Week 1 HW: Principles and Practices

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 am interested in creating biosensors by using modified bacterial cultures to detect toxins and contaminants in the environment. Currently, to test for substances such as heavy metals, biotoxins, and organic pollutants, we often need expensive and often inaccessible detection tools, such as mass spectrometers and analytical chemistry kits. By creating biosensors out of bacteria, we can simplify contaminant detection.

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.

Biological containment: built-in safety mechanisms such as kill switches, dependence on essential nutrients to control spread, and self-limiting lifespans to prevent uncontrolled survival or evolution outside intended use.

Environmental risk assessment: Require pre-deployment ecological impact studies assessing horizontal gene transfer, ecosystem disruption, and long-term persistence.

Dual-use risk mitigation: Establish oversight to prevent misuse of synthetic organisms for harmful surveillance, bioweaponization, or covert environmental manipulation.

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. Purpose: What is done now and what changes are you proposing? There is a need for simple detection of various contaminants in our environment. The current state requires a lot of effort and equipment to do this job. Developing biosensing bacteria can simplify contaminant detection and expand access, especially in situations where current methods are impractical or inaccessible.

   2. Design: What is needed to make it “work”? (including the actor(s) involved - who must opt-in, fund, approve, or implement, etc) Various industries are in constant need of rapid detection of contaminants and toxins. Pharma and the food industry are always tightly controlled for contaminants, but detection is often limited and can lead to serious problems. This method would involve funding and approval from industry and government bodies, such as the FDA in US.

   3. Assumptions: What could you have wrong (incorrect assumptions, uncertainties)?

   4. Risks of Failure & “Success”: How might this fail, including any unintended consequences of the “success” of your proposed actions? Require continuous monitoring, reporting, and mitigation mechanisms if harms emerge from synthetic organisms. The main risks are that engineered bacteria may reproduce uncontrollably, or evolve outside their intended environment, its genetic material could be transferred to natural organisms, alteringthe environment. As this technology becomes widely available, its oversight/monitoring becomes harder to enforce. It is difficult to predict long-term effects of any engineered organism.

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.

I would prioritize a risk-based hybrid governance approach that integrates binding regulation, mandatory technical standards, and participatory oversight. No single governance option sufficiently addresses the biological, social, and ethical risks associated with bacterial biosensors. Binding regulation is necessary to prevent harm by mandating authorization, biosafety review, and post-deployment monitoring. Technical standards and ethics-by-design requirements mitigate risk at the design stage by embedding safety and containment mechanisms. Participatory and deliberative oversight addresses concerns related to consent, equity, and public trust, which technical regulation alone cannot resolve. The primary trade-offs include slower innovation, increased costs due to stricter oversight, reduced regulatory flexibility as technologies evolve, and greater time demands from inclusive governance processes. However, these trade-offs are justified by the potential for irreversible ecological or social harm.

Homework Questions from Professor Jacobson:

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?

There are DNA polymerases with different fidelity and mechanisms for proofreading and repair, which differ between prokaryotes and eukaryotes. For humans, it is estimated at 1:10000-100000. The human genome is 3.1Gb, meaning that without an additional proofreading or error-correction mechanism, we would accumulate many DNA errors. To help with such a problem, 3´•5´ exonucleases (Pols δ and ε) are used to remove the mismatch to allow correct DNA synthesis to proceed, increasing fidelity by 100-1000x, making the resulting new copy DNA even at a 3Gb virtually errorless. Proofreading also occurs during mRNA translation, preventing the incorporation of incorrect amino acids. Different DNA repair mechanisms are used to correct DNA (like MutS repair system); they are highly conserved, and any loss of function can lead to serious problems, including cancer.

Wang F, He Q, O’Donnell ME, Li H. The proofreading mechanism of the human leading-strand DNA polymerase ε holoenzyme. Proc Natl Acad Sci U S A. 2025 Jun 3;122(22)

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?

Average human protein is about 300 AAs so if we have 61 codons and 20 AAs and assume that those AA equally spread, leading to an average of 3 codons per aminoacid we can estimate that there are astronomical 3^300 ways to encode a random protein in DNA. In practice, not all codons are used with the same frequency; there are limiting factors, such as the amount of tRNA available to “service” all 61 codons. Secondly, if DNA has high CG content, it would not be easily accessible for translation other factors like stability of resulting mRNA and posibility of results with secondary structures will limit coding options, For synthetic DNA an important step of codon optimization used to address these issues and improve an expresion.

Homework Questions from Dr. LeProust:

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

Solid-phase phosphoramidite chemistry is most common method currently however it has limitation on size of product and problems with speed and reagent and product stability

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

Due to depurination in which the β-N-glycosidic bond is hydrolytically cleaved releasing a nucleic base (A/G) in acidic environment required for synthesis leads to incomplete product and errors in resulting DNA.

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

   Because of these limits, larger DNA is assembled from high-quality oligos (typically 60–200nt) using methods like Gibson Assembly or PCA

   Yin Y, Arneson R, Yuan Y, Fang S. Long oligos: direct chemical synthesis of genes with up to 1728 nucleotides. Chem Sci. 2024 Dec 18;16(4):1966-1973. doi: 10.1039/d4sc06958g. PMID: 39759933; PMCID: PMC11694485.

Homework Question from George Church:

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

1. [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”?

There are nine essential amino acids (histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine) Arginine is often added to this list, but can be synthesised in animals from glutamate. Lysine contingency refers to an organism that cannot synthesise lysine and is therefore dependent on lysine-containing media. This idea was used in the Jurassic Park Hollywood movie, but it does not make sense as a contingency measure due to the availability of lysine in a food chain (lysine produced in plants->herbivores->…->T-Rex). This idea might work if organism is dependent on a non-natural amino acid, so it can survive only if provided such AA

Ostrov N, Landon M, Guell M, Kuznetsov G, Teramoto J, Cervantes N, Zhou M, Singh K, Napolitano MG, Moosburner M, Shrock E, Pruitt BW, Conway N, Goodman DB, Gardner CL, Tyree G, Gonzales A, Wanner BL, Norville JE, Lajoie MJ, and Church GM. Design, synthesis, and testing toward a 57-codon genome. Science 2016; 353:819–22

Week 5 HW: Protein Design Part 2

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.
2. Using the PepMLM Colab linked from the HuggingFace PepMLM-650M model card:
3. Generate four peptides of length 12 amino acids conditioned on the mutant SOD1 sequence.
4. To your generated list, add the known SOD1-binding peptide FLYRWLPSRRGG for comparison.
5. Record the perplexity scores that indicate PepMLM’s confidence in the binders.

mutant: MATKVVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

1. WHSGAVAAELKK 8.491361
2. WLSYAVAAELWE 17.841335
3. WLSGPAALEHKK 11.617893
4. WRVYAAGAALKX 6.121234

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.
3. Record the ipTM score and briefly describe where the peptide appears to bind. Does it localize near the N-terminus where A4V sits? Does it engage the β-barrel region or approach the dimer interface? Does it appear surface-bound or partially buried?
4. In a short paragraph, describe the ipTM values you observe and whether any PepMLM-generated peptide matches or exceeds the known binder.

ipTM pTM

1. WHSGAVAAELKK 0.32 0.79
2. WLSYAVAAELWE 0.28 0.78
3. WLSGPAALEHKK 0.38 0.84
4. WRVYAAGAALKX invalid
5. FLYRWLPSRRGG 0.32 0.81

AlphaFold’s 𝑖𝑝𝑇𝑀 (Interface Predicted Template Modelling) metric is used to assess the accuracy of structural predictions of protein-protein interactions (PPIs) and to estimate the probability that two proteins interact. Values >0.8 are considered confident, while values <0.6 suggest a potential but not guaranteed failure. While pTM (Predicted Template Modelling) measures the global accuracy of the entire complex model, with values >0.5 indicating a reasonable prediction.

Part 3: Evaluate Properties of Generated Peptides in the PeptiVerse

Structural confidence alone is insufficient for therapeutic development. Using PeptiVerse, let’s evaluate the therapeutic properties of your peptide! For each PepMLM-generated peptide:

1. Paste the peptide sequence.
2. Paste the A4V mutant SOD1 sequence in the target field.
3. Check the boxes
   1. Predicted binding affinity
   2. Solubility
   3. Hemolysis probability
   4. Net charge (pH 7)
   5. Molecular weight

Scores ≥ 9 correspond to tight binders (K ≤ 10⁻⁹ M, nanomolar to picomolar range) Scores between 7 and 9 correspond to medium binders (10⁻⁷–10⁻⁹ M, nanomolar to micromolar range) Scores < 7 correspond to weak binders (K ≥ 10⁻⁶ M, micromolar and weaker) A difference of 1 unit in score corresponds to an approximately tenfold change in binding affinity.

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.

All are weak binders as per PeptiVerse and Alphafold. Peptide with least ipTM score (WLSYAVAAELWE) in AF3 reported as most promissing binder in Peptiverse. All peptides are soluble and non-hemolytic. However high scoring peptide carry a charge which mekes it difficult to cross membranes.

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.

1. Open the moPPit Colab linked from the HuggingFace moPPIt model card
2. Make a copy and switch to a GPU runtime.
3. In the notebook:
   1. Paste your A4V mutant SOD1 sequence.
   2. 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).
   3. Set peptide length to 12 amino acids.
   4. Enable motif and affinity guidance (and solubility/hemolysis guidance if available). Generate peptides.
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?

Week 6 HW: Genetic Circuits Part I: Assembly Technologies

Assignment: DNA Assembly

What are some components in the Phusion High-Fidelity PCR Master Mix and what is their purpose?

as per ¹

• High fidelity DNA polymerase (50 times more accurate than “standard” Thermus aquaticus DNApoly)
• Phusion HF buffer
• Mg²⁺ as 1.5mM MgCl₂ for DNApoly to function
• dNTPs

What are some factors that determine primer annealing temperature during PCR?

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.

How can you ensure that the DNA sequences that you have digested and PCR-ed will be appropriate for Gibson cloning?

How does the plasmid DNA enter the E. coli cells during transformation?

Describe another assembly method in detail (such as Golden Gate Assembly)

Explain the other method in 5 - 7 sentences plus diagrams (either handmade or online).

Model this assembly method with Benchling or Asimov Kernel!

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project