅したDNA断片を制限酵素サイトを使ってベクターに挿入する。プラスミドが形成される。

3.5. [Optional] How does it work in nature/biological systems?

Describe how a single gene codes for multiple proteins at the transcriptional level. Try aligning the DNA sequence, the transcribed RNA, and also the resulting translated Protein!!! See example below. [Example shows the biomolecular flow in central dogma from DNA to RNA to Protein] Special note that all “T” were transcribed into “U” and that the 3-nt codon represents 1-AA.

NA

Part 4: Prepare a Twist DNA Synthesis Order

This is a practice exercise, not necessarily your real Twist order!

4.1. Create a Twist account and a Benchling account Yes

4.2. Build Your DNA Insert Sequence For example, let’s make a sequence that will make E. coli glow fluorescent green under UV light by constitutively (always) expressing sfGFP (a green fluorescent protein):

In Benchling, select New DNA/RNA sequence

[https://benchling.com/s/seq-WzXC0VtZQgv3NjmBV9eD?m=slm-lDALmve76xdyPzXXyvGW]

4.3. On Twist, Select The “Genes” Option

yes

4.4. Select “Clonal Genes” option

For this demonstration, we’ll choose Clonal Genes. You’ll select clonal genes or gene fragments depending on your final project.

Historically, HTGAA projects using clonal genes (circular DNA) have reached experimental results 1-2 weeks quicker because they can be transformed directly into E. coli without additional assembly.

Gene fragments (linear DNA) offer greater design flexibility but typically require an assembly or cloning step prior to transformation. An advantage is If designed with the appropriate exonuclease protection, gene fragments can be used directly in cell-free expression.

For this exercise, I selected Clonal Genes.

4.5. Import your sequence

You just took an amino acid sequence of interest and converted it into DNA, codon optimized it, and built an expression cassette around it! Choose the Nucleotide Sequence option and Upload Sequence File to upload your FASTA file.

yes

4.6. Choose Your Vector

Since we’re ordering a clonal gene, you will need to refer to Twist’s Vector Catalog to choose your circular backbone. You can think of this as taking your linear expression cassette for your protein of interest, and completing the rest of the circle!

The backbone confers many special properties like antibiotic resistance, an origin of replication, and more. Discuss with your node to decide on appropriate antibiotic options. At MIT/Harvard, you can use Ampicillin, Chloramphenicol, or Kanamycin resistance.

Twist vectors do not contain restriction sites near the insert fragment, so make sure to flank your design with cut sites if you are intending to extract this DNA insert fragment later.

For this demonstration, choose a Twist cloning vectors like pTwist Amp High Copy.

sfGFPのfastaデータ constitutive_sfGFP_his_tag.fasta

constitutive_sfGFP_his_tag.gb

Go back to your Benchling account. Inside of a folder, click the import DNA/RNA sequence button and upload the GenBank file you just downloaded.

Part 5: DNA Read/Write/Edit

5.1 DNA Read

(i) What DNA would you want to sequence (e.g., read) and why? This could be DNA related to human health (e.g. genes related to disease research), environmental monitoring (e.g., sewage waste water, biodiversity analysis), and beyond (e.g. DNA data storage, biobank).

I am interested in sequencing the DNA of organisms and bacteria that thrive under extreme or specialized conditions, such as those capable of converting CO₂ into O₂ or withstanding intense heat, as they may be relevant to my “firework art” project aimed at improving the environment

  1. CO₂-to-O₂ Converting Bacteria
    • Cyanobacteria, for example, perform photosynthesis by fixing CO₂ and releasing O₂
    • Understanding their genes and metabolic pathways could pave the way for applications in environmental cleanup or carbon reduction
  2. Heat-Resistant Microorganisms
    • Some bacteria found in volcanic areas or hot springs are highly thermotolerant
    • Sequencing their genomes may reveal genes that produce heat-stable enzymes or proteins, which could be applied to create new materials or sustainable technologies—even in high-temperature settings like firework art

By analyzing the DNA of these organisms, I hope to discover novel approaches or materials that can contribute to environmental improvement and ultimately realize an eco-friendly “firework art”

(ii) In lecture, a variety of sequencing technologies were mentioned. What technology or technologies would you use to perform sequencing on your DNA and why?  Also answer the following questions:

  1. Is your method first-, second- or third-generation or other? How so?

    Chosen Technology: Oxford Nanopore (3rd generation) due to its ability to provide long reads and real-time sequencing, ideal for de novo assembly of bacterial genomes

    https://www.youtube.com/watch?v=E9-Rm5AoZGw

  2. What is your input? How do you prepare your input (e.g. fragmentation, adapter ligation, PCR)? List the essential steps.

    Input and Preparation: High-quality genomic DNA is extracted, adapters are ligated, and the library is loaded onto the nanopore device

  3. What are the essential steps of your chosen sequencing technology, how does it decode the bases of your DNA sample (base calling)?

    Core Steps (Base Calling): Single-stranded DNA passes through a nanopore, causing changes in electrical current that are measured and decoded by software into base sequences

  4. What is the output of your chosen sequencing technology?

    Output: Long-read sequences in FASTQ format, facilitating detailed genomic analyses

5.2 DNA Write

(i) What DNA would you want to synthesize (e.g., write) and why?

These could be individual genes, clusters of genes or genetic circuits, whole genomes, and beyond. As described in class thus far, applications could range from therapeutics and drug discovery (e.g., mRNA vaccines and therapies) to novel biomaterials (e.g. structural proteins), to sensors (e.g., genetic circuits for sensing and responding to inflammation, environmental stimuli, etc.), to art (DNA origamis). If possible, include the specific genetic sequence(s) of what you would like to synthesize! You will have the opportunity to actually have Twist synthesize these DNA constructs! :)

DNA to be Edited (Designed)

I intend to engineer a DNA aptamer system that can detect human metabolism–derived substances (trace gases from breath or skin) with high sensitivity.

I call this non-invasive sensor the “Vital Nano-Sniffer”

Concept Overview

  • Utilize genome editing techniques (e.g., expanded nucleobases) to enhance and optimize DNA aptamers that bind to volatile organic compounds (VOCs) and small molecules originating from living organisms
  • Implement these aptamers in a cell-free chip device that detects the characteristic metabolic byproducts released when a human passes by, triggering a signal
  • AI robots would find it difficult to replicate the exact same metabolites as humans, so this helps in determining “human identity”

Why This Is Necessary

  • In the near future, the number of humanoid AI robots may increase to the point where visual appearance alone cannot distinguish them from humans
  • Traditional methods (e.g., iris recognition) can be bypassed, but real-time biological metabolism is harder to fake
  • By combining DNA aptamers and synthetic biology, we can potentially develop a new high-sensitivity, rapid security technique for detecting VOCs

Advantages and Potential

  • Non-invasive and Rapid Detection: Unlike conventional chemical sensors, DNA sequences can be freely edited to improve specificity and sensitivity
  • If it can capture the complex metabolic patterns unique to humans, it could be applied not only for personal identification but also for health checkups and disease screening
  • Starting from an SF scenario of distinguishing AI from humans, this technology could actually advance the development of biosensors in the real world

(ii) What technology or technologies would you use to perform this DNA synthesis and why?

Also answer the following questions:

1. What are the essential steps of your chosen sequencing methods?

Enzymatic Assembly (Gibson Assembly, Golden Gate, etc.)

  • What: Uses enzymes to join multiple short DNA fragments (from chemical synthesis) into a longer construct
  • Why :

    1. Longer Sequences: Suitable for kilobase-scale or more (genes, libraries)

    2. Seamless Cloning: Can avoid leaving restriction sites, allowing flexible designs

    3. Complex Constructs: Perfect for assembling aptamer libraries or multi-fragment genetic circuits

2. What are the limitations of your sequencing method (if any) in terms of speed, accuracy, scalability?

Next-Generation Sequencing (Illumina, etc.) https://jp.illumina.com/

  1. Library Prep: Fragment if necessary, add adapters, possibly PCR

  2. Cluster Generation: DNA binds to a flow cell, forming millions of clusters

  3. Sequencing-by-Synthesis: Each nucleotide incorporation produces a fluorescent signal

  4. Data Processing: Base-calling, quality checks, and assembly if needed

5.3 DNA Edit

(i) What DNA would you want to edit and why?

In class, George shared a variety of ways to edit the genes and genomes of humans and other organisms. Such DNA editing technologies have profound implications for human health, development, and even human longevity and human augmentation. DNA editing is also already commonly leveraged for flora and fauna, for example in nature conservation efforts, (animal/plant restoration, de-extinction), or in agriculture (e.g. plant breeding, nitrogen fixation). What kinds of edits might you want to make to DNA (e.g., human genomes and beyond) and why?

“Antiphoton microbe”

Editing Target:

A dark-hued fungus or slime mold as the base organism (since it already exhibits some degree of black coloration)

Editing Objective:

Endow it with extremely high light-absorption capability so that it reflects almost no light, essentially becoming a “living darkness”

Bio-Art Aspect:

By producing a “writhing black mass,” we can provide an immersive experience of living darkness – a dramatic piece of bio-art

Optical Application:

There is potential to research this as a “biologically derived jet-black coating” that suppresses stray light, possibly benefiting optical instruments or novel material development

(ii) What technology or technologies would you use to perform these DNA edits and why?

Chosen Technology: CRISPR/Cas9

  • Reason for Choice:

    CRISPR/Cas9 allows relatively precise targeting of specific genes with fewer off-target effects

    It has increasingly been applied to black fungi and slime molds, making it suitable for modifying pigment pathways and morphological genes to enhance darkness and introduce nano-scale structures

  • Goal:

    Use CRISPR/Cas9 to edit genes involved in pigment synthesis and surface morphology

    By doing so, transform an already dark-hued microbe into an “ultra-black” organism with potential nano structures

Also answer the following questions:
1. How does your technology of choice edit DNA? What are the essential steps?

Designing Guide RNA (gRNA)

Identify target genes responsible for light absorption (pigment synthesis) or structural morphology, then select the guide sequences

Introducing Cas9

Deliver the Cas9 protein or a plasmid expressing Cas9 into the microbial cells, enabling the gene-cutting process

(At this stage, this is about as much detail as I can address)

2. What preparation do you need to do (e.g. design steps) and what is the input (e.g. DNA template, enzymes, plasmids, primers, guides, cells) for the editing?

Planning/Design

  1. Identify which pigment biosynthesis genes to upregulate or which inhibitory factors to knock out
  2. Determine the morphological control genes that affect cell-surface nano-structures

Inputs

  • Guide RNAs (gRNAs)
  • Cas9 Protein or Cas9-Expressing Plasmid
3. What are the limitations of your editing methods (if any) in terms of efficiency or precision?

Editing Efficiency

  • There’s a risk the microbe may die off if it experiences excess heat or oxidative stress from absorbing extreme amounts of light

Biosafety

  • If these microbes escape into the environment, unforeseen ecological impacts could occur. Proper containment and safety measures are essential

Reading & Resources (click to expand)

Resources

・DNA Sequencing at 40: Past, Present, and Future (2017) Shendure, J., Balasubramanian, S., Church, G. et al.

https://doi.org/10.1038/nature24286

・DNA Synthesis Technologies to Close the Gene Writing Gap (2023), Hoose, A., Vellacott, R., Storch, M. et al.

https://doi.org/10.1038/s41570-022-00456-9

・Recombineering and MAGE (2021), Wannier T, et al. Nat Rev Methods Primers,

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9083505/

・CRISPR Technology: A Decade of Genome Editing is Only the Beginning, Wang, Doudna, et al.,

https://www.science.org/doi/10.1126/science.add8643

Databases

・GenBank overview: https://www.ncbi.nlm.nih.gov/genbank/

・NCBI: https://www.ncbi.nlm.nih.gov/genome/

・Ensembl: https://useast.ensembl.org/index.html

・UCSC Genome Browser: https://genome.ucsc.edu/

・Protective and Enhancing Alleles: https://arep.med.harvard.edu/gmc/protect.html

Editors and tutorials

・CRISPR/Cas9

　　　・Short tutorial for designing gRNAs: https://blog.addgene.org/how-to-design-your-grna-for-crispr-genome-editing

　　　・Benchling specific tutorial for designing gRNAs: https://www.benchling.com/blog/how-to-design-grnas-to-target-your-favorite-gene

　　　・List of Cas editors and their PAM sites: https://www.synthego.com/guide/how-to-use-crispr/pam-sequence

・Base Editors

　　　・Base editors contain a nicking or dead Cas9 enzyme fused to a deaminase. a.) PAM requirement: Base editors contain a nicking or dead Cas9 enzyme fused to a deaminase. For designing your guide RNA for base editing you will therefore have a PAM requirement like you would have for any Cas9 experiment. b.) Deamination window: An additional design constraint is that the sequence window in which deamination occurs is only a few base pairs long. You can find information on the deamination windows in the review below (even though some new editors are not included).

　　　　　・BE4 and ABE7.10 are good starting points and both use SpCas9 with NGG Pam requirement. Base editors with other PAM sites have been 　　　　　　　constructed too.

　　　・Review of base editors (2018) including a list of all base editors, their editing window and PAM requirement:

　　https://www.nature.com/articles/s41576-018-0059-1?WT.feed_name=subjects_animal-biotechnology

・Other editors:

　　　・Prime editor https://www.nature.com/articles/s41586-019-1711-4

　　　　　　　・Tutorials/tools:

　　　　　　　　　　　　・https://primeedit.nygenome.org/

　　　　　　　　　　　　・https://www.nature.com/articles/s41551-020-00622-8

　　　　　　　　　　　　・http://pegfinder.sidichenlab.org/

・TALEN For TALENs, you can assume no sequence restrictions – One of the technology’s previous restrictions was a T starting base, but this has since been overcome. In contrast to the CRISPR/Cas technologies above, your DNA sequence is recognized through interactions between the DNA and the TALEN: each TAL in the array recognizes one base. (Note: In order to introduce a double strand break, you will need to design to TALENs targeting the opposing strands.)

　　　・Short guide: https://www.addgene.org/talen/guide/

　　　・One of the available design resources: https://tale-nt.cac.cornell.edu/node/add/talen

　　　・Directed evolution for overcoming starting base restriction:https://academic.oup.com/nar/article/41/21/9779/1276340

Additional Resources:

・Gel Purification of DNA: after DNA gel electrophoresis, cutting a band of DNA out of the agarose gel allows isolation and purification of a specific DNA fragment:

　　　・Addgene: Protocol - How to Purify DNA from an Agarose Gel

・Overview of synthetic, unnatural organisms using recoding:

・Synthetic genomes with altered genetic codes (2020)

・DNA recorders, Sense+Read+Write:

　　　・Lineage tracing and analog recording in mammalian cells by single-site DNA writing (2021)

・Molecular electronics, integrating single molecules into electronic chips:

　　　・Molecular electronics sensors on a scalable semiconductor chip: A platform for single-molecule measurement of binding kinetics and enzyme activity (2022)

・Review of genome editors (zinc finger nucleases, TALENs, CRISPR) at the time CRISPR was emerging as editing technology:

　https://www.cell.com/trends/biotechnology/pdf/S0167-7799(13)00087-5.pdf

・Clinical trials of genome-editing therapies: https://www.nature.com/articles/d41573-020-00096-y

Week 03 HW: Lab Automation

Assignment: Python Script for Opentrons Artwork — DUE BY YOUR LAB TIME!

Your task this week is to Create a Python file to run on an Opentrons liquid handling robot.

0. Review this week’s recitation and this week’s lab for details on the Opentrons and programming it.

1. Generate an artistic design using the GUI at opentrons-art.rcdonovan.com.

2. Using the coordinates from the GUI, follow the instructions in the HTGAA26 Opentrons Colab to write your own Python script which draws your design using the Opentrons.

　　・You may use AI assistance for this coding — Google Gemini is integrated into Colab (see the stylized star bottom center);   
     it will do a good job writing functional Python, while you probably need to take charge of the art concept.
　　・If you’re a proficient programmer and you’d rather code something mathematical or algorithmic instead of using your GUI coordinates,  
     you may do that instead.

3. If the Python component is proving too problematic even with AI and human assistance, download the full Python script from the GUI website and submit that:

4. If you use AI to help complete this homework or lab, document how you used AI and which models made contributions.

5. Sign up for a robot time slot if you are at MIT/Harvard/Wellesley or at a Node offering Opentrons automation.

   The Python script you created will be run on the robot to produce your work of art!

・At MIT/Harvard? Lab times are on Thursday Feb.19 between 10AM and 6PM.

・At other Nodes? Please coordinate with your Node.

6. Submit your Python file via this form.

Colab Link HTGAA26 Opentrons Colab _ShimadaSayaka

[https://colab.research.google.com/drive/13-Wv0oNY5XePGTKNubb3rZWO9Zlw5PAz?usp=sharing]

This is the character for “flower,” written by my late grandmother. She did not have access to education, so she taught herself to read and write after she was over 80 years old. These “flower” characters were practiced during that time. After she passed away, these practice writings were discovered. I want to bring my grandmother’s handwriting back to life. Last year when I participated in HTGAA, I had the idea but was unable to realize it, so this year I would like to make it happen.

First, I created my design using the Automation Art Interface. However, even after publishing it to the gallery, clicking on my own work did not display it properly. Therefore, I copied the coordinates from the “Coordinates” section and used them in Colab. In the “your code” section, Cyan and Blue were not defined in the well_colors dictionary. However, since they were listed as available colors, I added them manually by defining D1 as Cyan and E1 as Blue.

[https://opentrons-art.rcdonovan.com/?id=6o741ir7g0gj0ri]





Post-Lab Questions

One of the great parts about having an automated robot is being able to precisely mix, deposit, and run reactions without much intervention, and design and deploy experiments remotely.

For this week, we’d like for you to do the following:


1. Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications.
  1. AssemblyTron: flexible automation of DNA assembly with Opentrons OT-2 lab robots

Author: John A Bryant Jr 1, Mason Kellinger 2, Cameron Longmire 3, Ryan Miller 4, R Clay Wright 5,

Published: 22 December 2022

[https://doi.org/10.1093/synbio/ysac032]

この論文は、j5というDNAアセンブリ設計ソフトウェアとOpentrons OT-2液体ハンドリングロボットを統合したオープンソースのPythonパッケージ「AssemblyTron」を紹介している。
複数のDNA組み立て手法（PCR、Golden Gate assembly、相同性依存型IVAなど）を、自動化し、手動と同等の精度まで成功したことが実証された。

AssemblyTronは合成生物学における時間・コスト・廃棄物を削減でき、合成生物学をより広かれることで、生物学研究そのものを加速させる可能性があると言える。

This paper introduces “AssemblyTron,” an open-source Python package that integrates j5 DNA assembly design software with the Opentrons OT-2 liquid handling robot. It was demonstrated that multiple DNA assembly methods — including PCR, Golden Gate assembly, and homology-dependent IVA — were successfully automated, achieving accuracy comparable to manual methods. AssemblyTron has the potential to reduce time, cost, and waste in synthetic biology, and by making synthetic biology more widely accessible, it could accelerate biological research itself.

2. Write a description about what you intend to do with automation tools for your final project. You may include example pseudocode, Python scripts, 3D printed holders, a plan for how to use Ginkgo Nebula, and more. You may reference this week’s recitation slide deck for lab automation details.


While your description/project idea doesn’t need to be set in stone, we would like to see core details of what you would automate. This is due at the start of lecture and does not need to be tested on the Opentrons yet.

Example 1: You are creating a custom fabric, and want to deposit art onto specific parts that need to be intertwined in odd ways. You can design a 3D printed holder to attach this fabric to it, and be able to deposit bio art on top. Check out the Opentrons 3D Printing Directory.

Example 2: You are using the cloud laboratory to screen an array of biosensor constructs that you design, synthesize, and express using cell-free protein synthesis.

(1). Echo transfer biosensor constructs and any required cofactors into specified wells.

(2). Bravo stamp in CPFS reagent master mix into all wells of a 96-well / 384-well plate.

(3). Multiflo dispense the CFPS lysate to all wells to start protein expression.

(4). PlateLoc seal the plate.

(5). Inheco incubate the plate at 37°C while the biosensor proteins are synthesized.

(6). XPeel remove the seal.

(7). PHERAstar measure fluorescence to compare biosensor responses.

Final Project Ideas

As explained in this week’s recitation, add 1-3 slides with 3 ideas you have for an Individual Final Project in the appropriate slide deck for MIT/Harvard/Wellesley students or for Commited Listeners. Be sure to put your name on your slide(s); for CLs, also put your city and country on your slide(s) and be sure you’re putting your slide(s) in your Node’s section of the deck.

Week 04 HW: Protein Design Part I

‘Week 4 HW: Protein Design Part I’

Documentation

Homework: Protein Design I — DUE BY START OF MAR 3 LECTURE

Part A. Conceptual Questions

Answer any NINE of the following questions from Shuguang Zhang: (i.e. you can select two to skip):

1.  How many molecules of amino acids do you take with a piece of 500 grams of meat? (on average an amino acid is ~100 Daltons)
2.  Why do humans eat beef but do not become a cow, eat fish but do not become fish?
3.  Why are there only 20 natural amino acids?
4.  Can you make other non-natural amino acids? Design some new amino acids.
5.  Where did amino acids come from before enzymes that make them, and before life started?
6.  If you make an α-helix using D-amino acids, what handedness (right or left) would you expect?
7.  Can you discover additional helices in proteins?
8.  Why are most molecular helices right-handed?
9.  Why do β-sheets tend to aggregate?
    ・What is the driving force for β-sheet aggregation?
10. Why do many amyloid diseases form β-sheets?
    ・Can you use amyloid β-sheets as materials?
11. Design a β-sheet motif that forms a well-ordered structure.

☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆

1. How many molecules of amino acids do you take with a piece of 500 grams of meat? (on average an amino acid is ~100 Daltons)

(500gの肉に含まれるアミノ酸分子の数は？)

First, assuming this meat is beef, we calculate the protein content in 500 g of beef

（まず、この肉を牛肉と仮定して、牛肉500gに含まれるタンパク質の量を計算する）

100gあたり約22gのタンパク質とすると、
There are about 22 g of protein per 100 g, so for 500 g, that becomes 110 g of protein 500g × 0.22 = 110g
平均アミノ酸 1分子 ≈ 100 Dalton
Although some references suggest an average amino acid mass of ~110 Daltons, I follow the problem statement (~100 Daltons) for this calculation.

https://ecampusontario.pressbooks.pub/bioc2580/chapter/bioc2580-lecture-1-biological-macromolecules-amino-acids/#:~:text=Large%20numbers%20of%20amino%20acids,amino%20acid%20(Figure%201.9)

1 Dalton ≈ 1 g/mol
100 Dalton ≈ 100 g/mol
110g / 100g/mol = 1.1 mol

アボガドロ定数:物質1モル（mol）中に含まれる原子や分子などの粒子数で、その値は正確に 6.02214076 *10²³ molecules/mol である。
Avogadro constant: The number of particles, such as atoms or molecules, contained in one mole (mol) of a substance. Its value is exactly particles/mol.

1.1 mol × 6.02 × 10²³ molecules/mol ≈ 6.6 × 10²³ molecules

つまり、 6.6 × 10²³ molecules 個のアミノ酸分子

2. Why do humans eat beef but do not become a cow, eat fish but do not become fish?

（なぜヒトは牛肉を食べても牛にはならず、魚を食べても魚にはならないのか）

https://bio-sta.jp/biokids/chapter1/section7/

Quora: “Why don’t I turn into a fish when I eat fish or a cow when I eat beef? There are chemicals in our bodies that prevent foreign genetic material in a cell from being read and translated into mRNA. What are these chemical processes and how do they work?”

https://www.quora.com/Why-dont-I-turn-into-a-fish-when-I-eat-fish-or-a-cow-when-I-eat-beef-There-are-chemicals-in-our-bodies-that-prevent-foreign-genetic-material-in-a-cell-from-being-read-and-translated-into-mRNA-What-are-these-chemical-processes-and-how-do-they-work

Digestion (消化)
牛のタンパク質は完全に分解され、アミノ酸になり、もとの構造情報（配列情報）は失われる
Beef proteins are completely broken down into amino acids; their original structural information is lost.

ペプシン・トリプシン・キモトリプシンで分解 個々のアミノ酸・小ペプチド
Broken down by proteases such as pepsin, trypsin, and chymotrypsin Individual amino acids and small peptides
↓
Absorption(吸収)
アミノ酸は「設計図」ではなく、分子レベルの「材料」として吸収される（小腸） Amino acids are absorbed as molecular “building blocks,” not as blueprints.（absorbed in the small intestine）
↓
Synthesis（合成）
ゲノム（DNA）が設計図として機能し、ヒトのタンパク質として合成を指示する DNA → mRNA → ribosomes
Resynthesized as human proteins

牛肉を摂取すると、牛のタンパク質は胃や消化酵素によって分解され、アミノ酸になる。この過程で、もとのアミノ酸配列という構造情報は失われる。 その後、アミノ酸は小腸で吸収され、血液中へと取り込まれる。 吸収されるのは「牛のタンパク質」ではなく、あくまでタンパク質を構成する材料。そして最終的に、ヒトのDNAが設計図として働き、これらのアミノ酸を用いてヒトのタンパク質が新たに合成される。よって、牛を食べてもヒトは牛にはならない。

When we consume beef, the cow’s proteins are broken down by gastric acid and digestive enzymes into individual amino acids. During this process, the original structural information—the amino acid sequence—is lost.

The amino acids are then absorbed in the small intestine and transported into the bloodstream. What is absorbed is not “cow protein” itself, but merely the molecular building blocks that make up proteins. Finally, human DNA functions as the blueprint, directing the synthesis of new human proteins from these amino acids. Therefore, eating beef does not turn a human into a cow.

3. Why are there only 20 natural amino acids?

（なぜ天然のアミノ酸は20種類しかないのか？）

(A) Assumed early and late stages of amino acid alphabet incorporation during protein evolution and (B) design of peptide libraries based on this order(from Journal of the American Chemical Society Cite this: J. Am. Chem. Soc. 2023, 145, 9, 5320–5329)

From the above information, we can conclude:

01 Early Amino Acids (10) vs. Late Amino Acids (10)

原始地球の環境では、グリシン（Gly）やアラニン（Ala）などの「初期アミノ酸」は構造が単純で生成しやすく、自然界に豊富に存在していたと考えられている。 その後の進化の過程で、フェニルアラニン（Phe）やトリプトファン（Trp）のような、より複雑な「後期アミノ酸」が生合成経路を通じて取り込まれ、 最終的に約20種類の標準的なアミノ酸の集合へと収束した。
In the primordial environment, certain “early amino acids” (such as Gly and Ala), which were simpler and easier to form, were naturally abundant. Later in evolution, more complex “late amino acids” (e.g., Phe, Trp) were incorporated via biosynthetic pathways, eventually converging on a standard set of about 20 amino acids.

02 Optimization for Protein Folding

ジョンズ・ホプキンス大学の Steven Fried らによる研究では、機能的な球状タンパク質へと折りたたまれる能力（foldability）が、数多くの非タンパク質性アミノ酸の中から現在 の20種類が選ばれた重要な要因であった可能性が示唆されている。適切なアミノ酸の組み合わせは、より優れたタンパク質の折りたたみと機能をもたらした。
Research led by Steven Fried at Johns Hopkins University suggests that the ability to fold into functional globular proteins (i.e., “foldability”) was a critical factor in why these particular 20 amino acids were chosen over countless non-proteinogenic alternatives. The right blend of amino acids provided superior folding and functional performance.

03 Evolutionary Selection and Elimination

生命が正式に誕生する以前であっても、タンパク質形成のレベルで原始的な「自然選択」が起こっていた可能性がある。 タンパク質の折りたたみや触媒機能に有利なアミノ酸は残り、あまり有効でないものは自然に淘汰されていったと考えられる。
Even before the formal emergence of life, primitive “natural selection” may have taken place at the level of protein formation. Amino acids beneficial for folding and catalytic functions were retained, while less effective ones were naturally discarded.

04 From GNC Code → SNS Code → Universal Genetic Code

　　ある仮説では、最も初期の遺伝暗号はグリシンやアラニンなど、ごく少数のアミノ酸しか扱わなかったとされている。その後、性能を高めるアミノ酸が一つずつ追加され、現在の20種類 のアミノ酸の体系へと発展した。後から追加されたアミノ酸は、より複雑な側鎖を持ち、タンパク質の機能的多様性や安定性を高める役割を果たしている。
One hypothesis is that the earliest genetic code involved only a few amino acids (like Gly, Ala) . Over time, performance-enhancing amino acids were added one by one, leading to today’s repertoire of 20. Late-added amino acids have more complex side chains and contribute greater functional variety and stability to proteins.

05 Exceptions (21st, 22nd, etc.)

　　一部の生物では、セレノシステイン（21番目）やピロリジン（22番目）といった追加のアミノ酸が利用されている。 しかし、これらは特別な翻訳機構を必要とする例外的な存在であり、標準的な20種類のアミノ酸体系の外に位置づけられている。
A few organisms utilize selenocysteine (21st) or pyrrolysine (22nd), but these require specialized translational machinery They remain exceptions outside the standard 20.

06 Why Non-Proteinogenic Amino Acids Were Not Adopted

分岐していないアルキル鎖を持つアミノ酸など、一部の非タンパク質性アミノ酸は、前生物的環境では豊富に存在していた可能性がある。 しかし、タンパク質の折りたたみや分子間相互作用にとって不利であったため、生命のタンパク質構成要素としては適さなかったと考えられる。 その結果、現在の20種類のアミノ酸の集合は、生命にとって非常に効率的でバランスの取れた構成であると見なされている。
Some non-proteinogenic amino acids—such as those with unbranched alkyl chains—might have been abundantly available prebiotically, but proved detrimental for folding or interaction, making them poor choices.
The set of 20 is viewed as a highly effective balance for life’s needs.

初期地球では、タンパク質の折りたたみ能力に優れたアミノ酸を選び出すような、自然選択と化学的な選別が起こっていたと考えられる。 生命が誕生し進化する過程で、いくつかの「高性能」なアミノ酸が追加され、最終的に20種類の標準的アミノ酸（加えて例外的な21番目・22番目）に至った。 この最終的なセットは、生物が機能するために必要な性質のバランスを最もよく満たす組み合わせであった可能性が高い。

In summary, natural selection and chemical filtering took place on the early Earth, favoring amino acids that excelled at protein folding. Once life arose and evolved, several “high-performance” amino acids were added, culminating in the total of 20 standard amino acids (plus the exceptional 21st and 22nd). This final set likely struck the optimal balance of functionality for living organisms.

4. Can you make other non-natural amino acids? Design some new amino acids.

（自然界に存在しない非天然アミノ酸を作ることは可能でしょうか。また、いくつか新しいアミノ酸を設計してみてください。）

[https://www.nature.com/articles/nature24031]

[https://www.nature.com/articles/nchembio.203]

自然界に存在しない非天然アミノ酸は作れる。アミノ酸は共通の骨格（NH2-CH-COOH）を持ち、側鎖（R基)を変えることで新しいアミノ酸が作れるからである。そのため、蛍光基、光反応基、金属結合基などを持つ新しいアミノ酸を人工的に設計できる。

Yes, it is possible to design non-natural amino acids.All amino acids share the same core structure (NH₂–CH–COOH) but differ in their side chain (R group). By modifying the side chain, scientists can create new amino acids with novel chemical properties. For example, one could design amino acids with fluorescent groups, photo-reactive groups, or metal-binding groups, expanding the functional diversity of proteins.

1 Fluorescent Amino Acid

通常のアミノ酸骨格にR基に蛍光分子をつける。→タンパク質を光らせる

   NH2–CH–COOH   
         |   
     fluorophore

R-group: a fluorescent moiety

Function: This amino acid would allow proteins to emit light, enabling visualization of protein location and dynamics in living cells.

2 Magnetic metal-binding amino acid （磁性金属結合アミノ酸）: 金属結合させて磁性を形成するタンパク質

鉄（Fe）やコバルト（Co）イオンと結合できる配位子（リガンド）を持つ側鎖 →このアミノ酸は金属イオンを配位して結合することができ、それにより、タンパク質が磁性中心や触媒中心を形成できるようになる可能性がある

NH2–CH–COOH
      |
      ligand (metal-binding ligand)
      |
      Co

binds “Fe” or “Co” ions

R-group: a ligand capable of binding iron or cobalt ions

Function: This amino acid could coordinate metal ions, enabling proteins to form magnetic or catalytic centers.

5. Where did amino acids come from before enzymes that make them, and before life started?

（生命やアミノ酸合成酵素が存在する以前、アミノ酸はどのようにして生まれたのか？）

生命の起源となったアミノ酸は、宇宙から隕石に乗ってやってきたのか？
[https://wired.jp/article/did-the-seeds-of-life-ride-to-earth-inside-an-asteroid/]

生命のもとは、暗闇ででたらめに生まれた(ユーリ・ミラー)
[https://lne.st/2016/01/14/the-origin-of-life/]

隕石衝突でアミノ酸生成　生命誕生に手掛かり　東北大など実証
[https://www.sankei.com/article/20200614-CZWXTGRD3BN57HKSS7KAQTC4AY/]

Based on the references above, there are several possibilities regarding where amino acids came from before life and its enzymes existed:

  1. Meteorite/Interstellar Cloud Origin

    It is widely known that meteorites contain various amino acids. During the early formation of the solar system, small bodies known as “asteroids” could have delivered amino acids to Earth upon impact. In interstellar clouds, ice and gas may have generated amino acids when exposed to ultraviolet or cosmic rays, which were then brought to Earth via these “cosmic time capsules” (i.e., meteorites). In the near future, uncontaminated samples returned from asteroids Bennu and Ryugu are expected to shed more light on this scenario.

  2. Natural Synthesis on Earth

    Experiments like the Miller-Urey experiment and scenarios involving deep-sea hydrothermal vents point to the possibility that amino acids could have been produced right here on Earth. Factors such as lightning, volcanic activity, temperature, and pressure may have triggered organic molecules to form and accumulate into the amino acids that eventually led to proteins.

  3. A Combined, Multistep Process

    It is also possible that amino acids came from both meteorites and Earth-based synthesis, mixing to form a “primordial soup” . Multiple routes—asteroid impacts, hydrothermal vents, and interstellar ices—could have simultaneously supplied amino acids to Earth, thereby facilitating the emergence of life.

生命が誕生する前のアミノ酸は
・原始地球の化学反応
・宇宙由来の有機分子
・深海の化学反応
などによって作られたと考えられる。

Before life began, amino acids are thought to have been formed through several processes, including chemical reactions on the early Earth, organic molecules delivered from space, and chemical reactions occurring in deep-sea environments such as hydrothermal vents.

6. If you make an α-helix using D-amino acids, what handedness (right or left) would you expect?

（D型アミノ酸でαヘリックスを作った場合、そのらせんは右巻きと左巻きのどちらになると予想されるか？）

from “Biological Homochirality: One of Life’s Greatest Mysteries”

Alpha helix αヘリックス
[https://en.wikipedia.org/wiki/Alpha_helix]
[https://ja.wikipedia.org/wiki/%CE%91%E3%83%98%E3%83%AA%E3%83%83%E3%82%AF%E3%82%B9]

[https://www.nature.com/scitable/topicpage/protein-structure-14122136/]

“The amino acids in an α-helix are arranged in a right-handed helical structure.”

Science Direct “Right-Handed Alpha-Helix”
[https://www.sciencedirect.com/topics/chemistry/right-handed-alpha-helix]

L-amino acids can form only right-handed α-helices in protein structures.

National Library of Medicine “A mixed chirality α-helix in a stapled bicyclic and a linear antimicrobial peptide revealed by X-ray crystallography”
[https://pmc.ncbi.nlm.nih.gov/articles/PMC8637766/]

L-chiral amino acids form right-handed helices,
D-chiral amino acids form left-handed helices.

Natural proteins are composed almost entirely of L-amino acids, and these amino acids preferentially form right-handed α-helices due to their stereochemistry.

7. Can you discover additional helices in proteins?

（タンパク質の中に、さらに別のヘリックス構造を発見することは可能か？）

Nature communications “Exo-chirality of the α-helix”
[https://www.nature.com/articles/s41467-024-51072-8]

最近の研究（例えば「αヘリックスの外部ヘリカル対称性（Exo-helical symmetries of the α-helix）」に関する研究）では、これまでαヘリックスの「主鎖ヘリックス（main-chain helix）」として一括りにされてきた構造に、周期的な側鎖パターンによって形成される 「外部ヘリックス（exo-helix）」 が伴っている可能性が示されている。 この発見は、以下のような理由から、タンパク質の中にはまだ 「未発見のヘリックス構造」 が存在している可能性を示唆しています。
Recent studies (such as the one on “Exo-helical symmetries of the α-helix”) indicate that what was previously grouped together as the “main-chain helix” of an α-helix may, in fact, be accompanied by an “external helix (exo-helix)” formed through periodic side-chain patterns This discovery suggests that there may still be “unidentified helices” hidden in proteins, for several reasons:

  1. Helices beyond the α-helix main chain:

    Traditionally, attention has been focused on the rotation angle and hydrogen bonding of the α-helix backbone

    However, it has recently been shown that side chains, which follow periodic repeat patterns (i, i + x), can assume a different helical symmetry (exo-helical symmetry)

  2. Theoretical and spectroscopic confirmation:

    In the cited work, the authors integrated NBD (nitrobenzoxadiazole) chromophores into peptide side chains, then employed CD spectroscopy and molecular dynamics simulations to demonstrate the presence of an exo-helix

    In other words, even though the backbone forms the same α-helix, periodic side chains can produce a separate, outward-facing helix that may be right-handed or left-handed

  3. A framework for finding “additional helices”:

    This exo-helix concept implies that, even in existing PDB structures, certain local or partial helical features might have gone unnoticed, depending on computational/experimental conditions

    In effect, alongside the “standard secondary structure,” there may be a “hidden external helix”—an entirely new perspective that strongly suggests there is still ample room to discover unidentified helical motifs in proteins

Potential:

The exo-helix concept could be valuable for protein design and the creation of functional synthetic peptides.
It points to a new direction in which “additional helices” could significantly affect molecular interactions and structural conformations

8. Why are most molecular helices right-handed?

（なぜ多くの分子ヘリックス（らせん構造）は右巻きなのか？）

・“Why are α-helices in proteins mostly right handed?
[https://www.ch.ic.ac.uk/rzepa/blog/?p=3802]

・Alpha Helix “Bioinformatics: Concepts, Methods, and Data”
[https://www.sciencedirect.com/topics/medicine-and-dentistry/alpha-helix]

  • YouTube Erik Lindahl “Lecture 05, concept 08: The alpha helix is right-handed due to L amino acids "
    [https://www.youtube.com/watch?v=rdkXOxLHDws]

ほとんどすべての生物のタンパク質は、L型アミノ酸（「左手型」の形）のみから構成されているため、αヘリックスは自然に右巻き構造をとる。
理論的には、もし**D型アミノ酸（L型アミノ酸の鏡像）**を使用すれば、左巻きのαヘリックスを作ることも可能である。
しかし、生物は主にL型アミノ酸を使用するように進化しており（そのキラリティが固定されているため）、この条件下で形成されるヘリックスは結果として右巻きになる。
Because virtually all biological proteins are built exclusively from L-amino acids (the “left-handed” form), α-helices naturally adopt a right-handed conformation.
Theoretically, if one were to use D-amino acids (the mirror image of L-amino acids), left-handed α-helices would be possible.
However, since living organisms predominantly chose L-amino acids (their chirality is fixed), the helices formed under these conditions end up being right-handed.

Ramachandran Plot Stability
ラマチャンドランプロットにおける安定性

L型アミノ酸は、ラマチャンドランプロットの中で右巻きαヘリックスの領域に安定して収まる。
そのため、この構造はエネルギー的にも立体的にも有利である。
L-amino acids fit securely into the Ramachandran plot’s region for right-handed α-helices, making this conformation energetically and sterically favorable.

Mirror-Image (Chirality) Influence 鏡像関係（キラリティ）の影響

物理法則そのものが、右巻きか左巻きかのどちらかを必ず選ばせるわけではない。
しかし、生命で使われているアミノ酸はすべてL型（特定のキラリティ）であるため、それらを組み合わせると結果として右巻きのヘリックスが形成される。
Physics itself does not mandate a preference for right- or left-handed forms. But because the amino acids in life are all L-type (with a specific chirality), assembling them leads to a right-handed helix.

Left-Handed Helices Are Rare in Practice
左巻きヘリックスは実際には非常にまれ

理論的には、D型アミノ酸を使えば左巻きαヘリックスが形成される可能性がある。しかし、このような構造は自然のタンパク質では非常にまれである。そのため、実際には「αヘリックス」と言うと、通常は右巻きのヘリックスを指す。
Although D-amino acids would theoretically yield left-handed α-helices, these are extremely uncommon in natural proteins Hence, for all practical purposes, “α-helix” refers to the right-handed version.

9. Why do β-sheets tend to aggregate?

・What is the driving force for β-sheet aggregation?
（なぜβシートは凝集（集まる）しやすいのか？ βシート凝集の駆動力は何か？）

“Natural β-sheet proteins use negative design to avoid edge-to-edge aggregation” [https://pmc.ncbi.nlm.nih.gov/articles/PMC122420/#:~:text=In%20hindsight%2C%20it%20seems%20obvious,other%20%CE%B2%20strands%20they%20encounter]

From the above cited paper, the following points can be understood:

Exposed “Edge” Strands
βシートでは、それぞれのストランドが主鎖の水素結合によって整列している。
しかし、端（エッジ）に位置するストランドは、近くにある別のβストランドと追加の水素結合を形成できる状態のまま残る。
そのため、これらのエッジが保護されていない場合、複数のβシートが積み重なったり結合したりして、大きな凝集体を形成する可能性がある。

In a β-sheet, each strand aligns via hydrogen bonds along its backbone.
However, the edge strands are still available to form additional hydrogen bonds with any nearby β-strand.
Consequently, if those edges remain unprotected, multiple β-sheets can stack or merge, leading to large aggregates.

Natural “Edge Protection” in Proteins
多くの天然のβシートタンパク質では、制御されない凝集を防ぐためにさまざまな設計上の工夫が存在する。
例：
・ループ構造がシートの端を覆う
・プロリン残基や「バルジ（bulge）」を挿入して、規則的なβ構造をわずかに歪ませる
・内側に向いた帯電残基を配置する
・βバレルのような閉じた構造を形成し、露出したエッジをなくす
一方で、このような保護機構を持たない短いβシート断片や、人工的に設計されたβタンパク質は、容易に凝集して不溶性の繊維（ファイバー）を形成しやすくなる。

・Many native β-sheet proteins avoid uncontrolled aggregation by employing various design strategies:
loops covering the edges, inserted prolines or “bulges” that distort regular β-structure, charged residues pointing inward, or forming closed barrels so that no true edges remain exposed.
・In contrast, short β-sheet fragments or artificially designed β-proteins without these protective features readily aggregate into insoluble fibers.

Driving Forces of Aggregation 凝集の駆動力

Extended Hydrogen-Bond Network 水素結合ネットワークの拡張:

When an edge strand meets another β-strand, a series of hydrogen bonds form, creating a significant energetic gain that propels aggregation βシートの端にあるストランドが別のβストランドと接触すると、一連の水素結合が形成される。このときエネルギー的に大きな安定化が得られるため、凝集が進みやすくなる。

Hydrophobic Interactions 疎水性相互作用:
Clustering β-sheets often bury hydrophobic side chains, lowering the system’s free energy and favoring aggregation βシートが集まると、疎水性の側鎖が内部に埋もれるように配置される。これによって系の自由エネルギーが低下し、凝集がより起こりやすくなる。

Other Interactions その他の相互作用:
Electrostatic and van der Waals (dispersion) forces can further stabilize the growing assembly, making β-sheet clusters more likely to form 静電相互作用やファンデルワールス力（分散力）も、形成されつつある集合体をさらに安定化させる。その結果、βシートのクラスターは形成されやすくなる。

10. Why do many amyloid diseases form β-sheets?

・Can you use amyloid β-sheets as materials? （なぜ多くのアミロイド病ではβシート構造が形成されるのか？ また、アミロイドβシートを材料として利用することはできるのか？）

アミロイドーシスに関する調査研究班
[http://amyloidosis-research-committee.jp/about/]

What Are Amyloid and Amyloidosis? アミロイドとアミロイドーシスとは何か
アミロイドとは、体内のタンパク質の形や性質が変化することで生じる物質であり、その結果タンパク質が集合して、水や血液に溶けにくい繊維状の沈着物を形成したものを指す。
このアミロイドが体内の組織に蓄積する病気は、総称して アミロイドーシス（amyloidosis） と呼ばれる。
Amyloid is a substance formed when the shape and properties of proteins in our bodies change, causing them to aggregate into fibrous deposits that are poorly soluble in water or blood. Diseases in which amyloid accumulates within the body’s tissues are collectively referred to as amyloidosis.

Microscopic Images of Amyloid Deposited in Tissue (Biopsy of the Carpal Ligament)

  • In the left image, the red-stained area is amyloid (stained with Congo red)
  • In the right image, when viewed under polarized light, the amyloid fluoresces with an apple-green color

Structure “Biology of Amyloid: Structure, Function, and Regulation”
[https://www.sciencedirect.com/science/article/pii/S0969212610003084]

Amyloid fibrils are fibrous structures composed of repeatedly aligned “cross-β sheet” arrangements, and they have long been linked to numerous diseases including Alzheimer’s disease (AD), Parkinson’s disease, and prion diseases.
More recently, researchers have discovered “functional amyloids” that carry out physiological roles despite having a similar structural basis.
The fundamental architecture of amyloid fibrils consists of protein chains extended and stacked in a regular cross-β configuration.
This fibrous arrangement is highly stable, so once protein misfolding initiates, it can recruit numerous identical molecules and rapidly grow into large aggregates.
Consequently, it is almost inevitable that the amyloid deposits observed in many neurodegenerative disorders (e.g., Alzheimer’s and Parkinson’s) take the form of β sheet–based fibrils.

Thus, in response to the question “Why do amyloid diseases form β sheets?”, one can argue that misfolded proteins adopt stable cross-β fibrils, leading to extensive aggregation characteristic of these conditions

アミロイド線維（amyloid fibrils）は、繰り返し整列した 「クロスβシート（cross-β sheet）」 配列から構成される繊維状構造であり、長い間、アルツハイマー病（AD）、パーキンソン病、プリオン病など多くの疾患と関連していることが知られている。 近年では、同様の構造的基盤を持ちながらも、生理的な役割を果たす 「機能性アミロイド（functional amyloids）」 の存在も発見されている。
アミロイド線維の基本構造は、タンパク質鎖が伸びた状態で規則的に積み重なった クロスβ構造 によって形成されている。
この繊維状の配置は非常に安定しており、一度タンパク質の誤った折りたたみ（ミスフォールディング）が始まると、同じ分子を次々と取り込みながら急速に大きな凝集体へと成長する。

その結果、アルツハイマー病やパーキンソン病など多くの神経変性疾患で観察されるアミロイド沈着は、ほぼ必然的に βシートを基盤とする線維構造をとる。

したがって、「なぜアミロイド病ではβシートが形成されるのか？」という問いに対しては、誤って折りたたまれたタンパク質が安定なクロスβ線維構造をとり、その結果として広範な凝集が生じるためであると説明することができる。

・Can you use amyloid β-sheets as materials?

PMC “The relationship between amyloid structure and cytotoxicity”
https://pmc.ncbi.nlm.nih.gov/articles/PMC4189889/

According to the link above, it is stated that the following can be utilized as materials

Tissue Repair and Cell Culture Scaffolds 組織修復および細胞培養用スキャフォールド

Utilizing amyloid fibrils as a scaffold can promote cell adhesion and proliferation via interactions with the cell membrane, making them highly effective in tissue engineering.

For example, composite hydrogels made from aloe vera and bovine serum albumin (BSA) have demonstrated strong wound-healing capabilities, and there are applications that combine 3D printing with bio-inks.

アミロイド線維をスキャフォールド（足場材料）として利用すると、細胞膜との相互作用によって細胞の接着や増殖を促進することができまる。そのため、組織工学の分野で非常に有効。
例：アロエベラとウシ血清アルブミン（BSA）から作られた複合ハイドロゲルは高い創傷治癒能力を示唆。3Dプリンティングとバイオインクを組み合わせた応用も報告されている。

Drug Delivery

The fibrous structure of amyloid can transport hydrophobic or positively charged molecules, which has led to its use in drug delivery systems. Reports include cases where methylene blue or riboflavin are effectively carried using amyloid-based materials.

アミロイドの繊維構造は、疎水性分子や正電荷を持つ分子を運搬する能力がある。 そのため、ドラッグデリバリーシステムへの応用が研究されている。 例：メチレンブルーやリボフラビンをアミロイド材料によって効率よく運搬できる。

Macroscale Fibers and Composites マクロスケール繊維および複合材料

Thanks to their mechanical strength and chemical stability, amyloid fibrils can be formed into hydrogels, macro-fibers, composite materials, and sensors. In addition, by incorporating conductive peptides, researchers aim to develop biodegradable and biocompatible conductive materials.

アミロイド線維は、機械的強度と化学的安定性が高い。

  • ハイドロゲル
  • マクロファイバー
  • 複合材料
  • センサー材料
    などに加工することが可能

さらに、導電性ペプチドを組み込むことで、生分解性かつ生体適合性を持つ導電材料の開発も。

Amyloid as a Catalytic Material 触媒材料としてのアミロイド

Esterase Activity Enhancement エステラーゼ活性の向上

Introducing residues such as tyrosine or histidine within the amyloid fibril can create a microenvironment that displays esterase-like activity. For instance, peptide fibers containing histidine or hydrophobic side chains (e.g., leucine, isoleucine) can form reversible hydrogels with significant catalytic potential.
アミロイド線維の内部にチロシンやヒスチジンなどの残基を導入すると、エステラーゼ様活性を示す微小環境を形成することができる。 例えば、ヒスチジンや疎水性側鎖（ロイシン、イソロイシンなど）を含むペプチド線維は、可逆的なハイドロゲルを形成し、優れた触媒活性を示す可能性がある。

11. Design a β-sheet motif that forms a well-ordered structure.

（秩序だった構造を形成するβシートモチーフを設計しなさい。）

条件：

  • Val (V) は βシートを作りやすい
  • Thr (T) は極性があって並びを整えやすい
  • 疎水性 / 親水性 が交互なのは、βストランドで良い
🧬　V–T–V–T–V–T–V–T

This motif has alternating hydrophobic and polar residues, which is favorable for β-sheet formation. Valine promotes β-sheet stability, and threonine helps create an ordered structure.

Part B. Protein Analysis and Visualization

In this part of the homework, you will be using online resources and 3D visualization software to answer questions about proteins. Pick any protein (from any organism) of your interest that has a 3D structure and answer the following questions:

1.Briefly describe the protein you selected and why you selected it.

I chose Green Fluorescent Protein (GFP) because I am interested in biological light emission and how light-producing mechanisms in living organisms work. GFP, originally derived from the jellyfish Aequorea victoria, absorbs light in the ultraviolet to blue range and emits green fluorescence. This unique property has made GFP widely used as a molecular marker in biological research to visualize proteins and cellular processes.

2. Identify the amino acid sequence of your protein.(選んだタンパク質のアミノ酸配列を特定しなさい。)

UniProt “P42212 · GFP_AEQVI”
[https://www.uniprot.org/uniprotkb/P42212/entry]

- How long is it? What is the most frequent amino acid? You can use this Colab notebook to count the frequency of amino acids.
  (その長さはどのくらいか？最も頻度の高いアミノ酸は何か？（このColabノートを使ってアミノ酸の出現頻度を数えることができます）

Colab
[https://colab.research.google.com/drive/1vlAU_Y84lb04e4Nnaf1axU8nQA6_QBP1?usp=sharing]

>sp|P42212|GFP_AEQVI Green fluorescent protein OS=Aequorea victoria OX=6100 GN=GFP PE=1 SV=1
MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTL
VTTFSYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLV
NRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLAD
HYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYK

for amino, count in amino_acid_count.most_common():
print(amino, count)

sequence_length = len(cleaned_sequence)
print(f"The total length of the amino acid sequence is: {sequence_length} residues.”)

The amino acid sequence of GFP has a total length of 238 residues, and the most frequent amino acid is glycine (G), appearing 22 times

- How many protein sequence homologs are there for your protein? Hint: Use Uniprot’s BLAST tool to search for homologs.   
（あなたのタンパク質にはどれくらいの配列ホモログ（類似タンパク質）がありますか？ヒント：Uniprot の BLAST ツールを使って検索してください。）

UniProt BLAST [https://www.uniprot.org/blast]

IDにGFP（Aequorea victoria）のUniProt ID：P42212を入力しRun BLASTする

A BLAST search using UniProt identified 205 homologous protein sequences related to GFP across different organisms.

- Does your protein belong to any protein family?
(あなたのタンパク質はどのタンパク質ファミリーに属していますか？)

[https://www.uniprot.org/uniprotkb/P42212/entry]

Belongs to the GFP family

3. Identify the structure page of your protein in RCSB   
(RCSB（Protein Data Bank）で、あなたが選んだタンパク質の構造ページを特定しなさい。)

[https://www.rcsb.org/]
ここで、Green fluorescent proteinで検索すると

一番上の1EMAが出てくる
GREEN FLUORESCENT PROTEIN FROM EQUOREA VICTORIA

[https://www.rcsb.org/structure/1EMA]

  • Classification: Fluorescent Protein
  • Organism(s): Aequorea victoria（クラゲ）
  • Method: X-ray crystallography
  • Resolution: 1.9 Å
- When was the structure solved? Is it a good quality structure? Good quality structure is the one with good resolution. Smaller the better (Resolution: 2.70 Å)
(そのタンパク質の構造はいつ解かれましたか？また、その構造は良い品質の構造ですか？   
良い構造とは解像度が良いものを指します。解像度は小さいほど良いです（2.70 Å）。)

[https://www.rcsb.org/structure/1EMA]

  • Released: 1996-11-08
  • Resolution: 1.90 Å
    Yes, it is a good quality structure.
- Are there any other molecules in the solved structure apart from protein?
(その構造には、タンパク質以外の分子は含まれていますか？)

[https://www.rcsb.org/structure/1EMA]

CRO and MSE

- Does your protein belong to any structure classification family?
(あなたが選んだタンパク質は、構造分類ファミリー（structure classification family）のどれかに属していますか？)

UnitProt “P42212・GFP_AEQVI” [https://www.uniprot.org/uniprotkb/P42212/entry]

[https://www.ebi.ac.uk/pdbe/scop/term/8096045]

Alpha and beta proteins ⇔ GFP-like ⇔ GFP-like ⇔ Fluorescent proteins

GFP belongs to the SCOP structural classification family as follows:
Class: Alpha and beta proteins (α+β)
Fold: GFP-like
Superfamily: GFP-like
Structure Family: Fluorescent proteins

According to the SCOP structural classification database, GFP belongs to the fluorescent protein family.

4. Open the structure of your protein in any 3D molecule visualization software:
（あなたが選んだタンパク質の構造を、任意の3D分子可視化ソフトウェアで開きなさい。）    

- PyMol Tutorial Here (hint: ChatGPT is good at PyMol commands)
(PyMolのチュートリアルはこちら（ヒント：ChatGPTはPyMolコマンドを作るのが得意です）)   

- Visualize the protein as “cartoon”, “ribbon” and “ball and stick”.
（タンパク質を3種類の表示方法で可視化してください）

CHAT GTP Auto

</> pymol
fetch 1ema

cartoon

hide everything
show cartoon

ribbon

hide everything
show ribbon

ball and stick

hide everything
show stick show spheres

　　　　　

- Color the protein by secondary structure. Does it have more helices or sheets?

hide everything show cartoon

color red, ss h # αヘリックスを赤色で表示

color yellow, ss s # βシートを黄色で表示

color green, ss l+’’ # ループ（コイル部分）を緑色で表示

α-helix: red
β-sheet: yellow
coil: green

　　　

The structure has more yellow regions, indicating that it is rich in β-sheets.

- Color the protein by residue type. What can you tell about the distribution of hydrophobic vs hydrophilic residues?

＃まずタンパク質をリセットします

hide everything　　　　

show cartoon

＃疎水性アミノ酸をオレンジに色付け

color orange, resn ALA+VAL+ILE+LEU+MET+PHE+TYR+TRP+PRO

＃親水性アミノ酸をシアン色に色付け

color cyan, resn ARG+ASN+ASP+GLN+GLU+HIS+LYS+SER+THR+CYS

＃中性または特定が難しいグリシン(G)を白に色付け

color white, resn GLY　　　　

Hydrophobic residues : orange
Hydrophilic residues in : cyan
Neutral or ambiguous residues (such as glycine) : white

The distribution of hydrophobic and hydrophilic residues in GFP appears to be relatively balanced, with both types of residues evenly distributed throughout the structure

- Visualize the surface of the protein. Does it have any “holes” (aka binding pockets)?

hide everything show surface set transparency, 0.2

video

The protein surface visualization reveals clear binding pockets or “holes” on the protein surface.

Part C. Using ML-Based Protein Design Tools

In this section, we will learn about the capabilities of modern protein AI models and test some of them in your chosen protein.

1. Copy the HTGAA_ProteinDesign2026.ipynb notebook and set up a colab instance with GPU.

2. Choose your favorite protein from the PDB.

3. We will now try multiple things in the three sections below; report each of these results in your homework writeup on your HTGAA website:

Colob [https://colab.research.google.com/drive/1L6ok_BjbmiIM_xB99dT4k0mHO9RnNSBy#scrollTo=33580eea]

For this section I continued using Green Fluorescent Protein (PDB ID: 1EMA), which I selected earlier from the Protein Data Bank.

Download the PDB file

C1. Protein Language Modeling

**Deep Mutational Scans**

a. Use ESM2 to generate an unsupervised deep mutational scan of your protein based on language model likelihoods.     
(ESM2を使って、言語モデルの尤度（likelihood）に基づいた非教師ありのディープミューテーショナルスキャンをあなたのタンパク質について生成しなさい。)

b. Can you explain any particular pattern? (choose a residue and a mutation that stands out)     
（何か特徴的なパターンは見られますか？（特に目立つ残基や変異を1つ選んで説明しなさい)）

c. (Bonus) Find sequences for which we have experimental scans, and compare the prediction of the language model to experiment.    
(ボーナス問題 実験的なミューテーションスキャンのデータがあるタンパク質配列を見つけ、言語モデルの予測と実験結果を比較しなさい。)

a. GFPのヒートマップを作る
[https://colab.research.google.com/drive/1L6ok_BjbmiIM_xB99dT4k0mHO9RnNSBy#scrollTo=09FwbZ6v1AUs]

An unsupervised deep mutational scan of GFP was generated using the ESM2 language model. The resulting heatmap shows the predicted likelihood of all possible amino acid substitutions across the protein sequence.

b.

c. (Bonus)　NA

2. Latent Space Analysis

a. Use the provided sequence dataset to embed proteins in reduced dimensionality.
（提供されたタンパク質配列データセットを使い、タンパク質を低次元空間（例：2次元）に埋め込みなさい。）


b. Analyze the different formed neighborhoods: do they approximate similar proteins?
（形成された近傍（clusters）を分析し、それらが似たタンパク質を表しているかを説明しなさい。）

c. Place your protein in the resulting map and explain its position and similarity to its neighbors.
（そのマップの中にあなたのタンパク質（GFP）を配置し、どのタンパク質に近いかを説明しなさい。）

a.

Protein sequences from the provided dataset were embedded using the ESM2 language model and projected into a reduced three-dimensional latent space using t-SNE. This visualization allows relationships between protein sequences to be explored.

b. The visualization shows that proteins form neighborhoods in the latent space. This suggests that the language model captures meaningful relationships between sequences, as similar proteins tend to cluster together.

C.

GFP appears as a single point in the latent space because only one GFP sequence was added. Its position within a dense neighborhood suggests that GFP shares meaningful sequence-level features with nearby proteins.

C2. Protein Folding

Folding a protein

1. Fold your protein with ESMFold. Do the predicted coordinates match your original structure?

PDB: 1EMA

[https://www.rcsb.org/structure/1EMA]

The ESMFold-predicted structure of GFP closely matches the original crystal structure (PDB: 1EMA). Both show the characteristic 11-stranded β-barrel, which is the hallmark of GFP. However, the predicted structure appears slightly less compact compared to the experimentally determined structure.

2. Try changing the sequence, first try some mutations, then large segments. Is your protein structure resilient to mutations?

First, I tried a small mutation by changing the last amino acid from K (Lysine) to A (Alanine). As shown in the figure below, the overall structure remained almost identical to the original.

Next, I tried a larger mutation by replacing the last 9 amino acids with Alanine (AAAAAAAA). Again, the β-barrel structure was largely preserved. These results suggest that GFP is highly resilient to mutations, especially at the C-terminal region. The core β-barrel scaffold is robust and maintained even when significant sequence changes are introduced at the terminal end.

C3. Protein Generation

Inverse-Folding a protein: Let’s now use the backbone of your chosen PDB to propose sequence candidates via ProteinMPNN （選んだPDBの骨格構造を使って、ProteinMPNNで配列の候補を提案する）

1. Analyze the predicted sequence probabilities and compare the predicted sequence vs the original one.

（予測された配列の確率を分析し、予測された配列と元の配列を比較する）

sequence probability heatmap

横軸（positions） → タンパク質の各位置（0〜150番目のアミノ酸） 縦軸（amino acids） → 20種類のアミノ酸（A, C, D, E, F…） 黄色 → 確率高い（0.9以上）→ ProteinMPNNが強く推薦

Colabより

New Sequence:

ALTPEEAAKLAAAWAPVAANAAANGKAFILTLFEKYPEIAEKFPEFKGKSLEEIKASPKLPAISSAFFATLDTLVAVADDAAKMAALLDALAKAHVALGIGAEDFEKVRAIFPGFVASIAPPPAGADAAWDKLFGDIIAALRAAGA

この配列を使って、Colabに入れ直す

2. Input this sequence into ESMFold and compare the predicted structure to your original.

（ProteinMPNNが提案したこの配列をESMFoldに入力し、予測された構造を元の構造と比較してください。）

The new sequence folded into alpha-helices instead of the original β-barrel structure.

I input the ProteinMPNN-proposed sequence into ESMFold and compared it to the original GFP structure (1EMA). The original GFP folded into a characteristic β-barrel structure, while the new sequence predicted by ProteinMPNN folded into α-helices.

Part D. Group Brainstorm on Bacteriophage Engineering

1. Find a group of ~3–4 students
2. Read through the Phage Reading material listed under “Reading & Resources” below.
3. Review the Bacteriophage Final Project Goals for engineering the L Protein:
（バクテリオファージのファイナルプロジェクト目標 - Lタンパク質の工学：）

   ・Increased stability （安定性の向上）(easiest)
   
   ・Higher titers力価の向上 (medium)...どれだけファジーが多く作れるか。ファジー療法では、多くのファジーが作れれないと治療に使えない。
   
   ・Higher toxicity of lysis protein 溶菌タンパク質の毒性向上(hard)
   
   ...Lタンパク質（Lysis protein）= MS2ファージがE. coliの細胞膜に穴を開けて破壊するタンパク質。   
   毒性を高めると少ないLタンパク質でE. coliを早く、多く、壊せるようにする
4. Brainstorm Session

   ・Choose one or two main goals from the list that you think you can address computationally (e.g., “We’ll try to stabilize the lysis protein,” or “We’ll attempt to disrupt its interaction with E. coli DnaJ.”).

   
   ・Write a 1-page proposal (bullet points or short paragraphs) describing:
              ・Which tools/approaches from recitation you propose using (e.g., “Use Protein Language Models to do in silico mutagenesis, then AlphaFold-Multimer to check complexes.”).
              
              ・Why do you think those tools might help solve your chosen sub-problem?
              
              ・Name one or two potential pitfalls (e.g., “We lack enough training data on phage–bacteria interactions.”).
              
              ・Include a schematic of your pipeline.
              
   ・This resource may be useful: HTGAA Protein Engineering Tools
5. Each individually put your plan on your HTGAA website

   ・Include your group’s short plan for engineering a bacteriophage

Week 05 HW: Protein Design Part II

‘Week 5 — Protein Design Part II’

Documentation

Homework: Protein Design II

Part A: SOD1 Binder Peptide Design (From Pranam)

Superoxide dismutase 1 (SOD1) is a cytosolic antioxidant enzyme that converts superoxide radicals into hydrogen peroxide and oxygen. In its native state, it forms a stable homodimer and binds copper and zinc.

Mutations in SOD1 cause familial Amyotrophic Lateral Sclerosis (ALS). Among them, the A4V mutation (Alanine → Valine at residue 4) leads to one of the most aggressive forms of the disease. The mutation subtly destabilizes the N-terminus, perturbs folding energetics, and promotes toxic aggregation.

Your challenge:

  1. Design short peptides that bind mutant SOD1.
  2. Then decide which ones are worth advancing toward therapy.

You will use three models developed in our lab:

・PepMLM: target sequence-conditioned peptide generation via masked language modeling ・PeptiVerse: therapeutic property prediction ・moPPIt: motif-specific multi-objective peptide design using Multi-Objective Guided Discrete Flow Matching (MOG-DFM)

Part 1: Generate Binders with PepMLM**

1. Begin by retrieving the human SOD1 sequence from UniProt (P00441) and introducing the A4V mutation.   
（SOD1配列の取得とA4V変異の導入。UniProt（ID: P00441）からヒトSOD1の配列を取得し、4番目のアミノ酸をアラニン(A)→バリン(V)に変える。）

2. Using the PepMLM Colab linked from the HuggingFace PepMLM-650M model card:  
（PepMLM Colabを開く。HuggingFaceのPepMLM-650MモデルカードからColabノートブックを開き、自分のGoogle Driveにコピーして実行する。）

3. Generate four peptides of length 12 amino acids conditioned on the mutant SOD1 sequence.  
（ペプチドを4本生成。変異SOD1配列を入力として、12アミノ酸長のペプチドを4本生成する。）

4. To your generated list, add the known SOD1-binding peptide FLYRWLPSRRGG for comparison.  
（既知バインダーを追加。生成した4本に加えて、既知のSOD1結合ペプチド FLYRWLPSRRGG をリストに追加する（比較用）。

5. Record the perplexity scores that indicate PepMLM's confidence in the binders.  
（パープレキシティスコアを記録。PepMLMが各ペプチドのバインディングにどれだけ自信があるかを示す「パープレキシティスコア」を記録する。スコアが低いほど自信が高い）

Uniprot（ID: P00441）human SOD1 [https://www.uniprot.org/uniprotkb/P00441/entry#sequences]

sequence

🧬 >sp|P00441|SODC_HUMAN Superoxide dismutase [Cu-Zn] OS=Homo sapiens OX=9606 GN=SOD1 PE=1 SV=2
MATKAVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTS
AGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVV
HEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

MATKAVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTS AGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVV HEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

memo The fourth residue is actually K, not A
Why? Because the initial methionine (M), which comes from the start codon, is often cleaved after translation
In this case, it is assumed to be removed, so the numbering starts from the next residue, A

Therefore, the fourth position refers to that A

A→V MATKVVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTS AGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVV HEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

PepMLM Colab link [https://colab.research.google.com/drive/12Z7MaSyPhik1gfX_TpW2mOggOYj4z98L?usp=sharing]

FLYRWLPSRRGG　

Added code to calculate the Pseudo Perplexity of the known SOD1-binding peptide FLYRWLPSRRGG for comparison

#FLYRWLPSRRGG 評価のためのコードを追加 
#Calculate perplexity for known binder FLYRWLPSRRGG

binder = "FLYRWLPSRRGG"
protein_seq = "MATKVVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ"

ppl = compute_pseudo_perplexity(model, tokenizer, protein_seq, binder)
print(f"{binder} pseudo perplexity: {ppl}")

FLYRWLPSRRGG pseudo perplexity: 20.63523127283615

Its pseudo perplexity score was 20.63523127283615, which is comparable to the PepMLM-generated binders

Part 2: Evaluate Binders with AlphaFold3

1. Navigate to the AlphaFold Server: alphafoldserver.com

2. For each peptide, submit the mutant SOD1 sequence followed by the peptide sequence as separate chains to model the protein-peptide complex.
（5本のペプチド（4本＋FLYRWLPSRRGG）それぞれについて、SOD1配列とペプチド配列を別々のチェーンとして入力して実行）

3. Record the ipTM score and briefly describe where the peptide appears to bind. Does it localize near the N-terminus where A4V sits?
（A4V変異があるN末端付近に結合しているか？）

   Does it engage the β-barrel region or approach the dimer interface? 
   （βバレル領域に結合しているか、それとも二量体界面に近いか？）
   
   Does it appear surface-bound or partially buried?
　 （表面に結合しているか、それともタンパク質の中に埋まっているか？）
  
4. In a short paragraph, describe the ipTM values you observe and whether any PepMLM-generated peptide matches or exceeds the known binder.
（観察したipTM値について、またPepMLMが生成したペプチドの中に既知バインダー（FLYRWLPSRRGG）と同等かそれ以上のスコアを示すものがあるかどうかを、短いパラグラフで説明する。）

Regenerated peptides because X (unknown amino acid) appeared in 2 of 4 sequences. AlphaFold3 does not accept X as it cannot predict structure for unknown amino acids.

X replaced with A (alanine) as X = unknown amino acid, not accepted by AlphaFold3

・WRYYAVVVRHKX →　WRYYAVVVRHKA
・WLYPAAAVEHWK
・WHYGATGLAHKX →　WHYGATGLAHKA
・WHYPVVALAHKE

・FLYRWLPSRRGG

1. WRYYAVVVRHKA

2. WLYPAAAVEHWK

3. WHYGATGLAHKA

4. WHYPVVALAHKE

5. FLYRWLPSRRGG

pTM（interface predicted TM-score） とは： 2つのタンパク質がどれだけうまくくっついているかを示すスコア
ipTM (interface predicted TM-score) is a score that measures how well two proteins interact with each other.

・Ranges from 0 to 1
・Higher is better (stronger interaction)
・Above 0.7 → reliable binding
・Below 0.5 → low confidence

In this homework, it is used to evaluate how well each peptide binds to the mutant SOD1 protein.

BinderPseudo PerplexityipTM
WRYYAVVVRHKA17.260.40
WLYPAAAVEHWK18.450.19
WHYGATGLAHKA11.620.37
WHYPVVALAHKE12.030.30
FLYRWLPSRRGG (known binder)20.640.30

Results：

  • The highest ipTM score was observed in WRYYAVVVRHKA (0.40) （一番高いipTM： WRYYAVVVRHKA（0.40））
  • 3 out of 4 PepMLM-generated peptides exceeded the known binder FLYRWLPSRRGG (0.30)
    (既知バインダーFLYRWLPSRRGG（0.30）より 3本がスコアを上回った )
  • However, all ipTM scores were below 0.7, suggesting low confidence in binding (しかし全体的にipTMが低い（0.7以下）→ 結合の信頼度はまだ低い )

Part 3: Evaluate Properties of Generated Peptides in the PeptiVerse
（PeptiVerseで生成ペプチドの特性を評価する）

Structural confidence alone is insufficient for therapeutic development. Using PeptiVerse, let’s evaluate the therapeutic properties of your peptide! For each PepMLM-generated peptide:
（構造的な信頼度だけでは治療薬開発には不十分である。PeptiVerseを使って各ペプチドの治療特性を評価すること）

1. Paste the peptide sequence. (ペプチドの配列を貼り付ける)

2. Paste the A4V mutant SOD1 sequence in the target field.（ターゲット欄にA4V変異SOD1の配列を貼り付ける）

3. Check the boxes
   1. Predicted binding affinity
   2. Solubility
   3. Hemolysis probability
   4. Net charge (pH 7)
   5. Molecular weight

Compare these predictions to what you observed structurally with AlphaFold3. In a short paragraph, describe what you see.  
(AlphaFold3で観察した構造結果と比較して、短いパラグラフで以下を説明する)  

・Do peptides with higher ipTM also show stronger predicted affinity? Are any strong binders predicted to be hemolytic or poorly soluble? 
（ipTMが高いペプチドは予測結合親和性も高いか？）  

・Which peptide best balances predicted binding and therapeutic properties?
（強いバインダーの中に溶血性が高いものや溶解性が低いものはあるか？）

・Choose one peptide you would advance and justify your decision briefly.
（結合と治療特性のバランスが最も良いペプチドはどれか？）

PeptiVerse

WRYYAVVVRHKA

  • Solubility: Soluble (1.000)
  • Hemolysis: Non-hemolytic (0.057)
  • Binding Affinity: Weak binding (5.826 pKd/pKi)
  • Molecular Weight: 1547.8 Da
  • Net Charge (pH 7): 2.84

WLYPAAAVEHWK

  • Solubility: Soluble (1.000)
  • Hemolysis: Non-hemolytic (0.027)
  • Binding Affinity: Weak binding (5.820 pKd/pKi)
  • Molecular Weight: 1470.7 Da
  • Net Charge (pH 7): -0.15

WHYGATGLAHKA

  • Solubility: Soluble (1.000)
  • Hemolysis: Non-hemolytic (0.026)
  • Binding Affinity: Weak binding (5.523 pKd/pKi)
  • Molecular Weight: 1311.4 Da
  • Net Charge (pH 7): 0.93

WHYPVVALAHKE

  • Solubility: Soluble (1.000)
  • Hemolysis: Non-hemolytic (0.021)
  • Binding Affinity: Weak binding (5.568 pKd/pKi)
  • Molecular Weight: 1449.7 Da
  • Net Charge (pH 7): -0.06

FLYRWLPSRRGG

  • Solubility: Soluble (1.000)
  • Hemolysis: Non-hemolytic (0.047)
  • Binding Affinity: Weak binding (5.968 pKd/pKi)
  • Molecular Weight: 1507.7 Da
  • Net Charge (pH 7): 2.76
BinderSolubilityHemolysisBinding AffinityMW (Da)Net Charge (pH 7)
WRYYAVVVRHKASoluble (1.000)Non-hemolytic (0.057)Weak binding (5.826)1547.82.84
WLYPAAAVEHWKSoluble (1.000)Non-hemolytic (0.027)Weak binding (5.820)1470.7-0.15
WHYGATGLAHKASoluble (1.000)Non-hemolytic (0.026)Weak binding (5.523)1311.40.93
WHYPVVALAHKESoluble (1.000)Non-hemolytic (0.021)Weak binding (5.568)1449.7-0.06
FLYRWLPSRRGGSoluble (1.000)Non-hemolytic (0.047)Weak binding (5.968)1507.72.76

Observations

  • All 5 peptides were predicted to be soluble (probability 1.000) and non-hemolytic
    （全5本ともペプチドが溶解性（確率1.000）＝非溶血性を示している。よって全部水に溶けても赤血球も壊さない。治療薬として安全である。）

  • All peptides showed weak binding affinity, ranging from 5.523 to 5.968 pKd/pKi
    （全ペプチドの結合親和性は弱い。5.523~5.968pKd/pKi の範囲。つまり、くっつく力が弱い。バインダーが弱いため改善の余地あり。）

  • The peptide with the highest ipTM (WRYYAVVVRHKA, ipTM = 0.40) did not show the strongest predicted binding affinity
    （ipTMが最も高い"WRYYAVVVRHKA(0.40)“が最も強い結合親和性が高いわけではなかった…。結果が一致しない。）

  • This suggests that structural confidence (ipTM) and binding affinity do not always correlate
    （ipTMと結合親和性が必ずしも一致しない）

Peptide I would advance: WRYYAVVVRHKA

  • Highest ipTM score (0.40) among all peptides（最も高いipTM…Part2の結果より）
  • Soluble and non-hemolytic (溶解性があり、非溶血性…全結果同じ)
  • Reasonable molecular weight (1547.8 Da) (適切な分子量が他もほぼ同じで大差はない)

治療用ペプチドの分子量目安 500Da以下: 小さすぎる…結合部位をカバーできない 2000Da以上: 大きすぎる…体内に吸収されにくい、製造コストが高い

Part 4: Generate Optimized Peptides with moPPIt

Now, move from sampling to controlled design. moPPIt uses Multi-Objective Guided Discrete Flow Matching (MOG-DFM) to steer peptide generation toward specific residues and optimize binding and therapeutic properties simultaneously.    
Unlike PepMLM, which samples plausible binders conditioned on just the target sequence, moPPIt lets you choose where you want to bind and optimize multiple objectives at once.   
（PepMLMのような「サンプリング」から「制御されたデザイン」に移る。moPPItはMOG-DFM（多目的ガイド付き離散フローマッチング）を使って、特定の残基に向けてペプチド生成を誘導し、結合と治療特性を同時に最適化する。  
PepMLMがターゲット配列だけを条件にして、ペプチドをサンプリングするのに対して、moPPItはどこに結合させたいかを指定して、複数の目的を同時に最適化できる。）


1. Open the moPPit Colab linked from the HuggingFace moPPIt model card   
（HuggingFaceのmoPPItモデルカードからmoPPIt Colabを開く）

2. Make a copy and switch to a GPU runtime.(コピーを作成してGPUランタイムに切り替える)  
3. In the notebook:

   1. Paste your A4V mutant SOD1 sequence. 
      (A4V変異SOD1配列を貼り付ける)  
   
   2. Choose specific residue indices on SOD1 that you want your peptide to bind
      (ペプチドを結合させたいSOD1上の特定の残基番号を選ぶ)   
      (for example, residues near position 4, the dimer interface, or another surface patch).  
      (例：4番目の変異付近、二量体界面、または別の表面パッチ）
   
   3. Set peptide length to 12 amino acids.
      (ペプチド長を12アミノ酸に設定する)
   
   4. Enable motif and affinity guidance (and solubility/hemolysis guidance if available). Generate peptides.
      (モチーフと親和性のガイダンスを有効にして（可能であれば溶解性・溶血性のガイダンスも）ペプチドを生成する)

4. After generation, briefly describe how these moPPit peptides differ from your PepMLM peptides. How would you evaluate these peptides before advancing them to clinical studies?
(生成後、moPPItのペプチドがPepMLMのペプチドとどう違うかを簡単に説明する。また、臨床試験に進める前にこれらのペプチドをどのように評価するかを述べる。)

moPPit Colab [https://colab.research.google.com/drive/1_wHRlIdfaZbW1T1lcRMv-zJg6-JzugTn?usp=sharing]

moPPIt Output Log (Click here)

/content/moPPIt/flow_matching/path/path_sample.py:45: SyntaxWarning: invalid escape sequence ‘\s’ x_t (Tensor): the sample along the path :math:X_t \sim p_t. /content/moPPIt/flow_matching/path/scheduler/scheduler.py:72: SyntaxWarning: invalid escape sequence ‘\s’ SchedulerOutput: :math:\alpha_t,\sigma_t,\frac{\partial}{\partial t}\alpha_t,\frac{\partial}{\partial t}\sigma_t /content/moPPIt/flow_matching/path/scheduler/scheduler.py:79: SyntaxWarning: invalid escape sequence ‘\k’ Computes :math:t from :math:\kappa_t. Target Motifs: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] tensor([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], device=‘cuda:0’) Some weights of EsmModel were not initialized from the model checkpoint at facebook/esm2_t33_650M_UR50D and are newly initialized: [’esm.pooler.dense.bias’, ’esm.pooler.dense.weight’] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. Some weights of EsmModel were not initialized from the model checkpoint at facebook/esm2_t33_650M_UR50D and are newly initialized: [’esm.pooler.dense.bias’, ’esm.pooler.dense.weight’] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. Some weights of EsmModel were not initialized from the model checkpoint at facebook/esm2_t33_650M_UR50D and are newly initialized: [’esm.pooler.dense.bias’, ’esm.pooler.dense.weight’] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

NFE: 0: 0%| | 0/0.9990000128746033 [00:00<?, ?it/s]Weight Vector: tensor([1., 1., 1., 1.], device=‘cuda:0’) [0.7495816946029663, 0.4166666269302368, 0.6292190551757812, 0.3727186322212219]

NFE: 0: 1%| | 0.009999999776482582/0.9990000128746033 [00:03<05:25, 329.27s/it] NFE: 1: 1%| | 0.009999999776482582/0.9990000128746033 [00:03<05:25, 329.29s/it][0.7495816946029663, 0.4166666269302368, 0.6292190551757812, 0.3727186322212219]

NFE: 1: 2%|▏ | 0.019999999552965164/0.9990000128746033 [00:05<04:13, 258.56s/it] NFE: 2: 2%|▏ | 0.019999999552965164/0.9990000128746033 [00:05<04:13, 258.57s/it][0.7495816946029663, 0.4166666269302368, 0.6292190551757812, 0.3727186322212219]

NFE: 2: 3%|▎ | 0.029999999329447746/0.9990000128746033 [00:07<03:47, 234.45s/it] NFE: 3: 3%|▎ | 0.029999999329447746/0.9990000128746033 [00:07<03:47, 234.45s/it][0.7495816946029663, 0.4166666269302368, 0.6292190551757812, 0.3727186322212219]

NFE: 3: 4%|▍ | 0.03999999910593033/0.9990000128746033 [00:08<03:33, 222.41s/it] NFE: 4: 4%|▍ | 0.03999999910593033/0.9990000128746033 [00:08<03:33, 222.42s/it][0.7495816946029663, 0.4166666269302368, 0.6292190551757812, 0.3727186322212219]

NFE: 4: 5%|▌ | 0.05000000074505806/0.9990000128746033 [00:10<03:24, 215.20s/it] NFE: 5: 5%|▌ | 0.05000000074505806/0.9990000128746033 [00:10<03:24, 215.21s/it][0.7495816946029663, 0.4166666269302368, 0.6292190551757812, 0.3727186322212219]

NFE: 5: 6%|▌ | 0.05999999865889549/0.9990000128746033 [00:12<03:17, 210.46s/it] NFE: 6: 6%|▌ | 0.05999999865889549/0.9990000128746033 [00:12<03:17, 210.46s/it][0.7495816946029663, 0.4166666269302368, 0.6292190551757812, 0.3727186322212219]

NFE: 6: 7%|▋ | 0.07000000029802322/0.9990000128746033 [00:14<03:12, 207.05s/it] NFE: 7: 7%|▋ | 0.07000000029802322/0.9990000128746033 [00:14<03:12, 207.06s/it][0.7495816946029663, 0.4166666269302368, 0.6292190551757812, 0.3727186322212219]

NFE: 7: 8%|▊ | 0.07999999821186066/0.9990000128746033 [00:16<03:07, 204.46s/it] NFE: 8: 8%|▊ | 0.07999999821186066/0.9990000128746033 [00:16<03:07, 204.46s/it]Jump! [0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 8: 9%|▉ | 0.09000000357627869/0.9990000128746033 [00:18<03:04, 202.53s/it] NFE: 9: 9%|▉ | 0.09000000357627869/0.9990000128746033 [00:18<03:04, 202.53s/it][0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 9: 10%|█ | 0.10000000149011612/0.9990000128746033 [00:20<03:00, 200.94s/it] NFE: 10: 10%|█ | 0.10000000149011612/0.9990000128746033 [00:20<03:00, 200.94s/it][0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 10: 11%|█ | 0.10999999940395355/0.9990000128746033 [00:21<02:57, 199.62s/it] NFE: 11: 11%|█ | 0.10999999940395355/0.9990000128746033 [00:21<02:57, 199.62s/it][0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 11: 12%|█▏ | 0.11999999731779099/0.9990000128746033 [00:23<02:54, 198.54s/it] NFE: 12: 12%|█▏ | 0.11999999731779099/0.9990000128746033 [00:23<02:54, 198.54s/it][0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 12: 13%|█▎ | 0.12999999523162842/0.9990000128746033 [00:25<02:51, 197.62s/it] NFE: 13: 13%|█▎ | 0.12999999523162842/0.9990000128746033 [00:25<02:51, 197.62s/it][0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 13: 14%|█▍ | 0.14000000059604645/0.9990000128746033 [00:27<02:49, 196.84s/it] NFE: 14: 14%|█▍ | 0.14000000059604645/0.9990000128746033 [00:27<02:49, 196.84s/it][0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 14: 15%|█▌ | 0.15000000596046448/0.9990000128746033 [00:29<02:46, 196.23s/it] NFE: 15: 15%|█▌ | 0.15000000596046448/0.9990000128746033 [00:29<02:46, 196.23s/it][0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 15: 16%|█▌ | 0.1599999964237213/0.9990000128746033 [00:31<02:44, 195.67s/it] NFE: 16: 16%|█▌ | 0.1599999964237213/0.9990000128746033 [00:31<02:44, 195.67s/it][0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 16: 17%|█▋ | 0.17000000178813934/0.9990000128746033 [00:33<02:41, 195.16s/it] NFE: 17: 17%|█▋ | 0.17000000178813934/0.9990000128746033 [00:33<02:41, 195.16s/it][0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 17: 18%|█▊ | 0.18000000715255737/0.9990000128746033 [00:35<02:39, 194.69s/it] NFE: 18: 18%|█▊ | 0.18000000715255737/0.9990000128746033 [00:35<02:39, 194.69s/it][0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 18: 19%|█▉ | 0.1899999976158142/0.9990000128746033 [00:36<02:37, 194.27s/it] NFE: 19: 19%|█▉ | 0.1899999976158142/0.9990000128746033 [00:36<02:37, 194.27s/it][0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 19: 20%|██ | 0.20000000298023224/0.9990000128746033 [00:38<02:34, 193.87s/it] NFE: 20: 20%|██ | 0.20000000298023224/0.9990000128746033 [00:38<02:34, 193.88s/it][0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 20: 21%|██ | 0.20999999344348907/0.9990000128746033 [00:40<02:32, 193.55s/it] NFE: 21: 21%|██ | 0.20999999344348907/0.9990000128746033 [00:40<02:32, 193.55s/it][0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 21: 22%|██▏ | 0.2199999988079071/0.9990000128746033 [00:42<02:30, 193.26s/it] NFE: 22: 22%|██▏ | 0.2199999988079071/0.9990000128746033 [00:42<02:30, 193.26s/it][0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 22: 23%|██▎ | 0.23000000417232513/0.9990000128746033 [00:44<02:28, 192.99s/it] NFE: 23: 23%|██▎ | 0.23000000417232513/0.9990000128746033 [00:44<02:28, 192.99s/it][0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 23: 24%|██▍ | 0.23999999463558197/0.9990000128746033 [00:46<02:26, 192.73s/it] NFE: 24: 24%|██▍ | 0.23999999463558197/0.9990000128746033 [00:46<02:26, 192.73s/it][0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 24: 25%|██▌ | 0.25/0.9990000128746033 [00:48<02:24, 192.49s/it]
NFE: 25: 25%|██▌ | 0.25/0.9990000128746033 [00:48<02:24, 192.49s/it][0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 25: 26%|██▌ | 0.25999999046325684/0.9990000128746033 [00:49<02:22, 192.28s/it] NFE: 26: 26%|██▌ | 0.25999999046325684/0.9990000128746033 [00:49<02:22, 192.28s/it][0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 26: 27%|██▋ | 0.27000001072883606/0.9990000128746033 [00:51<02:20, 192.09s/it] NFE: 27: 27%|██▋ | 0.27000001072883606/0.9990000128746033 [00:51<02:20, 192.09s/it][0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 27: 28%|██▊ | 0.2800000011920929/0.9990000128746033 [00:53<02:17, 191.87s/it] NFE: 28: 28%|██▊ | 0.2800000011920929/0.9990000128746033 [00:53<02:17, 191.87s/it][0.8327690064907074, 0.4166666269302368, 0.6426528692245483, 0.42955923080444336]

NFE: 28: 29%|██▉ | 0.28999999165534973/0.9990000128746033 [00:55<02:15, 191.67s/it] NFE: 29: 29%|██▉ | 0.28999999165534973/0.9990000128746033 [00:55<02:15, 191.67s/it]Jump! [0.8858508765697479, 0.5, 0.6378693580627441, 0.5126941800117493]

NFE: 29: 30%|███ | 0.30000001192092896/0.9990000128746033 [00:57<02:13, 191.51s/it] NFE: 30: 30%|███ | 0.30000001192092896/0.9990000128746033 [00:57<02:13, 191.51s/it][0.8858508765697479, 0.5, 0.6378693580627441, 0.5126941800117493]

NFE: 30: 31%|███ | 0.3100000023841858/0.9990000128746033 [00:59<02:11, 191.36s/it] NFE: 31: 31%|███ | 0.3100000023841858/0.9990000128746033 [00:59<02:11, 191.36s/it][0.8858508765697479, 0.5, 0.6378693580627441, 0.5126941800117493]

NFE: 31: 32%|███▏ | 0.3199999928474426/0.9990000128746033 [01:01<02:09, 191.23s/it] NFE: 32: 32%|███▏ | 0.3199999928474426/0.9990000128746033 [01:01<02:09, 191.23s/it][0.8858508765697479, 0.5, 0.6378693580627441, 0.5126941800117493]

NFE: 32: 33%|███▎ | 0.33000001311302185/0.9990000128746033 [01:03<02:07, 191.08s/it] NFE: 33: 33%|███▎ | 0.33000001311302185/0.9990000128746033 [01:03<02:07, 191.08s/it][0.8858508765697479, 0.5, 0.6378693580627441, 0.5126941800117493]

NFE: 33: 34%|███▍ | 0.3400000035762787/0.9990000128746033 [01:04<02:05, 190.94s/it] NFE: 34: 34%|███▍ | 0.3400000035762787/0.9990000128746033 [01:04<02:05, 190.94s/it][0.8858508765697479, 0.5, 0.6378693580627441, 0.5126941800117493]

NFE: 34: 35%|███▌ | 0.3499999940395355/0.9990000128746033 [01:06<02:03, 190.79s/it] NFE: 35: 35%|███▌ | 0.3499999940395355/0.9990000128746033 [01:06<02:03, 190.79s/it][0.8858508765697479, 0.5, 0.6378693580627441, 0.5126941800117493]

NFE: 35: 36%|███▌ | 0.36000001430511475/0.9990000128746033 [01:08<02:01, 190.67s/it] NFE: 36: 36%|███▌ | 0.36000001430511475/0.9990000128746033 [01:08<02:01, 190.67s/it][0.8858508765697479, 0.5, 0.6378693580627441, 0.5126941800117493]

NFE: 36: 37%|███▋ | 0.3700000047683716/0.9990000128746033 [01:10<01:59, 190.55s/it] NFE: 37: 37%|███▋ | 0.3700000047683716/0.9990000128746033 [01:10<01:59, 190.55s/it]Jump! [0.8231243938207626, 0.5, 0.6209778785705566, 0.6177530288696289]

NFE: 37: 38%|███▊ | 0.3799999952316284/0.9990000128746033 [01:12<01:57, 190.44s/it] NFE: 38: 38%|███▊ | 0.3799999952316284/0.9990000128746033 [01:12<01:57, 190.44s/it][0.8231243938207626, 0.5, 0.6209778785705566, 0.6177530288696289]

NFE: 38: 39%|███▉ | 0.38999998569488525/0.9990000128746033 [01:14<01:55, 190.36s/it] NFE: 39: 39%|███▉ | 0.38999998569488525/0.9990000128746033 [01:14<01:55, 190.36s/it][0.8231243938207626, 0.5, 0.6209778785705566, 0.6177530288696289]

NFE: 39: 40%|████ | 0.4000000059604645/0.9990000128746033 [01:16<01:53, 190.30s/it] NFE: 40: 40%|████ | 0.4000000059604645/0.9990000128746033 [01:16<01:53, 190.30s/it][0.8231243938207626, 0.5, 0.6209778785705566, 0.6177530288696289]

NFE: 40: 41%|████ | 0.4099999964237213/0.9990000128746033 [01:17<01:52, 190.22s/it] NFE: 41: 41%|████ | 0.4099999964237213/0.9990000128746033 [01:17<01:52, 190.22s/it]Jump! [0.8616571724414825, 0.5, 0.6517865657806396, 0.6071006655693054]

NFE: 41: 42%|████▏ | 0.41999998688697815/0.9990000128746033 [01:19<01:50, 190.14s/it] NFE: 42: 42%|████▏ | 0.41999998688697815/0.9990000128746033 [01:19<01:50, 190.15s/it][0.8616571724414825, 0.5, 0.6517865657806396, 0.6071006655693054]

NFE: 42: 43%|████▎ | 0.4300000071525574/0.9990000128746033 [01:21<01:48, 190.07s/it] NFE: 43: 43%|████▎ | 0.4300000071525574/0.9990000128746033 [01:21<01:48, 190.07s/it][0.8616571724414825, 0.5, 0.6517865657806396, 0.6071006655693054]

NFE: 43: 44%|████▍ | 0.4399999976158142/0.9990000128746033 [01:23<01:46, 190.00s/it] NFE: 44: 44%|████▍ | 0.4399999976158142/0.9990000128746033 [01:23<01:46, 190.00s/it][0.8616571724414825, 0.5, 0.6517865657806396, 0.6071006655693054]

NFE: 44: 45%|████▌ | 0.44999998807907104/0.9990000128746033 [01:25<01:44, 189.92s/it] NFE: 45: 45%|████▌ | 0.44999998807907104/0.9990000128746033 [01:25<01:44, 189.92s/it][0.8616571724414825, 0.5, 0.6517865657806396, 0.6071006655693054]

NFE: 45: 46%|████▌ | 0.46000000834465027/0.9990000128746033 [01:27<01:42, 189.87s/it] NFE: 46: 46%|████▌ | 0.46000000834465027/0.9990000128746033 [01:27<01:42, 189.87s/it][0.8616571724414825, 0.5, 0.6517865657806396, 0.6071006655693054]

NFE: 46: 47%|████▋ | 0.4699999988079071/0.9990000128746033 [01:29<01:40, 189.79s/it] NFE: 47: 47%|████▋ | 0.4699999988079071/0.9990000128746033 [01:29<01:40, 189.79s/it][0.8616571724414825, 0.5, 0.6517865657806396, 0.6071006655693054]

NFE: 47: 48%|████▊ | 0.47999998927116394/0.9990000128746033 [01:31<01:38, 189.71s/it] NFE: 48: 48%|████▊ | 0.47999998927116394/0.9990000128746033 [01:31<01:38, 189.71s/it][0.8616571724414825, 0.5, 0.6517865657806396, 0.6071006655693054]

NFE: 48: 49%|████▉ | 0.49000000953674316/0.9990000128746033 [01:32<01:36, 189.63s/it] NFE: 49: 49%|████▉ | 0.49000000953674316/0.9990000128746033 [01:32<01:36, 189.63s/it][0.8616571724414825, 0.5, 0.6517865657806396, 0.6071006655693054]

NFE: 49: 50%|█████ | 0.5/0.9990000128746033 [01:34<01:34, 189.57s/it]
NFE: 50: 50%|█████ | 0.5/0.9990000128746033 [01:34<01:34, 189.57s/it][0.8616571724414825, 0.5, 0.6517865657806396, 0.6071006655693054]

NFE: 50: 51%|█████ | 0.5099999904632568/0.9990000128746033 [01:36<01:32, 189.51s/it] NFE: 51: 51%|█████ | 0.5099999904632568/0.9990000128746033 [01:36<01:32, 189.51s/it][0.8616571724414825, 0.5, 0.6517865657806396, 0.6071006655693054]

NFE: 51: 52%|█████▏ | 0.5199999809265137/0.9990000128746033 [01:38<01:30, 189.45s/it] NFE: 52: 52%|█████▏ | 0.5199999809265137/0.9990000128746033 [01:38<01:30, 189.45s/it][0.8616571724414825, 0.5, 0.6517865657806396, 0.6071006655693054]

NFE: 52: 53%|█████▎ | 0.5299999713897705/0.9990000128746033 [01:40<01:28, 189.39s/it] NFE: 53: 53%|█████▎ | 0.5299999713897705/0.9990000128746033 [01:40<01:28, 189.39s/it][0.8616571724414825, 0.5, 0.6517865657806396, 0.6071006655693054]

NFE: 53: 54%|█████▍ | 0.5400000214576721/0.9990000128746033 [01:42<01:26, 189.33s/it] NFE: 54: 54%|█████▍ | 0.5400000214576721/0.9990000128746033 [01:42<01:26, 189.34s/it][0.8616571724414825, 0.5, 0.6517865657806396, 0.6071006655693054]

NFE: 54: 55%|█████▌ | 0.550000011920929/0.9990000128746033 [01:44<01:24, 189.28s/it] NFE: 55: 55%|█████▌ | 0.550000011920929/0.9990000128746033 [01:44<01:24, 189.28s/it][0.8616571724414825, 0.5, 0.6517865657806396, 0.6071006655693054]

NFE: 55: 56%|█████▌ | 0.5600000023841858/0.9990000128746033 [01:45<01:23, 189.23s/it] NFE: 56: 56%|█████▌ | 0.5600000023841858/0.9990000128746033 [01:45<01:23, 189.23s/it][0.8616571724414825, 0.5, 0.6517865657806396, 0.6071006655693054]

NFE: 56: 57%|█████▋ | 0.5699999928474426/0.9990000128746033 [01:47<01:21, 189.17s/it] NFE: 57: 57%|█████▋ | 0.5699999928474426/0.9990000128746033 [01:47<01:21, 189.17s/it][0.8616571724414825, 0.5, 0.6517865657806396, 0.6071006655693054]

NFE: 57: 58%|█████▊ | 0.5799999833106995/0.9990000128746033 [01:49<01:19, 189.13s/it] NFE: 58: 58%|█████▊ | 0.5799999833106995/0.9990000128746033 [01:49<01:19, 189.13s/it][0.8616571724414825, 0.5, 0.6517865657806396, 0.6071006655693054]

NFE: 58: 59%|█████▉ | 0.5899999737739563/0.9990000128746033 [01:51<01:17, 189.08s/it] NFE: 59: 59%|█████▉ | 0.5899999737739563/0.9990000128746033 [01:51<01:17, 189.09s/it][0.8616571724414825, 0.5, 0.6517865657806396, 0.6071006655693054]

NFE: 59: 60%|██████ | 0.6000000238418579/0.9990000128746033 [01:53<01:15, 189.04s/it] NFE: 60: 60%|██████ | 0.6000000238418579/0.9990000128746033 [01:53<01:15, 189.04s/it]Jump! [0.9183544814586639, 0.5833333134651184, 0.7453092932701111, 0.6523084044456482]

NFE: 60: 61%|██████ | 0.6100000143051147/0.9990000128746033 [01:55<01:13, 188.99s/it] NFE: 61: 61%|██████ | 0.6100000143051147/0.9990000128746033 [01:55<01:13, 188.99s/it][0.9183544814586639, 0.5833333134651184, 0.7453092932701111, 0.6523084044456482]

NFE: 61: 62%|██████▏ | 0.6200000047683716/0.9990000128746033 [01:57<01:11, 188.96s/it] NFE: 62: 62%|██████▏ | 0.6200000047683716/0.9990000128746033 [01:57<01:11, 188.96s/it][0.9183544814586639, 0.5833333134651184, 0.7453092932701111, 0.6523084044456482]

NFE: 62: 63%|██████▎ | 0.6299999952316284/0.9990000128746033 [01:59<01:09, 188.92s/it] NFE: 63: 63%|██████▎ | 0.6299999952316284/0.9990000128746033 [01:59<01:09, 188.92s/it]Jump! [0.9653539806604385, 0.6666666269302368, 0.7603995203971863, 0.5541437864303589]

NFE: 63: 64%|██████▍ | 0.6399999856948853/0.9990000128746033 [02:00<01:07, 188.89s/it] NFE: 64: 64%|██████▍ | 0.6399999856948853/0.9990000128746033 [02:00<01:07, 188.89s/it]Jump! [0.971489554271102, 0.75, 0.7158400416374207, 0.6807365417480469]

NFE: 64: 65%|██████▌ | 0.6499999761581421/0.9990000128746033 [02:02<01:05, 188.87s/it] NFE: 65: 65%|██████▌ | 0.6499999761581421/0.9990000128746033 [02:02<01:05, 188.87s/it]Jump! [0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 65: 66%|██████▌ | 0.6600000262260437/0.9990000128746033 [02:04<01:04, 188.83s/it] NFE: 66: 66%|██████▌ | 0.6600000262260437/0.9990000128746033 [02:04<01:04, 188.83s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 66: 67%|██████▋ | 0.6700000166893005/0.9990000128746033 [02:06<01:02, 188.79s/it] NFE: 67: 67%|██████▋ | 0.6700000166893005/0.9990000128746033 [02:06<01:02, 188.79s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 67: 68%|██████▊ | 0.6800000071525574/0.9990000128746033 [02:08<01:00, 188.75s/it] NFE: 68: 68%|██████▊ | 0.6800000071525574/0.9990000128746033 [02:08<01:00, 188.75s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 68: 69%|██████▉ | 0.6899999976158142/0.9990000128746033 [02:10<00:58, 188.71s/it] NFE: 69: 69%|██████▉ | 0.6899999976158142/0.9990000128746033 [02:10<00:58, 188.71s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 69: 70%|███████ | 0.699999988079071/0.9990000128746033 [02:12<00:56, 188.68s/it] NFE: 70: 70%|███████ | 0.699999988079071/0.9990000128746033 [02:12<00:56, 188.68s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 70: 71%|███████ | 0.7099999785423279/0.9990000128746033 [02:13<00:54, 188.66s/it] NFE: 71: 71%|███████ | 0.7099999785423279/0.9990000128746033 [02:13<00:54, 188.66s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 71: 72%|███████▏ | 0.7200000286102295/0.9990000128746033 [02:15<00:52, 188.64s/it] NFE: 72: 72%|███████▏ | 0.7200000286102295/0.9990000128746033 [02:15<00:52, 188.64s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 72: 73%|███████▎ | 0.7300000190734863/0.9990000128746033 [02:17<00:50, 188.61s/it] NFE: 73: 73%|███████▎ | 0.7300000190734863/0.9990000128746033 [02:17<00:50, 188.61s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 73: 74%|███████▍ | 0.7400000095367432/0.9990000128746033 [02:19<00:48, 188.58s/it] NFE: 74: 74%|███████▍ | 0.7400000095367432/0.9990000128746033 [02:19<00:48, 188.58s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 74: 75%|███████▌ | 0.75/0.9990000128746033 [02:21<00:46, 188.54s/it]
NFE: 75: 75%|███████▌ | 0.75/0.9990000128746033 [02:21<00:46, 188.54s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 75: 76%|███████▌ | 0.7599999904632568/0.9990000128746033 [02:23<00:45, 188.50s/it] NFE: 76: 76%|███████▌ | 0.7599999904632568/0.9990000128746033 [02:23<00:45, 188.50s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 76: 77%|███████▋ | 0.7699999809265137/0.9990000128746033 [02:25<00:43, 188.49s/it] NFE: 77: 77%|███████▋ | 0.7699999809265137/0.9990000128746033 [02:25<00:43, 188.49s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 77: 78%|███████▊ | 0.7799999713897705/0.9990000128746033 [02:26<00:41, 188.46s/it] NFE: 78: 78%|███████▊ | 0.7799999713897705/0.9990000128746033 [02:26<00:41, 188.46s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 78: 79%|███████▉ | 0.7900000214576721/0.9990000128746033 [02:28<00:39, 188.43s/it] NFE: 79: 79%|███████▉ | 0.7900000214576721/0.9990000128746033 [02:28<00:39, 188.43s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 79: 80%|████████ | 0.800000011920929/0.9990000128746033 [02:30<00:37, 188.41s/it] NFE: 80: 80%|████████ | 0.800000011920929/0.9990000128746033 [02:30<00:37, 188.41s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 80: 81%|████████ | 0.8100000023841858/0.9990000128746033 [02:32<00:35, 188.38s/it] NFE: 81: 81%|████████ | 0.8100000023841858/0.9990000128746033 [02:32<00:35, 188.38s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 81: 82%|████████▏ | 0.8199999928474426/0.9990000128746033 [02:34<00:33, 188.35s/it] NFE: 82: 82%|████████▏ | 0.8199999928474426/0.9990000128746033 [02:34<00:33, 188.35s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 82: 83%|████████▎ | 0.8299999833106995/0.9990000128746033 [02:36<00:31, 188.34s/it] NFE: 83: 83%|████████▎ | 0.8299999833106995/0.9990000128746033 [02:36<00:31, 188.34s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 83: 84%|████████▍ | 0.8399999737739563/0.9990000128746033 [02:38<00:29, 188.33s/it] NFE: 84: 84%|████████▍ | 0.8399999737739563/0.9990000128746033 [02:38<00:29, 188.33s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 84: 85%|████████▌ | 0.8500000238418579/0.9990000128746033 [02:40<00:28, 188.31s/it] NFE: 85: 85%|████████▌ | 0.8500000238418579/0.9990000128746033 [02:40<00:28, 188.31s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 85: 86%|████████▌ | 0.8600000143051147/0.9990000128746033 [02:41<00:26, 188.29s/it] NFE: 86: 86%|████████▌ | 0.8600000143051147/0.9990000128746033 [02:41<00:26, 188.29s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 86: 87%|████████▋ | 0.8700000047683716/0.9990000128746033 [02:43<00:24, 188.27s/it] NFE: 87: 87%|████████▋ | 0.8700000047683716/0.9990000128746033 [02:43<00:24, 188.27s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 87: 88%|████████▊ | 0.8799999952316284/0.9990000128746033 [02:45<00:22, 188.25s/it] NFE: 88: 88%|████████▊ | 0.8799999952316284/0.9990000128746033 [02:45<00:22, 188.25s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 88: 89%|████████▉ | 0.8899999856948853/0.9990000128746033 [02:47<00:20, 188.23s/it] NFE: 89: 89%|████████▉ | 0.8899999856948853/0.9990000128746033 [02:47<00:20, 188.23s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 89: 90%|█████████ | 0.8999999761581421/0.9990000128746033 [02:49<00:18, 188.21s/it] NFE: 90: 90%|█████████ | 0.8999999761581421/0.9990000128746033 [02:49<00:18, 188.21s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 90: 91%|█████████ | 0.9100000262260437/0.9990000128746033 [02:51<00:16, 188.19s/it] NFE: 91: 91%|█████████ | 0.9100000262260437/0.9990000128746033 [02:51<00:16, 188.19s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 91: 92%|█████████▏| 0.9200000166893005/0.9990000128746033 [02:53<00:14, 188.18s/it] NFE: 92: 92%|█████████▏| 0.9200000166893005/0.9990000128746033 [02:53<00:14, 188.18s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 92: 93%|█████████▎| 0.9300000071525574/0.9990000128746033 [02:55<00:12, 188.18s/it] NFE: 93: 93%|█████████▎| 0.9300000071525574/0.9990000128746033 [02:55<00:12, 188.18s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 93: 94%|█████████▍| 0.9399999976158142/0.9990000128746033 [02:56<00:11, 188.17s/it] NFE: 94: 94%|█████████▍| 0.9399999976158142/0.9990000128746033 [02:56<00:11, 188.17s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 94: 95%|█████████▌| 0.949999988079071/0.9990000128746033 [02:58<00:09, 188.17s/it] NFE: 95: 95%|█████████▌| 0.949999988079071/0.9990000128746033 [02:58<00:09, 188.17s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 95: 96%|█████████▌| 0.9599999785423279/0.9990000128746033 [03:00<00:07, 188.16s/it] NFE: 96: 96%|█████████▌| 0.9599999785423279/0.9990000128746033 [03:00<00:07, 188.16s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 96: 97%|█████████▋| 0.9700000286102295/0.9990000128746033 [03:02<00:05, 188.16s/it] NFE: 97: 97%|█████████▋| 0.9700000286102295/0.9990000128746033 [03:02<00:05, 188.16s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 97: 98%|█████████▊| 0.9800000190734863/0.9990000128746033 [03:04<00:03, 188.15s/it] NFE: 98: 98%|█████████▊| 0.9800000190734863/0.9990000128746033 [03:04<00:03, 188.15s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 98: 99%|█████████▉| 0.9900000095367432/0.9990000128746033 [03:06<00:01, 188.15s/it] NFE: 99: 99%|█████████▉| 0.9900000095367432/0.9990000128746033 [03:06<00:01, 188.15s/it][0.9666779488325119, 0.6666666269302368, 0.7214939594268799, 0.7797090411186218]

NFE: 99: 100%|██████████| 0.9990000128746033/0.9990000128746033 [03:06<00:00, 186.70s/it] NFE: 100: 100%|██████████| 0.9990000128746033/0.9990000128746033 [03:06<00:00, 186.70s/it] NFE: 100: 100%|██████████| 0.9990000128746033/0.9990000128746033 [03:06<00:00, 186.70s/it] 2026-03-11 19:44:10.444029: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0. 2026-03-11 19:44:10.461366: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered WARNING: All log messages before absl::InitializeLog() is called are written to STDERR E0000 00:00:1773258250.481215 8434 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered E0000 00:00:1773258250.487566 8434 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered W0000 00:00:1773258250.504596 8434 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1773258250.504634 8434 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1773258250.504636 8434 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1773258250.504638 8434 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. 2026-03-11 19:44:10.509156: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. [‘SETKVKTCRVVL’] [0.9666779488325119, 0.6666666269302368, 7.214939594268799, 0.7797090411186218]

NFE: 0: 0%| | 0/0.9990000128746033 [00:00<?, ?it/s]Weight Vector: tensor([1., 1., 1., 1.], device=‘cuda:0’) [0.9182402268052101, 0.4166666269302368, 0.5616710186004639, 0.0187290757894516]

NFE: 0: 1%| | 0.009999999776482582/0.9990000128746033 [00:01<03:06, 188.98s/it] NFE: 1: 1%| | 0.009999999776482582/0.9990000128746033 [00:01<03:06, 189.00s/it][0.9182402268052101, 0.4166666269302368, 0.5616710186004639, 0.0187290757894516]

NFE: 1: 2%|▏ | 0.019999999552965164/0.9990000128746033 [00:03<03:03, 187.81s/it] NFE: 2: 2%|▏ | 0.019999999552965164/0.9990000128746033 [00:03<03:03, 187.82s/it][0.9182402268052101, 0.4166666269302368, 0.5616710186004639, 0.0187290757894516]

NFE: 2: 3%|▎ | 0.029999999329447746/0.9990000128746033 [00:05<03:01, 187.40s/it] NFE: 3: 3%|▎ | 0.029999999329447746/0.9990000128746033 [00:05<03:01, 187.40s/it][0.9182402268052101, 0.4166666269302368, 0.5616710186004639, 0.0187290757894516]

NFE: 3: 4%|▍ | 0.03999999910593033/0.9990000128746033 [00:07<02:59, 187.16s/it] NFE: 4: 4%|▍ | 0.03999999910593033/0.9990000128746033 [00:07<02:59, 187.17s/it][0.9182402268052101, 0.4166666269302368, 0.5616710186004639, 0.0187290757894516]

NFE: 4: 5%|▌ | 0.05000000074505806/0.9990000128746033 [00:09<02:57, 187.15s/it] NFE: 5: 5%|▌ | 0.05000000074505806/0.9990000128746033 [00:09<02:57, 187.15s/it][0.9182402268052101, 0.4166666269302368, 0.5616710186004639, 0.0187290757894516]

NFE: 5: 6%|▌ | 0.05999999865889549/0.9990000128746033 [00:11<02:55, 187.20s/it] NFE: 6: 6%|▌ | 0.05999999865889549/0.9990000128746033 [00:11<02:55, 187.20s/it][0.9182402268052101, 0.4166666269302368, 0.5616710186004639, 0.0187290757894516]

NFE: 6: 7%|▋ | 0.07000000029802322/0.9990000128746033 [00:13<02:53, 187.21s/it] NFE: 7: 7%|▋ | 0.07000000029802322/0.9990000128746033 [00:13<02:53, 187.22s/it][0.9182402268052101, 0.4166666269302368, 0.5616710186004639, 0.0187290757894516]

NFE: 7: 8%|▊ | 0.07999999821186066/0.9990000128746033 [00:14<02:52, 187.21s/it] NFE: 8: 8%|▊ | 0.07999999821186066/0.9990000128746033 [00:14<02:52, 187.22s/it][0.9182402268052101, 0.4166666269302368, 0.5616710186004639, 0.0187290757894516]

NFE: 8: 9%|▉ | 0.09000000357627869/0.9990000128746033 [00:16<02:50, 187.18s/it] NFE: 9: 9%|▉ | 0.09000000357627869/0.9990000128746033 [00:16<02:50, 187.18s/it][0.9182402268052101, 0.4166666269302368, 0.5616710186004639, 0.0187290757894516]

NFE: 9: 10%|█ | 0.10000000149011612/0.9990000128746033 [00:18<02:48, 187.08s/it] NFE: 10: 10%|█ | 0.10000000149011612/0.9990000128746033 [00:18<02:48, 187.08s/it][0.9182402268052101, 0.4166666269302368, 0.5616710186004639, 0.0187290757894516]

NFE: 10: 11%|█ | 0.10999999940395355/0.9990000128746033 [00:20<02:46, 187.09s/it] NFE: 11: 11%|█ | 0.10999999940395355/0.9990000128746033 [00:20<02:46, 187.09s/it][0.9182402268052101, 0.4166666269302368, 0.5616710186004639, 0.0187290757894516]

NFE: 11: 12%|█▏ | 0.11999999731779099/0.9990000128746033 [00:22<02:44, 187.12s/it] NFE: 12: 12%|█▏ | 0.11999999731779099/0.9990000128746033 [00:22<02:44, 187.12s/it][0.9182402268052101, 0.4166666269302368, 0.5616710186004639, 0.0187290757894516]

NFE: 12: 13%|█▎ | 0.12999999523162842/0.9990000128746033 [00:24<02:42, 187.11s/it] NFE: 13: 13%|█▎ | 0.12999999523162842/0.9990000128746033 [00:24<02:42, 187.11s/it][0.9182402268052101, 0.4166666269302368, 0.5616710186004639, 0.0187290757894516]

NFE: 13: 14%|█▍ | 0.14000000059604645/0.9990000128746033 [00:26<02:40, 187.09s/it] NFE: 14: 14%|█▍ | 0.14000000059604645/0.9990000128746033 [00:26<02:40, 187.09s/it][0.9182402268052101, 0.4166666269302368, 0.5616710186004639, 0.0187290757894516]

NFE: 14: 15%|█▌ | 0.15000000596046448/0.9990000128746033 [00:28<02:38, 187.06s/it] NFE: 15: 15%|█▌ | 0.15000000596046448/0.9990000128746033 [00:28<02:38, 187.06s/it][0.9182402268052101, 0.4166666269302368, 0.5616710186004639, 0.0187290757894516]

NFE: 15: 16%|█▌ | 0.1599999964237213/0.9990000128746033 [00:29<02:36, 187.07s/it] NFE: 16: 16%|█▌ | 0.1599999964237213/0.9990000128746033 [00:29<02:36, 187.07s/it][0.9182402268052101, 0.4166666269302368, 0.5616710186004639, 0.0187290757894516]

NFE: 16: 17%|█▋ | 0.17000000178813934/0.9990000128746033 [00:31<02:35, 187.06s/it] NFE: 17: 17%|█▋ | 0.17000000178813934/0.9990000128746033 [00:31<02:35, 187.06s/it][0.9182402268052101, 0.4166666269302368, 0.5616710186004639, 0.0187290757894516]

NFE: 17: 18%|█▊ | 0.18000000715255737/0.9990000128746033 [00:33<02:33, 187.08s/it] NFE: 18: 18%|█▊ | 0.18000000715255737/0.9990000128746033 [00:33<02:33, 187.08s/it][0.9182402268052101, 0.4166666269302368, 0.5616710186004639, 0.0187290757894516]

NFE: 18: 19%|█▉ | 0.1899999976158142/0.9990000128746033 [00:35<02:31, 187.10s/it] NFE: 19: 19%|█▉ | 0.1899999976158142/0.9990000128746033 [00:35<02:31, 187.10s/it]Jump! [0.947677243500948, 0.5, 0.5999413728713989, 0.016267873346805573]

NFE: 19: 20%|██ | 0.20000000298023224/0.9990000128746033 [00:37<02:29, 187.10s/it] NFE: 20: 20%|██ | 0.20000000298023224/0.9990000128746033 [00:37<02:29, 187.10s/it][0.947677243500948, 0.5, 0.5999413728713989, 0.016267873346805573]

NFE: 20: 21%|██ | 0.20999999344348907/0.9990000128746033 [00:39<02:27, 187.10s/it] NFE: 21: 21%|██ | 0.20999999344348907/0.9990000128746033 [00:39<02:27, 187.10s/it][0.947677243500948, 0.5, 0.5999413728713989, 0.016267873346805573]

NFE: 21: 22%|██▏ | 0.2199999988079071/0.9990000128746033 [00:41<02:25, 187.10s/it] NFE: 22: 22%|██▏ | 0.2199999988079071/0.9990000128746033 [00:41<02:25, 187.10s/it][0.947677243500948, 0.5, 0.5999413728713989, 0.016267873346805573]

NFE: 22: 23%|██▎ | 0.23000000417232513/0.9990000128746033 [00:43<02:23, 187.09s/it] NFE: 23: 23%|██▎ | 0.23000000417232513/0.9990000128746033 [00:43<02:23, 187.09s/it][0.947677243500948, 0.5, 0.5999413728713989, 0.016267873346805573]

NFE: 23: 24%|██▍ | 0.23999999463558197/0.9990000128746033 [00:44<02:21, 187.08s/it] NFE: 24: 24%|██▍ | 0.23999999463558197/0.9990000128746033 [00:44<02:21, 187.08s/it][0.947677243500948, 0.5, 0.5999413728713989, 0.016267873346805573]

NFE: 24: 25%|██▌ | 0.25/0.9990000128746033 [00:46<02:20, 187.09s/it]
NFE: 25: 25%|██▌ | 0.25/0.9990000128746033 [00:46<02:20, 187.09s/it][0.947677243500948, 0.5, 0.5999413728713989, 0.016267873346805573]

NFE: 25: 26%|██▌ | 0.25999999046325684/0.9990000128746033 [00:48<02:18, 187.09s/it] NFE: 26: 26%|██▌ | 0.25999999046325684/0.9990000128746033 [00:48<02:18, 187.09s/it][0.947677243500948, 0.5, 0.5999413728713989, 0.016267873346805573]

NFE: 26: 27%|██▋ | 0.27000001072883606/0.9990000128746033 [00:50<02:16, 187.11s/it] NFE: 27: 27%|██▋ | 0.27000001072883606/0.9990000128746033 [00:50<02:16, 187.11s/it][0.947677243500948, 0.5, 0.5999413728713989, 0.016267873346805573]

NFE: 27: 28%|██▊ | 0.2800000011920929/0.9990000128746033 [00:52<02:14, 187.13s/it] NFE: 28: 28%|██▊ | 0.2800000011920929/0.9990000128746033 [00:52<02:14, 187.13s/it]Jump! [0.9575028233230114, 0.5833333134651184, 0.6630609631538391, 0.013635562732815742]

NFE: 28: 29%|██▉ | 0.28999999165534973/0.9990000128746033 [00:54<02:12, 187.14s/it] NFE: 29: 29%|██▉ | 0.28999999165534973/0.9990000128746033 [00:54<02:12, 187.14s/it][0.9575028233230114, 0.5833333134651184, 0.6630609631538391, 0.013635562732815742]

NFE: 29: 30%|███ | 0.30000001192092896/0.9990000128746033 [00:56<02:10, 187.14s/it] NFE: 30: 30%|███ | 0.30000001192092896/0.9990000128746033 [00:56<02:10, 187.14s/it][0.9575028233230114, 0.5833333134651184, 0.6630609631538391, 0.013635562732815742]

NFE: 30: 31%|███ | 0.3100000023841858/0.9990000128746033 [00:58<02:08, 187.15s/it] NFE: 31: 31%|███ | 0.3100000023841858/0.9990000128746033 [00:58<02:08, 187.15s/it][0.9575028233230114, 0.5833333134651184, 0.6630609631538391, 0.013635562732815742]

NFE: 31: 32%|███▏ | 0.3199999928474426/0.9990000128746033 [00:59<02:07, 187.20s/it] NFE: 32: 32%|███▏ | 0.3199999928474426/0.9990000128746033 [00:59<02:07, 187.20s/it][0.9575028233230114, 0.5833333134651184, 0.6630609631538391, 0.013635562732815742]

NFE: 32: 33%|███▎ | 0.33000001311302185/0.9990000128746033 [01:01<02:05, 187.20s/it] NFE: 33: 33%|███▎ | 0.33000001311302185/0.9990000128746033 [01:01<02:05, 187.20s/it][0.9575028233230114, 0.5833333134651184, 0.6630609631538391, 0.013635562732815742]

NFE: 33: 34%|███▍ | 0.3400000035762787/0.9990000128746033 [01:03<02:03, 187.19s/it] NFE: 34: 34%|███▍ | 0.3400000035762787/0.9990000128746033 [01:03<02:03, 187.19s/it][0.9575028233230114, 0.5833333134651184, 0.6630609631538391, 0.013635562732815742]

NFE: 34: 35%|███▌ | 0.3499999940395355/0.9990000128746033 [01:05<02:01, 187.19s/it] NFE: 35: 35%|███▌ | 0.3499999940395355/0.9990000128746033 [01:05<02:01, 187.20s/it][0.9575028233230114, 0.5833333134651184, 0.6630609631538391, 0.013635562732815742]

NFE: 35: 36%|███▌ | 0.36000001430511475/0.9990000128746033 [01:07<01:59, 187.19s/it] NFE: 36: 36%|███▌ | 0.36000001430511475/0.9990000128746033 [01:07<01:59, 187.19s/it][0.9575028233230114, 0.5833333134651184, 0.6630609631538391, 0.013635562732815742]

NFE: 36: 37%|███▋ | 0.3700000047683716/0.9990000128746033 [01:09<01:57, 187.20s/it] NFE: 37: 37%|███▋ | 0.3700000047683716/0.9990000128746033 [01:09<01:57, 187.20s/it][0.9575028233230114, 0.5833333134651184, 0.6630609631538391, 0.013635562732815742]

NFE: 37: 38%|███▊ | 0.3799999952316284/0.9990000128746033 [01:11<01:55, 187.20s/it] NFE: 38: 38%|███▊ | 0.3799999952316284/0.9990000128746033 [01:11<01:55, 187.20s/it]Jump! [0.9444788098335266, 0.6666666269302368, 0.6485329866409302, 0.03531733155250549]

NFE: 38: 39%|███▉ | 0.38999998569488525/0.9990000128746033 [01:13<01:54, 187.20s/it] NFE: 39: 39%|███▉ | 0.38999998569488525/0.9990000128746033 [01:13<01:54, 187.20s/it]Jump! [0.9489777311682701, 0.6666666269302368, 0.625001847743988, 0.18695953488349915]

NFE: 39: 40%|████ | 0.4000000059604645/0.9990000128746033 [01:14<01:52, 187.19s/it] NFE: 40: 40%|████ | 0.4000000059604645/0.9990000128746033 [01:14<01:52, 187.19s/it][0.9489777311682701, 0.6666666269302368, 0.625001847743988, 0.18695953488349915]

NFE: 40: 41%|████ | 0.4099999964237213/0.9990000128746033 [01:16<01:50, 187.19s/it] NFE: 41: 41%|████ | 0.4099999964237213/0.9990000128746033 [01:16<01:50, 187.19s/it][0.9489777311682701, 0.6666666269302368, 0.625001847743988, 0.18695953488349915]

NFE: 41: 42%|████▏ | 0.41999998688697815/0.9990000128746033 [01:18<01:48, 187.20s/it] NFE: 42: 42%|████▏ | 0.41999998688697815/0.9990000128746033 [01:18<01:48, 187.20s/it][0.9489777311682701, 0.6666666269302368, 0.625001847743988, 0.18695953488349915]

NFE: 42: 43%|████▎ | 0.4300000071525574/0.9990000128746033 [01:20<01:46, 187.21s/it] NFE: 43: 43%|████▎ | 0.4300000071525574/0.9990000128746033 [01:20<01:46, 187.21s/it][0.9489777311682701, 0.6666666269302368, 0.625001847743988, 0.18695953488349915]

NFE: 43: 44%|████▍ | 0.4399999976158142/0.9990000128746033 [01:22<01:44, 187.21s/it] NFE: 44: 44%|████▍ | 0.4399999976158142/0.9990000128746033 [01:22<01:44, 187.21s/it][0.9489777311682701, 0.6666666269302368, 0.625001847743988, 0.18695953488349915]

NFE: 44: 45%|████▌ | 0.44999998807907104/0.9990000128746033 [01:24<01:42, 187.20s/it] NFE: 45: 45%|████▌ | 0.44999998807907104/0.9990000128746033 [01:24<01:42, 187.20s/it][0.9489777311682701, 0.6666666269302368, 0.625001847743988, 0.18695953488349915]

NFE: 45: 46%|████▌ | 0.46000000834465027/0.9990000128746033 [01:26<01:40, 187.21s/it] NFE: 46: 46%|████▌ | 0.46000000834465027/0.9990000128746033 [01:26<01:40, 187.21s/it][0.9489777311682701, 0.6666666269302368, 0.625001847743988, 0.18695953488349915]

NFE: 46: 47%|████▋ | 0.4699999988079071/0.9990000128746033 [01:27<01:39, 187.20s/it] NFE: 47: 47%|████▋ | 0.4699999988079071/0.9990000128746033 [01:27<01:39, 187.20s/it][0.9489777311682701, 0.6666666269302368, 0.625001847743988, 0.18695953488349915]

NFE: 47: 48%|████▊ | 0.47999998927116394/0.9990000128746033 [01:29<01:37, 187.20s/it] NFE: 48: 48%|████▊ | 0.47999998927116394/0.9990000128746033 [01:29<01:37, 187.20s/it][0.9489777311682701, 0.6666666269302368, 0.625001847743988, 0.18695953488349915]

NFE: 48: 49%|████▉ | 0.49000000953674316/0.9990000128746033 [01:31<01:35, 187.21s/it] NFE: 49: 49%|████▉ | 0.49000000953674316/0.9990000128746033 [01:31<01:35, 187.21s/it][0.9489777311682701, 0.6666666269302368, 0.625001847743988, 0.18695953488349915]

NFE: 49: 50%|█████ | 0.5/0.9990000128746033 [01:33<01:33, 187.21s/it]
NFE: 50: 50%|█████ | 0.5/0.9990000128746033 [01:33<01:33, 187.21s/it]Jump! [0.9210547655820847, 0.6666666269302368, 0.6467683911323547, 0.22622990608215332]

NFE: 50: 51%|█████ | 0.5099999904632568/0.9990000128746033 [01:35<01:31, 187.19s/it] NFE: 51: 51%|█████ | 0.5099999904632568/0.9990000128746033 [01:35<01:31, 187.19s/it][0.9210547655820847, 0.6666666269302368, 0.6467683911323547, 0.22622990608215332]

NFE: 51: 52%|█████▏ | 0.5199999809265137/0.9990000128746033 [01:37<01:29, 187.19s/it] NFE: 52: 52%|█████▏ | 0.5199999809265137/0.9990000128746033 [01:37<01:29, 187.19s/it][0.9210547655820847, 0.6666666269302368, 0.6467683911323547, 0.22622990608215332]

NFE: 52: 53%|█████▎ | 0.5299999713897705/0.9990000128746033 [01:39<01:27, 187.19s/it] NFE: 53: 53%|█████▎ | 0.5299999713897705/0.9990000128746033 [01:39<01:27, 187.19s/it][0.9210547655820847, 0.6666666269302368, 0.6467683911323547, 0.22622990608215332]

NFE: 53: 54%|█████▍ | 0.5400000214576721/0.9990000128746033 [01:41<01:25, 187.18s/it] NFE: 54: 54%|█████▍ | 0.5400000214576721/0.9990000128746033 [01:41<01:25, 187.18s/it][0.9210547655820847, 0.6666666269302368, 0.6467683911323547, 0.22622990608215332]

NFE: 54: 55%|█████▌ | 0.550000011920929/0.9990000128746033 [01:42<01:24, 187.19s/it] NFE: 55: 55%|█████▌ | 0.550000011920929/0.9990000128746033 [01:42<01:24, 187.19s/it][0.9210547655820847, 0.6666666269302368, 0.6467683911323547, 0.22622990608215332]

NFE: 55: 56%|█████▌ | 0.5600000023841858/0.9990000128746033 [01:44<01:22, 187.19s/it] NFE: 56: 56%|█████▌ | 0.5600000023841858/0.9990000128746033 [01:44<01:22, 187.19s/it][0.9210547655820847, 0.6666666269302368, 0.6467683911323547, 0.22622990608215332]

NFE: 56: 57%|█████▋ | 0.5699999928474426/0.9990000128746033 [01:46<01:20, 187.19s/it] NFE: 57: 57%|█████▋ | 0.5699999928474426/0.9990000128746033 [01:46<01:20, 187.19s/it]Jump! [0.9526022113859653, 0.75, 0.6508082747459412, 0.11812040954828262]

NFE: 57: 58%|█████▊ | 0.5799999833106995/0.9990000128746033 [01:48<01:18, 187.19s/it] NFE: 58: 58%|█████▊ | 0.5799999833106995/0.9990000128746033 [01:48<01:18, 187.19s/it][0.9526022113859653, 0.75, 0.6508082747459412, 0.11812040954828262]

NFE: 58: 59%|█████▉ | 0.5899999737739563/0.9990000128746033 [01:50<01:16, 187.18s/it] NFE: 59: 59%|█████▉ | 0.5899999737739563/0.9990000128746033 [01:50<01:16, 187.18s/it]Jump! [0.9667159244418144, 0.75, 0.5794999599456787, 0.30711933970451355]

NFE: 59: 60%|██████ | 0.6000000238418579/0.9990000128746033 [01:52<01:14, 187.18s/it] NFE: 60: 60%|██████ | 0.6000000238418579/0.9990000128746033 [01:52<01:14, 187.18s/it][0.9667159244418144, 0.75, 0.5794999599456787, 0.30711933970451355]

NFE: 60: 61%|██████ | 0.6100000143051147/0.9990000128746033 [01:54<01:12, 187.17s/it] NFE: 61: 61%|██████ | 0.6100000143051147/0.9990000128746033 [01:54<01:12, 187.17s/it][0.9667159244418144, 0.75, 0.5794999599456787, 0.30711933970451355]

NFE: 61: 62%|██████▏ | 0.6200000047683716/0.9990000128746033 [01:56<01:10, 187.19s/it] NFE: 62: 62%|██████▏ | 0.6200000047683716/0.9990000128746033 [01:56<01:10, 187.19s/it][0.9667159244418144, 0.75, 0.5794999599456787, 0.30711933970451355]

NFE: 62: 63%|██████▎ | 0.6299999952316284/0.9990000128746033 [01:57<01:09, 187.18s/it] NFE: 63: 63%|██████▎ | 0.6299999952316284/0.9990000128746033 [01:57<01:09, 187.18s/it][0.9667159244418144, 0.75, 0.5794999599456787, 0.30711933970451355]

NFE: 63: 64%|██████▍ | 0.6399999856948853/0.9990000128746033 [01:59<01:07, 187.18s/it] NFE: 64: 64%|██████▍ | 0.6399999856948853/0.9990000128746033 [01:59<01:07, 187.18s/it][0.9667159244418144, 0.75, 0.5794999599456787, 0.30711933970451355]

NFE: 64: 65%|██████▌ | 0.6499999761581421/0.9990000128746033 [02:01<01:05, 187.19s/it] NFE: 65: 65%|██████▌ | 0.6499999761581421/0.9990000128746033 [02:01<01:05, 187.19s/it][0.9667159244418144, 0.75, 0.5794999599456787, 0.30711933970451355]

NFE: 65: 66%|██████▌ | 0.6600000262260437/0.9990000128746033 [02:03<01:03, 187.19s/it] NFE: 66: 66%|██████▌ | 0.6600000262260437/0.9990000128746033 [02:03<01:03, 187.19s/it][0.9667159244418144, 0.75, 0.5794999599456787, 0.30711933970451355]

NFE: 66: 67%|██████▋ | 0.6700000166893005/0.9990000128746033 [02:05<01:01, 187.19s/it] NFE: 67: 67%|██████▋ | 0.6700000166893005/0.9990000128746033 [02:05<01:01, 187.19s/it][0.9667159244418144, 0.75, 0.5794999599456787, 0.30711933970451355]

NFE: 67: 68%|██████▊ | 0.6800000071525574/0.9990000128746033 [02:07<00:59, 187.19s/it] NFE: 68: 68%|██████▊ | 0.6800000071525574/0.9990000128746033 [02:07<00:59, 187.19s/it][0.9667159244418144, 0.75, 0.5794999599456787, 0.30711933970451355]

NFE: 68: 69%|██████▉ | 0.6899999976158142/0.9990000128746033 [02:09<00:57, 187.19s/it] NFE: 69: 69%|██████▉ | 0.6899999976158142/0.9990000128746033 [02:09<00:57, 187.19s/it][0.9667159244418144, 0.75, 0.5794999599456787, 0.30711933970451355]

NFE: 69: 70%|███████ | 0.699999988079071/0.9990000128746033 [02:11<00:55, 187.19s/it] NFE: 70: 70%|███████ | 0.699999988079071/0.9990000128746033 [02:11<00:55, 187.19s/it]Jump! [0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 70: 71%|███████ | 0.7099999785423279/0.9990000128746033 [02:12<00:54, 187.18s/it] NFE: 71: 71%|███████ | 0.7099999785423279/0.9990000128746033 [02:12<00:54, 187.18s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 71: 72%|███████▏ | 0.7200000286102295/0.9990000128746033 [02:14<00:52, 187.17s/it] NFE: 72: 72%|███████▏ | 0.7200000286102295/0.9990000128746033 [02:14<00:52, 187.17s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 72: 73%|███████▎ | 0.7300000190734863/0.9990000128746033 [02:16<00:50, 187.16s/it] NFE: 73: 73%|███████▎ | 0.7300000190734863/0.9990000128746033 [02:16<00:50, 187.16s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 73: 74%|███████▍ | 0.7400000095367432/0.9990000128746033 [02:18<00:48, 187.16s/it] NFE: 74: 74%|███████▍ | 0.7400000095367432/0.9990000128746033 [02:18<00:48, 187.16s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 74: 75%|███████▌ | 0.75/0.9990000128746033 [02:20<00:46, 187.15s/it]
NFE: 75: 75%|███████▌ | 0.75/0.9990000128746033 [02:20<00:46, 187.15s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 75: 76%|███████▌ | 0.7599999904632568/0.9990000128746033 [02:22<00:44, 187.15s/it] NFE: 76: 76%|███████▌ | 0.7599999904632568/0.9990000128746033 [02:22<00:44, 187.15s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 76: 77%|███████▋ | 0.7699999809265137/0.9990000128746033 [02:24<00:42, 187.15s/it] NFE: 77: 77%|███████▋ | 0.7699999809265137/0.9990000128746033 [02:24<00:42, 187.15s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 77: 78%|███████▊ | 0.7799999713897705/0.9990000128746033 [02:25<00:40, 187.15s/it] NFE: 78: 78%|███████▊ | 0.7799999713897705/0.9990000128746033 [02:25<00:40, 187.15s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 78: 79%|███████▉ | 0.7900000214576721/0.9990000128746033 [02:27<00:39, 187.14s/it] NFE: 79: 79%|███████▉ | 0.7900000214576721/0.9990000128746033 [02:27<00:39, 187.14s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 79: 80%|████████ | 0.800000011920929/0.9990000128746033 [02:29<00:37, 187.14s/it] NFE: 80: 80%|████████ | 0.800000011920929/0.9990000128746033 [02:29<00:37, 187.15s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 80: 81%|████████ | 0.8100000023841858/0.9990000128746033 [02:31<00:35, 187.14s/it] NFE: 81: 81%|████████ | 0.8100000023841858/0.9990000128746033 [02:31<00:35, 187.14s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 81: 82%|████████▏ | 0.8199999928474426/0.9990000128746033 [02:33<00:33, 187.14s/it] NFE: 82: 82%|████████▏ | 0.8199999928474426/0.9990000128746033 [02:33<00:33, 187.14s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 82: 83%|████████▎ | 0.8299999833106995/0.9990000128746033 [02:35<00:31, 187.14s/it] NFE: 83: 83%|████████▎ | 0.8299999833106995/0.9990000128746033 [02:35<00:31, 187.14s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 83: 84%|████████▍ | 0.8399999737739563/0.9990000128746033 [02:37<00:29, 187.13s/it] NFE: 84: 84%|████████▍ | 0.8399999737739563/0.9990000128746033 [02:37<00:29, 187.13s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 84: 85%|████████▌ | 0.8500000238418579/0.9990000128746033 [02:39<00:27, 187.13s/it] NFE: 85: 85%|████████▌ | 0.8500000238418579/0.9990000128746033 [02:39<00:27, 187.13s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 85: 86%|████████▌ | 0.8600000143051147/0.9990000128746033 [02:40<00:26, 187.13s/it] NFE: 86: 86%|████████▌ | 0.8600000143051147/0.9990000128746033 [02:40<00:26, 187.13s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 86: 87%|████████▋ | 0.8700000047683716/0.9990000128746033 [02:42<00:24, 187.13s/it] NFE: 87: 87%|████████▋ | 0.8700000047683716/0.9990000128746033 [02:42<00:24, 187.13s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 87: 88%|████████▊ | 0.8799999952316284/0.9990000128746033 [02:44<00:22, 187.13s/it] NFE: 88: 88%|████████▊ | 0.8799999952316284/0.9990000128746033 [02:44<00:22, 187.13s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 88: 89%|████████▉ | 0.8899999856948853/0.9990000128746033 [02:46<00:20, 187.13s/it] NFE: 89: 89%|████████▉ | 0.8899999856948853/0.9990000128746033 [02:46<00:20, 187.13s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 89: 90%|█████████ | 0.8999999761581421/0.9990000128746033 [02:48<00:18, 187.12s/it] NFE: 90: 90%|█████████ | 0.8999999761581421/0.9990000128746033 [02:48<00:18, 187.12s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 90: 91%|█████████ | 0.9100000262260437/0.9990000128746033 [02:50<00:16, 187.11s/it] NFE: 91: 91%|█████████ | 0.9100000262260437/0.9990000128746033 [02:50<00:16, 187.11s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 91: 92%|█████████▏| 0.9200000166893005/0.9990000128746033 [02:52<00:14, 187.11s/it] NFE: 92: 92%|█████████▏| 0.9200000166893005/0.9990000128746033 [02:52<00:14, 187.11s/it][0.9714920222759247, 0.6666666269302368, 0.6005164980888367, 0.41679421067237854]

NFE: 92: 93%|█████████▎| 0.9300000071525574/0.9990000128746033 [02:54<00:12, 187.11s/it] NFE: 93: 93%|█████████▎| 0.9300000071525574/0.9990000128746033 [02:54<00:12, 187.11s/it]Jump! [0.9754907339811325, 0.75, 0.6045233607292175, 0.40398263931274414]

NFE: 93: 94%|█████████▍| 0.9399999976158142/0.9990000128746033 [02:55<00:11, 187.11s/it] NFE: 94: 94%|█████████▍| 0.9399999976158142/0.9990000128746033 [02:55<00:11, 187.11s/it][0.9754907339811325, 0.75, 0.6045233607292175, 0.40398263931274414]

NFE: 94: 95%|█████████▌| 0.949999988079071/0.9990000128746033 [02:57<00:09, 187.10s/it] NFE: 95: 95%|█████████▌| 0.949999988079071/0.9990000128746033 [02:57<00:09, 187.10s/it][0.9754907339811325, 0.75, 0.6045233607292175, 0.40398263931274414]

NFE: 95: 96%|█████████▌| 0.9599999785423279/0.9990000128746033 [02:59<00:07, 187.10s/it] NFE: 96: 96%|█████████▌| 0.9599999785423279/0.9990000128746033 [02:59<00:07, 187.10s/it][0.9754907339811325, 0.75, 0.6045233607292175, 0.40398263931274414]

NFE: 96: 97%|█████████▋| 0.9700000286102295/0.9990000128746033 [03:01<00:05, 187.10s/it] NFE: 97: 97%|█████████▋| 0.9700000286102295/0.9990000128746033 [03:01<00:05, 187.10s/it][0.9754907339811325, 0.75, 0.6045233607292175, 0.40398263931274414]

NFE: 97: 98%|█████████▊| 0.9800000190734863/0.9990000128746033 [03:03<00:03, 187.10s/it] NFE: 98: 98%|█████████▊| 0.9800000190734863/0.9990000128746033 [03:03<00:03, 187.10s/it][0.9754907339811325, 0.75, 0.6045233607292175, 0.40398263931274414]

NFE: 98: 99%|█████████▉| 0.9900000095367432/0.9990000128746033 [03:05<00:01, 187.11s/it] NFE: 99: 99%|█████████▉| 0.9900000095367432/0.9990000128746033 [03:05<00:01, 187.11s/it][0.9754907339811325, 0.75, 0.6045233607292175, 0.40398263931274414]

NFE: 99: 100%|██████████| 0.9990000128746033/0.9990000128746033 [03:05<00:00, 185.66s/it] NFE: 100: 100%|██████████| 0.9990000128746033/0.9990000128746033 [03:05<00:00, 185.66s/it] NFE: 100: 100%|██████████| 0.9990000128746033/0.9990000128746033 [03:05<00:00, 185.66s/it] [‘KEKPKYETIYTW’] [0.9754907339811325, 0.75, 6.045233726501465, 0.40398263931274414]

NFE: 0: 0%| | 0/0.9990000128746033 [00:00<?, ?it/s]Weight Vector: tensor([1., 1., 1., 1.], device=‘cuda:0’) [0.9430350884795189, 0.6666666269302368, 0.6217286586761475, 0.0018350780010223389]

NFE: 0: 1%| | 0.009999999776482582/0.9990000128746033 [00:01<03:05, 187.20s/it] NFE: 1: 1%| | 0.009999999776482582/0.9990000128746033 [00:01<03:05, 187.22s/it][0.9430350884795189, 0.6666666269302368, 0.6217286586761475, 0.0018350780010223389]

NFE: 1: 2%|▏ | 0.019999999552965164/0.9990000128746033 [00:03<03:03, 187.01s/it] NFE: 2: 2%|▏ | 0.019999999552965164/0.9990000128746033 [00:03<03:03, 187.01s/it][0.9430350884795189, 0.6666666269302368, 0.6217286586761475, 0.0018350780010223389]

NFE: 2: 3%|▎ | 0.029999999329447746/0.9990000128746033 [00:05<03:01, 186.81s/it] NFE: 3: 3%|▎ | 0.029999999329447746/0.9990000128746033 [00:05<03:01, 186.81s/it][0.9430350884795189, 0.6666666269302368, 0.6217286586761475, 0.0018350780010223389]

NFE: 3: 4%|▍ | 0.03999999910593033/0.9990000128746033 [00:07<02:59, 186.66s/it] NFE: 4: 4%|▍ | 0.03999999910593033/0.9990000128746033 [00:07<02:59, 186.67s/it][0.9430350884795189, 0.6666666269302368, 0.6217286586761475, 0.0018350780010223389]

NFE: 4: 5%|▌ | 0.05000000074505806/0.9990000128746033 [00:09<02:57, 186.86s/it] NFE: 5: 5%|▌ | 0.05000000074505806/0.9990000128746033 [00:09<02:57, 186.86s/it][0.9430350884795189, 0.6666666269302368, 0.6217286586761475, 0.0018350780010223389]

NFE: 5: 6%|▌ | 0.05999999865889549/0.9990000128746033 [00:11<02:55, 186.83s/it] NFE: 6: 6%|▌ | 0.05999999865889549/0.9990000128746033 [00:11<02:55, 186.83s/it][0.9430350884795189, 0.6666666269302368, 0.6217286586761475, 0.0018350780010223389]

NFE: 6: 7%|▋ | 0.07000000029802322/0.9990000128746033 [00:13<02:53, 186.79s/it] NFE: 7: 7%|▋ | 0.07000000029802322/0.9990000128746033 [00:13<02:53, 186.79s/it][0.9430350884795189, 0.6666666269302368, 0.6217286586761475, 0.0018350780010223389]

NFE: 7: 8%|▊ | 0.07999999821186066/0.9990000128746033 [00:14<02:51, 186.69s/it] NFE: 8: 8%|▊ | 0.07999999821186066/0.9990000128746033 [00:14<02:51, 186.69s/it][0.9430350884795189, 0.6666666269302368, 0.6217286586761475, 0.0018350780010223389]

NFE: 8: 9%|▉ | 0.09000000357627869/0.9990000128746033 [00:16<02:49, 186.62s/it] NFE: 9: 9%|▉ | 0.09000000357627869/0.9990000128746033 [00:16<02:49, 186.62s/it][0.9430350884795189, 0.6666666269302368, 0.6217286586761475, 0.0018350780010223389]

NFE: 9: 10%|█ | 0.10000000149011612/0.9990000128746033 [00:18<02:47, 186.54s/it] NFE: 10: 10%|█ | 0.10000000149011612/0.9990000128746033 [00:18<02:47, 186.54s/it][0.9430350884795189, 0.6666666269302368, 0.6217286586761475, 0.0018350780010223389]

NFE: 10: 11%|█ | 0.10999999940395355/0.9990000128746033 [00:20<02:45, 186.53s/it] NFE: 11: 11%|█ | 0.10999999940395355/0.9990000128746033 [00:20<02:45, 186.53s/it][0.9430350884795189, 0.6666666269302368, 0.6217286586761475, 0.0018350780010223389]

NFE: 11: 12%|█▏ | 0.11999999731779099/0.9990000128746033 [00:22<02:44, 186.59s/it] NFE: 12: 12%|█▏ | 0.11999999731779099/0.9990000128746033 [00:22<02:44, 186.59s/it][0.9430350884795189, 0.6666666269302368, 0.6217286586761475, 0.0018350780010223389]

NFE: 12: 13%|█▎ | 0.12999999523162842/0.9990000128746033 [00:24<02:42, 186.62s/it] NFE: 13: 13%|█▎ | 0.12999999523162842/0.9990000128746033 [00:24<02:42, 186.62s/it]Jump! [0.9574280381202698, 0.75, 0.6192697286605835, 0.008897013030946255]

NFE: 13: 14%|█▍ | 0.14000000059604645/0.9990000128746033 [00:26<02:40, 186.65s/it] NFE: 14: 14%|█▍ | 0.14000000059604645/0.9990000128746033 [00:26<02:40, 186.65s/it][0.9574280381202698, 0.75, 0.6192697286605835, 0.008897013030946255]

NFE: 14: 15%|█▌ | 0.15000000596046448/0.9990000128746033 [00:27<02:38, 186.64s/it] NFE: 15: 15%|█▌ | 0.15000000596046448/0.9990000128746033 [00:27<02:38, 186.64s/it][0.9574280381202698, 0.75, 0.6192697286605835, 0.008897013030946255]

NFE: 15: 16%|█▌ | 0.1599999964237213/0.9990000128746033 [00:29<02:36, 186.64s/it] NFE: 16: 16%|█▌ | 0.1599999964237213/0.9990000128746033 [00:29<02:36, 186.64s/it]Jump! [0.9627480544149876, 0.75, 0.6250566840171814, 0.013623252511024475]

NFE: 16: 17%|█▋ | 0.17000000178813934/0.9990000128746033 [00:31<02:34, 186.71s/it] NFE: 17: 17%|█▋ | 0.17000000178813934/0.9990000128746033 [00:31<02:34, 186.71s/it][0.9627480544149876, 0.75, 0.6250566840171814, 0.013623252511024475]

NFE: 17: 18%|█▊ | 0.18000000715255737/0.9990000128746033 [00:33<02:32, 186.74s/it] NFE: 18: 18%|█▊ | 0.18000000715255737/0.9990000128746033 [00:33<02:32, 186.74s/it][0.9627480544149876, 0.75, 0.6250566840171814, 0.013623252511024475]

NFE: 18: 19%|█▉ | 0.1899999976158142/0.9990000128746033 [00:35<02:31, 186.74s/it] NFE: 19: 19%|█▉ | 0.1899999976158142/0.9990000128746033 [00:35<02:31, 186.74s/it][0.9627480544149876, 0.75, 0.6250566840171814, 0.013623252511024475]

NFE: 19: 20%|██ | 0.20000000298023224/0.9990000128746033 [00:37<02:29, 186.75s/it] NFE: 20: 20%|██ | 0.20000000298023224/0.9990000128746033 [00:37<02:29, 186.75s/it][0.9627480544149876, 0.75, 0.6250566840171814, 0.013623252511024475]

NFE: 20: 21%|██ | 0.20999999344348907/0.9990000128746033 [00:39<02:27, 186.76s/it] NFE: 21: 21%|██ | 0.20999999344348907/0.9990000128746033 [00:39<02:27, 186.76s/it][0.9627480544149876, 0.75, 0.6250566840171814, 0.013623252511024475]

NFE: 21: 22%|██▏ | 0.2199999988079071/0.9990000128746033 [00:41<02:25, 186.76s/it] NFE: 22: 22%|██▏ | 0.2199999988079071/0.9990000128746033 [00:41<02:25, 186.76s/it][0.9627480544149876, 0.75, 0.6250566840171814, 0.013623252511024475]

NFE: 22: 23%|██▎ | 0.23000000417232513/0.9990000128746033 [00:42<02:23, 186.79s/it] NFE: 23: 23%|██▎ | 0.23000000417232513/0.9990000128746033 [00:42<02:23, 186.79s/it][0.9627480544149876, 0.75, 0.6250566840171814, 0.013623252511024475]

NFE: 23: 24%|██▍ | 0.23999999463558197/0.9990000128746033 [00:44<02:21, 186.82s/it] NFE: 24: 24%|██▍ | 0.23999999463558197/0.9990000128746033 [00:44<02:21, 186.82s/it][0.9627480544149876, 0.75, 0.6250566840171814, 0.013623252511024475]

NFE: 24: 25%|██▌ | 0.25/0.9990000128746033 [00:46<02:19, 186.84s/it]
NFE: 25: 25%|██▌ | 0.25/0.9990000128746033 [00:46<02:19, 186.84s/it][0.9627480544149876, 0.75, 0.6250566840171814, 0.013623252511024475]

NFE: 25: 26%|██▌ | 0.25999999046325684/0.9990000128746033 [00:48<02:18, 186.86s/it] NFE: 26: 26%|██▌ | 0.25999999046325684/0.9990000128746033 [00:48<02:18, 186.86s/it][0.9627480544149876, 0.75, 0.6250566840171814, 0.013623252511024475]

NFE: 26: 27%|██▋ | 0.27000001072883606/0.9990000128746033 [00:50<02:16, 186.84s/it] NFE: 27: 27%|██▋ | 0.27000001072883606/0.9990000128746033 [00:50<02:16, 186.85s/it][0.9627480544149876, 0.75, 0.6250566840171814, 0.013623252511024475]

NFE: 27: 28%|██▊ | 0.2800000011920929/0.9990000128746033 [00:52<02:14, 186.87s/it] NFE: 28: 28%|██▊ | 0.2800000011920929/0.9990000128746033 [00:52<02:14, 186.87s/it][0.9627480544149876, 0.75, 0.6250566840171814, 0.013623252511024475]

NFE: 28: 29%|██▉ | 0.28999999165534973/0.9990000128746033 [00:54<02:12, 186.88s/it] NFE: 29: 29%|██▉ | 0.28999999165534973/0.9990000128746033 [00:54<02:12, 186.88s/it][0.9627480544149876, 0.75, 0.6250566840171814, 0.013623252511024475]

NFE: 29: 30%|███ | 0.30000001192092896/0.9990000128746033 [00:56<02:10, 186.90s/it] NFE: 30: 30%|███ | 0.30000001192092896/0.9990000128746033 [00:56<02:10, 186.90s/it][0.9627480544149876, 0.75, 0.6250566840171814, 0.013623252511024475]

NFE: 30: 31%|███ | 0.3100000023841858/0.9990000128746033 [00:57<02:08, 186.89s/it] NFE: 31: 31%|███ | 0.3100000023841858/0.9990000128746033 [00:57<02:08, 186.89s/it]Jump! [0.9684211909770966, 0.75, 0.6283949613571167, 0.03799552470445633]

NFE: 31: 32%|███▏ | 0.3199999928474426/0.9990000128746033 [00:59<02:06, 186.88s/it] NFE: 32: 32%|███▏ | 0.3199999928474426/0.9990000128746033 [00:59<02:06, 186.88s/it]Jump! [0.9636527560651302, 0.8333333134651184, 0.6365193724632263, 0.013730952516198158]

NFE: 32: 33%|███▎ | 0.33000001311302185/0.9990000128746033 [01:01<02:05, 186.87s/it] NFE: 33: 33%|███▎ | 0.33000001311302185/0.9990000128746033 [01:01<02:05, 186.87s/it][0.9636527560651302, 0.8333333134651184, 0.6365193724632263, 0.013730952516198158]

NFE: 33: 34%|███▍ | 0.3400000035762787/0.9990000128746033 [01:03<02:03, 186.86s/it] NFE: 34: 34%|███▍ | 0.3400000035762787/0.9990000128746033 [01:03<02:03, 186.86s/it][0.9636527560651302, 0.8333333134651184, 0.6365193724632263, 0.013730952516198158]

NFE: 34: 35%|███▌ | 0.3499999940395355/0.9990000128746033 [01:05<02:01, 186.87s/it] NFE: 35: 35%|███▌ | 0.3499999940395355/0.9990000128746033 [01:05<02:01, 186.87s/it]Jump! [0.9542962573468685, 0.8333333134651184, 0.6484213471412659, 0.013894453644752502]

NFE: 35: 36%|███▌ | 0.36000001430511475/0.9990000128746033 [01:07<01:59, 186.88s/it] NFE: 36: 36%|███▌ | 0.36000001430511475/0.9990000128746033 [01:07<01:59, 186.88s/it]Jump! [0.9576348774135113, 0.8333333134651184, 0.6566989421844482, 0.019453784450888634]

NFE: 36: 37%|███▋ | 0.3700000047683716/0.9990000128746033 [01:09<01:57, 186.90s/it] NFE: 37: 37%|███▋ | 0.3700000047683716/0.9990000128746033 [01:09<01:57, 186.90s/it][0.9576348774135113, 0.8333333134651184, 0.6566989421844482, 0.019453784450888634]

NFE: 37: 38%|███▊ | 0.3799999952316284/0.9990000128746033 [01:11<01:55, 186.91s/it] NFE: 38: 38%|███▊ | 0.3799999952316284/0.9990000128746033 [01:11<01:55, 186.91s/it][0.9576348774135113, 0.8333333134651184, 0.6566989421844482, 0.019453784450888634]

NFE: 38: 39%|███▉ | 0.38999998569488525/0.9990000128746033 [01:12<01:53, 186.93s/it] NFE: 39: 39%|███▉ | 0.38999998569488525/0.9990000128746033 [01:12<01:53, 186.93s/it][0.9576348774135113, 0.8333333134651184, 0.6566989421844482, 0.019453784450888634]

NFE: 39: 40%|████ | 0.4000000059604645/0.9990000128746033 [01:14<01:51, 186.93s/it] NFE: 40: 40%|████ | 0.4000000059604645/0.9990000128746033 [01:14<01:51, 186.94s/it][0.9576348774135113, 0.8333333134651184, 0.6566989421844482, 0.019453784450888634]

NFE: 40: 41%|████ | 0.4099999964237213/0.9990000128746033 [01:16<01:50, 186.94s/it] NFE: 41: 41%|████ | 0.4099999964237213/0.9990000128746033 [01:16<01:50, 186.94s/it][0.9576348774135113, 0.8333333134651184, 0.6566989421844482, 0.019453784450888634]

NFE: 41: 42%|████▏ | 0.41999998688697815/0.9990000128746033 [01:18<01:48, 186.97s/it] NFE: 42: 42%|████▏ | 0.41999998688697815/0.9990000128746033 [01:18<01:48, 186.97s/it][0.9576348774135113, 0.8333333134651184, 0.6566989421844482, 0.019453784450888634]

NFE: 42: 43%|████▎ | 0.4300000071525574/0.9990000128746033 [01:20<01:46, 186.98s/it] NFE: 43: 43%|████▎ | 0.4300000071525574/0.9990000128746033 [01:20<01:46, 186.98s/it][0.9576348774135113, 0.8333333134651184, 0.6566989421844482, 0.019453784450888634]

NFE: 43: 44%|████▍ | 0.4399999976158142/0.9990000128746033 [01:22<01:44, 186.98s/it] NFE: 44: 44%|████▍ | 0.4399999976158142/0.9990000128746033 [01:22<01:44, 186.98s/it][0.9576348774135113, 0.8333333134651184, 0.6566989421844482, 0.019453784450888634]

NFE: 44: 45%|████▌ | 0.44999998807907104/0.9990000128746033 [01:24<01:42, 186.99s/it] NFE: 45: 45%|████▌ | 0.44999998807907104/0.9990000128746033 [01:24<01:42, 186.99s/it][0.9576348774135113, 0.8333333134651184, 0.6566989421844482, 0.019453784450888634]

NFE: 45: 46%|████▌ | 0.46000000834465027/0.9990000128746033 [01:26<01:40, 187.00s/it] NFE: 46: 46%|████▌ | 0.46000000834465027/0.9990000128746033 [01:26<01:40, 187.00s/it][0.9576348774135113, 0.8333333134651184, 0.6566989421844482, 0.019453784450888634]

NFE: 46: 47%|████▋ | 0.4699999988079071/0.9990000128746033 [01:27<01:38, 187.02s/it] NFE: 47: 47%|████▋ | 0.4699999988079071/0.9990000128746033 [01:27<01:38, 187.02s/it][0.9576348774135113, 0.8333333134651184, 0.6566989421844482, 0.019453784450888634]

NFE: 47: 48%|████▊ | 0.47999998927116394/0.9990000128746033 [01:29<01:37, 187.03s/it] NFE: 48: 48%|████▊ | 0.47999998927116394/0.9990000128746033 [01:29<01:37, 187.03s/it][0.9576348774135113, 0.8333333134651184, 0.6566989421844482, 0.019453784450888634]

NFE: 48: 49%|████▉ | 0.49000000953674316/0.9990000128746033 [01:31<01:35, 187.03s/it] NFE: 49: 49%|████▉ | 0.49000000953674316/0.9990000128746033 [01:31<01:35, 187.03s/it][0.9576348774135113, 0.8333333134651184, 0.6566989421844482, 0.019453784450888634]

NFE: 49: 50%|█████ | 0.5/0.9990000128746033 [01:33<01:33, 187.03s/it]
NFE: 50: 50%|█████ | 0.5/0.9990000128746033 [01:33<01:33, 187.03s/it][0.9576348774135113, 0.8333333134651184, 0.6566989421844482, 0.019453784450888634]

NFE: 50: 51%|█████ | 0.5099999904632568/0.9990000128746033 [01:35<01:31, 187.03s/it] NFE: 51: 51%|█████ | 0.5099999904632568/0.9990000128746033 [01:35<01:31, 187.03s/it][0.9576348774135113, 0.8333333134651184, 0.6566989421844482, 0.019453784450888634]

NFE: 51: 52%|█████▏ | 0.5199999809265137/0.9990000128746033 [01:37<01:29, 187.02s/it] NFE: 52: 52%|█████▏ | 0.5199999809265137/0.9990000128746033 [01:37<01:29, 187.02s/it][0.9576348774135113, 0.8333333134651184, 0.6566989421844482, 0.019453784450888634]

NFE: 52: 53%|█████▎ | 0.5299999713897705/0.9990000128746033 [01:39<01:27, 187.02s/it] NFE: 53: 53%|█████▎ | 0.5299999713897705/0.9990000128746033 [01:39<01:27, 187.02s/it][0.9576348774135113, 0.8333333134651184, 0.6566989421844482, 0.019453784450888634]

NFE: 53: 54%|█████▍ | 0.5400000214576721/0.9990000128746033 [01:40<01:25, 187.02s/it] NFE: 54: 54%|█████▍ | 0.5400000214576721/0.9990000128746033 [01:40<01:25, 187.02s/it][0.9576348774135113, 0.8333333134651184, 0.6566989421844482, 0.019453784450888634]

NFE: 54: 55%|█████▌ | 0.550000011920929/0.9990000128746033 [01:42<01:23, 187.03s/it] NFE: 55: 55%|█████▌ | 0.550000011920929/0.9990000128746033 [01:42<01:23, 187.03s/it]Jump! [0.9533324092626572, 0.8333333134651184, 0.6591528654098511, 0.07877840101718903]

NFE: 55: 56%|█████▌ | 0.5600000023841858/0.9990000128746033 [01:44<01:22, 187.03s/it] NFE: 56: 56%|█████▌ | 0.5600000023841858/0.9990000128746033 [01:44<01:22, 187.04s/it][0.9533324092626572, 0.8333333134651184, 0.6591528654098511, 0.07877840101718903]

NFE: 56: 57%|█████▋ | 0.5699999928474426/0.9990000128746033 [01:46<01:20, 187.04s/it] NFE: 57: 57%|█████▋ | 0.5699999928474426/0.9990000128746033 [01:46<01:20, 187.04s/it]Jump! [0.9659351706504822, 0.8333333134651184, 0.6427434682846069, 0.11301430314779282]

NFE: 57: 58%|█████▊ | 0.5799999833106995/0.9990000128746033 [01:48<01:18, 187.02s/it] NFE: 58: 58%|█████▊ | 0.5799999833106995/0.9990000128746033 [01:48<01:18, 187.02s/it]Jump! [0.9656420052051544, 0.8333333134651184, 0.6446895599365234, 0.1294279396533966]

NFE: 58: 59%|█████▉ | 0.5899999737739563/0.9990000128746033 [01:50<01:16, 187.01s/it] NFE: 59: 59%|█████▉ | 0.5899999737739563/0.9990000128746033 [01:50<01:16, 187.01s/it][0.9656420052051544, 0.8333333134651184, 0.6446895599365234, 0.1294279396533966]

NFE: 59: 60%|██████ | 0.6000000238418579/0.9990000128746033 [01:52<01:14, 187.02s/it] NFE: 60: 60%|██████ | 0.6000000238418579/0.9990000128746033 [01:52<01:14, 187.02s/it][0.9656420052051544, 0.8333333134651184, 0.6446895599365234, 0.1294279396533966]

NFE: 60: 61%|██████ | 0.6100000143051147/0.9990000128746033 [01:54<01:12, 187.02s/it] NFE: 61: 61%|██████ | 0.6100000143051147/0.9990000128746033 [01:54<01:12, 187.02s/it][0.9656420052051544, 0.8333333134651184, 0.6446895599365234, 0.1294279396533966]

NFE: 61: 62%|██████▏ | 0.6200000047683716/0.9990000128746033 [01:55<01:10, 187.01s/it] NFE: 62: 62%|██████▏ | 0.6200000047683716/0.9990000128746033 [01:55<01:10, 187.01s/it][0.9656420052051544, 0.8333333134651184, 0.6446895599365234, 0.1294279396533966]

NFE: 62: 63%|██████▎ | 0.6299999952316284/0.9990000128746033 [01:57<01:09, 187.01s/it] NFE: 63: 63%|██████▎ | 0.6299999952316284/0.9990000128746033 [01:57<01:09, 187.01s/it][0.9656420052051544, 0.8333333134651184, 0.6446895599365234, 0.1294279396533966]

NFE: 63: 64%|██████▍ | 0.6399999856948853/0.9990000128746033 [01:59<01:07, 187.00s/it] NFE: 64: 64%|██████▍ | 0.6399999856948853/0.9990000128746033 [01:59<01:07, 187.00s/it][0.9656420052051544, 0.8333333134651184, 0.6446895599365234, 0.1294279396533966]

NFE: 64: 65%|██████▌ | 0.6499999761581421/0.9990000128746033 [02:01<01:05, 187.00s/it] NFE: 65: 65%|██████▌ | 0.6499999761581421/0.9990000128746033 [02:01<01:05, 187.00s/it][0.9656420052051544, 0.8333333134651184, 0.6446895599365234, 0.1294279396533966]

NFE: 65: 66%|██████▌ | 0.6600000262260437/0.9990000128746033 [02:03<01:03, 187.00s/it] NFE: 66: 66%|██████▌ | 0.6600000262260437/0.9990000128746033 [02:03<01:03, 187.00s/it][0.9656420052051544, 0.8333333134651184, 0.6446895599365234, 0.1294279396533966]

NFE: 66: 67%|██████▋ | 0.6700000166893005/0.9990000128746033 [02:05<01:01, 186.99s/it] NFE: 67: 67%|██████▋ | 0.6700000166893005/0.9990000128746033 [02:05<01:01, 186.99s/it][0.9656420052051544, 0.8333333134651184, 0.6446895599365234, 0.1294279396533966]

NFE: 67: 68%|██████▊ | 0.6800000071525574/0.9990000128746033 [02:07<00:59, 186.99s/it] NFE: 68: 68%|██████▊ | 0.6800000071525574/0.9990000128746033 [02:07<00:59, 186.99s/it][0.9656420052051544, 0.8333333134651184, 0.6446895599365234, 0.1294279396533966]

NFE: 68: 69%|██████▉ | 0.6899999976158142/0.9990000128746033 [02:09<00:57, 187.00s/it] NFE: 69: 69%|██████▉ | 0.6899999976158142/0.9990000128746033 [02:09<00:57, 187.00s/it][0.9656420052051544, 0.8333333134651184, 0.6446895599365234, 0.1294279396533966]

NFE: 69: 70%|███████ | 0.699999988079071/0.9990000128746033 [02:10<00:55, 186.99s/it] NFE: 70: 70%|███████ | 0.699999988079071/0.9990000128746033 [02:10<00:55, 186.99s/it]Jump! [0.9506738372147083, 0.8333333134651184, 0.6492480039596558, 0.1404581218957901]

NFE: 70: 71%|███████ | 0.7099999785423279/0.9990000128746033 [02:12<00:54, 186.99s/it] NFE: 71: 71%|███████ | 0.7099999785423279/0.9990000128746033 [02:12<00:54, 186.99s/it]Jump! [0.9600984677672386, 0.9166666865348816, 0.6407340168952942, 0.26584258675575256]

NFE: 71: 72%|███████▏ | 0.7200000286102295/0.9990000128746033 [02:14<00:52, 187.01s/it] NFE: 72: 72%|███████▏ | 0.7200000286102295/0.9990000128746033 [02:14<00:52, 187.01s/it]Jump! [0.9486669488251209, 1.0, 0.6147314310073853, 0.339128702878952]

NFE: 72: 73%|███████▎ | 0.7300000190734863/0.9990000128746033 [02:16<00:50, 187.00s/it] NFE: 73: 73%|███████▎ | 0.7300000190734863/0.9990000128746033 [02:16<00:50, 187.00s/it][0.9486669488251209, 1.0, 0.6147314310073853, 0.339128702878952]

NFE: 73: 74%|███████▍ | 0.7400000095367432/0.9990000128746033 [02:18<00:48, 186.99s/it] NFE: 74: 74%|███████▍ | 0.7400000095367432/0.9990000128746033 [02:18<00:48, 186.99s/it]Jump! [0.9661266058683395, 1.0, 0.6008438467979431, 0.4007812440395355]

NFE: 74: 75%|███████▌ | 0.75/0.9990000128746033 [02:20<00:46, 186.98s/it]
NFE: 75: 75%|███████▌ | 0.75/0.9990000128746033 [02:20<00:46, 186.98s/it][0.9661266058683395, 1.0, 0.6008438467979431, 0.4007812440395355]

NFE: 75: 76%|███████▌ | 0.7599999904632568/0.9990000128746033 [02:22<00:44, 186.97s/it] NFE: 76: 76%|███████▌ | 0.7599999904632568/0.9990000128746033 [02:22<00:44, 186.97s/it][0.9661266058683395, 1.0, 0.6008438467979431, 0.4007812440395355]

NFE: 76: 77%|███████▋ | 0.7699999809265137/0.9990000128746033 [02:23<00:42, 186.96s/it] NFE: 77: 77%|███████▋ | 0.7699999809265137/0.9990000128746033 [02:23<00:42, 186.96s/it][0.9661266058683395, 1.0, 0.6008438467979431, 0.4007812440395355]

NFE: 77: 78%|███████▊ | 0.7799999713897705/0.9990000128746033 [02:25<00:40, 186.97s/it] NFE: 78: 78%|███████▊ | 0.7799999713897705/0.9990000128746033 [02:25<00:40, 186.97s/it][0.9661266058683395, 1.0, 0.6008438467979431, 0.4007812440395355]

NFE: 78: 79%|███████▉ | 0.7900000214576721/0.9990000128746033 [02:27<00:39, 186.97s/it] NFE: 79: 79%|███████▉ | 0.7900000214576721/0.9990000128746033 [02:27<00:39, 186.97s/it][0.9661266058683395, 1.0, 0.6008438467979431, 0.4007812440395355]

NFE: 79: 80%|████████ | 0.800000011920929/0.9990000128746033 [02:29<00:37, 186.97s/it] NFE: 80: 80%|████████ | 0.800000011920929/0.9990000128746033 [02:29<00:37, 186.97s/it][0.9661266058683395, 1.0, 0.6008438467979431, 0.4007812440395355]

NFE: 80: 81%|████████ | 0.8100000023841858/0.9990000128746033 [02:31<00:35, 186.97s/it] NFE: 81: 81%|████████ | 0.8100000023841858/0.9990000128746033 [02:31<00:35, 186.97s/it]Jump! [0.9618403874337673, 1.0, 0.5968782901763916, 0.5011692047119141]

NFE: 81: 82%|████████▏ | 0.8199999928474426/0.9990000128746033 [02:33<00:33, 186.97s/it] NFE: 82: 82%|████████▏ | 0.8199999928474426/0.9990000128746033 [02:33<00:33, 186.97s/it][0.9618403874337673, 1.0, 0.5968782901763916, 0.5011692047119141]

NFE: 82: 83%|████████▎ | 0.8299999833106995/0.9990000128746033 [02:35<00:31, 186.97s/it] NFE: 83: 83%|████████▎ | 0.8299999833106995/0.9990000128746033 [02:35<00:31, 186.97s/it][0.9618403874337673, 1.0, 0.5968782901763916, 0.5011692047119141]

NFE: 83: 84%|████████▍ | 0.8399999737739563/0.9990000128746033 [02:37<00:29, 186.97s/it] NFE: 84: 84%|████████▍ | 0.8399999737739563/0.9990000128746033 [02:37<00:29, 186.97s/it][0.9618403874337673, 1.0, 0.5968782901763916, 0.5011692047119141]

NFE: 84: 85%|████████▌ | 0.8500000238418579/0.9990000128746033 [02:38<00:27, 186.98s/it] NFE: 85: 85%|████████▌ | 0.8500000238418579/0.9990000128746033 [02:38<00:27, 186.98s/it]Jump! [0.9635799527168274, 1.0, 0.6060819029808044, 0.5376657843589783]

NFE: 85: 86%|████████▌ | 0.8600000143051147/0.9990000128746033 [02:40<00:25, 186.97s/it] NFE: 86: 86%|████████▌ | 0.8600000143051147/0.9990000128746033 [02:40<00:25, 186.97s/it]Jump! [0.9726648647338152, 1.0, 0.6095247864723206, 0.5780714154243469]

NFE: 86: 87%|████████▋ | 0.8700000047683716/0.9990000128746033 [02:42<00:24, 186.97s/it] NFE: 87: 87%|████████▋ | 0.8700000047683716/0.9990000128746033 [02:42<00:24, 186.97s/it][0.9726648647338152, 1.0, 0.6095247864723206, 0.5780714154243469]

NFE: 87: 88%|████████▊ | 0.8799999952316284/0.9990000128746033 [02:44<00:22, 186.97s/it] NFE: 88: 88%|████████▊ | 0.8799999952316284/0.9990000128746033 [02:44<00:22, 186.97s/it]Jump! [0.976908365264535, 1.0, 0.5532985329627991, 0.6531668901443481]

NFE: 88: 89%|████████▉ | 0.8899999856948853/0.9990000128746033 [02:46<00:20, 186.98s/it] NFE: 89: 89%|████████▉ | 0.8899999856948853/0.9990000128746033 [02:46<00:20, 186.98s/it][0.976908365264535, 1.0, 0.5532985329627991, 0.6531668901443481]

NFE: 89: 90%|█████████ | 0.8999999761581421/0.9990000128746033 [02:48<00:18, 186.98s/it] NFE: 90: 90%|█████████ | 0.8999999761581421/0.9990000128746033 [02:48<00:18, 186.98s/it][0.976908365264535, 1.0, 0.5532985329627991, 0.6531668901443481]

NFE: 90: 91%|█████████ | 0.9100000262260437/0.9990000128746033 [02:50<00:16, 186.99s/it] NFE: 91: 91%|█████████ | 0.9100000262260437/0.9990000128746033 [02:50<00:16, 186.99s/it][0.976908365264535, 1.0, 0.5532985329627991, 0.6531668901443481]

NFE: 91: 92%|█████████▏| 0.9200000166893005/0.9990000128746033 [02:52<00:14, 186.99s/it] NFE: 92: 92%|█████████▏| 0.9200000166893005/0.9990000128746033 [02:52<00:14, 186.99s/it][0.976908365264535, 1.0, 0.5532985329627991, 0.6531668901443481]

NFE: 92: 93%|█████████▎| 0.9300000071525574/0.9990000128746033 [02:53<00:12, 186.99s/it] NFE: 93: 93%|█████████▎| 0.9300000071525574/0.9990000128746033 [02:53<00:12, 186.99s/it][0.976908365264535, 1.0, 0.5532985329627991, 0.6531668901443481]

NFE: 93: 94%|█████████▍| 0.9399999976158142/0.9990000128746033 [02:55<00:11, 186.99s/it] NFE: 94: 94%|█████████▍| 0.9399999976158142/0.9990000128746033 [02:55<00:11, 186.99s/it][0.976908365264535, 1.0, 0.5532985329627991, 0.6531668901443481]

NFE: 94: 95%|█████████▌| 0.949999988079071/0.9990000128746033 [02:57<00:09, 187.00s/it] NFE: 95: 95%|█████████▌| 0.949999988079071/0.9990000128746033 [02:57<00:09, 187.00s/it][0.976908365264535, 1.0, 0.5532985329627991, 0.6531668901443481]

NFE: 95: 96%|█████████▌| 0.9599999785423279/0.9990000128746033 [02:59<00:07, 187.00s/it] NFE: 96: 96%|█████████▌| 0.9599999785423279/0.9990000128746033 [02:59<00:07, 187.00s/it][0.976908365264535, 1.0, 0.5532985329627991, 0.6531668901443481]

NFE: 96: 97%|█████████▋| 0.9700000286102295/0.9990000128746033 [03:01<00:05, 187.00s/it] NFE: 97: 97%|█████████▋| 0.9700000286102295/0.9990000128746033 [03:01<00:05, 187.00s/it][0.976908365264535, 1.0, 0.5532985329627991, 0.6531668901443481]

NFE: 97: 98%|█████████▊| 0.9800000190734863/0.9990000128746033 [03:03<00:03, 187.01s/it] NFE: 98: 98%|█████████▊| 0.9800000190734863/0.9990000128746033 [03:03<00:03, 187.01s/it][0.976908365264535, 1.0, 0.5532985329627991, 0.6531668901443481]

NFE: 98: 99%|█████████▉| 0.9900000095367432/0.9990000128746033 [03:05<00:01, 187.00s/it] NFE: 99: 99%|█████████▉| 0.9900000095367432/0.9990000128746033 [03:05<00:01, 187.00s/it][0.976908365264535, 1.0, 0.5532985329627991, 0.6531668901443481]

NFE: 99: 100%|██████████| 0.9990000128746033/0.9990000128746033 [03:05<00:00, 185.56s/it] NFE: 100: 100%|██████████| 0.9990000128746033/0.9990000128746033 [03:05<00:00, 185.56s/it] NFE: 100: 100%|██████████| 0.9990000128746033/0.9990000128746033 [03:05<00:00, 185.56s/it] [‘KDEQTGDCCKTT’] [0.976908365264535, 1.0, 5.532985210418701, 0.6531668901443481]

NFE: 0: 0%| | 0/0.9990000128746033 [00:00<?, ?it/s]Weight Vector: tensor([1., 1., 1., 1.], device=‘cuda:0’) [0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 0: 1%| | 0.009999999776482582/0.9990000128746033 [00:01<03:05, 187.85s/it] NFE: 1: 1%| | 0.009999999776482582/0.9990000128746033 [00:01<03:05, 187.87s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 1: 2%|▏ | 0.019999999552965164/0.9990000128746033 [00:03<03:03, 187.45s/it] NFE: 2: 2%|▏ | 0.019999999552965164/0.9990000128746033 [00:03<03:03, 187.45s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 2: 3%|▎ | 0.029999999329447746/0.9990000128746033 [00:05<03:01, 187.16s/it] NFE: 3: 3%|▎ | 0.029999999329447746/0.9990000128746033 [00:05<03:01, 187.16s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 3: 4%|▍ | 0.03999999910593033/0.9990000128746033 [00:07<02:59, 187.02s/it] NFE: 4: 4%|▍ | 0.03999999910593033/0.9990000128746033 [00:07<02:59, 187.03s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 4: 5%|▌ | 0.05000000074505806/0.9990000128746033 [00:09<02:57, 187.09s/it] NFE: 5: 5%|▌ | 0.05000000074505806/0.9990000128746033 [00:09<02:57, 187.10s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 5: 6%|▌ | 0.05999999865889549/0.9990000128746033 [00:11<02:55, 187.12s/it] NFE: 6: 6%|▌ | 0.05999999865889549/0.9990000128746033 [00:11<02:55, 187.13s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 6: 7%|▋ | 0.07000000029802322/0.9990000128746033 [00:13<02:53, 187.11s/it] NFE: 7: 7%|▋ | 0.07000000029802322/0.9990000128746033 [00:13<02:53, 187.11s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 7: 8%|▊ | 0.07999999821186066/0.9990000128746033 [00:14<02:51, 187.10s/it] NFE: 8: 8%|▊ | 0.07999999821186066/0.9990000128746033 [00:14<02:51, 187.10s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 8: 9%|▉ | 0.09000000357627869/0.9990000128746033 [00:16<02:50, 187.02s/it] NFE: 9: 9%|▉ | 0.09000000357627869/0.9990000128746033 [00:16<02:50, 187.03s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 9: 10%|█ | 0.10000000149011612/0.9990000128746033 [00:18<02:48, 187.04s/it] NFE: 10: 10%|█ | 0.10000000149011612/0.9990000128746033 [00:18<02:48, 187.04s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 10: 11%|█ | 0.10999999940395355/0.9990000128746033 [00:20<02:46, 187.08s/it] NFE: 11: 11%|█ | 0.10999999940395355/0.9990000128746033 [00:20<02:46, 187.08s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 11: 12%|█▏ | 0.11999999731779099/0.9990000128746033 [00:22<02:44, 187.08s/it] NFE: 12: 12%|█▏ | 0.11999999731779099/0.9990000128746033 [00:22<02:44, 187.08s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 12: 13%|█▎ | 0.12999999523162842/0.9990000128746033 [00:24<02:42, 187.05s/it] NFE: 13: 13%|█▎ | 0.12999999523162842/0.9990000128746033 [00:24<02:42, 187.05s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 13: 14%|█▍ | 0.14000000059604645/0.9990000128746033 [00:26<02:40, 187.07s/it] NFE: 14: 14%|█▍ | 0.14000000059604645/0.9990000128746033 [00:26<02:40, 187.07s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 14: 15%|█▌ | 0.15000000596046448/0.9990000128746033 [00:28<02:38, 187.06s/it] NFE: 15: 15%|█▌ | 0.15000000596046448/0.9990000128746033 [00:28<02:38, 187.06s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 15: 16%|█▌ | 0.1599999964237213/0.9990000128746033 [00:29<02:36, 187.09s/it] NFE: 16: 16%|█▌ | 0.1599999964237213/0.9990000128746033 [00:29<02:36, 187.09s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 16: 17%|█▋ | 0.17000000178813934/0.9990000128746033 [00:31<02:35, 187.09s/it] NFE: 17: 17%|█▋ | 0.17000000178813934/0.9990000128746033 [00:31<02:35, 187.09s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 17: 18%|█▊ | 0.18000000715255737/0.9990000128746033 [00:33<02:33, 187.09s/it] NFE: 18: 18%|█▊ | 0.18000000715255737/0.9990000128746033 [00:33<02:33, 187.09s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 18: 19%|█▉ | 0.1899999976158142/0.9990000128746033 [00:35<02:31, 187.08s/it] NFE: 19: 19%|█▉ | 0.1899999976158142/0.9990000128746033 [00:35<02:31, 187.08s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 19: 20%|██ | 0.20000000298023224/0.9990000128746033 [00:37<02:29, 187.07s/it] NFE: 20: 20%|██ | 0.20000000298023224/0.9990000128746033 [00:37<02:29, 187.07s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 20: 21%|██ | 0.20999999344348907/0.9990000128746033 [00:39<02:27, 187.09s/it] NFE: 21: 21%|██ | 0.20999999344348907/0.9990000128746033 [00:39<02:27, 187.09s/it][0.8911156952381134, 0.75, 0.5392359495162964, 0.06427273154258728]

NFE: 21: 22%|██▏ | 0.2199999988079071/0.9990000128746033 [00:41<02:25, 187.12s/it] NFE: 22: 22%|██▏ | 0.2199999988079071/0.9990000128746033 [00:41<02:25, 187.13s/it]Jump! [0.8977592140436172, 0.75, 0.5452988147735596, 0.10699160397052765]

NFE: 22: 23%|██▎ | 0.23000000417232513/0.9990000128746033 [00:43<02:23, 187.12s/it] NFE: 23: 23%|██▎ | 0.23000000417232513/0.9990000128746033 [00:43<02:23, 187.12s/it][0.8977592140436172, 0.75, 0.5452988147735596, 0.10699160397052765]

NFE: 23: 24%|██▍ | 0.23999999463558197/0.9990000128746033 [00:44<02:22, 187.12s/it] NFE: 24: 24%|██▍ | 0.23999999463558197/0.9990000128746033 [00:44<02:22, 187.12s/it]Jump! [0.8849005997180939, 0.75, 0.540800154209137, 0.5166702270507812]

NFE: 24: 25%|██▌ | 0.25/0.9990000128746033 [00:46<02:20, 187.12s/it]
NFE: 25: 25%|██▌ | 0.25/0.9990000128746033 [00:46<02:20, 187.12s/it][0.8849005997180939, 0.75, 0.540800154209137, 0.5166702270507812]

NFE: 25: 26%|██▌ | 0.25999999046325684/0.9990000128746033 [00:48<02:18, 187.09s/it] NFE: 26: 26%|██▌ | 0.25999999046325684/0.9990000128746033 [00:48<02:18, 187.10s/it][0.8849005997180939, 0.75, 0.540800154209137, 0.5166702270507812]

NFE: 26: 27%|██▋ | 0.27000001072883606/0.9990000128746033 [00:50<02:16, 187.07s/it] NFE: 27: 27%|██▋ | 0.27000001072883606/0.9990000128746033 [00:50<02:16, 187.08s/it][0.8849005997180939, 0.75, 0.540800154209137, 0.5166702270507812]

NFE: 27: 28%|██▊ | 0.2800000011920929/0.9990000128746033 [00:52<02:14, 187.09s/it] NFE: 28: 28%|██▊ | 0.2800000011920929/0.9990000128746033 [00:52<02:14, 187.09s/it][0.8849005997180939, 0.75, 0.540800154209137, 0.5166702270507812]

NFE: 28: 29%|██▉ | 0.28999999165534973/0.9990000128746033 [00:54<02:12, 187.10s/it] NFE: 29: 29%|██▉ | 0.28999999165534973/0.9990000128746033 [00:54<02:12, 187.10s/it][0.8849005997180939, 0.75, 0.540800154209137, 0.5166702270507812]

NFE: 29: 30%|███ | 0.30000001192092896/0.9990000128746033 [00:56<02:10, 187.10s/it] NFE: 30: 30%|███ | 0.30000001192092896/0.9990000128746033 [00:56<02:10, 187.10s/it][0.8849005997180939, 0.75, 0.540800154209137, 0.5166702270507812]

NFE: 30: 31%|███ | 0.3100000023841858/0.9990000128746033 [00:58<02:08, 187.10s/it] NFE: 31: 31%|███ | 0.3100000023841858/0.9990000128746033 [00:58<02:08, 187.10s/it]Jump! [0.9160678088665009, 0.75, 0.5461604595184326, 0.5178617835044861]

NFE: 31: 32%|███▏ | 0.3199999928474426/0.9990000128746033 [00:59<02:07, 187.12s/it] NFE: 32: 32%|███▏ | 0.3199999928474426/0.9990000128746033 [00:59<02:07, 187.12s/it][0.9160678088665009, 0.75, 0.5461604595184326, 0.5178617835044861]

NFE: 32: 33%|███▎ | 0.33000001311302185/0.9990000128746033 [01:01<02:05, 187.12s/it] NFE: 33: 33%|███▎ | 0.33000001311302185/0.9990000128746033 [01:01<02:05, 187.12s/it][0.9160678088665009, 0.75, 0.5461604595184326, 0.5178617835044861]

NFE: 33: 34%|███▍ | 0.3400000035762787/0.9990000128746033 [01:03<02:03, 187.14s/it] NFE: 34: 34%|███▍ | 0.3400000035762787/0.9990000128746033 [01:03<02:03, 187.14s/it]Jump! [0.9530380703508854, 0.75, 0.5512549877166748, 0.5998870730400085]

NFE: 34: 35%|███▌ | 0.3499999940395355/0.9990000128746033 [01:05<02:01, 187.13s/it] NFE: 35: 35%|███▌ | 0.3499999940395355/0.9990000128746033 [01:05<02:01, 187.14s/it][0.9530380703508854, 0.75, 0.5512549877166748, 0.5998870730400085]

NFE: 35: 36%|███▌ | 0.36000001430511475/0.9990000128746033 [01:07<01:59, 187.13s/it] NFE: 36: 36%|███▌ | 0.36000001430511475/0.9990000128746033 [01:07<01:59, 187.13s/it]Jump! [0.9427667930722237, 0.75, 0.5975725054740906, 0.7066122889518738]

NFE: 36: 37%|███▋ | 0.3700000047683716/0.9990000128746033 [01:09<01:57, 187.13s/it] NFE: 37: 37%|███▋ | 0.3700000047683716/0.9990000128746033 [01:09<01:57, 187.13s/it][0.9427667930722237, 0.75, 0.5975725054740906, 0.7066122889518738]

NFE: 37: 38%|███▊ | 0.3799999952316284/0.9990000128746033 [01:11<01:55, 187.12s/it] NFE: 38: 38%|███▊ | 0.3799999952316284/0.9990000128746033 [01:11<01:55, 187.12s/it][0.9427667930722237, 0.75, 0.5975725054740906, 0.7066122889518738]

NFE: 38: 39%|███▉ | 0.38999998569488525/0.9990000128746033 [01:12<01:53, 187.12s/it] NFE: 39: 39%|███▉ | 0.38999998569488525/0.9990000128746033 [01:12<01:53, 187.12s/it][0.9427667930722237, 0.75, 0.5975725054740906, 0.7066122889518738]

NFE: 39: 40%|████ | 0.4000000059604645/0.9990000128746033 [01:14<01:52, 187.13s/it] NFE: 40: 40%|████ | 0.4000000059604645/0.9990000128746033 [01:14<01:52, 187.13s/it][0.9427667930722237, 0.75, 0.5975725054740906, 0.7066122889518738]

NFE: 40: 41%|████ | 0.4099999964237213/0.9990000128746033 [01:16<01:50, 187.13s/it] NFE: 41: 41%|████ | 0.4099999964237213/0.9990000128746033 [01:16<01:50, 187.13s/it][0.9427667930722237, 0.75, 0.5975725054740906, 0.7066122889518738]

NFE: 41: 42%|████▏ | 0.41999998688697815/0.9990000128746033 [01:18<01:48, 187.14s/it] NFE: 42: 42%|████▏ | 0.41999998688697815/0.9990000128746033 [01:18<01:48, 187.14s/it][0.9427667930722237, 0.75, 0.5975725054740906, 0.7066122889518738]

NFE: 42: 43%|████▎ | 0.4300000071525574/0.9990000128746033 [01:20<01:46, 187.13s/it] NFE: 43: 43%|████▎ | 0.4300000071525574/0.9990000128746033 [01:20<01:46, 187.13s/it][0.9427667930722237, 0.75, 0.5975725054740906, 0.7066122889518738]

NFE: 43: 44%|████▍ | 0.4399999976158142/0.9990000128746033 [01:22<01:44, 187.12s/it] NFE: 44: 44%|████▍ | 0.4399999976158142/0.9990000128746033 [01:22<01:44, 187.12s/it][0.9427667930722237, 0.75, 0.5975725054740906, 0.7066122889518738]

NFE: 44: 45%|████▌ | 0.44999998807907104/0.9990000128746033 [01:24<01:42, 187.12s/it] NFE: 45: 45%|████▌ | 0.44999998807907104/0.9990000128746033 [01:24<01:42, 187.12s/it][0.9427667930722237, 0.75, 0.5975725054740906, 0.7066122889518738]

NFE: 45: 46%|████▌ | 0.46000000834465027/0.9990000128746033 [01:26<01:40, 187.12s/it] NFE: 46: 46%|████▌ | 0.46000000834465027/0.9990000128746033 [01:26<01:40, 187.12s/it][0.9427667930722237, 0.75, 0.5975725054740906, 0.7066122889518738]

NFE: 46: 47%|████▋ | 0.4699999988079071/0.9990000128746033 [01:27<01:38, 187.13s/it] NFE: 47: 47%|████▋ | 0.4699999988079071/0.9990000128746033 [01:27<01:38, 187.13s/it][0.9427667930722237, 0.75, 0.5975725054740906, 0.7066122889518738]

NFE: 47: 48%|████▊ | 0.47999998927116394/0.9990000128746033 [01:29<01:37, 187.12s/it] NFE: 48: 48%|████▊ | 0.47999998927116394/0.9990000128746033 [01:29<01:37, 187.12s/it][0.9427667930722237, 0.75, 0.5975725054740906, 0.7066122889518738]

NFE: 48: 49%|████▉ | 0.49000000953674316/0.9990000128746033 [01:31<01:35, 187.11s/it] NFE: 49: 49%|████▉ | 0.49000000953674316/0.9990000128746033 [01:31<01:35, 187.11s/it][0.9427667930722237, 0.75, 0.5975725054740906, 0.7066122889518738]

NFE: 49: 50%|█████ | 0.5/0.9990000128746033 [01:33<01:33, 187.10s/it]
NFE: 50: 50%|█████ | 0.5/0.9990000128746033 [01:33<01:33, 187.10s/it][0.9427667930722237, 0.75, 0.5975725054740906, 0.7066122889518738]

NFE: 50: 51%|█████ | 0.5099999904632568/0.9990000128746033 [01:35<01:31, 187.09s/it] NFE: 51: 51%|█████ | 0.5099999904632568/0.9990000128746033 [01:35<01:31, 187.09s/it][0.9427667930722237, 0.75, 0.5975725054740906, 0.7066122889518738]

NFE: 51: 52%|█████▏ | 0.5199999809265137/0.9990000128746033 [01:37<01:29, 187.10s/it] NFE: 52: 52%|█████▏ | 0.5199999809265137/0.9990000128746033 [01:37<01:29, 187.10s/it][0.9427667930722237, 0.75, 0.5975725054740906, 0.7066122889518738]

NFE: 52: 53%|█████▎ | 0.5299999713897705/0.9990000128746033 [01:39<01:27, 187.10s/it] NFE: 53: 53%|█████▎ | 0.5299999713897705/0.9990000128746033 [01:39<01:27, 187.10s/it][0.9427667930722237, 0.75, 0.5975725054740906, 0.7066122889518738]

NFE: 53: 54%|█████▍ | 0.5400000214576721/0.9990000128746033 [01:41<01:25, 187.10s/it] NFE: 54: 54%|█████▍ | 0.5400000214576721/0.9990000128746033 [01:41<01:25, 187.11s/it][0.9427667930722237, 0.75, 0.5975725054740906, 0.7066122889518738]

NFE: 54: 55%|█████▌ | 0.550000011920929/0.9990000128746033 [01:42<01:24, 187.09s/it] NFE: 55: 55%|█████▌ | 0.550000011920929/0.9990000128746033 [01:42<01:24, 187.09s/it]Jump! [0.9433723241090775, 0.8333333134651184, 0.6104594469070435, 0.621017575263977]

NFE: 55: 56%|█████▌ | 0.5600000023841858/0.9990000128746033 [01:44<01:22, 187.08s/it] NFE: 56: 56%|█████▌ | 0.5600000023841858/0.9990000128746033 [01:44<01:22, 187.08s/it][0.9433723241090775, 0.8333333134651184, 0.6104594469070435, 0.621017575263977]

NFE: 56: 57%|█████▋ | 0.5699999928474426/0.9990000128746033 [01:46<01:20, 187.07s/it] NFE: 57: 57%|█████▋ | 0.5699999928474426/0.9990000128746033 [01:46<01:20, 187.07s/it][0.9433723241090775, 0.8333333134651184, 0.6104594469070435, 0.621017575263977]

NFE: 57: 58%|█████▊ | 0.5799999833106995/0.9990000128746033 [01:48<01:18, 187.06s/it] NFE: 58: 58%|█████▊ | 0.5799999833106995/0.9990000128746033 [01:48<01:18, 187.06s/it][0.9433723241090775, 0.8333333134651184, 0.6104594469070435, 0.621017575263977]

NFE: 58: 59%|█████▉ | 0.5899999737739563/0.9990000128746033 [01:50<01:16, 187.06s/it] NFE: 59: 59%|█████▉ | 0.5899999737739563/0.9990000128746033 [01:50<01:16, 187.06s/it][0.9433723241090775, 0.8333333134651184, 0.6104594469070435, 0.621017575263977]

NFE: 59: 60%|██████ | 0.6000000238418579/0.9990000128746033 [01:52<01:14, 187.06s/it] NFE: 60: 60%|██████ | 0.6000000238418579/0.9990000128746033 [01:52<01:14, 187.06s/it][0.9433723241090775, 0.8333333134651184, 0.6104594469070435, 0.621017575263977]

NFE: 60: 61%|██████ | 0.6100000143051147/0.9990000128746033 [01:54<01:12, 187.06s/it] NFE: 61: 61%|██████ | 0.6100000143051147/0.9990000128746033 [01:54<01:12, 187.06s/it]Jump! [0.9582028836011887, 0.8333333134651184, 0.6309454441070557, 0.6528478860855103]

NFE: 61: 62%|██████▏ | 0.6200000047683716/0.9990000128746033 [01:55<01:10, 187.06s/it] NFE: 62: 62%|██████▏ | 0.6200000047683716/0.9990000128746033 [01:55<01:10, 187.06s/it][0.9582028836011887, 0.8333333134651184, 0.6309454441070557, 0.6528478860855103]

NFE: 62: 63%|██████▎ | 0.6299999952316284/0.9990000128746033 [01:57<01:09, 187.05s/it] NFE: 63: 63%|██████▎ | 0.6299999952316284/0.9990000128746033 [01:57<01:09, 187.05s/it][0.9582028836011887, 0.8333333134651184, 0.6309454441070557, 0.6528478860855103]

NFE: 63: 64%|██████▍ | 0.6399999856948853/0.9990000128746033 [01:59<01:07, 187.05s/it] NFE: 64: 64%|██████▍ | 0.6399999856948853/0.9990000128746033 [01:59<01:07, 187.05s/it][0.9582028836011887, 0.8333333134651184, 0.6309454441070557, 0.6528478860855103]

NFE: 64: 65%|██████▌ | 0.6499999761581421/0.9990000128746033 [02:01<01:05, 187.07s/it] NFE: 65: 65%|██████▌ | 0.6499999761581421/0.9990000128746033 [02:01<01:05, 187.07s/it][0.9582028836011887, 0.8333333134651184, 0.6309454441070557, 0.6528478860855103]

NFE: 65: 66%|██████▌ | 0.6600000262260437/0.9990000128746033 [02:03<01:03, 187.06s/it] NFE: 66: 66%|██████▌ | 0.6600000262260437/0.9990000128746033 [02:03<01:03, 187.06s/it][0.9582028836011887, 0.8333333134651184, 0.6309454441070557, 0.6528478860855103]

NFE: 66: 67%|██████▋ | 0.6700000166893005/0.9990000128746033 [02:05<01:01, 187.04s/it] NFE: 67: 67%|██████▋ | 0.6700000166893005/0.9990000128746033 [02:05<01:01, 187.04s/it][0.9582028836011887, 0.8333333134651184, 0.6309454441070557, 0.6528478860855103]

NFE: 67: 68%|██████▊ | 0.6800000071525574/0.9990000128746033 [02:07<00:59, 187.03s/it] NFE: 68: 68%|██████▊ | 0.6800000071525574/0.9990000128746033 [02:07<00:59, 187.03s/it][0.9582028836011887, 0.8333333134651184, 0.6309454441070557, 0.6528478860855103]

NFE: 68: 69%|██████▉ | 0.6899999976158142/0.9990000128746033 [02:09<00:57, 187.03s/it] NFE: 69: 69%|██████▉ | 0.6899999976158142/0.9990000128746033 [02:09<00:57, 187.03s/it][0.9582028836011887, 0.8333333134651184, 0.6309454441070557, 0.6528478860855103]

NFE: 69: 70%|███████ | 0.699999988079071/0.9990000128746033 [02:10<00:55, 187.04s/it] NFE: 70: 70%|███████ | 0.699999988079071/0.9990000128746033 [02:10<00:55, 187.04s/it][0.9582028836011887, 0.8333333134651184, 0.6309454441070557, 0.6528478860855103]

NFE: 70: 71%|███████ | 0.7099999785423279/0.9990000128746033 [02:12<00:54, 187.06s/it] NFE: 71: 71%|███████ | 0.7099999785423279/0.9990000128746033 [02:12<00:54, 187.06s/it][0.9582028836011887, 0.8333333134651184, 0.6309454441070557, 0.6528478860855103]

NFE: 71: 72%|███████▏ | 0.7200000286102295/0.9990000128746033 [02:14<00:52, 187.06s/it] NFE: 72: 72%|███████▏ | 0.7200000286102295/0.9990000128746033 [02:14<00:52, 187.06s/it][0.9582028836011887, 0.8333333134651184, 0.6309454441070557, 0.6528478860855103]

NFE: 72: 73%|███████▎ | 0.7300000190734863/0.9990000128746033 [02:16<00:50, 187.07s/it] NFE: 73: 73%|███████▎ | 0.7300000190734863/0.9990000128746033 [02:16<00:50, 187.07s/it]Jump! [0.9723214395344257, 0.8333333134651184, 0.6566857099533081, 0.6946128010749817]

NFE: 73: 74%|███████▍ | 0.7400000095367432/0.9990000128746033 [02:18<00:48, 187.08s/it] NFE: 74: 74%|███████▍ | 0.7400000095367432/0.9990000128746033 [02:18<00:48, 187.08s/it][0.9723214395344257, 0.8333333134651184, 0.6566857099533081, 0.6946128010749817]

NFE: 74: 75%|███████▌ | 0.75/0.9990000128746033 [02:20<00:46, 187.09s/it]
NFE: 75: 75%|███████▌ | 0.75/0.9990000128746033 [02:20<00:46, 187.09s/it][0.9723214395344257, 0.8333333134651184, 0.6566857099533081, 0.6946128010749817]

NFE: 75: 76%|███████▌ | 0.7599999904632568/0.9990000128746033 [02:22<00:44, 187.09s/it] NFE: 76: 76%|███████▌ | 0.7599999904632568/0.9990000128746033 [02:22<00:44, 187.09s/it][0.9723214395344257, 0.8333333134651184, 0.6566857099533081, 0.6946128010749817]

NFE: 76: 77%|███████▋ | 0.7699999809265137/0.9990000128746033 [02:24<00:42, 187.10s/it] NFE: 77: 77%|███████▋ | 0.7699999809265137/0.9990000128746033 [02:24<00:42, 187.11s/it][0.9723214395344257, 0.8333333134651184, 0.6566857099533081, 0.6946128010749817]

NFE: 77: 78%|███████▊ | 0.7799999713897705/0.9990000128746033 [02:25<00:40, 187.10s/it] NFE: 78: 78%|███████▊ | 0.7799999713897705/0.9990000128746033 [02:25<00:40, 187.10s/it][0.9723214395344257, 0.8333333134651184, 0.6566857099533081, 0.6946128010749817]

NFE: 78: 79%|███████▉ | 0.7900000214576721/0.9990000128746033 [02:27<00:39, 187.10s/it] NFE: 79: 79%|███████▉ | 0.7900000214576721/0.9990000128746033 [02:27<00:39, 187.10s/it][0.9723214395344257, 0.8333333134651184, 0.6566857099533081, 0.6946128010749817]

NFE: 79: 80%|████████ | 0.800000011920929/0.9990000128746033 [02:29<00:37, 187.09s/it] NFE: 80: 80%|████████ | 0.800000011920929/0.9990000128746033 [02:29<00:37, 187.09s/it][0.9723214395344257, 0.8333333134651184, 0.6566857099533081, 0.6946128010749817]

NFE: 80: 81%|████████ | 0.8100000023841858/0.9990000128746033 [02:31<00:35, 187.09s/it] NFE: 81: 81%|████████ | 0.8100000023841858/0.9990000128746033 [02:31<00:35, 187.09s/it][0.9723214395344257, 0.8333333134651184, 0.6566857099533081, 0.6946128010749817]

NFE: 81: 82%|████████▏ | 0.8199999928474426/0.9990000128746033 [02:33<00:33, 187.08s/it] NFE: 82: 82%|████████▏ | 0.8199999928474426/0.9990000128746033 [02:33<00:33, 187.08s/it]Jump! [0.9875771626830101, 0.9166666865348816, 0.571490466594696, 0.7535707354545593]

NFE: 82: 83%|████████▎ | 0.8299999833106995/0.9990000128746033 [02:35<00:31, 187.09s/it] NFE: 83: 83%|████████▎ | 0.8299999833106995/0.9990000128746033 [02:35<00:31, 187.09s/it][0.9875771626830101, 0.9166666865348816, 0.571490466594696, 0.7535707354545593]

NFE: 83: 84%|████████▍ | 0.8399999737739563/0.9990000128746033 [02:37<00:29, 187.10s/it] NFE: 84: 84%|████████▍ | 0.8399999737739563/0.9990000128746033 [02:37<00:29, 187.10s/it][0.9875771626830101, 0.9166666865348816, 0.571490466594696, 0.7535707354545593]

NFE: 84: 85%|████████▌ | 0.8500000238418579/0.9990000128746033 [02:39<00:27, 187.10s/it] NFE: 85: 85%|████████▌ | 0.8500000238418579/0.9990000128746033 [02:39<00:27, 187.10s/it]Jump! [0.9839042630046606, 0.9166666865348816, 0.5561251640319824, 0.7986380457878113]

NFE: 85: 86%|████████▌ | 0.8600000143051147/0.9990000128746033 [02:40<00:26, 187.09s/it] NFE: 86: 86%|████████▌ | 0.8600000143051147/0.9990000128746033 [02:40<00:26, 187.09s/it][0.9839042630046606, 0.9166666865348816, 0.5561251640319824, 0.7986380457878113]

NFE: 86: 87%|████████▋ | 0.8700000047683716/0.9990000128746033 [02:42<00:24, 187.07s/it] NFE: 87: 87%|████████▋ | 0.8700000047683716/0.9990000128746033 [02:42<00:24, 187.07s/it][0.9839042630046606, 0.9166666865348816, 0.5561251640319824, 0.7986380457878113]

NFE: 87: 88%|████████▊ | 0.8799999952316284/0.9990000128746033 [02:44<00:22, 187.06s/it] NFE: 88: 88%|████████▊ | 0.8799999952316284/0.9990000128746033 [02:44<00:22, 187.06s/it][0.9839042630046606, 0.9166666865348816, 0.5561251640319824, 0.7986380457878113]

NFE: 88: 89%|████████▉ | 0.8899999856948853/0.9990000128746033 [02:46<00:20, 187.05s/it] NFE: 89: 89%|████████▉ | 0.8899999856948853/0.9990000128746033 [02:46<00:20, 187.05s/it][0.9839042630046606, 0.9166666865348816, 0.5561251640319824, 0.7986380457878113]

NFE: 89: 90%|█████████ | 0.8999999761581421/0.9990000128746033 [02:48<00:18, 187.04s/it] NFE: 90: 90%|█████████ | 0.8999999761581421/0.9990000128746033 [02:48<00:18, 187.04s/it][0.9839042630046606, 0.9166666865348816, 0.5561251640319824, 0.7986380457878113]

NFE: 90: 91%|█████████ | 0.9100000262260437/0.9990000128746033 [02:50<00:16, 187.04s/it] NFE: 91: 91%|█████████ | 0.9100000262260437/0.9990000128746033 [02:50<00:16, 187.04s/it][0.9839042630046606, 0.9166666865348816, 0.5561251640319824, 0.7986380457878113]

NFE: 91: 92%|█████████▏| 0.9200000166893005/0.9990000128746033 [02:52<00:14, 187.05s/it] NFE: 92: 92%|█████████▏| 0.9200000166893005/0.9990000128746033 [02:52<00:14, 187.05s/it]Jump! [0.9804037809371948, 0.9166666865348816, 0.5661965012550354, 0.7954820990562439]

NFE: 92: 93%|█████████▎| 0.9300000071525574/0.9990000128746033 [02:53<00:12, 187.06s/it] NFE: 93: 93%|█████████▎| 0.9300000071525574/0.9990000128746033 [02:53<00:12, 187.06s/it][0.9804037809371948, 0.9166666865348816, 0.5661965012550354, 0.7954820990562439]

NFE: 93: 94%|█████████▍| 0.9399999976158142/0.9990000128746033 [02:55<00:11, 187.07s/it] NFE: 94: 94%|█████████▍| 0.9399999976158142/0.9990000128746033 [02:55<00:11, 187.07s/it][0.9804037809371948, 0.9166666865348816, 0.5661965012550354, 0.7954820990562439]

NFE: 94: 95%|█████████▌| 0.949999988079071/0.9990000128746033 [02:57<00:09, 187.07s/it] NFE: 95: 95%|█████████▌| 0.949999988079071/0.9990000128746033 [02:57<00:09, 187.07s/it][0.9804037809371948, 0.9166666865348816, 0.5661965012550354, 0.7954820990562439]

NFE: 95: 96%|█████████▌| 0.9599999785423279/0.9990000128746033 [02:59<00:07, 187.07s/it] NFE: 96: 96%|█████████▌| 0.9599999785423279/0.9990000128746033 [02:59<00:07, 187.07s/it][0.9804037809371948, 0.9166666865348816, 0.5661965012550354, 0.7954820990562439]

NFE: 96: 97%|█████████▋| 0.9700000286102295/0.9990000128746033 [03:01<00:05, 187.07s/it] NFE: 97: 97%|█████████▋| 0.9700000286102295/0.9990000128746033 [03:01<00:05, 187.07s/it][0.9804037809371948, 0.9166666865348816, 0.5661965012550354, 0.7954820990562439]

NFE: 97: 98%|█████████▊| 0.9800000190734863/0.9990000128746033 [03:03<00:03, 187.06s/it] NFE: 98: 98%|█████████▊| 0.9800000190734863/0.9990000128746033 [03:03<00:03, 187.06s/it][0.9804037809371948, 0.9166666865348816, 0.5661965012550354, 0.7954820990562439]

NFE: 98: 99%|█████████▉| 0.9900000095367432/0.9990000128746033 [03:05<00:01, 187.06s/it] NFE: 99: 99%|█████████▉| 0.9900000095367432/0.9990000128746033 [03:05<00:01, 187.06s/it][0.9804037809371948, 0.9166666865348816, 0.5661965012550354, 0.7954820990562439]

NFE: 99: 100%|██████████| 0.9990000128746033/0.9990000128746033 [03:05<00:00, 185.61s/it] NFE: 100: 100%|██████████| 0.9990000128746033/0.9990000128746033 [03:05<00:00, 185.61s/it] NFE: 100: 100%|██████████| 0.9990000128746033/0.9990000128746033 [03:05<00:00, 185.61s/it] [‘RKETTTQFCKKR’] [0.9804037809371948, 0.9166666865348816, 5.6619648933410645, 0.7954820990562439]

moPPIt Generated Peptides Summary　生成結果まとめ

#PeptideHemolysisSolubilityAffinity (pKd)Motif
1RKETTTQFCKKR0.9800.9175.6620.795
2SETKVKTCRVVL0.9670.6677.2150.780
3KEKPKYETIYTW0.9750.7506.0450.404
4KDEQTGDCCKTT0.9771.0005.5330.653

Pont：

  • SETKVKTCRVVL :achieved the highest binding affinity (7.215 pKd) （ Affinity 最高（7.215））
  • KDEQTGDCCKTT :achieved the highest solubility score (1.000) （Solubility 最高（1.000））
  • RKETTTQFCKKR :appeared in both peptide 1 and peptide 4, suggesting convergence（4本目でも同じ配列が出た（収束している）)

Week 06 HW -Genetic Circuits Part I: Assembly Technologies

‘Week 6 — Genetic Circuits Part I: Assembly Technologies’

Documentation

Homework: Genetic Circuits Part I: Assembly Technologies

Assignment: DNA Assembly

Answer these questions about the protocol in this week’s lab:

1. What are some components in the Phusion High-Fidelity PCR Master Mix and what is their purpose?
(Phusion High-Fidelity PCR Master Mix にはどのような成分が含まれていて、それぞれの目的は何ですか。)

https://www.youtube.com/watch?v=c07_5BfIDTw&t=115s

https://www.neb.com/ja-jp/protocols/2012/09/06/protocol-phusion-high-fidelity-pcr-master-mix-with-hf-buffer-m0531

  • Phusion High-Fidelity DNA Polymerase

    The enzyme that drives DNA synthesis with high fidelity. It has 3’→5’ exonuclease (proofreading) activity, which reduces the error rate during PCR.

  • dNTP（A, T, G, C）

    These are the nucleotide building blocks used to synthesize new DNA strands during PCR.

  • HF Buffer（High-Fidelity Buffer）

    This is an optimized reaction buffer that maintains the proper pH and ionic conditions for high-fidelity DNA amplification.

  • MgCl₂

    Magnesium ions are an essential cofactor for DNA polymerase activity and are provided at an optimal concentration (1.5 mM in the 1X final reaction).

Together, these components provide the enzyme, substrates, and chemical environment needed for accurate DNA amplification.

2. What are some factors that determine primer annealing temperature during PCR?
(PCR におけるプライマーのアニーリング温度は、どのような要因によって決まりますか。)

https://www.nippongene.com/siyaku/product/pcr/cat_pcr.pdf

The primer annealing temperature should be set about 5°C lower than the Tm, typically around 55–60 (50- 65?)°C. Higher annealing temperatures increase specificity. At a primer concentration of 0.2 µmol/L, annealing occurs within a few seconds

The extension reaction is commonly carried out at 72°C, and depending on other reaction conditions, the synthesis rate is approximately 35–100 nucleotides per second

3. There are two methods from this class that create linear fragments of DNA: PCR, and restriction enzyme digests. Compare and contrast these two methods, both in terms of protocol as well as when one may be preferable to use over the other.
(この授業で線状DNA断片を作る方法は2つあります。PCR と制限酵素消化です。
これら2つの方法について、プロトコルの面と、どのような場合にどちらがより適しているかという面の両方から、比較して説明しなさい。)

PCR obtains a desired DNA fragment by using primers to flank and amplify a specific region, requiring primers, dNTPs, and polymerase

Restriction enzyme digestion involves enzymes (e.g., EcoRI) that recognize specific restriction sites and cut the DNA

If no such sites exist in the vector or DNA sequence, digestion is not possible

Thus, PCR is used when the target restriction site is absent, while restriction digestion is preferred if the vector already contains suitable restriction sites and high reproducibility is desired

4. How can you ensure that the DNA sequences that you have digested and PCR-ed will be appropriate for Gibson cloning?
(消化したDNA配列およびPCRで増幅したDNA配列が、Gibsonクローニングに適したものになるようにするには、どうすればよいですか。)

https://docs.google.com/document/d/1_aSV7w8iRYc3EDmbueJ_hSEGy_jHLDfxT2wAezEtC4c/edit?tab=t.0#heading=h.a157u2dx9dhb

https://www.neb.com/ja-jp/applications/cloning-and-synthetic-biology/dna-assembly-and-cloning/gibson-assembly

https://docs.google.com/document/d/1_aSV7w8iRYc3EDmbueJ_hSEGy_jHLDfxT2wAezEtC4c/edit?tab=t.0#heading=h.ysntjrikaygh

  • Overlap Design

    Confirm that each fragment has the correct 20–40 bp overlaps required for Gibson Assembly

  • Fragment Size and Sequence Accuracy

    Check via gel electrophoresis (for size) and, if necessary, sequencing (for the correctness of overlap regions or introduced mutations)

  • Orientation and Molar Ratios

    Ensure each fragment is in the proper 5′→3′ orientation and use an appropriate insert-to-vector molar ratio (commonly 2:1) to maximize assembly efficiency

5. How does the plasmid DNA enter the E. coli cells during transformation?
(形質転換の際、プラスミドDNAはどのようにして E. coli 細胞の中に入りますか。)

https://www.thermofisher.com/jp/ja/home/life-science/cloning/cloning-learning-center/invitrogen-school-of-molecular-biology/molecular-cloning/transformation/bacterial-transformation-workflow.html

In order to introduce DNA into cells, it is necessary to temporarily increase the permeability of the cell membrane

Generally, there are two main methods for transforming E. coli: chemical transformation (e.g., the CaCl₂ method) and electroporation

(A) Chemical Transformation

  1. Mix chemically competent E. coli cells (prepared with CaCl₂, etc.) with ligated DNA and incubate for a set period
  2. Briefly subject the mixture to a high temperature (e.g., 42°C) for heat shock, creating temporary pores in the cell membrane through which the DNA can enter
  3. Transfer the cells into a recovery medium afterward to allow them to recover

(B) Electroporation

  1. Add purified DNA to electrocompetent E. coli cells
  2. Apply a high-voltage pulse (e.g., ~15 kV/cm), which forms transient micro-pores in the cell membrane, enabling the DNA to enter
  3. Finally, move the cells to a recovery medium to allow them to recuperate
6. Describe another assembly method in detail (such as Golden Gate Assembly)
別のアセンブリ法を1つ詳しく説明しなさい（例：Golden Gate Assembly）。

(1) Explain the other method in 5 - 7 sentences plus diagrams (either handmade or online).
(その方法を 5〜7文で説明し、図も付けなさい（手描きでも、オンラインの図でも可）。)

(2) Model this assembly method with Benchling or Asimov Kernel!
(Benchling または Asimov Kernel を使って、このアセンブリ法をモデル化しなさい。)

https://www.youtube.com/watch?v=NzQdLQ44I7w

https://www.youtube.com/watch?v=EpHeu44hitI

  1. Golden Gate Assembly is a method that employs Type IIS restriction enzymes to cleave DNA and efficiently ligate multiple fragments using custom-designed overhangs

    ※ An overhang refers to the single-stranded extension of DNA that protrudes from one strand when the DNA is cleaved

  2. Prepare each fragment so that it contains a Type IIS site—using primer design and PCR—ensuring the desired overhangs appear upon enzyme digestion

  3. Next, combine all fragments in a single tube with the Type IIS restriction enzyme (e.g., BsaI) and T4 DNA ligase, enabling digestion and ligation to occur simultaneously

  1. Under these reaction conditions, the enzyme repeatedly cuts the DNA to create transient overhangs, which then anneal, and the ligase seals the nicks

  2. Incorrect assemblies are recut, so only the properly matching fragments remain ligated, resulting in an efficient multi-fragment assembly in the correct order

  1. Because Type IIS enzymes cleave outside their recognition sequences, the final product is “scarless,” with no leftover restriction sites

  2. After the reaction, transform the assembled plasmid into E. coli, and confirm the intended construct by sequencing if necessary

Week 07 HW -Genetic Circuits Part II: Neuromorphic Circuits

‘Week 7 — Genetic Circuits Part II: Neuromorphic Circuits’

Documentation

Homework: Genetic Circuits Part II: Neuromorphic Circuits

Assignment Part 1: Intracellular Artificial Neural Networks (IANNs)

Answer these questions about the protocol in this week’s lab:

1. What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions? 
（入力と出力のふるまいがブール関数（ON/OFF）で表される従来の遺伝子回路に対して、IANNs にはどんな利点がありるか？）

https://www.sciencedirect.com/science/article/pii/S0303264724000492

IANNs = Intracellular Artificial Neural Networks
細胞内人工ニューラルネットワーク

入力 = ある DNA や RNA の量
計算 = 分子どうしの制御
出力 = 蛍光タンパク質など

・Tx = transcription（転写）
・Tl = translation（翻訳）

コンピュータのニューラルネットワークのような計算を、細胞の中で DNA・RNA・タンパク質を使って行う遺伝子回路。
従来の遺伝子回路が Boolean 的な ON/OFF 回路として扱われるのに対し、
IANNs は 重み付き入力の統合や、しきい値的な応答のような、ニューラルネットワーク的な計算を目指すもの

IANNs は、従来の Boolean 型遺伝子回路よりも、複数の入力を重み付きで統合し、連続的で柔軟な出力を出せる点が利点である。
さらに、多層化によって、単純なON/OFF論理では難しい複雑な判定やパターン認識が可能になる。

IANNs have advantages over traditional Boolean genetic circuits because they can integrate multiple inputs with different weights and produce continuous, flexible outputs. In addition, by using multilayer architectures, they can perform more complex decision-making and pattern recognition that would be difficult to achieve with simple ON/OFF logic alone.

2. Describe a useful application for an IANN; include a detailed description of input/output behavior, as well as any limitations an IANN might face to achieve your goal. 
(IANN の有用な応用例を1つ説明しなさい。  
その際、入力と出力がどのように振る舞うかを詳しく説明し、さらにその目的を達成するうえで IANN が直面しうる限界や課題についても述べなさい。)

Robust and tunable signal processing in mammalian cells via engineered covalent modification cycles
https://pmc.ncbi.nlm.nih.gov/articles/PMC8971529

Sensing and guiding cell-state transitions by using genetically encoded endoribonuclease-mediated microRNA sensors
https://pubmed.ncbi.nlm.nih.gov/38982158/　　

Small RNAs, big potential: Engineering microRNA-based synthetic gene circuits
https://www.sciencedirect.com/science/article/pii/S1367593126000013

IANN の応用例として、特定の細胞状態を見分けるセンサーが考えられる。
入力として複数の miRNA や RNA マーカーを使い、それらを重み付きで統合して、条件を満たしたときだけ蛍光タンパク質を出力する。
この方法は、1つのマーカーだけでなく、複数のシグナルの組み合わせで細胞を判断できる点が有用である。
一方で、細胞ごとのばらつき、漏れ発現、部品間干渉、細胞への負荷などが限界となる。

As an application of IANNs, a sensor that identifies specific cell states can be envisioned.
It would use multiple miRNAs or other RNA markers as inputs, integrate them with different weights, and produce a fluorescent protein output only when the required conditions are met.
This approach is useful because it can evaluate a cell based on a combination of signals rather than relying on a single marker.
On the other hand, its limitations include cell-to-cell variability, leaky expression, crosstalk between parts, and cellular burden.

3. Below is a diagram depicting an intracellular single-layer perceptron where the X1 input is DNA encoding for the Csy4 endoribonuclease and the X2 input is DNA encoding for a fluorescent protein output whose mRNA is regulated by Csy4. Tx: transcription; Tl: translation.
(以下の図は、細胞内の単層パーセプトロンを示している。
ここで、入力 X1 は Csy4 エンドリボヌクレアーゼをコードするDNA であり、入力 X2 は蛍光タンパク質をコードするDNA で、そのmRNAはCsy4によって制御される。  
Txはtranscription（転写）、Tlはtranslation（翻訳）を表す。)
Draw a diagram for an intracellular multilayer perceptron where layer 1 outputs an endoribonuclease that regulates a fluorescent protein output in layer 2.　　
第1層の出力が endoribonuclease となり、それが第2層の蛍光タンパク質出力を制御するような、細胞内多層パーセプトロンの図を描きなさい

https://www.nature.com/articles/s41467-022-30172-3

Assignment Part 2: Fungal Materials

What are some examples of existing fungal materials and what are they used for?   
すでに存在する菌類材料（fungal materials）にはどのような例があり、何に使われていますか。

Mushroom Packaging by Ecovative
https://mushroompackaging.com/

ecovative.com
https://ecovative.com/

Existing examples of fungal materials 既存の fungal materials の代表例

・Mycelium-based materials used for packaging, insulation, decorative elements, furniture, wall coverings, and textiles.
・There is also Mushroom® Packaging, which can be used as an alternative to Styrofoam and plastic, and fungal materials are also being developed for insulation applications.

・菌糸体ベース材料として packaging、insulation、decorative elements、furniture、wall coverings、textiles などに使われている。 ・発泡スチロールやプラスチックの代替として使えるMushroom® Packagingがあり、断熱用途の材料も扱っている。

What are their advantages and disadvantages over traditional counterparts?    
それらは従来の材料と比べて、どのような長所と短所がありますか。

Critical review of mycelium-bound product development to identify barriers to entry and paths to overcome them
https://www.sciencedirect.com/science/article/abs/pii/S0959652624013076

Mycelium-based composites: An updated comprehensive overview
https://www.um.edu.mt/library/oar/bitstream/123456789/130645/1/Mycelium_based_composites_an_updated_comprehensive_overview%282025%29.pdf

・Advantages： They can be grown from renewable feedstocks and agricultural waste, are lightweight, biodegradable and recyclable, and generally require relatively low energy for production. In particular, they are expected to serve as alternatives to petroleum-based foams in applications such as packaging and insulation.

・長所： 再生可能な原料や農業廃棄物を使って育てられること、軽量であること、生分解性やリサイクル性があること、製造エネルギーが比較的低いことである。
　　　　 特に梱包材や断熱材では、石油由来フォームの代替として期待されている。

・Disadvantages： They are often vulnerable to water and moisture, can show variability in strength and durability, and are difficult to standardize and scale up for mass production. Although mycelium-based materials are promising, major challenges still include insufficient optimization of manufacturing conditions, maintaining consistent quality control, and achieving large-scale production.

・短所： 水や湿気に弱くなりやすいこと、強度や耐久性のばらつき、標準化や大量生産の難しさである。
菌糸体材料は有望だが、製造条件の最適化不足や一貫した品質管理、スケールアップが大きな課題だとされている。

What might you want to genetically engineer fungi to do and why? What are the advantages of doing synthetic biology in fungi as opposed to bacteria?  
あなたなら菌類にどのような機能をもたせるように遺伝子工学的に改変したいですか。それはなぜですか。また、細菌ではなく菌類で合成生物学を行う利点は何ですか。

I would like to engineer fungi to produce building materials that are better suited to Japan’s hot and humid climate, with improved water resistance, fast drying, and the ability to signal deterioration when moisture levels become too high.
This is important because humidity and condensation in Japan often lead to mold growth, and building materials also need to withstand the heavy rains of the rainy season and typhoon season.
Compared with bacteria, fungi are more suitable for this purpose because their three-dimensional mycelial networks can themselves be grown into material structures, and they can also make use of plant-based waste.
For these reasons, fungi are a promising platform for synthetic biology in building materials.

日本の高温多湿な気候に合うように、耐水性と速乾性が高く、湿気が多いと劣化を知らせる菌類建材を作るように菌類を改変したい。
日本では湿気や結露がカビの原因になりやすく、梅雨や台風の時期の大雨にも対応できる建材が重要だからである。
菌類は細菌よりも、三次元の菌糸ネットワークそのものを材料として育てられ、植物系廃棄物も利用しやすいので、建材の合成生物学に向いていると考える。

Unlocking the magic in mycelium: Using synthetic biology to optimize filamentous fungi for biomanufacturing and sustainability
https://pmc.ncbi.nlm.nih.gov/articles/PMC9900623

Assignment Part 3: First DNA Twist Order

0. Review the Individual Final Project documentation guidelines.   

個人最終プロジェクトの記録・提出に関するガイドラインを確認する。


1. Submit this Google Form with your draft Aim 1, final project summary, HTGAA industry council selections, and shared folder for DNA designs. 
DUE MARCH 20 FOR MIT/HARVARD/WELLESLEY STUDENTS  

次の内容を含めて、この Google Form を提出する。
- Aim 1 のドラフト
- 最終プロジェクトの要約
- HTGAA industry council の希望選択
- DNA デザインを入れる共有フォルダ


2. Review Part 3: DNA Design Challenge of the week 2 homework.   
Design at least 1 insert sequence and place it into the Benchling/Kernel/Other folder you shared in the Google Form above.   
Document the backbone vector it will be synthesized in on your website.  

少なくとも1つの insert sequence（挿入配列）を設計し、上の Google Form で共有した Benchling／Kernel／Other フォルダに入れなさい。
また、その配列がどの backbone vector（バックボーンベクター）に入った状態で合成されるのかを、自分のウェブサイトに記載しなさい。

Week 09 HW -cell-free-systems

‘week-09-hw-cell-free-systems’

Documentation

Homework: Cell Free Systems

Homework Part A: General and Lecturer-Specific Questions

General homework questions

1. Explain the main advantages of cell-free protein synthesis over traditional in vivo methods, specifically in terms of flexibility and control over experimental variables. 　　
Name at least two cases where cell-free expression is more beneficial than cell production.

従来の in vivo（生細胞内）法と比べて、cell-free protein synthesis（無細胞タンパク質合成）の主な利点を説明しなさい。  
特に、柔軟性と実験条件の制御という観点から述べなさい。  
また、細胞内での生産より無細胞発現のほうが有利な例を少なくとも2つ挙げなさい。

Cell-free protein synthesis
https://en.wikipedia.org/wiki/Cell-free_protein_synthesis#:~:text=CFPS has many advantages over,required for such a reaction.

Traditional protein expression methods present multiple challenges
https://www.biocompare.com/Editorial-Articles/594727-Advantages-of-Cell-Free-Protein-Expression/

A User’s Guide to Cell-Free Protein Synthesis
https://pmc.ncbi.nlm.nih.gov/articles/PMC6481089

Main advantages of cell-free protein synthesis　　無細胞タンパク質合成の主な利点 Faster 速い
A cell-free reaction can often be completed within 1–2 days, so results can be obtained much more quickly than with in vivo expression.
無細胞反応は 1〜2 日で完了することが多く、in vivo 発現よりも短時間で結果を得られる。

Greater flexibility and control 柔軟性と制御性が高い
Because CFPS is an open system, components such as DNA concentration, salts, cofactors, and engineered tRNAs can be directly adjusted.
CFPS は開放系であるため、DNA 濃度、塩濃度、補因子、改変 tRNA などを直接調整できる。

Less concern about toxicity 毒性の影響を受けにくい
The target protein does not need to be produced inside living cells, so toxic proteins are easier to handle.
標的タンパク質を生きた細胞内で作る必要がないため、毒性タンパク質でも扱いやすい。

Useful for toxic proteins 毒性タンパク質の発現に有利
Proteins that would damage or kill host cells during in vivo expression can often be produced more easily in a cell-free system.
細胞内で作ると宿主にダメージを与えるタンパク質でも、無細胞系では発現しやすい。

Useful for proteins containing unnatural amino acids 非天然アミノ酸を含むタンパク質の生産に有利
Since the reaction is open, modified tRNAs and unnatural amino acids can be introduced more easily.
開放系なので、改変 tRNA や非天然アミノ酸を導入しやすい。

Advantageous for membrane proteins 膜タンパク質の発現にも有利
Membrane proteins, which are often difficult to express correctly in living cells, can be tested under more controllable conditions in CFPS.
生細胞内では正しく発現させにくい膜タンパク質でも、条件を調整しながら発現を試せる。

2. Describe the main components of a cell-free expression system and explain the role of each component.    
無細胞発現系の主な構成要素を説明し、それぞれの役割を述べなさい。

https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2019.00248/full

Main components of a cell-free expression system　
無細胞発現系の主な構成要素

Comparison of the workflows of an in vivo system and a CFPS system
in vivo system と CFPS system の流れの比較図

A cell-free expression system consists of a DNA template, transcription and translation machinery, amino acids, nucleotides, an energy regeneration system, and cofactors, salts, and buffers.
These components respectively serve as the genetic blueprint, the synthesis machinery, the building materials, the energy supply, and the reaction environment.
無細胞発現系は、DNA テンプレート、転写・翻訳装置、アミノ酸、ヌクレオチド、エネルギー再生系、補因子・塩・バッファー からできており、
それぞれが「設計図」「合成機械」「材料」「エネルギー」「反応環境」となっている。

3. Why is energy provision regeneration critical in cell-free systems? Describe a method you could use to ensure continuous ATP supply in your cell-free experiment.   
なぜ無細胞系ではエネルギー供給・再生が重要なのか説明しなさい。  
また、あなたの無細胞実験で ATP を継続的に供給するために使える方法を1つ述べなさい。

Energy regeneration is critical in cell-free systems because transcription and translation consume large amounts of ATP and GTP.
Without continuous energy supply, these molecules are rapidly depleted, the reaction stops, and protein yield decreases.
One method to ensure continuous ATP supply is to use phosphoenolpyruvate (PEP) as an energy substrate.
PEP can regenerate ATP from ADP through pyruvate kinase, making it a common energy regeneration strategy in cell-free expression systems.

無細胞系では、転写と翻訳の両方に大量の ATP や GTP が必要であり、これらは反応中にすぐ消費されるため。
エネルギー再生がないと反応は短時間で止まり、目的タンパク質の収量も低下する。
ATP を継続的に供給する方法としては、phosphoenolpyruvate（PEP）を用いる方法がある。
PEP は pyruvate kinase を介して ADP から ATP を再生できるため、無細胞発現系のエネルギー供給法としてよく利用される。

4. Compare prokaryotic versus eukaryotic cell-free expression systems. Choose a protein to produce in each system and explain why.   
原核生物由来と真核生物由来の無細胞発現系を比較しなさい。  
それぞれの系で作るタンパク質を1つずつ選び、なぜその系を選ぶのか説明しなさい。

Development of a robust Escherichia coli-based cell-free protein synthesis application platform
https://pmc.ncbi.nlm.nih.gov/articles/PMC7568173

Cell-Free Protein Synthesis: Pros and Cons of Prokaryotic and Eukaryotic Systems
https://pmc.ncbi.nlm.nih.gov/articles/PMC4676933

Prokaryotic cell-free expression system 原核生物由来の無細胞発現系 （for example, an E. coli extract 抽出液）　　

Characteristics 　　　 It is fast, easy to handle, and suitable for producing simple soluble proteins.
E. coli-based systems have been developed as platforms that can achieve high expression levels, and the experimental setup is relatively simple.
On the other hand, they are not well suited for complex eukaryotic post-translational modifications.

速くて扱いやすく、単純な可溶性タンパク質を作るのに向いている。
E. coli ベースの系では高い発現量が得られるプラットフォームが開発されており、実験系も比較的シンプル。
一方、複雑な真核生物型の翻訳後修飾は苦手。

Example protein
I would choose to produce sfGFP (superfolder GFP). sfGFP is soluble, easy to handle, and does not require complex glycosylation,
so it can be expressed efficiently in an E. coli-based system.
In fact, sfGFP is commonly used as a model protein in E. coli-based cell-free expression systems.
sfGFP（superfolder GFP） を作る。
sfGFP は可溶性で扱いやすく、複雑な糖鎖修飾を必要としないので、E. coli 系で十分に発現しやすい。
実際に、E. coli ベースの cell-free 系では sfGFP がよくモデルタンパク質として使われている。

Cell-Free Systems Based on CHO Cell Lysates: Optimization Strategies, Synthesis of “Difficult-to-Express” Proteins and Future Perspectives
https://pmc.ncbi.nlm.nih.gov/articles/PMC5042383

Cell-free synthesis of functional antibodies using a coupled in vitro transcription-translation system based on CHO cell lysates
https://pmc.ncbi.nlm.nih.gov/articles/PMC5607253

Production of G protein‐coupled receptors in an insect‐based cell‐free system
https://pmc.ncbi.nlm.nih.gov/articles/PMC5599999

Characterisation of a cell-free synthesised G-protein coupled receptor https://pmc.ncbi.nlm.nih.gov/articles/PMC5430785

Eukaryotic cell-free expression system　真核生物由来の無細胞発現系
（for example, systems derived from CHO cells or insect cells (CHO や昆虫細胞由来）)

Characteristics：
It is well suited for producing complex eukaryotic proteins and membrane proteins.
For example, CHO-based cell-free systems contain ER-derived microsomal structures, which can support membrane insertion and some post-translational processes.
Insect cell-derived cell-free systems have also been used to synthesize membrane proteins such as GPCRs.

複雑な真核タンパク質や膜タンパク質に向いている。
たとえば CHO 由来の無細胞系には ER 由来の microsomal structures が含まれており、膜への挿入や一部の翻訳後過程を助けることができる。
昆虫細胞由来の無細胞系でも、GPCR のような膜タンパク質の合成が行われている。

Example proteins
I would choose to produce complex proteins such as GPCRs (G protein-coupled receptors) or antibodies.
These proteins require an appropriate membrane environment, correct folding, and often more complex assembly or modification, so eukaryotic cell-free systems are more suitable than E. coli-based systems.
Functional antibody production has been reported in CHO-based systems, and GPCR synthesis has been reported in insect cell-based systems.

GPCR（G protein-coupled receptor） や 抗体 のような複雑なタンパク質を作る。
こうしたタンパク質は、膜環境、正しい折りたたみ、複雑な会合や修飾 が重要で、E. coli 系よりも真核由来の無細胞系のほうが適している。
CHO系では機能的な抗体、昆虫系では GPCR の合成例が報告されている。

5. How would you design a cell-free experiment to optimize the expression of a membrane protein? 
Discuss the challenges and how you would address them in your setup.    
膜タンパク質の発現を最適化するために、どのように無細胞実験を設計するか説明しなさい。   
また、想定される課題と、それに対して自分ならどう対処するかについて述べなさい。

High-throughput Cell-free Screening of Eukaryotic Membrane Protein Expression by R. Bruni, Q. Liu https://www.osti.gov/servlets/purl/1837204　　　

A cell-free system for functional studies of small membrane proteins
https://www.sciencedirect.com/science/article/pii/S0021925824023524

6. Imagine you observe a low yield of your target protein in a cell-free system. Describe three possible reasons for this and suggest a troubleshooting strategy for each.   
無細胞系で目的タンパク質の収量が低いと観察されたと仮定しなさい。   
考えられる理由を 3つ挙げ、それぞれについて トラブルシューティングの方法を提案しなさい。