Week 1 HW: Principles + Practices

1.1 A Neighborly Bio-literacy Learning System for Non-Scientists, Living in a Disaster-Prone World

Full disclosure: My house burned down in the Palisades, California fire last year with 5,000 other homes and it inspired me to see neighborhood disaster as a rich opportunity for study.

Rather than treating bio-literacy as isolated content mastery, this project frames bio-literacy as ethical sense-making within one’s own community and around community-based problems. Bio-literacy is understood as the ability to know ourselves and our world by asking questions, interpreting uncertainty, engaging responsibly, and building trust with biological systems. These capacities become more meaningful—and more powerful—when grounded in local concerns and lived experience. There is no shortage of biology-based shared community challenges: food security, extreme weather and fire, infectious disease, and environmental instability.

The project draws on local, embodied, and experimental pedagogies—such as role play, physical modeling, dialogue, and narrative—to make biological systems felt rather than merely understood abstractly. Participants develop bio-literacy in their “own backyard,” investigating biological questions that matter to them, their families, and their neighbors. In this way, Neighbor Gap Bridge (neighborgapbridge.com) reframes bio-literacy as a situated, relational practice rather than a distant technical competence.

Why this matters We are living in a world of disaster uncertainty in which consequential biological decisions—about health, environment, food systems, and governance—are increasingly made by non-biologists. Bio-literacy’s closest historical parallel is computer literacy: a decades-long project that succeeded in widespread participation, but not widespread understanding. This project reimagines the starting point of bio-literacy as the learner’s own backyard, privileging local problems as invitations into biological understanding, community participation, and compassion. This project is situated in the overall transformative experience of a disaster victim, creating opportunity for high level sensemaking and ability to know ourselves and our world by asking questions, and building trust with multiple systems including biological systems.

Existing bio- and AI-literacy efforts often optimize for scale, rigor, or engagement in isolation. This project instead optimizes for connection and meaning—situating learning within relationships, shared stakes, and ethical reflection.

Visionary (Infrastructure Design) What is missing is a governance-aligned learning infrastructure that treats ethical sense-making, uncertainty, pluralism, and participation as core learning outcomes rather than peripheral concerns. This project explores what such an infrastructure might look like, and what forms of governance, partnership, and institutional support would be required to sustain it.

Near-Future (Programmatic Pilot) Local, problem-based, intergenerational synthetic biology learning—using scaffolded play and embodied curriculum in partnership with LAUSD—focused on community-relevant biological questions.

Close-In (Rapid Prototyping / Extreme Events) Mobile syn-bio workshops and learning labs responding to “extreme events,” such as wildfire. For example, intergenerational workshops with residents affected by the Palisades fire to explore the biology of fire-resistant mycelium-based materials, alongside the design and fabrication of protective artifacts for future resilience.

1.2 Governance/Policy Goals for a Neighborly Bio-Literate Future

Governance Goal 1: Equitable Access Without Dilution

• Open access (low or no cost)
• Universal Design for Learning
• Multi-generational participation
• Not restricted to credentialed elites
• Engage Creative athletics
• Engages local biological problems

Governance Goal 2: Epistemic Pluralism

• Interdisciplinary sources
• Diverse instructors and perspectives
• Recognition that different perspectives change what becomes knowable
• Embodies Learning
• Learning is felt

Governance Goal 3: Trustworthy Sense-Making

• Transparency of sources
• Open and updatable materials
• Clear articulation of uncertainty
• Avoidance of false certainty or hype
• Care and Compassion based Learning
• Feminine Technology of learning
• Treat Error as opportunity

Governance Goal 4: Ethics as Infrastructure (Not Add-On)

• Ethics embedded in delivery, not add-ons
• Democratic dialogue and controversy included
• Anticipation of ethical roadblocks
• Delayed closure where appropriate

1.3 Potential Governance Actors + Actions

NSF-funded experimental bio literacy learning labs

Purpose Bio-education funding prioritizes content mastery and workforce development. This action proposes NSF funding streams specifically for experimental, embodied bio literacy learning environments aimed at non-specialists.

Design

• Competitive grants for interdisciplinary teams (science + education + design0
• Competitive Grants for Neighorhood non scientists
• Emphasis on process documentation, not standardized outcomes
• Publicly available learning artifacts and reflections
• Ethics embedded throughout the learning experience
• Robust digital share community spaces

Assumptions

• Embodied and experimental pedagogy improves ethical sense-making
• Non-specialists can meaningfully engage without technical mastery
• NSF will value exploratory education research
• People actually want to work when they have been affected by trauma

Risks of Failure

• Failure: Projects become performative or symbolic rather than substantive
• Failure: Difficulty evaluating progress without traditional metrics
• Say to day survival becomes more important

Department of Education Guidance on Bio-literacy + Trust

Purpose Currently, bio education standards focus on factual knowledge. This action proposes non-binding federal guidance recognizing bio literacy as an ethical and civic competency.

Design

• Advisory frameworks (not mandates)
• Alignment with Universal Design for Learning
• Encouragement of dialogue-based and participatory approaches
• Recognition of uncertainty and ethical debate as learning outcomes

Assumptions

• Federal guidance can shape discourse without enforcement
• Educators want permission to teach uncertainty and ethics
• Bio literacy can be framed as civic preparation
• Bio Literacy can be framed astrauma informed

Risks of Failure

• Failure: Guidance is ignored or politicized
• Failure: Oversimplification for scale
• Risk of “success”: Bio literacy reduced to compliance checklists

MIT Life-long Kindergarten as Model

Purpose Traditional science education often prioritizes correctness, abstraction, and expert authority. This governance action supports play-based, experimental science literacy models that cultivate curiosity, agency, and ethical orientation before formal expertise. Rather than teaching biology directly, the approach develops habits of inquiry—iteration, questioning, and reflection—that are transferable to bio literacy contexts.

Design

• Learning environments structured around play, making, and experimentation
• Tools that lower barriers to participation (no prerequisite mastery)
• Emphasis on remixing, peer learning, and public sharing
• Ethics embedded implicitly through collaboration, attribution, and care
• These workshops are part of a holistic plan for discovery and recovery

Assumptions

• Play supports deeper engagement and long-term learning
• Ethical orientation can emerge through participation, not instruction alone
• Habits of inquiry transfer across domains (e.g., from computation to biology)
• Framing can be sensitive enough to support engagement during or after a crisis
• these Workshops are optional

Risks of Failure

• Failure: Play is dismissed as insufficiently rigorous
• Failure: Ethical dimensions remain implicit and unarticulated
• Play becomes instrumentalized or gamified, losing its exploratory power and sensitivity
• This becomes very Kumbaya and does not move our collective inderstanding forward

1.4 Scoring Table

–> (1= lowest)

Week 1 HW: Principles and Practices

Week 02 Homework: DNA Read, Write and Edit

Part 1: Benchling & In-silico Gel Art

Part 2: Gel Art - Restriction Digests and Gel Electrophoresis

No lab access

Part 3: DNA Design Challenge

3.1. Choose your protein.

I chose Green Fluorescent Protein (GFP) because it is widely used in biotechnology as a visual reporter protein. Its ability to fluoresce green when exposed to blue light makes it an elegant example of how DNA sequences can encode observable biological functions. This is a random choice, I love green and this is HTGAA!

The 2008 Nobel Prize in Chemistry was awarded to Osamu Shimomura, Martin Chalfie, and Roger Y. Tsien for the discovery and development of Green Fluorescent Protein (GFP)

Protein Sequence: P42212|GFP_AEQVI Green fluorescent protein OS=Aequorea victoria MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTLTYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYK

3.2. Reverse Translate: Protein (amino acid) sequence to DNA (nucleotide) sequence. DNA Nucleotide Sequence 1 tacacacgaa taaaagataa caaagatgag taaaggagaa gaacttttca ctggagttgt 61 cccaattctt gttgaattag atggtgatgt taatgggcac aaattttctg tcagtggaga 121 gggtgaaggt gatgcaacat acggaaaact tacccttaaa tttatttgca ctactggaaa 181 actacctgtt ccatggccaa cacttgtcac tactttctct tatggtgttc aatgcttttc 241 aagataccca gatcatatga aacagcatga ctttttcaag agtgccatgc ccgaaggtta 301 tgtacaggaa agaactatat ttttcaaaga tgacgggaac tacaagacac gtgctgaagt 361 caagtttgaa ggtgataccc ttgttaatag aatcgagtta aaaggtattg attttaaaga 421 agatggaaac attcttggac acaaattgga atacaactat aactcacaca atgtatacat 481 catggcagac aaacaaaaga atggaatcaa agttaacttc aaaattagac acaacattga 541 agatggaagc gttcaactag cagaccatta tcaacaaaat actccaattg gcgatggccc 601 tgtcctttta ccagacaacc attacctgtc cacacaatct gccctttcga aagatcccaa 661 cgaaaagaga gaccacatgg tccttcttga gtttgtaaca gctgctggga ttacacatgg 721 catggatgaa ctatacaaat aaatgtccag acttccaatt gacactaaag tgtccgaaca 781 attactaaaa tctcagggtt cctggttaaa ttcaggctga gatattattt atatatttat 841 agattcatta aaattgtatg aataatttat tgatgttatt gatagaggtt attttcttat 901 taaacaggct acttggagtg tattcttaat tctatattaa ttacaatttg atttgacttg 961 ctcaaa

3.3. Codon optimization. ATGGTCTCAAAAGGTGAAGAATTGTTTACAGGTGTCGTACCTATACTTGTAGAACTCGATGGTGATGTTAATGGTCATAAATTTTCGGTCTCAGGAGAAGGTGAAGGAGACGCGACTTATGGTAAACTCACTTTAAAATTCATATGTACAACTGGTAAATTACCTGTTCCATGGCCGACTTTAGTGACAACGTTGACGTATGGTGTTCAATGTTTTAGTCGTTATCCTGATCATATGAAACAACATGATTTCTTTAAAAGTGCAATGCCTGAGGGTTATGTTCAAGAACGGACGATTTTCTTTAAAGATGATGGGAATTACAAAACTCGCGCAGAAGTCAAATTTGAAGGAGACACACTGGTAAATCGTATAGAACTTAAAGGTATTGACTTTAAAGAAGATGGAAATATTTTAGGTCATAAACTTGAATACAATTTGAACTCCCATAATGTCTACATAATGGCAGACAAACAGAAGAATGGAATAAAAGTTAATTTTAAAATACGCCATAATATTGAAGATGGTTCGGTCCAACTGGCAGATCATTATCAACAGAATACTCCAATTGGAGATGGTCCAGTCTTGTTACCAGATAATCATTATCTCAGTACTCAATCAGCGCTCTCTAAGGATCCAAATGAAAAGCGTGACCATATGGTGTTGCTCGAATTTGTTACAGCGGCAGGCATTACATTAGGAATGGATGAATTATATAAATAG

3.4. You have a sequence! Now what? 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. I read about how fluorescent green protein is used in molecular biology to identify and track protein movement and gene expression. I am not yet ready to describe how a DNA sequence can be transcribed and translated into this protein. The best I can do here “in my own words” is to go back to the central dogma / flow of genetic information: DNA >RNA> Protein This is what I think happened in this week’s HW

Part 4: Prepare a Twist DNA Synthesis Order: Design the full machine (Expression Cassette?) that makes bacteria glow.

4.1. Create a Twist account and a Benchling account

4.2. Build Your DNA Insert Sequence 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):

Promoter TTTACGGCTAGCTCAGTCCTAGGTATAGTGCTAGC RBS CATTAAAGAGGAGAAAGGTACC Start Codon ATG Coding Sequence GTCTCAAAAGGTGAAGAATTGTTTACAGGTGTCGTACCTATACTTGTAGAACTCGATGGTGATGTTAATGGTCATAAATTTTCGGTCTCAGGAGAAGGTGAAGGAGACGCGACTTATGGTAAACTCACTTTAAAATTCATATGTACAACTGGTAAATTACCTGTTCCATGGCCGACTTTAGTGACAACGTTGACGTATGGTGTTCAATGTTTTAGTCGTTATCCTGATCATATGAAACAACATGATTTCTTTAAAAGTGCAATGCCTGAGGGTTATGTTCAAGAACGGACGATTTTCTTTAAAGATGATGGGAATTACAAAACTCGCGCAGAAGTCAAATTTGAAGGAGACACACTGGTAAATCGTATAGAACTTAAAGGTATTGACTTTAAAGAAGATGGAAATATTTTAGGTCATAAACTTGAATACAATTTGAACTCCCATAATGTCTACATAATGGCAGACAAACAGAAGAATGGAATAAAAGTTAATTTTAAAATACGCCATAATATTGAAGATGGTTCGGTCCAACTGGCAGATCATTATCAACAGAATACTCCAATTGGAGATGGTCCAGTCTTGTTACCAGATAATCATTATCTCAGTACTCAATCAGCGCTCTCTAAGGATCCAAATGAAAAGCGTGACCATATGGTGTTGCTCGAATTTGTTACAGCGGCAGGCATTACATTAGGAATGGATGAATTATATAAA 7x His Tag CATCACCATCACCATCATCAC Stop Codon TAA Terminator CCAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTGTTGTTTGTCGGTGAACGCTCTCTACTAGAGTCACACTGGCTCACCTTCGGGTGGGCCTTTCTGCGTTTATA

4.3. On Twist, Select The “Genes” Option 4.4. Select “Clonal Genes” option 4.5. Import your sequence I had to stop here because Twist would not accept my .fasta file. When I loaded it kept adding.txt 4.6. Choose Your Vector No can do

Part 5: DNA Read/Write/Edit

5.1 DNA Read

What DNA would you want to sequence (e.g., read) and why?

The Smell of Renewal: mapping post-fire soil chemistry + culture as a recovery marker

I would want to sequence DNA from soil bacteria called Streptomyces that produce geosmin, a key molecule behind petrichor (the earthy smell after rain). I’m interested in this because after a major fire, soil ecosystems change dramatically, and I want to understand what microbial communities survive, return, or disappear during recovery.

Why sequence it? The big “why” is that my home burned down in the Palisades fire last year. As I recover from the trauma of the fire, I find myself deeply drawn to the repair and restoration of my neighborhood — both physically and emotionally. I am particularly interested in sequencing soil bacteria such as Streptomyces, which produce geosmin, a key molecule behind petrichor — the earthy smell after rain.

Sequencing would allow me to identify which Streptomyces species or strains are present in post-fire soil, compare them to soil from unaffected areas, and observe how the microbial “signature” of a burned landscape changes over time. This could support environmental monitoring by tracking soil recovery and ecosystem health after disaster.

Because petrichor is strongly tied to emotional memory and renewal, understanding the biology behind it could connect ecological recovery with human recovery after disaster.

(CHAT GPT: How would I do this?)To study these microbial communities, I would use 16S rRNA gene sequencing, a common method for identifying and comparing bacterial species in environmental samples. By extracting DNA from soil and sequencing this conserved bacterial marker gene, I could determine which Streptomyces strains are present and how their abundance changes over time following a fire.

What I learned this week

• Understood the Central Dogma in a functional way.
• Learned what promoters and RBS actually do.
• Codon-optimized a gene.
• Wrestled with file formats (real-world friction).
• Designed a sequencing project grounded in lived experience.
• Named 16S rRNA as a method.
• Connected six diverse interdisciplinary areas of inquiry

Fire Soil Microbes Memory Recovery Design Biology

Week 03 Homework: Lab Automation

1/Create a Python file to run on an Opentrons liquid handling robot.

This is what I want to do, but I am still working on it. Happy Late Valentines Day!

2/ Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications.

Bryant Jr. et al., 2023 — “AssemblyTron: Automated DNA Assembly Using the Opentrons OT-2.” Synthetic Biology (Oxford University Press).

This paper describes an automated workflow that connects DNA design software to the Opentrons OT-2 liquid-handling robot. Rather than manual pipetting, the robot executes highly standardized molecular biology workflows.The innovation is novel because it is integration of design software and robotic execution. This reduces human error and makes it easier to reproduce experiements. Although this is challening information for me, I can see how it might lower the bar for entry into syn bio experiements and and speed up design cycles. If HTGAA’s mission is to democratize access to cutting-edge bioengineering and synthetic biology education and foster global “biological literacy” by equipping diverse, distributed participants with the skills and laboratory knowledge to design, experiment, and create with living organisms, then this Opentron is a gamechanger.

3/ 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.

In my wildfire soil project, automation might add rigor to the process of detecting subtle microbial differences in post-fire environments. My samples might be: Burned soil, Unburned soil, Sunflower rhizosphere soil, Adjacent burned soil away from roots. For each sample I will need to: Create standardized slurry. Perform serial dilutions. Plate onto defined media, Record colony morphology and counts, Measure pH

4/ Three Final Project Ideas

What if post-fire soil holds a molecular archive of both hope + disturbance, and we could build biological instruments that translate that archive into visible signals — making ecological memory perceptible to communities rebuilding after disaster?

Week 4 HW: Protein Design

A protein created by RFdiffusion3, a newly released protein design tool from Nobel laureate David Baker’s lab, interacting with DNA. (UW Institute for Protein Design / Ian C. Haydon Image)

Protein Design I

Objective:

• Learn basic concepts:
• amino acid structure
• 3D protein visualization
• the variety of ML-based design tools

Part A. Conceptual Questions

1/ How many molecules of amino acids do you take with a piece of 500 grams of meat?

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?

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

4b/ Design some new amino acids.

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

Ran out of time. Will return!

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?

10/ What is the driving force for β-sheet aggregation?

11/ Why do many amyloid diseases form β-sheets?

12/ Can you use amyloid β-sheets as materials?

13/ Design a β-sheet motif that forms a well-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:

Dps (DNA-binding protein from starved cells)

I selected it because Dps is a bacterial stress protein that shows up strongly when cells are under starvation, oxidative stress, and other harsh conditions—exactly the kinds of conditions microbes face in disrupted environments (like post-fire soils). It binds and compacts DNA, helping preserve genetic information when conditions are bad. That’s very “molecular archive” which is something I proposed in mt final project. In addition, I became really interested in DNA storage after I went to the first BIOPUNK lab meeting. All 16 GB of English Wikipedia have been encoded into DNA? Dps is a natural DNA packaging and protection system. Dps is a gorgeous bridge between information and material protection before a fire.

Identify the amino acid sequence of your protein.

MSTAKLVKSKATNLLYTRNDVSDSEKKATVELLNRQVIQFIDLSLITKQAHWNMRGANFIAVHEMLDGFRTALIDHLDTMAERAVQLGGVALGTTQVINSKTPLKSYPLDIHNVQDHLKELADRYAIVANDVRKAIGEAKDDDTADILTAASRDLDKFLWFIESNIE

How long is it?

715 amino acids The length of the protein is: 167 aminoacids?

What is the most frequent amino acid? You can use this Colab notebook to count the frequency of amino acids.

The most common amino acid is: L, which appears 19 times.

How many protein sequence homologs are there for your protein? Hint: Use Uniprot’s BLAST tool to search for homologs.

250

Does your protein belong to any protein family?

Dps / Ferritin-like superfamily Dps family (DNA-binding proteins from starved cells)

Identify the structure page of your protein in RCSB

The structure of Dps from Escherichia coli can be found in the RCSB Protein Data Bank under PDB ID 9ZC2. The structure was determined using single-particle cryo-electron microscopy at 1.3 Å resolution, indicating excellent structural quality.

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)

It is 1.3 Å resolution. That’s good@

Are there any other molecules in the solved structure apart from protein?

Ligand Interaction (FE)

Does your protein belong to any structure classification family?

Dps belongs to the ferritin-like structural fold and assembles into a homo-dodecamer (A12) with tetrahedral symmetry. The biological assembly consists of 12 identical alpha-helical subunits forming a hollow spherical cage, characteristic of the ferritin-like superfamily.The structure contains iron (Fe) ions in addition to the protein subunits, consistent with the iron-binding function of Dps.

Open the structure of your protein in any 3D molecule visualization software:

PyMol Tutorial Here (hint: ChatGPT is good at PyMol commands)

Visualize the protein as “cartoon”, “ribbon” and “ball and stick”.

Dps =

• Mostly alpha helices
• Connected by short loops
• Assembled into a 12-subunit spherical cage
• With metal ions inside

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

Completely helices

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

A moderate amount of evenly distributed tiny dots. Chat gPT says: Coloring the protein by residue type reveals that hydrophobic residues are predominantly buried within the interior of each helical subunit, stabilizing the protein core. Hydrophilic and charged residues are enriched on the outer surface and within the internal cavity. This distribution is consistent with Dps being a soluble cytoplasmic protein that binds DNA and coordinates iron, both of which require surface-exposed charged residues

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

I see a center hole and small “insetion like shapes between the sub unites.Chat GPT says Surface visualization reveals that Dps forms a hollow spherical assembly with a large central cavity. Small pores and indentations are visible at the interfaces between subunits, particularly at symmetry axes. These openings likely facilitate iron transport into the internal cavity, consistent with the protein’s role in iron sequestration and stress protection.

Part C. Using ML-Based Protein Design Tools

Assignees for this section MIT/Harvard students Required Committed Listeners Required In this section, we will learn about the capabilities of modern protein AI models and test some of them in your chosen protein.

Copy the HTGAA_ProteinDesign2026.ipynb notebook and set up a colab instance with GPU. Choose your favorite protein from the PDB. We will now try multiple things in the three sections below; report each of these results in your homework writeup on your HTGAA website: C1. Protein Language Modeling Picture Source: Bordin, Nicola et al (2023). Novel machine learning approaches revolutionize protein knowledge. Trends in Biochemical Sciences, Volume 48, Issue 4, 345 - 359 Picture Source: Bordin, Nicola et al (2023). Novel machine learning approaches revolutionize protein knowledge. Trends in Biochemical Sciences, Volume 48, Issue 4, 345 - 359

Deep Mutational Scans Use ESM2 to generate an unsupervised deep mutational scan of your protein based on language model likelihoods. Can you explain any particular pattern? (choose a residue and a mutation that stands out) (Bonus) Find sequences for which we have experimental scans, and compare the prediction of the language model to experiment. Latent Space Analysis Use the provided sequence dataset to embed proteins in reduced dimensionality. Analyze the different formed neighborhoods: do they approximate similar proteins? Place your protein in the resulting map and explain its position and similarity to its neighbors. C2. Protein Folding Picture Source: Lin et al (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Picture Source: Lin et al (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model.

Folding a protein Fold your protein with ESMFold. Do the predicted coordinates match your original structure? Try changing the sequence, first try some mutations, then large segments. Is your protein structure resilient to mutations? C3. Protein Generation Picture Source: 1. Post from Sergey Ovchinnikov 2. Roney, Ovchinnikov et al (2022). State-of-the-art estimation of protein model accuracy using AlphaFold. Phys. Rev. Lett. 129, 238101 Picture Source: 1. Post from Sergey Ovchinnikov 2. Roney, Ovchinnikov et al (2022). State-of-the-art estimation of protein model accuracy using AlphaFold. Phys. Rev. Lett. 129, 238101

Inverse-Folding a protein: Let’s now use the backbone of your chosen PDB to propose sequence candidates via ProteinMPNN Analyze the predicted sequence probabilities and compare the predicted sequence vs the original one. Input this sequence into ESMFold and compare the predicted structure to your original. Part D. Group Brainstorm on Bacteriophage Engineering Assignees for this section MIT/Harvard students Optional Committed Listeners Required Find a group of ~3–4 students Read through the Phage Reading material listed under “Reading & Resources” below. Review the Bacteriophage Final Project Goals for engineering the L Protein: Increased stability (easiest) Higher titers (medium) Higher toxicity of lysis protein (hard) 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 Each individually put your plan on your HTGAA website Include your group’s short plan for engineering a bacteriophage

Week 5 HW: Protein Design Part Two

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:

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

PART 1

1/ The mutant SOD1 sequence MATKVLCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

4 generated 12-aa peptides from PepMLM

the comparison peptide: FLYRWLPSRRGG

the perplexity score for each generated peptide, since the prompt says to record PepMLM confidence values

probably a small table you can later paste into your homework document

Part 2: Evaluate Binders with AlphaFold3 The predicted complex produced an ipTM score of 0.68, suggesting a moderately confident interaction between the peptide and mutant SOD1. The SOD1 β-barrel core is predicted with very high confidence (dark blue), while the peptide shows lower confidence values, which is typical for short flexible peptides. The peptide appears primarily surface-associated rather than deeply buried within the protein structure.

Part 3: Evaluate Properties of Generated Peptides in the PeptiVerse (in progress) Structural confidence alone is insufficient for therapeutic development. Using PeptiVerse, let’s evaluate the therapeutic properties of your peptide! For each PepMLM-generated peptide:

Paste the peptide sequence. Paste the A4V mutant SOD1 sequence in the target field. Check the boxes Predicted binding affinity Solubility Hemolysis probability Net charge (pH 7) Molecular weight Compare these predictions to what you observed structurally with AlphaFold3. In a short paragraph, describe what you see. Do peptides with higher ipTM also show stronger predicted affinity? Are any strong binders predicted to be hemolytic or poorly soluble? Which peptide best balances predicted binding and therapeutic properties?

Choose one peptide you would advance and justify your decision briefly.

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.

Open the moPPit Colab linked from the HuggingFace moPPIt model card Make a copy and switch to a GPU runtime. In the notebook: Paste your A4V mutant SOD1 sequence. Choose specific residue indices on SOD1 that you want your peptide to bind (for example, residues near position 4, the dimer interface, or another surface patch). Set peptide length to 12 amino acids. Enable motif and affinity guidance (and solubility/hemolysis guidance if available). Generate peptides. 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?

Week 6 HW: Genetic Circuits Pt 1

https://kernel.asimov.com/ hello@biopunklab.com Biopunk2026!

Part 1 — Concept Questions

1. Components of Phusion High-Fidelity PCR Master Mix

Phusion PCR master mix typically contains:

• High-fidelity DNA polymerase • enzyme that synthesizes new DNA strands • has proofreading ability, reducing mutation errors

• dNTPs (deoxynucleotide triphosphates) • building blocks used to create new DNA strands

• Reaction buffer • maintains proper pH and salt conditions for enzyme activity

• Mg²⁺ ions (magnesium) • essential cofactor for polymerase activity

• Stabilizers and enhancers • improve enzyme stability and reaction efficiency

Purpose: To allow accurate amplification of DNA during PCR.

2. Factors that determine primer annealing temperature

Primer annealing temperature depends mainly on:

Primer length • longer primers bind more strongly

GC content • G-C pairs have stronger hydrogen bonding

Primer sequence complementarity • mismatches lower binding strength

Melting temperature (Tm) • annealing temperature is typically 3–5°C below Tm

Salt concentration in buffer • affects DNA duplex stability

3. PCR vs Restriction Enzyme Digests Feature PCR Restriction Digest Purpose Amplify DNA Cut DNA at specific sites Enzyme DNA polymerase Restriction endonuclease Output Many copies of a DNA fragment Linear fragments from cutting Flexibility Primers allow custom fragments Limited to existing restriction sites Use case Creating new DNA fragments Preparing plasmids or inserts

PCR is preferable when you need to generate or modify a DNA fragment.

Restriction digestion is useful when cutting DNA at known sequences for cloning.

4. Ensuring fragments work for Gibson Assembly

For Gibson assembly, DNA fragments must have:

overlapping homologous regions (20–40 bp) so fragments can anneal to each other.

These overlaps can be created by:

• designing primers with overlap sequences • cutting plasmids at appropriate sites

DNA fragments must also be:

• clean and correctly sized • free of incompatible ends

5. How plasmid DNA enters E. coli during transformation

Plasmids enter bacteria through competent cell transformation.

Common methods:

Heat shock transformation

cold competent cells + plasmid DNA ↓ heat shock (~42°C) ↓ pores form in membrane ↓ DNA enters cell

or

Electroporation

electric pulse ↓ temporary membrane pores ↓ DNA enters cell

After transformation, cells replicate the plasmid.

6. Another Assembly Method: Golden Gate Assembly

Golden Gate Assembly is a cloning method that uses Type IIS restriction enzymes and DNA ligase in the same reaction.

Type IIS enzymes cut DNA outside their recognition site, creating custom overhangs. These overhangs allow multiple DNA fragments to assemble in a predefined order.

The process cycles between digestion and ligation, allowing fragments to be cut and joined repeatedly until the correct construct forms.

Golden Gate is highly efficient and allows the assembly of many DNA fragments in a single reaction. It is commonly used in synthetic biology to build complex genetic circuits.

Simple diagram Fragment A Fragment B Fragment C | | |

Type IIS enzyme cuts → custom overhangs

A – B – C

DNA ligase seals the backbone

PART 2 - Create a Repository for your work

Asimov Kernel is the computational synthetic biology environment. 1️ Create a repository 2️ Create a notebook entry 3️ Rebuild the Repressilator (The repressilator is a famous synthetic gene circuit.)

Repressilator idea:

• Gene A represses Gene B
• Gene B represses Gene C
• Gene C represses Gene A

This results in an oscillating gene expression A ─| B B ─| C C ─| A

Design three synthetic circuits This assignment is teaching you how genetic circuits are engineered This is basically electrical engineering for cells.

• Promoters = switches
• Genes = components
• Repressors = logic gates

#1 TOGGLE SWITCH

Gene A represses Gene B Gene B represses Gene A

Result: stable on/off state.

#2 Inducible gene

Promoter → gene expression when signal present

#3 Oscillator variant Add feedback loops to control amplitude.