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:

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 10^6–108 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:

• [段階収率が累積して長鎖が難しい（150nt以上が難しい、0.99^150 の例など）] https://www.nature.com/articles/s42004-024-01216-0

• [長いオリゴで脱プリン（depurination）が制限要因になる（LeProust 2010, NAR）] https://academic.oup.com/nar/article/38/8/2522/3112266

• [化学反応効率の限界で150–200 basesが上限、エラーが累積（Ma 2012 review, PMC）] https://pmc.ncbi.nlm.nih.gov/articles/PMC3424320/

• [ホスホロアミダイト法の限界：~200nt、1サイクル収率の累積（Yoo 2021 review, PMC）] https://pmc.ncbi.nlm.nih.gov/articles/PMC8113751/

• [段階収率の計算で実用長が~200ntに制限（ACS Catalysis 2025 PDF）] https://pubs.acs.org/doi/pdf/10.1021/acscatal.5c05189

• [「200-merが最長とされる」長鎖の直接合成に挑戦した例（RSC 2025）] https://pubs.rsc.org/en/content/articlelanding/2025/sc/d4sc06958g

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？）

• [Synthetic DNA Synthesis and Assembly review; oligos up to ~200 nt; error sources] https://pmc.ncbi.nlm.nih.gov/articles/PMC5204324/

• [Gene assembly errors; typical 1–3 errors/kb noted] https://pmc.ncbi.nlm.nih.gov/articles/PMC3347765/

• [Gene assembly error rate ranges 1–10 errors/kb] https://currentprotocols.onlinelibrary.wiley.com/doi/10.1002/0471142727.mb0324s99

• [Errors largely attributed to oligo synthesis/purity in genome assembly work] https://www.pnas.org/doi/10.1073/pnas.2237126100

• [Review noting challenges beyond ~200 bases] https://pmc.ncbi.nlm.nih.gov/articles/PMC8113751/

• Direct chemical oligo synthesis adds one nucleotide per cycle, and each cycle is slightly imperfect.

• As length increases, the chance of getting a full-length product drops exponentially, so a 2000 bp “one-piece” product becomes essentially impossible to obtain in high purity.

• Errors and truncated byproducts accumulate, and purification becomes extremely difficult.

• Even if you try to build 2000 bp from many shorter oligos, oligo-derived errors compound across the assembly.

• Therefore, long genes are usually made by assembling shorter fragments and then verifying / error-correcting, rather than by direct

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　　

The 10 essential amino acids commonly listed for animals are: arginine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, and valine.

This affects my view of the “Lysine Contingency” because lysine is not a rare or artificial dependency. It is a natural essential amino acid and can be present in food, organisms, and environments. Therefore, making an organism dependent on lysine alone would probably be a weak biocontainment strategy. A stronger strategy would require layered safeguards, such as dependence on synthetic nutrients or non-natural amino acids that are not normally available outside the intended environment.

[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：）

• https://arpa-h.gov/explore-funding/programs/boss
• https://www.darpa.mil/research/programs/smart-rbc
• https://www.darpa.mil/research/programs/go

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

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。

2. 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　　　　　
3. 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. I used ChatGPT to help troubleshoot the Python script and clarify how to modify the well color dictionary. The final design concept and coordinate selection were my own.

[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

Shishir Shreyas Nair and Charles Naney

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

Part D: Group Brainstorm on Bacteriophage Engineering

I do not fully understand bacteriophage engineering yet, but my first idea is to explore mutations in the MS2 bacteriophage L protein.

The L protein seems to be related to lysis, so I would like to test whether changing parts of this protein could change how strongly or weakly it affects the host cell.

As a first computational step, I would use a protein language model to suggest possible mutations in the L protein. Then I would use AlphaFold or AlphaFold-Multimer to compare the predicted structures of the original and mutated proteins.

If the mutated protein still keeps a similar structure, but changes the predicted interaction with E. coli DnaJ, it may be an interesting candidate for later experimental testing.

まだ bacteriophage engineering を完全には理解できていないが、まずは MS2 bacteriophage の L protein に変異を入れることを考えた。　　 L protein は lysis に関係しているようなので、このタンパク質の一部を変えることで、host cell への影響が強くなったり弱くなったりするのではないかと考えた。　　 最初の計算ステップとして、protein language model で変異候補を出し、AlphaFold や AlphaFold-Multimer で元のタンパク質と変異タンパク質の構造を比較する。　 もし構造を大きく壊さずに、E. coli DnaJ との相互作用が変わるような変異があれば、後で実験的に試すことになるかもしれない。

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.

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

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 Generated Peptides Summary　生成結果まとめ

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）)

Part B: BRD4 Drug Discovery Platform Tutorial (Gabriele)

Optional

Part C: Final Project: L-Protein Mutants

High level summary: The objective of this assignment is to improve the stability and auto-folding of the lysis protein of a MS2-phage. This mechanism is key to the understanding of how phages can potentially solve antibiotic-resistance.

This homework requires computation that might take you a while to run, so please get started early.

I do not fully understand bacteriophage engineering yet, but I tried to think about how the MS2 L protein could be changed. The L protein seems to be related to lysis, so my idea is to make small mutations in the L protein and compare them with the original sequence. As a first step, I would use a protein language model to suggest possible mutations. Then I would use AlphaFold to see whether the mutated protein still keeps a reasonable structure. I do not know yet whether these mutations would really change lysis activity, but this could be a first way to choose candidates for later experiments.

まだ bacteriophage engineering を完全には理解できていないが、MS2 L protein をどのように変えられるかを考えた。

L protein は lysis に関係しているようなので、L protein に小さな変異を入れ、元の配列と比較する。

最初、protein language model で変異候補を出し、AlphaFold で変異後のタンパク質が大きく壊れていないかを確認する。

その変異が本当に lysis activity を変えるか？　後で実験する候補を選ぶ最初の方法になるかもしれない。

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

4. Under these reaction conditions, the enzyme repeatedly cuts the DNA to create transient overhangs, which then anneal, and the ligase seals the nicks

5. Incorrect assemblies are recut, so only the properly matching fragments remain ligated, resulting in an efficient multi-fragment assembly in the correct order

6. Because Type IIS enzymes cleave outside their recognition sequences, the final product is “scarless,” with no leftover restriction sites

7. After the reaction, transform the assembled plasmid into E. coli, and confirm the intended construct by sequencing if necessary

Assignment: Asimov Kernel

1. Create a Repository for your work

2. Create a blank Notebook entry to document the homework and save it to that Repository

3. Explore the devices in the Bacterial Demos Repo to understand how the parts work together by running the Simulator on various examples, following the instructions for the simulator found in the “Info” panel (click the “i” icon on the right to open the Info panel)

4. Create a blank Construct and save it to your Repository

• 1. Recreate the Repressilator in that empty Construct by using parts from the Characterized Bacterial Parts repository
• 2. Search the parts using the Search function in the right menu
• 3. Drag and drop the parts into the Construct
• 4. Confirm it works as expected by running the Simulator (“play” button) and compare your results with the Repressilator Construct found in the Bacterial Demos repository
• 5. Document all of this work in your Notebook entry - you can copy the glyph image and the simulator graphs, and paste them into your Notebook

https://kernel.asimov.com/htgaa-2026/repositories/repository/ab55bc16-c32f-4e11-b628-49a2e9d94b63/folder/7f22521c-46b9-464d-a01b-c02767759066/entry/3c7af5e6-a122-4d38-a7d6-382ae2f467d7

5. Build three of your own Constructs using the parts in the Characterized Bacterials Parts Repo (Characterized Bacterial Parts Repo にあるパーツを使って、自分で 3つの Construct を作りなさい。)

• 1. Explain in the Notebook Entry how you think each of the Constructs should function (それぞれの Construct がどのように機能すると思うかを Notebook Entry に説明する)
• 2. Run the simulator and share your results in the Notebook Entry (Simulator を実行し、その結果を Notebook Entry に共有する)
• 3. If the results don’t match your expectations, speculate on why and see if you can adjust the simulator settings to get the expected outcome (結果が予想と違った場合は、その理由を考察する)

・1 Simple GFP Construct

I expected this construct to express GFP.

Promoter → RBS → GFP → Terminator

Specifically, I used:

pTetR → A1 RBS → gfp → L3S2P4 Bacterial Terminator

pTetR as the promoter : it was a characterized bacterial promoter available in the repository. I chose it instead of an unspecified promoter because I wanted to use a defined part that could be simulated in Kernel. I also noticed that pTetR is commonly used in bacterial circuit examples, so it seemed like a reasonable promoter for a simple first construct. Although pTetR can be regulated by TetR, in this construct I used it mainly as a promoter to drive gfp transcription.
(未指定 promoter より、characterized されていて、Kernel内の bacterial circuit 文脈で使いやすそうだったから。)

A1 RBS as the RBS : RBS is necessary before the coding sequence to recruit ribosomes and start translation.
(発現強度の最適化ではなく、まず translation を開始させる基本部品として使った。)

gfp : GFP is an easy reporter gene to understand. If the construct works, the expected output is GFP expression.
(出力がわかりやすい reporter だから)

L3S2P4 Bacterial Terminator as the Terminator : terminator is needed after the coding sequence to stop transcription. It was a characterized bacterial terminator available in the repository. I chose it instead of an unspecified terminator because I wanted a defined part to stop transcription after gfp.
(未指定 terminator ではなく、characterized された bacterial terminator として使えるから。)

I ran the simulator with the default settings: E. coli chassis, 72 hours duration, 10 min timestep, and transient transfection.

I expected the construct to express GFP because pTetR should drive transcription of gfp, A1 RBS should support translation, and the L3S2P4 Bacterial Terminator should stop transcription after the coding sequence.

・2 Metal-responsive GFP Construct

For Construct 2, I attempted to build a metal-responsive GFP construct related to my final project.　　　

Place the two expression units within the same construct　　　 2つの発現ユニットを同じConstruct内に入れる

構造　　 metal ion → sensor / regulator protein → promoter regulation → GFP expression

1st cassette:
Constitutive promoter → RBS → copper sensor/regulator → terminator

2nd cassette:
PcopZA-copper inducer promoter → RBS → gfp → terminator

1st cassette:
CUER_ECOLI という copper-related regulator を常に発現させる　　

Constitutive promoter → RBS → copper sensor/regulator → terminator
　　　　　　　　　　　　　　　　↓
BBa_J23101 → A1 RBS → CUER_ECOLI → L3S2P4 Bacterial Terminator

BBa_J23101 as Constitutive promoter :

(BBa_J23101 はよく使われる BioBrick の constitutive promoter なので、regulator protein を常に発現させるために選んだ。金属応答 promoter が働くには、先に copper sensor / regulator protein が存在している必要がある。そのため、regulator cassette には inducible promoter より constitutive promoter の方が適している。未指定 promoter ではなく、characterized bacterial part として説明しやすい J23101 を使った。)

A1 RBS as RBS

CUER_ECOLI as copper sensor/regulator
CueR = copper-responsive regulator

1st cassette

2nd cassette:
copper-responsive promoter によって gfp を制御する

PcopZA-copper inducer promoter → RBS → gfp → terminator
↓
PcopZA-copper inducer promoter → A1 RBS → gfp → L3S2P4 Bacterial Terminator

1st + 2nd cassette

Sumilation

I wanted to test a copper-responsive GFP construct, but the simulator ligand options only included IPTG, aTc, L-arabinose, and Doxycycline.
I could not add copper as a ligand condition.
Therefore, I ran the simulation without a copper ligand.
If the result does not show the expected metal-dependent GFP response, one likely reason is that the required copper ligand condition is not available in the simulator settings.

本来は copper-responsive GFP construct として、copper / Cu²⁺ を ligand として入れたかった。
しかし simulator の ligand options には IPTG、aTc、L-arabinose、Doxycycline しかなく、copper を追加できなかった。
そのため、copper ligand なしで simulation を行った。
期待した metal-dependent GFP response が出ない場合、その理由の一つは、必要な copper ligand 条件が simulator settings に存在しないことだと考えられる。

Run

In the simulation, I observed RNA output for CUER_ECOLI, but I did not observe clear GFP protein expression.
One possible reason is that the simulator ligand options did not include copper / Cu²⁺, so I could not add the metal input needed for this circuit.
(Another possible reason is that CUER_ECOLI and PcopZA may not be modeled as a matched regulator-promoter pair in Kernel.)
Therefore, this construct did not behave as I originally expected, but it helped me understand that a metal-responsive genetic circuit requires not only a promoter and reporter gene, but also the correct regulator, ligand condition, and simulation model.

シミュレーションでは、CUER_ECOLI の RNA は見えたが、明確な GFP protein expression は見えなかった。
理由として、simulator の ligand options に copper / Cu²⁺ がなく、金属入力を加えられなかったことではないか？。
（また、CUER_ECOLI と PcopZA が Kernel 内で正しい regulator-promoter pair としてモデル化されていない可能性もある。）
この construct は期待通りには動かなかったが、metal-responsive genetic circuit には promoter と reporter gene だけでなく、正しい regulator、ligand condition、simulation model が必要だとわかった。

・3 Metallothionein Expression Construct

For Construct 3, I wanted to design a genetic circuit related to trace metal concentration in my final project.
Construct 3 では、Final Project に関係する「微量金属の濃縮」を直接シミュレーションするのではなく、濃縮に関わる可能性のある metallothionein を発現する遺伝子回路を設計することにした。

Kernel cannot directly simulate metal binding or metal concentration itself, but it can simulate the expression of a protein that may be involved in metal binding. Therefore, this construct represents the first biological step toward trace metal concentration: producing a protein that could bind metals.

Kernelでは金属結合や金属濃縮そのものは扱えないが、metallothionein のような金属結合タンパク質を作る回路は表現できる。
そのため、この construct は、微量金属を集めるための最初の生物学的ステップとして、金属結合タンパク質を発現させるモデルである。

BBa_J23101 → A1 RBS → metallothionein → L3S2P4 Bacterial Terminator

BBa_J23101 as promoter:

A1 RBS as RBS

Metallothionein_CDS as metallothionein
CDS means coding sequence, and this part should encode the metallothionein protein.
Metallothionein is a metal-binding protein, so expressing this CDS is a first genetic-circuit step toward producing a protein that may bind trace metals.
(CDS は coding sequence の意味で、タンパク質をコードする領域である。
Metallothionein_CDS は metallothionein protein を作るための配列なので、金属結合タンパク質を発現させる construct の中心部分として使える。 )

L3S2P4 Bacterial Terminator as terminator

Sumilation

• RNAP flux is visible
  → This suggests that transcription is occurring from the promoter.

  (promoter から transcription が起きていると考えられる)

• RNA concentration is visible
  → This suggests that a transcript for A1 RBS → Metallothionein_CDS is being produced.

  (A1 RBS → Metallothionein_CDS の transcript が作られている)

• Ribosome flux is visible
  → This suggests that translation may be occurring.

  (translation が起きている可能性がある)

• Protein concentration is N/A / almost zero
  → This suggests that Kernel may not be modeling the concentration of the metallothionein protein properly.

  (Kernel上では metallothionein protein の濃度まではうまくモデル化されていない可能性がある)

I chose Metallothionein_CDS because metallothionein is a metal-binding protein. Kernel cannot directly simulate metal binding or metal concentration, but it can simulate the genetic circuit that would express a protein related to metal binding.

In the simulation, I observed RNAP flux, RNA concentration, and ribosome flux for the Metallothionein_CDS construct.
This suggests that the construct was transcribed and may be translated in the simulation.

However, the protein concentration plot showed N/A or no clear protein output. One possible reason is that Kernel may not have a complete protein model for this metallothionein part.

Metallothionein は金属結合タンパク質なので、微量金属の濃縮に関係する可能性がある。Kernelでは金属結合や濃縮そのものはシミュレーションできないが、金属結合に関わるタンパク質を発現させる遺伝子回路は表現できる。
シミュレーションでは、RNAP flux、RNA concentration、ribosome flux が見られたため、この construct は転写され、翻訳も起きている可能性がある。

一方で、protein concentration のグラフは N/A または明確な出力が見えなかった。 これは、Kernel内で metallothionein protein のモデルが十分に定義されていない可能性や、Kernelが金属結合・濃縮プロセスまでは扱えないためだと考えられる。

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。

Troubleshooting Guide for NEBExpress™ Cell-free E. coli Protein Synthesis System (NEB #E5360)
https://www.neb.com/ja-jp/tools-and-resources/troubleshooting-guides/troubleshooting-guide-for-nebexpress-cell-free-e-coli-protein-synthesis-system-neb-e5360

・Possible causes of low yield in cell-free protein synthesis include contamination of the template DNA, RNase contamination, inappropriate DNA concentration, and problems in template design.　　　　

cell-free protein synthesis で収量が低い原因として、template DNA の汚染、RNase contamination、DNA濃度の不適切さ、template design の問題などが挙げられている。

NEB. NEBExpress™ Cell-free E. coli Protein Synthesis System Manual.
https://www.neb.com/en/-/media/nebus/files/manuals/manuale5360.pdf　　　　　

・The amount and purity of the DNA template, the difference between linear DNA and plasmid DNA, and mRNA stability can affect the efficiency of a cell-free reaction.

DNA template の量や純度、linear DNA / plasmid DNA の違い、mRNA stability などが、cell-free reaction の効率に影響することが説明されている。　　　　

Zemella A. et al. Cell-Free Protein Synthesis: Pros and Cons of Prokaryotic and Eukaryotic Systems.
https://pmc.ncbi.nlm.nih.gov/articles/PMC4676933/

・In cell-free protein synthesis, it is important to optimize reaction conditions such as Mg²⁺, K⁺, amino acids, and the energy system.　　　　 　cell-free protein synthesis では Mg²⁺、K⁺、amino acids、energy system などの反応条件を最適化することが重要だと述べられている。

Homework question from Kate Adamala

Design an example of a useful synthetic minimal cell as follows:
以下の条件に沿って、有用な synthetic minimal cell（合成ミニマル細胞） の例を設計しなさい。

1. Pick a function and describe it. 機能を1つ選び、それについて説明しなさい。

a. What would your synthetic cell do? What is the input and what is the output?

あなたの synthetic cell は何をするものですか？ その input（入力） と output（出力） は何ですか？

Input:
K⁺ contained in body-derived samples such as sweat.（汗などの身体由来サンプルに含まれる K⁺)

Output:
A fluorescent signal produced by fluorescent proteins such as GFP.（GFP などの蛍光タンパク質による蛍光シグナル）

Explain
This synthetic minimal cell receives body-derived K⁺ and outputs its presence as fluorescence.
K⁺ itself is invisible, but by converting it into a biological reaction inside the artificial cell, its presence can be visually detected.　　

この synthetic minimal cell は、身体由来のK⁺を受け取り、その存在を蛍光として出力する。
K⁺そのものは目に見えないが、人工細胞の内部で生物学的な反応に変換されることで、視覚的に確認できるようになる。

b. Could this function be realized by cell-free Tx/Tl alone, without encapsulation?   
その機能は、カプセル化なしに、cell-free Tx/Tl（無細胞転写・翻訳系）だけ で実現できますか？

Conditions where cell-free Tx/Tl alone would be sufficient （cell-free Tx/Tl だけでできる条件）

Ref:https://www.mdpi.com/2073-4425/9/3/144

Ref:https://www.mdpi.com/2075-1729/11/12/1367

-If the goal is simply to express a fluorescent protein such as GFP in response to K⁺, this function could be partly realized by cell-free Tx/Tl alone.
Cell-free Tx/Tl is a system that can synthesize RNA and proteins from DNA in vitro, without using living cells.

目的が、K⁺に応答して GFP などの蛍光タンパク質を発現することだけであれば、cell-free Tx/Tl だけでもある程度実現できる。
Cell-free Tx/Tl は、生きた細胞を使わずに、試験管内の反応液中で DNA から RNA、タンパク質を合成できるシステムである。

https://pmc.ncbi.nlm.nih.gov/articles/PMC7211207/

-Cell-free Tx/Tl is useful when the reaction conditions need to be directly controlled.
DNA concentration, salt concentration, K⁺ concentration, Mg²⁺ concentration, and the energy system can be adjusted from outside the reaction.
This makes it suitable for testing a K⁺-responsive genetic circuit.

　反応条件を直接コントロールしたい場合には、cell-free Tx/Tl は有利である。
DNA濃度、塩濃度、K⁺濃度、Mg²⁺濃度、energy system などを外から調整できるため、K⁺応答回路のテストには適している。

https://pmc.ncbi.nlm.nih.gov/articles/PMC10196276/

At the initial testing stage, where the goal is to check whether the genetic circuit responds to K⁺ and produces GFP, a bulk cell-free reaction without encapsulation would be sufficient.
In other words, cell-free Tx/Tl alone is suitable for early testing and reaction optimization.　
（まず遺伝子回路が本当にK⁺に応答するかを確認する段階では、カプセル化なしの bulk solution で十分である。つまり、初期テストや条件最適化には cell-free Tx/Tl alone が向いている。）

Conditions where encapsulation would be necessary （カプセル化が必要な条件）

Ref: “TXTL-based approach to synthetic cells”
Jonathan Garamella, David Garenne, Vincent Noireaux
https://www.noireauxlab.org/html%20pages/docs%20website/publications/Garamella%20et%20al%20-%202019A.pdf

• Encapsulation is necessary if the goal is to make a synthetic minimal cell with a cell-like boundary.
  Without encapsulation, the reaction would only occur in a bulk solution, and there would be no clear distinction between inside and outside. synthetic minimal cell として「細胞のような境界」を持たせたい場合には、カプセル化が必要である。
  カプセル化しない場合、反応は単なる溶液中で起こるだけで、内側と外側の区別がない。

  https://pmc.ncbi.nlm.nih.gov/articles/PMC7613214/

• f the system is designed to receive K⁺ from an external body-derived sample and respond internally through the membrane, a compartment such as a liposome would be necessary.
  This allows the artificial cell to receive an external molecular input and produce an internal output.

  外部の身体由来サンプルからK⁺を受け取り、膜を通して内部で応答するシステムにしたい場合には、liposome などの compartment が必要になる。
  これにより、人工細胞が外部環境から input を受け取り、内部で output を作るという構造を持てる。

  https://www.sciencedirect.com/science/article/pii/S0958166918301629

• Encapsulation is also important if each synthetic minimal cell needs to behave as an independent small reaction unit.
  By placing cell-free Tx/Tl inside microcompartments, the system can mimic cell size, cellular individuality, and cell-like behavior.

  個々の synthetic minimal cell が、それぞれ独立した小さな反応単位としてふるまうことを示したい場合にも、カプセル化が重要である。
  Cell-free Tx/Tl を microcompartment に入れることで、cell size、cell individuality、cell-like behavior を模倣できる。

  https://www.sciencedirect.com/science/article/pii/S0958166918301629

It is important that body-derived K⁺ enters from the “outside” and is converted into fluorescence on the “inside” of the artificial cell.
Therefore, encapsulation would be necessary for the final synthetic minimal cell design.　
（身体由来のK⁺が「外側」から入り、人工細胞の「内側」で蛍光へ変換されることが重要なので、最終的にはカプセル化が必要である。）

c. Could this function be realized by genetically modified natural cell?   
その機能は、遺伝子改変された自然細胞 によって実現できますか？   

・Technically possible（技術的には可能）：

Bacteria already have mechanisms to regulate K⁺ homeostasis. For example, the KdpD/KdpE two-component system is known to regulate the expression of the K⁺ transport system, the Kdp-ATPase / kdpFABC operon.
If a K⁺-responsive circuit and a GFP reporter were introduced into a genetically modified cell such as E. coli, it may be possible to create a cell that responds to K⁺ by producing fluorescence.

細菌にはもともと K⁺ homeostasis を制御する仕組みがある。たとえば KdpD/KdpE two-component system は、K⁺輸送系である Kdp-ATPase / kdpFABC operon の発現制御に関わるシステムとして知られている。 遺伝子改変 E. coli などに K⁺応答回路と GFP reporter を入れれば、K⁺に応答して蛍光を出す細胞は作れる可能性がある。

Ref:The KdpD/KdpE Two-Component System: Integrating K+ Homeostasis and Virulence
Zoë N Freeman, Steve Dorus, Nicholas R Waterfield
https://pmc.ncbi.nlm.nih.gov/articles/PMC3610689

However, a synthetic minimal cell is more suitable for the purpose of this project
（しかし今回の目的には synthetic minimal cell の方が適している）

Natural cells involve issues such as growth, metabolism, biosafety, and environmental release.
In contrast, a synthetic minimal cell can be designed as a more controlled system by extracting only the necessary sensing and expression functions. This would allow body-derived K⁺ to be converted into fluorescence inside an artificial cell.

自然細胞は増殖・代謝・安全性・環境放出の問題を持つ。
一方、synthetic minimal cell なら、必要な sensing と expression だけを取り出し、身体由来K⁺を人工細胞内部で蛍光に変換する、より制御されたシステムとして設計できる。

d. Describe the desired outcome of your synthetic cell operation.    
あなたの synthetic cell が作動した結果として、どのような成果・状態が望まれますか？

Scientific outcome （科学的な成果）：
The synthetic minimal cell operates successfully by detecting body-derived K⁺ and visualizing it as GFP fluorescence.
身体由来K⁺を検出し、GFP蛍光として可視化する synthetic minimal cell が作動すること。

Design outcome（設計上の成果）：
The external K⁺ input is converted into a fluorescent output inside the membrane-enclosed artificial cell.
外部のK⁺入力が、膜で囲まれた人工細胞の内部で蛍光出力に変換されること。

Conceptual outcome（コンセプト上の成果）：
Invisible body-derived material is transformed into visible light through an artificial biological system.
見えない身体由来の物質が、人工的な生命システムを通して、目に見える光へ変換されること。

2. Design all components that would need to be part of your synthetic cell.   
(synthetic cell に必要なすべての構成要素を設計しなさい)

a. What would be the membrane made of?   
(膜は何で作られますか？)

Membrane design / 膜の設計

• 膜は phospholipid bilayer でできた liposome / vesicle とする。
  Liposome は、cell-free Tx/Tl system を封入して synthetic cell を作るためによく使われる compartment である。
  https://pmc.ncbi.nlm.nih.gov/articles/PMC7613214/

• Phospholipid vesicle は、自然細胞の膜に近い lipid bilayer を持つため、synthetic minimal cell に「内側」と「外側」の境界を与えることができる。
  https://pmc.ncbi.nlm.nih.gov/articles/PMC5518703/

• 今回の synthetic minimal cell では、外部の身体由来K⁺を受け取り、内部でGFP蛍光に変換する必要がある。
  そのため、単なる bulk solution ではなく、liposome membrane による compartmentalization が重要である。
  https://pmc.ncbi.nlm.nih.gov/articles/PMC7613214/

Body-derived K⁺ comes from the “outside” and is converted into GFP fluorescence on the “inside” of the artificial cell.
（身体由来のK⁺が「外側」から来て、それが人工細胞の「内側」でGFP蛍光に変換される。）

Outside: body-derived samples such as sweat (汗などの身体由来サンプル)
Membrane(膜): the boundary of the artificial cell（人工細胞の境界）
Inside: cell-free Tx/Tl system
Output: GFP fluorescence

This structure is necessary for the design.

In other words, the phospholipid bilayer is necessary as the “boundary” of a small artificial cell that converts K⁺ into fluorescence.
つまり、リン脂質二重膜は、K⁺を蛍光に変える小さな人工細胞の「境界」として必要である。

An important point is that K⁺ does not easily pass through a phospholipid bilayer by itself. Because K⁺ is a charged ion, it cannot easily cross a pure phospholipid bilayer.
ここで重要なのは、K⁺はそのままだとリン脂質二重膜を通りにくいという点である。K⁺は電荷を持つイオンなので、純粋な phospholipid bilayer は簡単には通過できない。

Therefore, it is not enough to encapsulate the cell-free Tx/Tl system inside a liposome and create a small cell-like compartment. The synthetic minimal cell would also need a mechanism that allows K⁺ to enter from the outside to the inside.
そのため、liposome に cell-free Tx/Tl を封入して細胞に似た小さな区画を作るだけでなく、K⁺が外部から内部へ入るための 仕組みも必要になる。

b. What would you encapsulate inside? Enzymes, small molecules.   
(内部には何を封入しますか？酵素や小分子などを含めて説明しなさい。)

I would encapsulate the complete cell-free Tx/Tl reaction system needed to express GFP after receiving K⁺.
K⁺を受け取ったあとに GFP を発現するための cell-free Tx/Tl reaction 一式を封入する。

Components to encapsulate（封入するもの）：

・DNA template
This would include a K⁺-responsive genetic circuit and a GFP reporter gene. When K⁺ enters from the outside to the inside, this change would be converted into gene expression and output as GFP fluorescence.
（K⁺応答性の genetic circuit と GFP reporter gene を含む。K⁺が外部から内部に入ると、その変化が遺伝子発現に変換され、GFP fluorescence として出力される。）

・RNA polymerase
To transcribe mRNA from the DNA template.
（DNA template から mRNA を転写するため。）

・Ribosomes
To read the mRNA and synthesize proteins.
（mRNA を読み取り、タンパク質を合成するため。）

・tRNAs
To carry the amino acids corresponding to each mRNA codon to the ribosome.
（mRNA の codon に対応する amino acids を ribosome に運ぶため。）

・Amino acids
As the building blocks for proteins such as GFP. （GFP などのタンパク質を作るための材料。）

・Nucleotides As the building blocks for synthesizing mRNA. ATP and GTP are also involved in the energy required for transcription and translation.
（mRNA を合成するための材料。また ATP や GTP は転写・翻訳のエネルギーにも関わる。）

・Energy regeneration system
To continuously supply and regenerate ATP / GTP required for transcription and translation.
（転写・翻訳に必要な ATP / GTP を継続的に供給・再生するため。）

・Salts
To adjust the ionic environment, including Mg²⁺, K⁺, and other ions, and to support the activity of RNA polymerase and ribosomes.
（Mg²⁺、K⁺、その他のイオン環境を整え、RNA polymerase や ribosome の活性を支えるため。）

・Buffer
To stabilize the pH and maintain a reaction environment suitable for cell-free Tx/Tl.　　　 （pH を安定させ、cell-free Tx/Tl reaction が進みやすい反応環境を保つため。）

・GFP reporter system
To generate GFP fluorescence as the output in response to K⁺ input. However, this would basically be designed as a GFP reporter gene included in the DNA template.
（K⁺入力に対する出力として、GFP fluorescence を発生させるため。ただし、これは基本的には DNA template に含まれる GFP reporter gene として設計する。）

• Cell-Free Protein Synthesis: A Promising Option for Future Drug Development Srujan Kumar Dondapati, Marlitt Stech, Anne Zemella, Stefan Kubick
  https://pmc.ncbi.nlm.nih.gov/articles/PMC7211207/

• Cell-Free Protein Synthesis: Pros and Cons of Prokaryotic and Eukaryotic Systems
  Anne Zemella, Lena Thoring, Christian Hoffmeister, Stefan Kubick
  https://pmc.ncbi.nlm.nih.gov/articles/PMC4676933/

• The KdpD/KdpE Two-Component System: Integrating K+ Homeostasis and Virulence
  Zoë N Freeman, Steve Dorus, Nicholas R Waterfield
  https://pmc.ncbi.nlm.nih.gov/articles/PMC3610689/

c. Which organism your Tx/Tl system will come from? Is bacterial OK, or do you need a mammalian system for some reason? (hint: for example, if you want to use small molecule modulated promotors, like Tet-ON, you need mammalian)   
(Tx/Tl system はどの生物由来のものを使いますか？
細菌由来でよいですか？ それとも何らかの理由で哺乳類由来の system が必要ですか？
ヒント：たとえば Tet-ON のような small molecule modulated promoters を使いたい場合は、哺乳類系が必要です。)

I would use a bacterial cell-free Tx/Tl system derived from E. coli.
今回は E. coli 由来の bacterial cell-free Tx/Tl system*を使う。

This is because the goal of this synthetic minimal cell is to receive K⁺ input and express GFP, and it does not require complex mammalian proteins or glycosylation. (理由は、今回の synthetic minimal cell の目的が、K⁺入力を受け取って GFP を発現することであり、複雑な哺乳類タンパク質や糖鎖修飾を必要としないためである。)

Also, since I do not plan to use a mammalian promoter system such as Tet-ON, a mammalian Tx/Tl system is not necessary. (また、Tet-ON のような mammalian promoter system も使わないため、哺乳類由来の Tx/Tl system は必要ない。)

d. How will your synthetic cell communicate with the environment? (hint: are substrates permeable? or do you need to express the membrane channel?)   
あなたの synthetic cell は環境とどのように communication しますか？
ヒント：基質は膜を透過できますか？ それとも membrane channel を発現する必要がありますか？

This synthetic minimal cell receives K⁺ from an external body-derived sample, such as sweat, as its input.
However, K⁺ is a charged ion and cannot easily pass through a pure phospholipid bilayer. Therefore, the liposome membrane needs a mechanism that allows K⁺ to enter from the outside to the inside.
Possible mechanisms include a potassium channel or an ionophore.
（この synthetic minimal cell は、外部の身体由来サンプル、たとえば汗に含まれる K⁺ を input として受け取る。 しかし、K⁺は電荷を持つイオンなので、純粋な phospholipid bilayer を簡単には通過できない。そのため、liposome membrane には、K⁺が外部から内部へ入るための仕組みが必要になる。
具体的な方法としては、potassium channel、pore-forming protein、または ionophore を使うことが考えられる。）

potassium channel:
A potassium channel is a membrane protein that selectively allows K⁺ to pass through the membrane. This would be suitable for a more cell-like and selective communication system.
（K⁺を選択的に通す膜タンパク質である。より細胞らしく、選択的な communication を作る場合に向いている。）

ionophore:
An ionophore is a small molecule that binds ions and transports them across a lipid membrane. For example, valinomycin is known as a K⁺-selective ionophore and has been used to study K⁺ transport across lipid membranes.
（イオンを結合して脂質膜を横切らせる小分子である。たとえば valinomycin は K⁺に選択性を持つ ionophore として知られ、K⁺を脂質膜越しに輸送する仕組みの研究に使われている。）

For this design, the simplest option would be to use a K⁺ ionophore such as valinomycin. In a more advanced and cell-like version, a potassium channel could be incorporated into the liposome membrane.
(今回の設計では、最もシンプルには valinomycin のような K⁺ ionophore を使う案が考えられる。より cell-like な設計にするなら、liposome membrane に potassium channel を組み込む方法も考えられる。)

Ion channels form aqueous pores across lipid bilayers and allow specific inorganic ions to pass through the membrane.
https://www.ncbi.nlm.nih.gov/books/NBK26910

Valinomycin is a K⁺-selective ionophore that can transport potassium ions across lipid membranes.
https://pubs.acs.org/doi/10.1021/acs.langmuir.1c01500

3.Experimental details

a. List all lipids and genes. (bonus: find the specific genes; for example, instead of just saying “small molecule membrane channel” pick the actual gene.)

Lipids / membrane components　(脂質・膜の構成要素)

• POPC POPC would be used as the main phospholipid component of the liposome membrane. It forms a phospholipid bilayer that creates the boundary of the synthetic minimal cell.

  (POPC は、liposome membrane の主なリン脂質成分として使う。リン脂質二重膜を形成し、synthetic minimal cell の境界を作る。)

• Cholesterol Cholesterol would be added to improve membrane stability and make the liposome membrane less fragile.

  (Cholesterol は、膜の安定性を高め、liposome membrane を壊れにくくするために加える。)

• Valinomycin Valinomycin is not a lipid, but it would be included as a K⁺ ionophore in the membrane. It would help transport K⁺ across the phospholipid bilayer.

  (Valinomycin は lipid ではないが、K⁺ ionophore として膜に含める。K⁺がリン脂質二重膜を通過するのを助ける。)

Genes

• K⁺-responsive regulatory element / promoter
  This regulatory element would connect K⁺ input to gene expression. It could be designed based on bacterial K⁺ homeostasis systems, such as the KdpD/KdpE system or the kdpFABC regulatory region. (この制御要素は、K⁺ input を gene expression につなげるために使う。細菌の K⁺ homeostasis system、たとえば KdpD/KdpE system や kdpFABC regulatory region をもとに設計できる。)

• GFP reporter gene
  The GFP gene would be used as the output. When the K⁺-responsive circuit is activated, GFP would be expressed and produce fluorescence. (GFP gene は output として使う。K⁺ responsive circuit が活性化されると、GFP が発現し、蛍光を発する。)

• Optional: potassium channel gene
  In a more advanced version, a potassium channel gene could be included to express a membrane channel that selectively allows K⁺ to enter the synthetic cell. However, in the simple version of this design, valinomycin would be used instead, so this gene is optional. (より発展的なバージョンでは、K⁺を選択的に synthetic cell 内へ入れる membrane channel を発現するために、potassium channel gene を含めることもできる。
  ただし、このシンプルな設計では valinomycin を使うため、この gene は optional である。)

TXTL-based approach to synthetic cells
Jonathan Garamella, David Garenne, Vincent Noireaux
https://pubmed.ncbi.nlm.nih.gov/30784403/

Preparing Protein Producing Synthetic Cells using Cell Free Bacterial Extracts, Liposomes and Emulsion Transfer
https://pmc.ncbi.nlm.nih.gov/articles/PMC7613214

The all-E. coliTXTL toolbox 3.0: new capabilities of a cell-free synthetic biology platform David Garenne, Seth Thompson, Amaury Brisson, Aset Khakimzhan, Vincent Noireaux
https://academic.oup.com/synbio/article/6/1/ysab017/6320565

b. How will you measure the function of your system?   
この synthetic minimal cell が本当に機能したかを、どう測定するか

I would measure the function by detecting GFP fluorescence.
(システムの機能は、GFP fluorescence（GFP蛍光）を検出することで測定する。)

I would prepare samples with different K⁺ concentrations, such as(K⁺濃度の異なるサンプルを用意する。たとえば):

• no K⁺
• low K⁺
• high K⁺

These samples would be added outside the synthetic minimal cells.(これらのサンプルを、synthetic minimal cell の外側に加える。)

If K⁺ enters the liposome and activates the K⁺-responsive circuit, GFP will be expressed.
(K⁺が liposome の内部に入り、K⁺ responsive circuit を活性化すれば、GFP が発現する。)

The output would be measured by fluorescence microscopy or a plate reader.
(出力は、fluorescence microscopy（蛍光顕微鏡）または plate readerを使って測定する。)

I would compare the fluorescence intensity between the no K⁺ condition and the K⁺ conditions.( K⁺なしの条件と、K⁺ありの条件の蛍光強度を比較する。)

The expected result is that higher K⁺ concentration produces stronger GFP fluorescence.
期待される結果は、K⁺濃度が高いほど GFP fluorescence が強くなることである。

Homework question from Peter Nguyen

Freeze-dried cell-free systems can be incorporated into all kinds of materials as biological sensors or as inducible enzymes to modify the material itself or the surrounding environment. Choose one application field — Architecture, Textiles/Fashion, or Robotics — and propose an application using cell-free systems that are functionally integrated into the material. Answer each of these key questions for your proposal pitch:   
(凍結乾燥された cell-free system は、生物学的センサーとして、または材料そのものや周囲の環境を変化させる誘導型酵素として、さまざまな素材に組み込むことができます   
Architecture（建築）・Textiles/Fashion（テキスタイル／ファッション）・Robotics（ロボティクス） の中から、応用分野を1つ選び、cell-free system が素材の中に機能的に統合された応用例を提案しなさい。   
提案ピッチでは、以下の質問に答えてください。)

・Write a one-sentence summary pitch sentence describing your concept.   
(あなたのコンセプトを説明する、1文の短いピッチ文を書きなさい。)

wearable biological sensor
A sweat-activated wearable biological sensor that uses freeze-dried cell-free systems embedded in textile to visualize invisible bodily stress through fluorescence or color change.
汗で起動する凍結乾燥 cell-free system を布に組み込み、見えない身体ストレスを蛍光や色変化として可視化するウェアラブル・バイオセンサー。

・How will the idea work, in more detail? Write 3-4 sentences or more.   
(そのアイデアは、具体的にどのように機能しますか？3〜4文以上で説明しなさい。)

The freeze-dried cell-free system would be embedded into the textile as small biosensor patches.

In the dry state, the system would remain inactive, but it would be reactivated when moisture from sweat is added.

Body-derived ions such as K⁺ in sweat would act as the input, and the cell-free Tx/Tl system would express fluorescent proteins such as GFP.
As a result, invisible bodily conditions related to dehydration or heat stress would be displayed as fluorescent signals on the textile.

(凍結乾燥された cell-free system を、布の中の小さなバイオセンサーパッチとして組み込む。
乾燥状態では反応せず、汗によって水分が加わると再び活性化する。
汗に含まれる K⁺ などの身体由来イオンが入力となり、cell-free Tx/Tl system が GFP などの蛍光タンパク質を発現する。
その結果、脱水や熱ストレスに関係する見えない身体状態が、布の上の蛍光シグナルとして表示される)

・What societal challenge or market need will this address?   
(そのアイデアは、どのような 社会的課題 または 市場のニーズ に応えるものですか？)

This idea addresses social challenges related to heat stress, dehydration, outdoor labor, and sports safety.

Changes in sweat, such as K⁺ concentration and moisture, are related to the condition of the body, but they are usually invisible.
This wearable biological sensor would be activated by sweat and display invisible bodily changes as fluorescent signals on the textile.

This could help the wearer or people around them notice the risk of dehydration or heat stress earlier.
It could be especially useful for outdoor workers, athletes, elderly people, and children who are more vulnerable in hot environments.

このアイデアは、熱中症、脱水、屋外労働、スポーツ時の身体ストレスなどの社会的課題に対応する。 汗に含まれるK⁺や水分量の変化は、身体の状態と関係しているが、通常は目に見えない。
この wearable biological sensor は、汗によって起動し、身体の見えない変化を布の上の蛍光シグナルとして表示する。
これにより、着用者本人や周囲の人が、脱水や熱ストレスのリスクに早く気づくことができる。
特に、屋外労働者、アスリート、高齢者、子どもなど、暑熱環境でリスクの高い人々に役立つ可能性がある。

・How do you envision addressing the limitation of cell-free reactions (e.g., activation with water, stability, one-time use)?   
(cell-free reaction の制限、たとえば、水による活性化、安定性、一回限りの使用 といった問題に、どのように対応するつもりですか？)

The system would be designed as a dry, replaceable patch.
It would remain inactive until sweat rehydrates it and activates the reaction.
Because the reaction may be one-time use, the patch would be replaced after activation.

このシステムは、乾燥した交換可能なパッチとして設計する。
普段は不活性で、汗によって再水和されると反応が始まる。
一回使用を前提とし、使用後は新しいパッチに交換する。

Homework question from Ally Huang

Freeze-dried cell-free reactions have great potential in space, where resources are constrained. As described in my talk, the Genes in Space competition challenges students to consider how biotechnology, including cell-free reactions, can be used to solve biological problems encountered in space. While the competition is limited to only high school students, your assignment will be to develop your own mock Genes in Space proposal to practice thinking about biotech applications in space!   

(凍結乾燥された cell-free reaction は、資源が限られている宇宙環境において、大きな可能性を持っています。   
私の講義で説明したように、Genes in Space competition は、cell-free reaction を含むバイオテクノロジーを使って、宇宙で直面する生物学的な問題をどのように解決できるかを学生に考えさせるコンペティションです。   
このコンペティション自体は高校生のみを対象としていますが、今回の課題では、宇宙におけるバイオテクノロジー応用について考える練習として、あなた自身の mock Genes in Space proposal（模擬 Genes in Space 提案） を作成します。)


For this particular assignment, your proposal is required to incorporate the BioBits® cell-free protein expression system, but you may also use the other tools in the Genes in Space toolkit (the miniPCR® thermal cycler and the P51 Molecular Fluorescence Viewer). 


(この課題では、あなたの提案に BioBits® cell-free protein expression system を必ず組み込む必要があります。   
ただし、Genes in Space toolkit に含まれる他のツール、たとえば miniPCR® thermal cycler や P51 Molecular Fluorescence Viewer も使ってかまいません。)

For more inspiration, check out (さらにアイデアの参考にしたい場合は、以下のサイトを見てください。)   
https://www.genesinspace.org/ .

Human Body Mine in Space

This project treats human-derived waste produced inside a spacecraft, such as hair, nails, urine, and feces, as a potential resource. These materials would first be decomposed by microorganisms, and reusable ions, minerals, and nitrogen-containing compounds would then be detected using the BioBits cell-free system.
(宇宙船内で発生する髪、爪、尿、排泄物などの人体由来廃棄物を、微生物によって分解し、そこに含まれる再利用可能なイオン・ミネラル・窒素化合物を BioBits cell-free system で検出する。)

Useful components(使える要素)

• Hair and nails:
  Solid body-derived waste rich in keratin.(keratin を多く含む固形の身体由来廃棄物)

• Urine 尿:
  Liquid waste containing nitrogen-containing compounds, K⁺, Na⁺, and other ions.(窒素化合物、K⁺、Na⁺などを含む液体廃棄物)

• Feces 排泄物:
  Complex biological waste containing organic matter, minerals, and trace elements.(有機物、ミネラル、微量元素を含む複合的廃棄物)

System components

• Microorganisms 微生物:
  Used for decomposition and pretreatment of the body-derived waste.(分解・前処理を担う)

• BioBits®:
  Used to detect useful components through fluorescence.(有用成分の存在を蛍光で検出する)

Significance in space: In a closed environment where resources cannot be easily brought from Earth, waste can be read as a circular resource rather than something to discard.
(資源を持ち込めない閉鎖環境で、廃棄物を循環資源として読む)

1. Provide background information that describes the space biology question or challenge you propose to address. Explain why this topic is significant for humanity, relevant for space exploration, and scientifically interesting. (Maximum 100 words)　　　

あなたが取り組もうとしている 宇宙生物学上の問い、または課題 について、背景情報を述べなさい。   
そのテーマが、   
・なぜ人類にとって重要なのか　　　
・なぜ宇宙探査に関係するのか　　　
・なぜ科学的に興味深いのか　　　
を説明しなさい。(最大100語。)

In long-duration space missions, human-derived waste such as hair, nails, urine, and feces should not be treated only as trash. These materials contain organic matter, ions, minerals, and nitrogen-containing compounds that could become reusable resources in a closed space habitat. I propose to explore whether these body-derived materials can be decomposed by microorganisms and then analyzed using the BioBits® cell-free protein expression system. This topic is significant because future space exploration will require circular resource systems. Scientifically, it reimagines the human body as a temporary “mine” within a closed ecological environment. (88 words)

長期の宇宙ミッションでは、髪、爪、尿、排泄物などの人体由来廃棄物を、単なるゴミとして扱うべきではない。これらの物質には、有機物、イオン、ミネラル、窒素化合物が含まれており、閉鎖された宇宙居住環境では再利用可能な資源になりうる。私は、これらの身体由来物質を微生物によって分解し、その後 BioBits cell-free protein expression system を用いて分析できるかを探究することを提案する。このテーマは、将来の宇宙探査に循環型資源システムが必要になるため重要であると考える。いわば、人間の身体を閉鎖生態環境の中にある一時的な「鉱山」として再考するものである。

2. Name the molecular or genetic target that you propose to study. Examples of molecular targets include individual genes and proteins, DNA and RNA sequences, or broader -omics approaches. (Maximum 30 words)

あなたが研究対象として提案する molecular target（分子ターゲット） または genetic target（遺伝的ターゲット） の名前を書きなさい。   
分子ターゲットの例としては、
・個別の遺伝子
・タンパク質
・DNA配列
・RNA配列
・あるいは、より広い omics 的アプローチ　　　
例：metabolomics, proteomics, transcriptomics など　　　
が含まれます。(最大30語)

Option　　　 Reusable ions, minerals, and nitrogen-containing compounds released from microbially decomposed human-derived waste.　　　 微生物分解された人体由来廃棄物から放出される、再利用可能なイオン、ミネラル、窒素化合物。

Components such as K⁺, Na⁺, Ca²⁺, Mg²⁺, ammonium, and urea contained in decomposed urine, hair, nails, and feces.　　　 尿・髪・爪・排泄物の分解物に含まれる K⁺、Na⁺、Ca²⁺、Mg²⁺、アンモニウム、尿素などの成分。

Cell-free biosensor targets that respond to K⁺ and nitrogen-containing compounds detectable after the decomposition of human-derived waste.　　　 人体由来廃棄物の分解後に検出可能な、K⁺や窒素化合物に応答する cell-free biosensor target。

K⁺, Na⁺, Ca²⁺, Mg²⁺, ammonium, urea, phosphate, sulfate, iron, copper, and keratin-derived peptides released from microbially decomposed urine, feces, hair, and nails. (22 words)
微生物分解された尿、排泄物、髪、爪から放出される K⁺、Na⁺、Ca²⁺、Mg²⁺、アンモニウム、尿素、リン酸、硫酸、鉄、銅、ケラチン由来ペプチド。

3.  how your molecular or genetic target relates to the space biology question or challenge your proposal addresses. (Maximum 100 words)　　　
あなたが選んだ molecular target / genetic target が、提案で取り組む 宇宙生物学上の問い・課題 とどのように関係しているかを説明しなさい。(最大100語)

These targets are indicators for evaluating human-derived waste as a resource in a closed space environment. Urine and feces contain ions, minerals, and nitrogen-containing compounds, while hair and nails contain keratin-derived components.
If these materials are released through microbial decomposition, they could potentially be reused for life support, plant growth, or material recycling. By detecting these targets with the BioBits® cell-free system, waste inside a spacecraft can be reinterpreted as a circular resource rather than something to discard.

これらのターゲットは、人体由来廃棄物が宇宙船内で再利用可能な資源になりうるかを判断するための指標である。尿や排泄物にはイオン、ミネラル、窒素化合物が含まれ、髪や爪にはケラチン由来の成分が含まれる。
微生物分解によってこれらの成分が放出されれば、閉鎖環境内での資源循環に利用できる可能性がある。
BioBits cell-free system によってこれらを蛍光で検出できれば、宇宙船内の廃棄物を「捨てるもの」ではなく、回収・再利用可能な物質の供給源として評価できる。

4.Clearly state your hypothesis or research goal and explain the reasoning behind it. (Maximum 150 words)　　　
あなたの提案における hypothesis（仮説） または research goal（研究目標） をはっきり書きなさい。
さらに、なぜその仮説・目標を立てるのか、その理由も説明しなさい。(最大150語)

Hypothesis（仮説）
Human-derived waste decomposed by microorganisms contains reusable resource components that can be detected by the BioBits® cell-free system.
微生物によって分解された人体由来廃棄物には、BioBits cell-free system で検出可能な再利用資源成分が含まれている。

Research goal(研究目標)
The goal is to test whether ions, minerals, nitrogen-containing compounds, and keratin-derived peptides released from microbially decomposed urine, feces, hair, and nails can be detected as fluorescent signals. These targets may include K⁺, Na⁺, Ca²⁺, Mg²⁺, ammonium, urea, phosphate, sulfate, iron, copper, and keratin-derived peptides.
尿、排泄物、髪、爪などを微生物分解したあと、そこから放出される K⁺、Na⁺、Ca²⁺、Mg²⁺、アンモニウム、尿素、リン酸、硫酸、鉄、銅、ケラチン由来ペプチドなどを、蛍光シグナルとして検出できるかを調べる。

Reasoning
Resources are limited in space, so evaluating waste as a source of reusable materials is important for long-duration missions and closed-loop life support systems.
宇宙では資源が限られており、廃棄物を再利用可能な成分として評価することが、長期滞在や閉鎖型生命維持システムに重要だから。

My hypothesis is that human-derived waste decomposed by microorganisms contains reusable resource components that can be detected by the BioBits® cell-free system. The research goal is to test whether ions, minerals, nitrogen-containing compounds, and keratin-derived components released from microbially decomposed urine, feces, hair, and nails can be detected as fluorescent signals. In a closed environment such as a spacecraft, waste should not only be processed but also reevaluated as a resource. Therefore, this research tests whether human-derived waste can be treated as part of a circular resource system. (91 words)

私の仮説は、微生物によって分解された人体由来廃棄物には、BioBits® cell-free system によって検出可能な再利用資源成分が含まれている、というものである。研究目標は、尿、排泄物、髪、爪などを微生物分解した後、そこから放出されるイオン、ミネラル、窒素化合物、ケラチン由来成分を蛍光シグナルとして検出できるかを調べることである。宇宙船のような閉鎖環境では、廃棄物を単に処理するだけでなく、資源として再評価する必要がある。そのため、この研究は、人体由来廃棄物を循環型資源システムの一部として扱えるかを検証する。

5. Outline your experimental plan - identify the sample(s) you will test in your experiment, including any necessary controls, the type of data or measurements that will be collected, etc. (Maximum 100 words)　　　

あなたの実験でテストする sample（サンプル） を明確にしなさい。
必要な control（対照実験）、集める data / measurements（データや測定値） なども含めて、実験の流れを説明しなさい。(最大100 words)

Sample:サンプル：
Liquid extracts from urine, feces, hair, and nails after microbial decomposition.
尿、排泄物、髪、爪を微生物分解した抽出液

Controls コントロール： ・Undecomposed samples. (分解していないサンプル)
・Positive controls with known concentrations of K⁺, ammonium, or other target compounds.
(K⁺やアンモニウムなど既知濃度の positive control)
・A negative control without target compounds.(ターゲットなしの negative control)

Measurement 測定：
・Fluorescence produced by the BioBits® cell-free system.
(BioBits cell-free system による蛍光)

・Fluorescence intensity would be observed using the P51 Molecular Fluorescence Viewer.
(P51 Molecular Fluorescence Viewer で蛍光強度を見る)　　　

Expected result 期待：
Samples containing higher amounts of useful components would produce stronger fluorescence.
有用成分が多いほど蛍光が強くなる

I would test liquid extracts from microbially decomposed human-derived waste samples, including urine, feces, hair, and nails. These extracts would be added to BioBits® cell-free protein expression reactions designed to produce fluorescence in response to target compounds.
Controls would include undecomposed samples, a negative control without target compounds, and positive controls containing known concentrations of K⁺, ammonium, or urea.
Fluorescence would be measured using the P51 Molecular Fluorescence Viewer. The main data would be fluorescence intensity compared across samples and controls, indicating whether useful resource components were released by microbial decomposition.

微生物分解された人体由来廃棄物サンプル、具体的には尿、排泄物、髪、爪から得た液体抽出物をテストする。これらの抽出物を、ターゲット化合物に応答して蛍光を発するように設計された BioBits® cell-free protein expression reaction に加える。
コントロールとして、分解していないサンプル、ターゲット化合物を含まない negative control、そして既知濃度の K⁺、アンモニウム、尿素を含む positive control を用意する。
蛍光は P51 Molecular Fluorescence Viewer を使って測定する。主なデータは、各サンプルとコントロール間の蛍光強度の比較であり、微生物分解によって有用な資源成分が放出されたかを示す。

Homework Part B: Individual Final Project

We’d like students to start exploring their final project in depth this week! Of your three Aims, for this week you should have at least Aim 1 decided and written down.　　　
今週から、最終プロジェクトをより深く探り始めてください。
あなたの3つの Aim のうち、今週は少なくとも Aim 1 を決定し、文章として書いておく必要があります。

1. Put your chosen final project slide in the appropriate slide deck following the instructions on slide 1:
   MIT/Harvard/Wellesley ONE FINAL PROJECT IDEA
   Committed Listener ONE FINAL PROJECT IDEA

Slide 1 の指示に従って、自分が選んだ final project のスライドを、該当するスライドデッキに入れてください。

My slide page https://docs.google.com/presentation/d/142YNBXXcDJBfGO_OaF0DpeaF_287YsDeH1-Acp7kUI0/edit?slide=id.g3d87645a083_0_0#slide=id.g3d87645a083_0_0

Aim 1 - Experimental:

I will design a first biological system to detect and eventually concentrate body-derived ions and trace metals from materials such as sweat, hair, nails, and menstrual blood.
As an initial test, I will use a cell-free or synthetic minimal cell system to convert body-derived ions such as K⁺ into a fluorescent signal.
In parallel, I will explore engineered microorganisms expressing metallothionein as a future strategy for concentrating trace metals from body-derived materials.

(私はまず、汗、髪、爪、経血などの身体由来物質から、身体由来イオンや微量金属を検出し、最終的には濃縮するための最初の生物学的システムを設計する。
初期実験として、K⁺ などの身体由来イオンを蛍光シグナルに変換する cell-free system、または synthetic minimal cell system を用いる。
並行して、身体由来物質から微量金属を濃縮する将来的な方法として、metallothionein を発現する遺伝子改変微生物の可能性も探る。)

2. Submit this Final Project selection form if you have not already.　　　
まだ提出していない場合は、Final Project selection form を提出してください。

Done

3. BBegin planning how you will write your final project documentation based on these guidelines　　
ガイドラインに基づいて、最終プロジェクトの documentation をどのように書くか計画を始めてください。

Planned documentation structure:

• Background 背景 / Motivation 動機
  Body, matter, East Asian alchemy, gunpowder, and fireworks.

  身体、物質、東アジアの錬丹術、火薬、花火。

• Research Question 研究課題
  Can body-derived materials be detected, transformed, or concentrated using synthetic biology?

  身体由来物質は、合成生物学によって検出・変換・濃縮できるのか。

• Project Aims
  Aim 1: detecting and concentrating body-derived ions and trace metals.

  Aim 1：身体由来イオンと微量金属の検出・濃縮。

• Experimental Design 実験設計
  Cell-free / synthetic minimal cell system, GFP fluorescence, and DNA construct design.

  Cell-free / synthetic minimal cell system、GFP蛍光、DNA construct design。

• Results 結果 / Troubleshooting
  Protocols, test conditions, images, fluorescence data, failures, and improvements. プロトコル、実験条件、画像、蛍光データ、失敗、改善点。

• Artistic Development / Future Work
  Connection to pigments, inks, smoke, pyrotechnics, and the larger Flying Humanoid vision.

  顔料、インク、煙、花火表現、そして Flying Humanoid の大きな構想への接続。

4. Prepare your first DNA order and put it in the “Twist (MIT)” or “Twist (Nodes)” tab of the 2026 HTGAA Ordering: DNA, Reagents, Consumables spreadsheet, as appropriate.
・First Twist order deadline for MIT/Harvard/Wellesley students is Friday, April 3 at 11PM ET
・First Twist order deadline for Committed Listeners is Friday, April 10 at 11PM ET. (Your Node Lead will place the Twist order, so please work with them to finalize your constructs and ordering decisions.)

最初の DNA order を準備し、2026 HTGAA Ordering: DNA, Reagents, Consumables spreadsheet の該当タブに入力してください。

MIT / Harvard / Wellesley の学生： 4/3
Nodes / Committed Listener：4/10

For my first DNA order, I am considering two constructs related to Aim 1. 最初のDNA注文として、Aim 1 に関係する2つの construct を検討している。

Construct 1: GFP expression construct (GFP発現 construct)

T7 promoter → RBS → GFP → T7 terminator

This construct will be used as a basic test to confirm that the cell-free Tx/Tl system can express a fluorescent reporter.
これは、cell-free Tx/Tl system が蛍光 reporter を発現できるか確認するための基本テストとして使う。

Construct 2: K⁺-responsive GFP construct(K⁺応答性 GFP construct)

K⁺-responsive regulatory element / promoter → RBS → GFP → terminator

This construct will be used to test whether body-derived K⁺ can be converted into a GFP fluorescent signal.
This is more directly related to Aim 1, but the design of the K⁺-responsive regulatory element still needs to be confirmed before ordering.
これは、身体由来K⁺を GFP蛍光シグナルに変換できるかを調べるために使う。
Aim 1 により直接関係するが、K⁺応答性の regulatory element の設計は、注文前に確認する必要がある。

I will discuss these construct options with my Node Lead before placing the Twist order.
Twist order を出す前に、これらの construct option を Node Lead と相談する。

🧪TWIST ORDER (May 22th)🔬

However, this construct is difficult to order immediately because it requires further consideration of ….
(しかし、この construct をすぐに注文するには)

・K⁺-responsive regulatory element (K⁺応答性 regulatory element の選定 )
・Background K⁺ concentration in the cell-free system (cell-free system 内の背景K⁺濃度)
・how K⁺ would enter the vesicle. (K⁺が vesicle 内に入る仕組み)

などを検討する必要がある。

Therefore, for the first DNA order, I am considering a metallothionein expression construct that is more directly related to the concentration of trace metals from body-derived materials.
(そのため、最初の DNA order では、身体由来物質から微量金属を濃縮する方向により直接つながる metallothionein expression construct を検討する。)

🧬Construct: Metallothionein expression construct🧬

T7 promoter → RBS → metallothionein → T7 terminator

This construct would be used to express metallothionein as a first step toward binding and concentrating trace metals from body-derived materials such as sweat, hair, nails, and menstrual blood.
(この construct は、汗、髪、爪、経血などの身体由来物質から微量金属を結合・濃縮するための最初のステップとして、metallothionein を発現させるために使う。)

Week 10 HW -Advanced Imaging & Measurement Technology

‘week-10-hw-imaging-and-measurement’

Documentation

Homework: Advanced Imaging & Measurement Technology

Homework: Final Project

For your final project:

・Please identify at least one (ideally many) aspect(s) of your project that you will measure. It could be the mass or sequence of a protein, the presence, absence, or quantity of a biomarker, etc.　

測定する対象を少なくとも1つ、できれば複数、特定してください。
あなたの最終プロジェクトの中で、何を測定するのかを考えてください。
たとえば、以下のようなものが考えられます。

・タンパク質の質量
・タンパク質の配列
・biomarker の有無
・biomarker の量
・その他、プロジェクトに関係する測定対象

What I will measure in my final project (Final Projectで測定するもの)

In my final project, I would like to measure several aspects of the transformation from body-derived materials into detectable or recoverable substances.(身体由来物質が検出可能、または回収可能な物質へ変換される過程を測定したい。)

• Body-derived ions and trace metals(身体由来イオンと微量金属)
  I would measure ions and trace metals contained in body-derived materials such as sweat, hair, nails, and menstrual blood.
  Examples include K⁺, Na⁺, Ca²⁺, Mg²⁺, Fe, Cu, and Zn.

  汗、髪、爪、経血などの身体由来物質に含まれるイオンや微量金属を測定する。
  例として、K⁺、Na⁺、Ca²⁺、Mg²⁺、Fe、Cu、Zn などがある。

• Protein expression(タンパク質発現)
  If I use a GFP reporter or metallothionein construct, I would measure whether the target protein is expressed.

  GFP reporter や metallothionein construct を使う場合、目的タンパク質が発現しているかを測定する。

• Fluorescent signal(蛍光シグナル)
  I would measure GFP fluorescence to test whether body-derived ions such as K⁺ can be converted into a visible biological signal.

  K⁺ などの身体由来イオンが、目に見える生物学的シグナルへ変換されるかを調べるため、GFP蛍光を測定する。

• Metal binding or concentration(金属結合・濃縮)
  If I use metallothionein, I would measure whether trace metals are bound or concentrated after biological treatment.

  metallothionein を使う場合、生物学的処理後に微量金属が結合・濃縮されたかを測定する。

• DNA construct accuracy
  I would confirm whether the ordered DNA constructs have the correct sequence and size.

  注文した DNA construct が、正しい配列とサイズを持っているかを確認する。。

・Please describe all of the elements you would like to measure, and furthermore describe how you will perform these measurements.
測定したい要素をすべて説明し、それをどのように測定するかも説明してください。
自分のプロジェクトで測りたいものをリストアップし、それぞれについて、どの方法で測定するのかを説明してください。

I would like to measure the following elements(測定したい要素は以下の通りである。):

• Body-derived ions and trace metals(身体由来イオンと微量金属)
  I would measure K⁺, Na⁺, Ca²⁺, Mg²⁺, Fe, Cu, and Zn in body-derived samples such as sweat, hair, nails, and menstrual blood. These would be measured by ion / metal analysis such as ICP-MS, ion chromatography, or mass spectrometry. 汗、髪、爪、経血などの身体由来サンプルに含まれる K⁺、Na⁺、Ca²⁺、Mg²⁺、Fe、Cu、Zn などを測定する。ICP-MS、ion chromatography、mass spectrometry などによって測定する。

• GFP fluorescence(GFP蛍光)
  I would measure GFP fluorescence to test whether body-derived ions such as K⁺ can be converted into a visible biological signal. This would be measured using fluorescence microscopy, a plate reader, or a fluorescence viewer.

  K⁺などの身体由来イオンが、目に見える生物学的シグナルに変換されるかを確認するため、GFP蛍光を測定する。蛍光顕微鏡、plate reader、fluorescence viewer などを使う。

• Protein expression(タンパク質発現)
  If I use a GFP reporter or metallothionein construct, I would measure whether the target protein is expressed. This could be checked by SDS-PAGE or fluorescence measurement.

  GFP reporter や metallothionein construct を使う場合、目的タンパク質が発現しているかを測定する。SDS-PAGE や蛍光測定によって確認する。

• DNA construct accuracy(DNA construct の正確性)
  I would confirm whether the ordered DNA constructs have the correct size and sequence. This would be measured by gel electrophoresis and DNA sequencing.

  注文した DNA construct が正しいサイズと配列を持っているかを確認する。Gel electrophoresis と DNA sequencing によって測定する。

• Metal binding or concentration(金属結合・濃縮)
  If I use metallothionein, I would compare metal levels before and after biological treatment to test whether trace metals are bound or concentrated.

  metallothionein を使う場合、生物学的処理の前後で金属量を比較し、微量金属が結合・濃縮されたかを調べる。

・What are the technologies you will use (e.g., gel electrophoresis, DNA sequencing, mass spectrometry, etc.)? Describe in detail.
どの技術を使いますか？ 詳しく説明してください。
使用する測定技術を書いてください。 たとえば：

・gel electrophoresis ・DNA sequencing ・mass spectrometry ・その他の測定技術

それぞれ、何のために使うのかを詳しく説明してください。

For my final project, I would use several measurement technologies.
Final Projectでは、いくつかの測定技術を使う予定である。

• Gel electrophoresis
  To confirm the size of DNA constructs and, if needed, protein expression by SDS-PAGE.

  (DNA construct のサイズを確認するために使う。また必要に応じて、SDS-PAGE によってタンパク質発現を確認する。)

• DNA sequencing
  To verify that the ordered DNA constructs, including promoter, RBS, coding sequence, and terminator, are correct.

  (注文した DNA construct の配列が正しいかを確認するために使う。promoter、RBS、coding sequence、terminator などが正しく含まれているかを確認する。)

• Fluorescence measurement
  To measure GFP expression and test whether body-derived ions such as K⁺ can be converted into a fluorescent signal.

  (GFP の発現を測定し、K⁺ などの身体由来イオンが蛍光シグナルに変換されるかを確認するために使う。)

• Mass spectrometry
  To analyze proteins or trace metals in body-derived samples before and after biological treatment.

  (生物学的処理の前後で、身体由来サンプルに含まれるタンパク質や微量金属を分析するために使う。)

• Ion / metal analysis
  To quantify ions and trace metals such as K⁺, Na⁺, Ca²⁺, Mg²⁺, Fe, Cu, and Zn from sweat, hair, nails, or menstrual blood.

  (汗、髪、爪、経血などに含まれる K⁺、Na⁺、Ca²⁺、Mg²⁺、Fe、Cu、Zn などのイオンや微量金属を定量するために使う。)

Homework: Waters Part I — Molecular Weight

We will analyze an eGFP standard on a Waters Xevo G3 QTof MS system to determine the molecular weight of intact eGFP and observe its charge state distribution in the native and denatured (unfolded) states. The conditions for LC-MS analysis of intact protein cause it to unfold and be detected in its denatured form (due to the solvents and pH used for analysis).   

(Waters Xevo G3 QTof MS system を使って eGFP standard を分析し、intact eGFP、つまり分解していない完全な eGFP の分子量を決定します。
また、native state、つまり自然に折りたたまれた状態と、denatured / unfolded state、つまり変性して折りたたみがほどけた状態における charge state distribution（電荷状態の分布） を観察します。   
Intact protein の LC-MS 分析条件では、使用される溶媒や pH の影響によってタンパク質が unfolded / denatured state になり、変性した形で検出されます。)

1. Based on the predicted amino acid sequence of eGFP (see below) and any known modifications, what is the calculated molecular weight? You can use an online calculator like the one at https://web.expasy.org/compute_pi/

(下に示されている eGFP の予測アミノ酸配列 と、既知の修飾をもとに、計算上の分子量 はいくつになりますか？
ExPASy の Compute pI/Mw のようなオンライン計算ツールを使ってもかまいません。)

eGFP Sequence:

MVSKGEELFTG VVPILVELDG DVNGHKFSVS GEGEGDATYG KLTLKFICTT
GKLPVPWPTL VTTLTYGVQC FSRYPDHMKQ HDFFKSAMPE GYVQERTIFF
KDDGNYKTRA EVKFEGDTLV NRIELKGIDF KEDGNILGHK LEYNYNSHNV
YIMADKQKNG IKVNFKIRHN IEDGSVQLAD HYQQNTPIGD GPVLLPDNHY
LSTQSALSKD PNEKRDHMVL LEFVTAAGIT LGMDELYKLE HHHHHH

Note: This contains a His-purification tag (HHHHHH) and a linker (LE before it).

MVSKGEELFTG = 11 amino acids VVPILVELDG = 10
DVNGHKFSVS = 10
GEGEGDATYG = 10
KLTLKFICTT = 10
GKLPVPWPTL = 10
VTTLTYGVQC = 10
FSRYPDHMKQ = 10
HDFFKSAMPE = 10
GYVQERTIFF = 10
KDDGNYKTRA = 10
EVKFEGDTLV = 10
NRIELKGIDF = 10
KEDGNILGHK = 10
LEYNYNSHNV = 10
YIMADKQKNG = 10
IKVNFKIRHN = 10
IEDGSVQLAD = 10
HYQQNTPIGD = 10
GPVLLPDNHY = 10
LSTQSALSKD = 10
PNEKRDHMVL = 10
LEFVTAAGIT = 10
LGMDELYKLE = 10
HHHHHH = 6

Total: 247 amino acids

Calculated molecular weight: 28,006.6 Da
= 28.01 kDa

Based on the predicted amino acid sequence of eGFP, including the LE linker and the C-terminal 6xHis tag, the calculated molecular weight is approximately 28,006.6 Da, or 28.01 kDa.
The sequence contains 247 amino acids.

2. Calculate the molecular weight of the eGFP using the adjacent charge state approach described in the recitation. Select two charge states from the intact LC-MS data (Figure 1) and:

(Recitation で説明された adjacent charge state approach（隣接する電荷状態を使う方法） を用いて、eGFP の分子量を計算しなさい。 Intact LC-MS data、つまり Figure 1 の分解されていない eGFP の LC-MS データ から、隣り合う2つの charge state のピークを選び、以下を行いなさい。)

2-1. Determine $z$ for each adjacent pair of peaks $(n, n+1)$ using:

$$ {\large z} = {\Large \frac{\frac{m}{z_{n+1}}}{\frac{m}{z_n} - \frac{m}{z_{n+1}}}} $$

隣り合うピークのペア (n,n+1) について、次の式を使って z（charge state / 電荷数） を求めなさい。

From Figure 1, I selected two adjacent peaks and calculated the charge state of each peak.
Figure 1のピーク2つを選んで、「このピークは何価のイオンか？」を計算する。

The two adjacent peaks selected from Figure 1 are:
Figure 1から、隣り合う2つのピークとして

m/z 1000.4302
m/z 965.9684

を選んだ。

First, I calculated (z) using the adjacent charge state equation:
まず、隣接するcharge stateの式を使って z を計算した。

z = 965.9684 / (1000.4302 − 965.9684) z = 965.9684 / 34.4618 z ≈ 28.0

Therefore,
m/z 1000.4302 のピーク = 28+
m/z 965.9684 のピーク = 29+

These two peaks are considered to correspond to adjacent charge states.
に対応すると考えられる。

2-2. Determine the MW of the protein using the relationship between $\frac{m}{z_n}$, $MW$, and $z$

m/z、MW、z の関係を使って、タンパク質の MW（molecular weight / 分子量） を求めなさい。

Using the charge state (z) calculated in 2-1, I calculated the experimental molecular weight of eGFP from the LC-MS data.
2-1で求めた charge state z を使って、LC-MSデータからeGFPの実測分子量を計算する。

Next, I calculated the molecular weight using:
次に、以下の計算式も使って分子量を求めた。

MW = z × (m/z − 1.0073)

Using the peak at m/z 1000.4302
MW = 28 × (1000.4302 − 1.0073)
MW = 28 × 999.4229
MW ≈ 27,983.8 Da

Using the peak at m/z 965.9684:
MW = 29 × (965.9684 − 1.0073) MW = 29 × 964.9611 MW ≈ 27,983.9 Da

Therefore, the experimental molecular weight of eGFP calculated from the LC-MS data is:　　　 したがって、LC-MSデータから求めた eGFP の実測分子量は

MWexperiment ≈ 27,984 Da = 27.984 kDa

The experimental molecular weight is approximately 27.984 kDa. 27.984 kDa である。

2-3. Calculate the accuracy of the measurement using the deconvoluted MW from 2.2 and the predicted weight of the protein from 2.1 using:

$$ \text{Accuracy} = \frac{|MW_{\text{experiment}} - MW_{\text{theory}}|}{MW_{\text{theory}}} $$

2-2で求めた実測分子量と、1で求めた予測分子量を使って、測定の accuracy（正確さ／誤差率） を計算しなさい。

Compared with the theoretical molecular weight calculated in Question 1, 28,006.6 Da:　　　 1で求めた理論分子量 28,006.6 Da と比較すると、

Accuracy = |27,983.9 − 28,006.6| / 28,006.6
Accuracy = 22.7 / 28,006.6
Accuracy ≈ 0.00081

Therefore, the error is approximately 0.081%.　　　 つまり、誤差は約 0.081% である。

3. Can you observe the charge state for the zoomed-in peak in the mass spectrum for the intact eGFP? If yes, what is it? If no, why not?　　　

intact eGFP の mass spectrum で、拡大表示されたピークの charge state を観察できますか？　 もし観察できるなら、それは何ですか？ もし観察できないなら、なぜ観察できないのですか？

ここでいう charge state は、さっき計算したような 28+ や 29+ のこと。

ただし、zoomed-in peak で直接見えているのは charge state ではなく、通常は isotope pattern / isotopic peaks になる。

タンパク質の質量スペクトルでは、同じ分子でも自然同位体の違いによって、少しずつ m/z が異なる isotopic peaks が現れる。

電荷が 1+ の場合、同位体ピークの間隔は約 1 m/z になる。

しかし、電荷が z+ の場合、同位体ピークの間隔は：

1 / z

になる。

したがって、28+ の場合は：

1 / 28 ≈ 0.036 m/z

となる。

そのため、zoomed-in peak の中で isotope peaks の細かい間隔が見えれば、その間隔から charge state を推定できる。

Figure 1 の zoomed-in peak が m/z 1000.4302 周辺のピークであれば、前の計算からこのピークは 28+ charge state に対応すると考えられる。

Yes, the charge state can be inferred from the zoomed-in peak by looking at the isotope spacing.
The zoomed-in peak shows an isotope pattern, and the spacing between isotopic peaks is approximately 1/z.
For the peak around m/z 1000.4302, the adjacent charge state calculation indicates that this peak corresponds to the 28+ charge state.
For a 28+ ion, the expected isotope spacing is about 1/28 = 0.036 m/z.

Homework: Waters Part II — Secondary/Tertiary structure

We will analyze eGFP in its native, folded state and compare it to its denatured, unfolded state on a quadrupole time-of-flight MS. We will be doing MS-only analysis (no liquid chromatography, also known as “direct infusion” experiments) on the Waters Xevo G3-QToF MS.

eGFP を native / folded state（自然に折りたたまれた状態） で分析し、それを denatured / unfolded state（変性してほどけた状態） と比較する。 分析には quadrupole time-of-flight MS を使う。

今回は LC、つまり液体クロマトグラフィーは使わず、MS-only analysis を行う。(= direct infusion experiment とも呼ばれる）
装置は Waters Xevo G3-QToF MS を使用。

1. Based on learnings in the lab, please explain the difference between native and denatured protein conformations. For example, what happens when a protein unfolds? How is that determined with a mass spectrometer? What changes do you see in the mass spectrum between the native and denatured protein analyses (Figure 2)?

ラボで学んだことをもとに、native protein conformation と denatured protein conformation の違いを説明しなさい。

たとえば：

・タンパク質が unfolding すると何が起こるのか？
・それは mass spectrometer でどのように判断できるのか？
・native protein analysis と denatured protein analysis の間で、mass spectrum にどのような違いが見えるか？
※ Figure 2 を参照

Native state では、タンパク質は自然に折りたたまれたコンパクトな構造をとっている。
この状態では、プロトンが結合できる部位があまり露出していないため、比較的低い charge state で検出される。
そのため、mass spectrum では高い m/z 側に少数のピークとして現れやすい。

一方、denatured state では、タンパク質の折りたたみ構造がほどけ、より伸びた状態になる。
内部に隠れていたアミノ酸やプロトン化可能な部位が露出するため、より多くのプロトンを受け取り、高い charge state で検出される。
その結果、mass spectrum では低い m/z 側に、多数の charge state のピークとして現れる。

Figure 2 では、native eGFP では m/z 2500〜2800 付近に大きなピークが見られ、低い charge state を示している。
一方、denatured eGFP では m/z 700〜1300 付近に多くのピークが分布しており、高い charge state distribution を示している。
この違いから、native eGFP は foldedでコンパクトな状態、denatured eGFP は unfoldedでより多くの電荷を持つ状態として観察されていると考えられる。

Native eGFP is folded and compact, so fewer protonation sites are exposed.
As a result, it carries fewer charges and appears at higher m/z values.
Denatured eGFP is unfolded, exposing more protonation sites, so it carries more charges and appears as many peaks at lower m/z values.
In Figure 2, the native spectrum shows strong peaks around m/z 2500–2800, while the denatured spectrum shows many peaks around m/z 700–1300.
This shift in charge state distribution shows the difference between folded and unfolded eGFP.

2. Zooming into the native mass spectrum of eGFP from the Waters Xevo G3 QTof MS (see Figure 3), can you discern the charge state of the peak at ~2800 $\frac{m}{z}$? What is the charge state? How can you tell?

Figure 3 の native eGFP mass spectrum を拡大して見たとき、m/z 約2800のピークの charge state は判別できますか？
もし判別できるなら、その charge state は何ですか？また、どうやってそれがわかりますか？

Yes, it can be determined.判別できる。

The peak around m/z 2800 is likely to correspond to approximately the 10+ charge state, because the molecular weight of eGFP is about 27,984 Da.　　　 m/z 約2800 のピークは、eGFP の分子量がおよそ 27,984 Da であることから、約 10+ の charge state に対応すると考えられる。

Since MW / z ≈ m/z:

27,984 / 10 ≈ 2,798

This matches the peak around m/z 2800.　　　 m/z 約2800 のピークと一致する。

The charge state can also be confirmed from the isotope spacing in the zoomed-in spectrum.
When the charge state is z+, the isotope peak spacing is approximately 1/z.
For a 10+ ion, the expected isotope spacing is about:　　　

また、zoomed-in spectrum で isotope peaks が見える場合、同位体ピーク間隔からも charge state を確認できる。
charge state が z+ のとき、isotope peak spacing は 1/z になる。
10+ の場合、間隔は約 0.1 m/z になるため、ピーク内の isotope spacing が約 0.1 m/z であれば、このピークは 10+ charge state と判断できる。

1 / 10 = 0.1 m/z

Therefore, if the isotope peaks within the zoomed-in peak are spaced by about 0.1 m/z, the peak can be assigned as the 10+ charge state.

Homework: Waters Part III — Peptide Mapping - primary structure

We will digest the eGFP protein standard into peptides using trypsin (an enzyme that selectively cleaves the peptide bond after Lysine (K) and Arginine (R) residues. The resulting peptides will be analyzed on the Waters BioAccord LC-MS to measure their molecular weights and fragmented to confirm the amino acid sequence within each peptide – generating a “peptide map”. This process is used to confirm the primary structure of the protein.

There are a variety of tools available online to calculate protein molecular weight and predict a list of peptides generated from a tryptic digest. We will be using tools within the online resource Expasy (the bioinformatics resource portal of the Swiss Institute of Bioinformatics (SIB)) to predict a list of tryptic peptides from eGFP.

(eGFP タンパク質標準品を trypsin を使ってペプチドに分解します。 Trypsin は、Lysine（K） と Arginine（R） の後ろのペプチド結合を選択的に切断する酵素です。生成されたペプチドは、Waters BioAccord LC-MS で分析されます。 これにより、それぞれのペプチドの分子量を測定し、さらに断片化して、各ペプチド内のアミノ酸配列を確認します。 この一連のプロセスによって peptide map が作成されます。この方法は、タンパク質の primary structure（一次構造） を確認するために使われます。 タンパク質の分子量を計算したり、trypsin digest によって生成されるペプチドのリストを予測したりするためのオンラインツールはいくつかあります。 今回は、Swiss Institute of Bioinformatics（SIB）の bioinformatics resource portal である ExPASy のツールを使い、eGFP から生成される tryptic peptides のリストを予測します。)

1. How many Lysines (K) and Arginines (R) are in eGFP? Please circle or highlight them in the eGFP sequence given in Waters Part I question 1 above. (Note: adding the sequence to Benchling as an amino acid file and clicking biochemical properties tab will show you a count for each amino acid).

(eGFP には、Lysine（K） と Arginine（R） がいくつ含まれていますか？ Waters Part I Question 1 で与えられた eGFP 配列の中で、それらを 丸で囲む、または ハイライトしてください。 (注：Benchling にアミノ酸ファイルとして配列を追加し、biochemical properties タブをクリックすると、各アミノ酸の数を確認できます。))

I could not use the highlight function properly, so I made the K and R residues bold instead.　　　

MVSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTLTYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITLGMDELYKLEHHHHHH

Lysine (K): 20 Arginine (R): 6

There are 26 total K/R residues that can be potential trypsin cleavage sites.

2. How many peptides will be generated from tryptic digestion of eGFP? (eGFP を trypsin digestion した場合、いくつのペプチドが生成されますか？)

1. Navigate to https://web.expasy.org/peptide_mass/

2. Copy/paste the sequence above into the input box in the PeptideMass tool to generate expected list of peptides. (上にある eGFP 配列を、PeptideMass tool の入力欄にコピー＆ペーストする。)

3. Use Figure 4 below as a guide for the relevant parameters to predict peptides from eGFP. (eGFP から生成されるペプチドを予測するために、下の Figure 4 を参考にして、関連するパラメータを設定する。)

4. Click “Perform the Cleavage” button in the PeptideMass tool and report the number of peptides generated when using trypsin to perform the digest.(PeptideMass tool の “Perform the Cleavage” (ボタンをクリックし、trypsin を使って digest した場合に生成されるペプチド数を報告する。)

I used the ExPASy PeptideMass tool with the following settings:

• Enzyme: Trypsin
• Missed cleavages: 0
• Cysteines: reduced form
• Methionines: not oxidized
• Mass calculation: monoisotopic
• Ion: [M+H]+

The PeptideMass tool predicted 27 tryptic peptides from the eGFP sequence.

3. Based on the LC-MS data for the Peptide Map data generated in lab (please use Figure 5a as a reference) how many chromatographic peaks do you see in the eGFP peptide map between 0.5 and 6 minutes? You may count all peaks that are >10% relative abundance.

(ラボで生成された Peptide Map の LC-MS データをもとに、eGFP peptide map において 0.5分から6分の間に、いくつの chromatographic peaks が見えますか？ Figure 5a を参考にしてください。相対存在量が 10%を超えるピークはすべて数えてかまいません。 Figure 5a：eGFP peptide map の total ion chromatogram（TIC）。 2.78分のピークが丸で囲まれており、その MS データは下の Figure 5b の mass spectrum に示されています。)

Based on the TIC chromatogram in Figure 5a, I counted approximately 17 chromatographic peaks between 0.5 and 6 minutes that are above 10% relative abundance.

The main peaks I counted are around 0.61, 0.79, 1.43, 1.80, 1.85, 1.93, 2.17, 2.26, 2.54, 2.78, 3.27, 3.53, 3.59, 3.70, 4.48, 4.64, and 4.87 minutes.

Approximately 17

4. Assuming all the peaks are peptides, does the number of peaks match the number of peptides predicted from question 2 above? Are there more peaks in the chromatogram or fewer?

(すべてのピークがペプチドであると仮定した場合、chromatogram のピーク数は、上の Question 2 で予測されたペプチド数と一致しますか？ chromatogram には、予測されたペプチド数よりも 多くのピークがありますか？それとも 少ないピークがありますか？)

The number of observed peaks does not match the number of predicted peptides.

In Question 2, the ExPASy PeptideMass tool predicted 27 tryptic peptides from the eGFP sequence.
In Figure 5a, I counted approximately 17 chromatographic peaks between 0.5 and 6 minutes above 10% relative abundance.

Therefore, there are fewer peaks in the chromatogram than the number of predicted peptides.

This difference may occur because not every predicted peptide is detected as a separate chromatographic peak. Some peptides may be present at low abundance, ionize poorly, co-elute with other peptides, or fall below the 10% relative abundance threshold.

観察されたピーク数は、予測されたペプチド数とは一致しない。　 Question 2 では、ExPASy PeptideMass tool により、eGFP 配列から 27個の tryptic peptides が予測された。　 一方、Figure 5a では、0.5分から6分の間で、10%以上の相対存在量を持つ chromatographic peaks を約 17個 数えた。
したがって、chromatogram には、予測されたペプチド数よりも 少ないピーク が見られる。
この違いは、予測されたすべてのペプチドが、それぞれ独立した chromatographic peak として検出されるとは限らないために起こる可能性がある。
一部のペプチドは存在量が低かったり、イオン化効率が悪かったり、他のペプチドと同じ時間に溶出してピークが重なったり、10% relative abundance の閾値を下回ったりする可能性がある。

5. Identify the mass-to-charge ($\frac{m}{z}$) of the peptide shown in Figure 5b. What is the charge ($z$) of the most abundant charge state of the peptide (use the separation of the isotopes to determine the charge state). Calculate the mass of the singly charged form of the peptide ($\small{[M\!\!+\!\!H]^+}$) based on its $\frac{m}{z}$ and $z$.

Figure 5b に示されているペプチドの mass-to-charge ratio、つまり m/z を特定しなさい。 そのペプチドの最も多い charge state、つまり 最も強く観察されている電荷状態 z は何ですか？ charge state は、同位体ピーク同士の間隔を使って判断しなさい。 さらに、その m/z と z をもとに、そのペプチドの 一価イオンの質量、つまり [M+H]⁺ を計算しなさい。

“Figure 5b.
Mass spectrum figure to show $\frac{m}{z}$ for the chromatographic peak at 2.78 min from Figure 5a above. The inset is a zoom-in of the peak at M/Z 525.76 to discern the isotope peaks."　　　 2.78分の chromatographic peak に対応する mass spectrum です。 m/z 525.76 のピークが拡大されており、isotope peaks を見分けられるようになっています。

“Figure 5c.
Fragmentation spectrum of the peptide eluting at retention time 2.78 minutes in Figure 5a (above).
Figure 5a の 2.78分に溶出したペプチドの fragmentation spectrum です。

Figure 5b を見ると、
2.78分のピークに対応する主な peptide peak は：m/z = 525.76712
拡大図では、同位体ピークが：

525.76712
526.25918
526.76845
527.26098

同位体ピークの間隔

526.25918 − 525.76712 = 0.49206
約 0.5 m/z

同位体ピーク間隔は"1 / z"のため

1 / z ≈ 0.5
z ≈ 2

つまり、この peptide の最も多い charge state はz = 2+

観測されたピークは 2価イオンなので：

m/z = 525.76712
z = 2

一価の peptide mass、つまり [M+H]⁺ は：
[M+H]⁺ = z × (m/z) − (z − 1) × 1.0073

[M+H]⁺ = 2 × 525.76712 − 1.0073
[M+H]⁺ = 1051.53424 − 1.0073
[M+H]⁺ ≈ 1050.5269 Da

Figure 5b には実際に m/z 1050.52438 のピークも見えているので、これは同じ peptide の 1+ charge state と考えられる。

The mass-to-charge ratio of the peptide shown in Figure 5b is m/z 525.76712.

The isotope peaks are separated by approximately 0.5 m/z:

526.25918 − 525.76712 = 0.49206

Because isotope spacing is approximately 1/z, this indicates that the most abundant charge state is 2+.

The singly charged peptide mass, [M+H]⁺, can be calculated as:

[M+H]⁺ = z × (m/z) − (z − 1) × 1.0073
[M+H]⁺ = 2 × 525.76712 − 1.0073
[M+H]⁺ ≈ 1050.5269 Da

Therefore, the singly charged form of the peptide is approximately 1050.53 Da.

6. Identify the peptide based on comparison to expected masses in the PeptideMass tool.
What is mass accuracy of measurement? Please calculate the error in ppm.

PeptideMass tool で予測されたペプチドの質量と比較して、Figure 5b のペプチドがどのペプチドかを同定しなさい。 また、その測定の mass accuracy はどのくらいですか？誤差を ppm で計算しなさい。

(Recall that $ \text{Accuracy} = \frac{|MW_{\text{experiment}} - MW_{\text{theory}}|}{MW_{\text{theory}}} $ )

Note:
Accuracy = |MWexperiment − MWtheory| / MWtheory
ppm で表す場合は、これに 1,000,000 をかける。　　 ppm error = |MWexperiment − MWtheory| / MWtheory × 1,000,000　　　

The singly charged form calculated in Question 5 was approximately 1050.5269 Da.
By comparing this value with the expected peptide masses from the ExPASy PeptideMass tool, the closest predicted peptide is FEGDTLVNR, with a theoretical mass of 1050.5214 Da.

Therefore, the peptide can be identified as FEGDTLVNR.

Figure 5b のペプチドについて、設問5で計算した singly charged form は約 1050.5269 Da であった。　 PeptideMass tool の予測質量と比較すると、最も近いペプチドは FEGDTLVNR であり、理論質量は 1050.5214 Da である。　 したがって、このペプチドは FEGDTLVNR と同定できる。　

The peptide is identified as FEGDTLVNR, because its theoretical mass from the ExPASy PeptideMass tool is 1050.5214 Da, which closely matches the experimentally calculated singly charged mass of 1050.5269 Da.

1050.5214 115-123 FEGDTLVNR

The ppm error was calculated as:

ppm error = |1050.5269 − 1050.5214| / 1050.5214 × 1,000,000
ppm error ≈ 5.2 ppm　

Therefore, the mass accuracy is approximately 5.2 ppm.

7. What is the percentage of the sequence that is confirmed by peptide mapping? (see Figure 6)

ペプチドマッピングによって確認された配列の割合はどれくらいですか？（図6を参照）

Figure 6 Amino Acid Coverage Map of eGFP based on BioAccord LC-MS peptide identification data.

The percentage of the eGFP sequence confirmed by peptide mapping is 88%.

According to Figure 6, the identified sequence coverage is 88%, meaning that 88% of the eGFP amino acid sequence was confirmed by the peptide map.

Figure 6 では “Identified: 88%” と表示されているため、peptide mapping によって確認された eGFP 配列の割合は 88% である。

Bonus Peptide Map Questions

8. Can you determine the peptide sequence for the peptide fragmentation spectrum shown in Figure 5c?

Figure 5c に示されている peptide fragmentation spectrum から、ペプチド配列を決定できますか？

(HINT: Use your results from Question 2 above to match the peptide molecular weight that is closest to that shown in Figure 5b. Copy and paste its sequence into this tool online to predict the fragmentation pattern based on its amino acid sequence: http://db.systemsbiology.net/proteomicsToolkit/FragIonServlet.html. What is the sequence of the eGFP peptide that best matches the fragmentation spectrum in Figure 5c?

ヒント： 上の Question 2 の結果を使って、Figure 5b に示された peptide molecular weight に最も近いペプチドを探します。 そのペプチド配列を、オンラインの fragmentation prediction tool にコピー＆ペーストし、アミノ酸配列にもとづく fragmentation pattern を予測します。 そして、Figure 5c の fragmentation spectrum に最もよく一致する eGFP peptide の配列を答えなさい。

The peptide sequence that best matches the fragmentation spectrum in Figure 5c is FEGDTLVNR.
I entered the peptide sequence FEGDTLVNR into the Fragment Ion Calculator and compared the predicted b/y ions with the observed peaks in Figure 5c.

Several predicted fragment ions match the observed spectrum. For example, the predicted y ions at approximately 903.45, 774.41, and 602.36 m/z match observed peaks around 903.44, 774.41, and 602.35 m/z in Figure 5c.

Therefore, the fragmentation spectrum supports the identification of the peptide as FEGDTLVNR.

Figure 5c の fragmentation spectrum に最もよく一致するペプチド配列は FEGDTLVNR である。
Fragment Ion Calculator に FEGDTLVNR を入力し、予測された b/y ions と Figure 5c のピークを比較した。
予測された y ions のうち、約 903.45、774.41、602.36 m/z のピークが、Figure 5c の 903.44、774.41、602.35 m/z 付近のピークと一致している。
したがって、Figure 5c の fragmentation spectrum は FEGDTLVNR を支持している。

9. Does the peptide map data make sense, i.e. do the results indicate the protein is the eGFP standard? Why or why not? Consult with Figure 6, which depicts the % amino acid coverage of peptides positively identified using their calculated mass and fragmentation pattern.

Peptide map data は妥当ですか？つまり、今回の peptide mapping の結果は、分析したタンパク質が eGFP standard であることを示していますか？ なぜそう言えますか？ または、なぜそう言えませんか？
Figure 6 を参照してください。 Figure 6 では、計算された質量と fragmentation pattern によって確実に同定されたペプチドが、eGFPアミノ酸配列の何％をカバーしているかが示されています。

Yes, the peptide map data supports that the protein is the eGFP standard.
Figure 6 shows 88% sequence coverage, meaning that most of the eGFP amino acid sequence was confirmed by peptide mapping. This high coverage indicates that many observed peptides match the predicted eGFP sequence.

In addition, as shown in Questions 6–8, the peptide eluting at 2.78 minutes was identified as FEGDTLVNR based on both its mass and fragmentation pattern. This supports the identification because the peptide matches the expected eGFP sequence.

peptide map data は、サンプルが eGFP standard であることを支持している。 Figure 6 では、eGFP配列の 88% が peptide mapping によって同定されている。これは高い sequence coverage であり、多くのペプチドが予測された eGFP 配列と一致していることを示している。

また、設問6〜8で確認したように、2.78分のピークから得られたペプチドは、質量と fragmentation spectrum の両方から FEGDTLVNR と同定できた。
このように、複数のペプチドが質量と断片化パターンによって確認されているため、分析したタンパク質は eGFP standard であると考えられる。

Homework: Waters Part IV — Oligomers

We will determine Keyhole Limpet Hemocyanin (KLH)’s oligomeric states using charge detection mass spectrometry (CDMS). CDMS single-particle measurements of KLH allow us to make direct mass measurements to determine what oligomeric states (that is, how many protein subunits combine) are present in solution. Using the known masses of the polypeptide subunits (Table 1) for KLH, identify where the following oligomeric species are on the spectrum shown below from the CDMS (Figure 7):　　　

今回は、charge detection mass spectrometry（CDMS） を使って、Keyhole Limpet Hemocyanin（KLH） の oligomeric states を調べます。　　 CDMS では、KLH の単一粒子を測定することで、直接その質量を測定できます。　　 それによって、溶液中にどのような oligomeric state、つまり 何個のタンパク質サブユニットが集まった状態 が存在しているかを判断します。 KLH の polypeptide subunit の既知の質量は Table 1 に示されています。　　　

このサブユニット質量を使って、Figure 7 の CDMS spectrum 上で、次の oligomeric species がどこに現れるかを同定しなさい。

• 7FU Decamer
• 8FU Didecamer
• 8FU 3-Decamer
• 8FU 4-Decamer

Figure 7
Mass spectrum of Keyhole Limpet Hemocyanin (KLH) acquired on the CDMS.

Decamer は、10個のサブユニットが集まった状態。なので

oligomer mass = subunit mass × subunit number

で計算する。

7FU Decamer　　　 7FU の subunit mass は 340 kDa。　　　 Decamer は10量体なので：

340 kDa × 10 = 3,400 kDa
7FU Decamer = 3.4 MDa

8FU Didecamer
8FU の subunit mass は 400 kDa。
Didecamer は decamer が2つ、つまり20量体なので：

400 kDa × 20 = 8,000 kDa
8FU Didecamer = 8.0 MDa

8FU 3-Decamer
3-Decamer は decamer が3つ、つまり30量体。
400 kDa × 30 = 12,000 kDa
8FU 3-Decamer = 12.0 MDa

8FU 4-Decamer
4-Decamer は decamer が4つ、つまり40量体。
400 kDa × 40 = 16,000 kDa
8FU 4-Decamer = 16.0 MDa

Therefore, on the CDMS spectrum in Figure 7, these species should appear around 3.4 MDa, 8.0 MDa, 12.0 MDa, and 16.0 MDa, respectively.

したがって、図7のCDMSスペクトル上では、これらの同位体はそれぞれ3.4 MDa、8.0 MDa、12.0 MDa、および16.0 MDa付近に現れるはずである。

Homework: Waters Part V — Did I make GFP?

Please fill out this table with the data you acquired from the lab work done at the Waters Immerse Lab in Cambridge, or else the data screenshots in this document if you were unable to have lab work done at Waters.

• Theoretical: 28.0066 kDa
• Observed / measured on intact LC-MS: 27.984 kDa
• PPM Mass Error: 約 810 ppm

Theoretical molecular weight は、アミノ酸配列から計算した 28.0066 kDa。
Observed molecular weight は、intact LC-MS の adjacent charge state calculation から求めた 27.984 kDa。
PPM mass error は約 810 ppm。

PPM error = |MWexperiment − MWtheory| / MWtheory × 1,000,000

PPM error = |27.984 − 28.0066| / 28.0066 × 1,000,000 PPM error ≈ 807 ppm

Week 11HW — Bioproduction & Cloud Labs

‘week-11-hw-building-genomes

Documentation

Homework: Bioproduction & Cloud Labs

Part A: The 1,536 Pixel Artwork Canvas | Collective Artwork

1. Contribute at least one pixel to this global artwork experiment before the editing ends on Sunday 4/19 at 11:59 PM EST.

• A personalized URL was sent to the email address associated with your Discourse account, and you can discuss the artwork on the Discourse.
• If you did not have a chance to contribute, it’s okay, just make sure you become a TA this fall! 😉

2. Make a note on your HTGAA webpages including:

• what you contributed to the community bioart project (e.g., “I made part of the DNA on the bottom right plate”)
• what you liked about the project, and
• what about this collaborative art experiment could be made better for next year.

https://rcdonovan.com/1536

Part A: Community Bioart Project

I liked that the project allowed many people to create one artwork together through small individual actions. Even though each contribution was simple, the final image became much richer through collaboration. It was a fun and interesting way to participate in a collective bioart experiment.

I also thought it was a beautiful form of collaboration that people discussed and worked together toward one shared image.
For next year, it might also be interesting to try a different format that is less about drawing a specific picture and more based on game-like rules.
For example, it could work like a territory game, where participants form teams by node or across nodes, and pixels expand or disappear according to certain rules.

Another possibility would be to limit how participants can edit the image. Instead of drawing freely, each person could only use fixed patterns, such as a three-pixel line, a cross shape, or diagonal marks. The final image might not become a beautiful picture in the usual sense, but the process itself could become more experimental and interesting.

このプロジェクトでは、小さな個人の操作が集まって、ひとつの共同作品になるところが面白かった。
また、参加者が話し合いながら一つの形を作っていくことも、素晴らしい連携だと思った。

来年もし行うなら、あえて具体的な絵を描くのではなく、ゲーム的なルールを設けても面白いと思う。 たとえば陣取りゲームのように、ノードごと、あるいはノードを超えたチームごとに、ピクセルがルールに従って増殖・消滅する仕組みにする。 また、参加者が自由に描くのではなく、「3ピクセル直線」「十字」「斜めだけ」など、決められたパターンでしか編集できないようにしても面白いかもしれない。
完成する絵は通常の意味では美しくないかもしれないが、プロセス自体はより実験的になると思う。

I contributed to the global community bioart project and made 109 contributions, ranking 19th overall.

私は global community bioart project に参加し、109回貢献して全体で19位だった。

Part B: Cell-Free Protein Synthesis | Cell-Free Reagents　　

無細胞タンパク質合成

1. Referencing the cell-free protein synthesis reaction composition (the middle box outlined in yellow on the image above, also listed below), provide a 1-2 sentence description of what each component’s role is in the cell-free reaction.

上の画像の 中央の黄色い枠で囲まれた cell-free protein synthesis reaction composition を参照し、それぞれの成分が cell-free reaction の中でどのような役割を持つのかを、1〜2文で説明してください。

E. coli Lysate　大腸菌ライセート

• BL21 (DE3) Star Lysate (includes T7 RNA Polymerase)

The E. coli lysate provides the cellular machinery needed for transcription and translation, including ribosomes, tRNAs, enzymes, and other factors. Because this lysate includes T7 RNA polymerase, it can transcribe DNA templates controlled by a T7 promoter.

E. coli lysate は、転写と翻訳に必要な細胞内の仕組みを提供する。これには ribosome、tRNA、酵素、その他の因子が含まれる。この lysate には T7 RNA polymerase が含まれているため、T7 promoter によって制御された DNA template を転写することができる。

Salts/Buffer

• Potassium Glutamate

  Potassium glutamate helps maintain ionic strength and provides potassium ions, which are important for translation and enzyme activity. It also helps mimic the intracellular environment of E. coli.

  Potassium glutamate は、反応の ionic strength を維持し、translation や酵素活性に重要な potassium ions を供給する。また、E. coli の細胞内環境を模倣するのにも役立つ。

• HEPES-KOH pH 7.5

  HEPES-KOH acts as a buffer to keep the reaction at a stable pH. Maintaining pH around 7.5 is important because transcription and translation enzymes are sensitive to pH changes.

  HEPES-KOH は buffer として働き、反応の pH を安定に保つ。転写や翻訳に関わる酵素は pH の変化に敏感なため、pH 7.5 付近を維持することが重要である。

• Magnesium Glutamate

  Magnesium ions are essential cofactors for many reactions in cell-free protein synthesis. They are especially important for ribosome function, nucleotide reactions, and RNA polymerase activity.

  Magnesium ions は、cell-free protein synthesis における多くの反応に必要な cofactor である。特に、ribosome の機能、nucleotide reaction、RNA polymerase activity に重要である。

• Potassium phosphate monobasic

  Potassium phosphate monobasic contributes phosphate and potassium ions to the reaction. It also helps support buffering and energy-related phosphate chemistry.

  Potassium phosphate monobasic は、反応に phosphate と potassium ions を供給する。また、buffering や energy-related phosphate chemistry を支える役割もある。

• Potassium phosphate dibasic

  Potassium phosphate dibasic works together with monobasic phosphate to help maintain phosphate balance and pH buffering. The ratio of monobasic and dibasic phosphate helps set the chemical environment of the reaction.

  Potassium phosphate dibasic は、monobasic phosphate と一緒に働き、phosphate balance と pH buffering を維持する。monobasic と dibasic phosphate の比率は、反応の化学的環境を調整するのに役立つ。

Energy / Nucleotide System

• Ribose

  Ribose is a sugar component used in nucleotide metabolism. In this system, it helps support the regeneration or production of nucleotide-related molecules needed for transcription.

  Ribose は、nucleotide metabolism に使われる糖成分である。この system では、transcription に必要な nucleotide-related molecules の再生や生成を支える。

• Glucose

  Glucose provides a carbon and energy source for the cell-free reaction. It can be metabolized by enzymes in the lysate to help regenerate energy molecules.

  Glucose は、cell-free reaction に炭素源と energy source を提供する。lysate 内の酵素によって代謝され、energy molecules の再生を助ける。

• AMP

  AMP is a nucleotide precursor that can be converted into ATP-related molecules. It supports the nucleotide and energy system needed for transcription and translation.

  AMP は nucleotide precursor であり、ATP-related molecules に変換されることがある。transcription と translation に必要な nucleotide / energy system を支える。

• CMP

  CMP is a cytidine nucleotide precursor. It helps provide the building blocks needed to regenerate CTP for RNA synthesis.

  CMP は cytidine nucleotide precursor である。RNA synthesis に必要な CTP を再生するための building block を提供する。

• GMP

  GMP is a guanosine nucleotide precursor. It supports the production or regeneration of GTP, which is needed for RNA synthesis and translation. GMP は guanosine nucleotide precursor である。RNA synthesis や translation に必要な GTP の生成または再生を支える。

• UMP

  UMP is a uridine nucleotide precursor. It helps provide the building blocks for UTP, which is required for transcription.

  UMP は uridine nucleotide precursor である。transcription に必要な UTP の building block を提供する。

• Guanine

  Guanine is a nucleobase that can be used in nucleotide salvage pathways. It helps support nucleotide regeneration in the reaction.

  Guanine は nucleobase であり、nucleotide salvage pathway に利用される。反応内で nucleotide regeneration を支える。

Translation Mix (Amino Acids)

• 17 Amino Acid Mix

  The 17 amino acid mix provides most of the amino acids needed to synthesize proteins. These amino acids are used by ribosomes during translation.

  17 amino acid mix は、タンパク質合成に必要なほとんどの amino acids を供給する。これらの amino acids は ribosome による translation の際に使われる。

• Tyrosine

  Tyrosine is supplied separately because it can have solubility or stability issues in amino acid mixtures. It is still required as one of the amino acids incorporated into the protein.

  Tyrosine は、amino acid mixture の中で solubility や stability の問題があるため、別に供給される。タンパク質に組み込まれる amino acid の一つとして必要である。

• Cysteine

  Cysteine is supplied separately because it is chemically reactive and can be unstable. It is needed for protein synthesis and may also affect folding through sulfur-containing chemistry.

  Cysteine は化学的に反応性が高く、不安定になりやすいため、別に供給される。タンパク質合成に必要であり、硫黄を含む化学性質によって protein folding にも影響する可能性がある。

Additives　添加物

• Nicotinamide

  Nicotinamide supports cofactor-related metabolism in the lysate, especially pathways involving NAD-related chemistry. It may help sustain enzyme activity and energy regeneration during the reaction.

  Nicotinamide は、lysate 内の cofactor-related metabolism、特に NAD-related chemistry を支える。反応中の酵素活性や energy regeneration を維持する助けになる可能性がある。

Backfill　体積調整用

• Nuclease Free Water

  Nuclease-free water is used to bring the reaction to the final desired volume. It avoids introducing nucleases that could degrade DNA or RNA templates.

  Nuclease-free water は、反応を最終的な必要 volume に調整するために使われる。また、DNA や RNA template を分解する nuclease を反応に持ち込まないために使用される。

2. Describe the main differences between the 1-hour optimized PEP-NTP master mix and the 20-hour NMP-Ribose-Glucose master mix shown in the Google Slide above. (2-3 sentences)

上の Google Slide に示されている 1時間反応用に最適化された PEP-NTP master mix と、20時間反応用に最適化された NMP-Ribose-Glucose master mix の主な違いを説明してください。(2~3文で）

「左の1時間用ミックス」と「中央の20時間用ミックス」は、何が違うのか？　　　 特に、エネルギー源・ヌクレオチド供給方法・反応時間の違いを説明する

1-hour PEP-NTP mix:

The 1-hour PEP-NTP master mix uses pre-supplied NTPs and a PEP-based energy system, so it is designed for rapid transcription and protein expression over a short incubation time.

ATP/GTP/CTP/UTP などの NTPを直接入れている。また、PEP / PEP-mono を使って、短時間で強く反応させるタイプ。

→ It starts quickly, but it is not designed to sustain the reaction for a long time.
すぐ動くけど、長時間持続する設計ではない。

20-hour NMP-Ribose-Glucose mix:

The 20-hour NMP-Ribose-Glucose master mix uses NMPs, ribose, glucose, and guanine to regenerate nucleotides and energy through enzymes in the lysate. This makes the reaction more sustainable and suitable for longer cell-free protein synthesis.

NTPを直接入れるのではなく、AMP/CMP/GMP/UMP などの NMP、ribose、glucose、guanine を使い、lysate 内の代謝酵素でヌクレオチドやエネルギーを再生する

→ It is more sustainable and suitable for long-duration cell-free protein production.
より持続的で、長時間のcell-free protein productionに向いている。

3. Bonus question: How can transcription occur if GMP is not included but Guanine is?

   GMP が含まれていないのに Guanine が含まれている場合、どうやって transcription（転写）が起こるのですか？

GTP should be necessary for RNA synthesis.  
However, this master mix does not contain GMP; it contains only guanine.  
Even so, how is transcription possible?  

RNAを作るには GTP が必要なはず。  
でもこの master mix には GMP がなくて Guanine だけが入っている。  
それでもどうして転写できるの？

Ref: An economical method for cell-free protein synthesis using glucose and nucleoside monophosphates
Kara A Calhoun 1, James R Swartz
https://pubmed.ncbi.nlm.nih.gov/16080695/

E. coli xanthine-guanine phosphoribosyltransferase（gpt）
https://www.uniprot.org/uniprotkb/P0A9M5/entry

Even if GMP is not directly included, guanine may be converted into GMP through the nucleotide salvage pathway remaining in the E. coli lysate.

GMP が直接入っていなくても、E. coli lysate に残っている nucleotide salvage pathway によって、guanine から GMP が作られる可能性がある。

Part C: Planning the Global Experiment | Cell-Free Master Mix Design

1. Given the 6 fluorescent proteins we used for our collaborative painting, identify and explain at least one biophysical or functional property of each protein that affects expression or readout in cell-free systems. (Hint: options include maturation time, acid sensitivity, folding, oxygen dependence, etc) (1-2 sentences each)

共同制作のペインティングで使った 6種類の蛍光タンパク質について、それぞれ cell-free system での発現や読み取りに影響する biophysical / functional property を少なくとも1つ挙げて説明しなさい。

1. sfGFP　　 2. mRFP1　　 3. mKO2　　 4. mTurquoise2　　 5. mScarlet_I　　 6. Electra2　　

The amino acid sequences are shown in the HTGAA Cell-Free Benchling folder.

In a cell-free system, even if a protein is produced from DNA, fluorescence may not appear immediately. Therefore, the readout can be affected by the following properties.　　 (cell-free system では、DNAからタンパク質が作られても、すぐに蛍光が見えるとは限らないので、読み取りには以下の性質が影響する。)　　

・maturation time
The time required after translation for the protein to form a fluorescent structure. A shorter maturation time allows the signal to appear faster.
タンパク質が翻訳されたあと、蛍光を出す構造になるまでの時間。短いほど早くシグナルが見える。

・acid sensitivity / pH sensitivity
The fluorescence may become weaker or disappear depending on the pH of the reaction mixture.
反応液のpHによって蛍光が弱くなったり消えたりすることがある。

・folding
If the protein does not fold correctly, it will not fluoresce. In cell-free systems, proteins that fold efficiently are easier to read reliably.
タンパク質が正しく折りたたまれないと、蛍光が出ない。cell-freeではfoldingしやすいタンパク質の方が安定して読める。

・oxygen dependence
Many fluorescent proteins require oxygen for chromophore maturation, so oxygen conditions can affect fluorescence development.
多くの蛍光タンパク質は chromophore maturation に酸素が必要なので、酸素条件が蛍光の発生に影響する。

・brightness
Even if the same amount of protein is produced, a brighter fluorescent protein will give a stronger signal.
同じ量のタンパク質が作られても、明るいタンパク質ほど強く見える。

・photostability
This refers to how resistant the fluorescence is to bleaching during light exposure. It can affect observation and imaging time.
光を当て続けたときに蛍光がどれくらい退色しにくいか。観察や撮影時間に影響する。

1. sfGFP

sfGFP stands for superfolder GFP. It is designed to fold efficiently, so it can produce a reliable fluorescent signal even in cell-free systems where protein folding conditions may not be perfect.
It is also reported to mature rapidly, so green fluorescence can appear relatively soon after translation.

sfGFP は superfolder GFP の略で、通常のGFPよりも折りたたみやすいように設計された蛍光タンパク質である。　　 sfGFP は folding しやすく設計されているため、cell-free system のように folding 条件が完全ではない環境でも reporter として使いやすい。
また、成熟が速いとされているため、翻訳後に比較的早く緑色蛍光が見えやすい。

Ref:

FPbase: Superfolder GFP https://www.fpbase.org/protein/sfgfp : sfGFP が very rapidly-maturing な green fluorescent protein として説明されている。

RCSB PDB: 2B3P / Crystal structure of superfolder GFP: sfGFP が、folding の悪いポリペプチドに融合してもよく折りたたまれるように作られた robustly folded GFP と記載あり。　　

2. mRFP1

mRFP1 is a monomeric red fluorescent protein, which makes it useful as a reporter because it is less likely to aggregate than tetrameric red fluorescent proteins.
However, it has lower brightness and photostability than DsRed, so its red fluorescence may be weaker or less stable during observation.
It also has relatively low acid sensitivity, which can make its fluorescence more stable under moderate pH variation.

mRFP1 は monomeric red fluorescent protein であり、四量体の赤色蛍光タンパク質よりも凝集しにくいため、reporter として使いやすい。
一方で、DsRed と比べると brightness や photostability が低いため、cell-free system での赤色蛍光シグナルはやや弱く見えたり、観察中に安定性が下がったりする可能性がある。
また、acid sensitivity は低いとされているため、ある程度の pH 変化に対しては蛍光が比較的安定しやすい。

Ref: FPbase: mRFP1　https://www.fpbase.org/protein/mrfp1/　　 mRFP1 は Discosoma sp. 由来の赤色蛍光タンパク質で、somewhat slowly-maturing monomer、かつ low acid sensitivity と説明

Campbell et al., 2002, “A monomeric red fluorescent protein”
mRFP1 は DsRed より extinction coefficient、quantum yield、photostability が低い一方で、10倍以上速く成熟すると説明されている。　　　 https://pubmed.ncbi.nlm.nih.gov/12060735/

3. mKO2

mKO2 is an orange fluorescent protein with relatively rapid maturation, which can help the orange fluorescence appear sooner after translation in a cell-free reaction.　　　 It also has moderate acid sensitivity, so the pH of the cell-free reaction may affect the strength of the fluorescence readout.

mKO2 はオレンジ色の蛍光タンパク質で、比較的 maturation が速いとされているため、cell-free reaction では翻訳後にオレンジ色蛍光が早く見えやすい可能性がある。　　　 一方で、moderate acid sensitivity を持つため、反応液の pH によって蛍光強度が影響を受ける可能性がある。

Ref:

FPbase: mKO2　

MBL Life Science: Kusabira-Orange / mKO2 　

https://ruo.mbl.co.jp/bio/e/product/flprotein/ko.html　　　

mKO2 は mKO1 の変異体で、rapid maturation を特徴とし、reporter assay に使える

4. mTurquoise2

mTurquoise2
mTurquoise2 is a bright cyan fluorescent protein with faster maturation, high photostability, and high quantum yield. 　　 These properties are useful in cell-free systems because the cyan fluorescence can appear efficiently and remain stable during observation.

mTurquoise2 は、明るく、maturation が速く、photostability が高く、quantum yield も高いシアン色蛍光タンパク質である。　　 cell-free system では、翻訳後に蛍光が早く見えやすく、観察中もシグナルが安定しやすい点が重要である。

brighter variant → 明るい蛍光タンパク質なので、cell-free system でシグナルが読み取りやすい。 faster maturation → 翻訳後、蛍光が見えるまでの成熟が速いので、短時間の反応でも読み取りやすい。 high photostability → 観察中に蛍光が退色しにくい。 highest quantum yield → 効率よく蛍光を出すため、強いシグナルにつながる。

Ref:

FPbase: mTurquoise2 https://www.fpbase.org/protein/mturquoise2/

Structure-guided evolution of cyan fluorescent proteins towards a quantum yield of 93%

https://www.researchgate.net/publication/221877718_Structure-guided_evolution_of_cyan_fluorescent_proteins_towards_a_quantum_yield_of_93

———　　

5. mScarlet_I

mScarlet-I

mScarlet-I is a monomeric red fluorescent protein related to the mScarlet family, which was engineered for very high brightness and quantum yield.
In a cell-free system, high brightness is useful because it can make the red fluorescence easier to detect even if the amount of expressed protein is limited.

mScarlet-I は mScarlet 系列の単量体赤色蛍光タンパク質であり、mScarlet は高い brightness と quantum yield を持つように設計されている。
cell-free system では、発現量が少ない場合でも、蛍光タンパク質自体が明るいほど赤色シグナルを検出しやすくなる。

Ref:

FPbase: mScarlet_I https://www.fpbase.org/protein/mscarlet/

mScarlet: a bright monomeric red fluorescent protein for cellular imaging

https://experiments.springernature.com/articles/10.1038/nmeth.4074

6. Electra2

Electra2 is a blue fluorescent protein.
Blue fluorescent proteins are often less bright than fluorescent proteins in other color ranges, so the blue signal may be harder to detect in a cell-free readout.
Spectral separation from the other fluorescent proteins is also important when reading the final collaborative painting.

Electra2 は青色蛍光タンパク質である。
Blue fluorescent proteins は、他の色の蛍光タンパク質に比べて brightness が低い傾向があるため、cell-free system では青色シグナルが弱く見える可能性がある。
また、共同制作のように複数の蛍光タンパク質を使う場合、他の色との spectral separation も読み取りに影響する。

Ref:

FPbase: Electra2 https://www.fpbase.org/protein/electra2/

Dual-expression system for blue fluorescent protein optimization

https://www.nature.com/articles/s41598-022-13214-0

2. Create a hypothesis for how adjusting one or more reagents in the cell-free mastermix could improve a specific biophysical or functional property you identified above, in order to maximize fluorescence over a 36-hour incubation. Clearly state the protein, the reagent(s), and the expected effect.

上で特定した蛍光タンパク質の biophysical / functional property を改善するために、cell-free mastermix の中の1つ以上の試薬を調整すると、36時間の incubation で蛍光を最大化できるという仮説を立ててください。

その際、以下を明確に書いてください。

• 対象とする蛍光タンパク質
• 調整する試薬
• 期待される効果

36時間という長い cell-free reaction で、蛍光をより強く・長く・安定して見せるには、mastermix のどの成分を調整すればよいか？

mScarlet-I

I chose mScarlet-I as the target protein.
mScarlet-I is already a bright red fluorescent protein, so I thought that if expression could be maintained for a longer time during the 36-hour cell-free artwork incubation, it could produce a stronger red fluorescence signal.

Hypothesis:
By adjusting ribose and NMPs (AMP, CMP, GMP, UMP), the reaction could maintain energy and nucleotide regeneration for a longer period, which could make mScarlet-I fluorescence stronger after 36 hours.

This is because the ribose and NMP system is important for supporting long-duration transcription and translation.

対象タンパク質として mScarlet-I を選ぶ。
mScarlet-I はもともと明るい赤色蛍光タンパク質であるため、36時間の cell-free artwork incubation では、発現を長く維持できれば強い赤色シグナルが得られると考えた。

仮説： riboseとNMPs（AMP, CMP, GMP, UMP）を調整してエネルギーとヌクレオチド再生を長く維持し、mScarlet-I の蛍光を36時間でより強くできるというものである。

ribose と NMP 系は、長時間の転写・翻訳を支えるために重要であるためである。

3. The second phase of this lab will be to define the precise reagent concentrations for your cell-free experiment. You will be assigned artwork wells with specific fluorescent proteins and receive an email with instructions this week (by April 24). You can begin composing master mix compositions here. https://rcdonovan.com/cfps

このラボの第2段階では、cell-free experiment に使う 正確な reagent concentration（試薬濃度） を決めます。

あなたには、特定の蛍光タンパク質が割り当てられた artwork well が指定されます。 今週中、4月24日までに、メールで詳しい指示が届きます。

以下のサイトで、master mix composition の作成を始めることができます。

https://rcdonovan.com/cfps

Based on my hypothesis, I used the CFPS Optimization Interface to make a preliminary master mix design.
I started from the 20-hour NMP-Ribose-Glucose master mix and slightly increased ribose and the NMP-related components.

先ほどの仮説に基づき、CFPS Optimization Interface を使って preliminary master mix design を作成した。20-hour NMP-Ribose-Glucose master mix を baseline とし、ribose と NMP 系成分を少し増やした。

The changes were:

• Ribose: 11.625 g/L → 11.875 g/L
• AMP: 0.625 mM → 0.750 mM
• CMP: 0.375 mM → 0.500 mM
• GMP: 0 mM → 0.125 mM
• UMP: 0.375 mM → 0.500 mM

I kept the adjustment small because this was a preliminary design, not a finalized recipe. 　　 The expected effect is that sustained nucleotide regeneration could support longer transcription and translation, allowing more mScarlet-I to be produced and matured over 36 hours.

I explored the HTGAA:1536 CFPS page and understood that the completed pixel artwork is connected to cell-free reaction compositions. Some wells already showed assigned fluorescent proteins, contributors, and saved reagent conditions.

I contributed to the pixel artwork, but I was not listed as an approved CFPS contributor for editing the final reaction compositions. Therefore, I could not directly adjust or save reagent concentrations on the final artwork page.　😭

I also checked an existing finalized mScarlet-I reagent composition on the HTGAA:1536 CFPS page. Its reagent concentrations appeared to be close to the original 20-hour NMP-Ribose-Glucose baseline, before the increases I tested in my preliminary design.

実際に実行された mScarlet-I の reagent composition を確認したところ、ribose や NMP 系成分は、自分が増やす前の baseline に近い値だった。　　

4. The final phase of this lab will be analyzing the fluorescence data we collect to determine whether we can draw any conclusions about favorable reagent compositions for our fluorescent proteins. This will be due a week after the data is returned (date TBD!). The reaction composition for each well will be as follows:

• 6 μL of Lysate
• 10 μL of 2X Optimized Master Mix from above
• 2 μL of assigned fluorescent protein DNA template
• 2 μL of your custom reagent supplements

Total: 20 μL reaction

このラボの最終段階では、収集された蛍光データを解析します。 その解析によって、各蛍光タンパク質にとって有利な reagent composition、つまりどの試薬配合がよかったのかについて、何らかの結論を出せるかを判断します。

この課題は、データが返却されてから1週間後に提出となります。 日付はまだ未定です。

各wellの reaction composition は以下のようになります。

Lysate：6 μL 上で作成した 2X Optimized Master Mix：10 μL 割り当てられた fluorescent protein DNA template：2 μL 自分の custom reagent supplements：2 μL

合計：20 μL reaction

To test my hypothesis, I would compare whether the condition with increased ribose and NMP-related components produces stronger mScarlet-I fluorescence than the baseline condition.

If the fluorescence becomes stronger, it may suggest that sustained nucleotide regeneration supported longer transcription and translation.
If there is no improvement, it may mean that ribose and NMPs were not the limiting factors, and that other factors such as pH, magnesium concentration, oxygen availability, or maturation were more important.

自分の仮説の確認のために、ribose と NMP 系成分を増やした条件が、baseline よりも mScarlet-I の蛍光を強くするかを見る。

もし蛍光が強くなっていれば、nucleotide regeneration の維持が長時間の transcription / translation を支えた可能性がある。
改善がなければ、ribose や NMPs は制限要因ではなく、pH、magnesium、酸素、maturation など別の要因が重要だった可能性がある。

Part D: Build-A-Cloud-Lab | (optional) Bonus Assignment

1. Use this simulation tool to create an interesting looking cloud lab out of the Ginkgo Reconfigurable Automation Carts. This is just a minimal implementation so far, but I would love to see some fun designs!

Tip!!! Note from Ronan: If you are interested in helping me build out future HTGAA cloud lab software, please fill out this form!

https://docs.google.com/forms/d/e/1FAIpQLScxtSh187245nsYRNHaKn93FTUoRMPjEbNwzltEAibt90g0ew/viewform