HTGAA - Week 4: Protein Design Part I

My Homework
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This week focuses on how sequence, structure, and energetics can be modeled and manipulated to create or optimize proteins with specified functions.
Lecture (Tues, Feb 10)
Protein Design Part I
(▶️Recording)
Thras Karydis, Jon Kaufman
Recitation (Wed, Feb 11)
Protein folding
(▶️Recording)
Allan Costa
Protein Design I
Objective:
- Learn basic concepts:
- amino acid structure
- 3D protein visualization
- the variety of ML-based design tools
- Brainstorm as a group how to apply these tools to engineer a better bacteriophage (setting the stage for the final project).
Part A. Conceptual Questions
Assignees for the following sections
| MIT/Harvard students | Required |
| Committed Listeners | Required |
Answer any NINE of the following questions from Shuguang Zhang: (i.e. you can select two to skip)
- 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)
- Why do humans eat beef but do not become a cow, eat fish but do not become fish?
- Why are there only 20 natural amino acids?
- Can you make other non-natural amino acids? Design some new amino acids.
- Where did amino acids come from before enzymes that make them, and before life started?
- If you make an α-helix using D-amino acids, what handedness (right or left) would you expect?
- Can you discover additional helices in proteins?
- Why are most molecular helices right-handed?
- Why do β-sheets tend to aggregate?
- What is the driving force for β-sheet aggregation?
- Why do many amyloid diseases form β-sheets?
- Can you use amyloid β-sheets as materials?
- Design a β-sheet motif that forms a well-ordered structure.
Part B: Protein Analysis and Visualization
Assignees for the following sections
| MIT/Harvard students | Required |
| Committed Listeners | Required |
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:
- Briefly describe the protein you selected and why you selected it.
- Identify the amino acid sequence of your protein.
- How long is it? What is the most frequent amino acid? You can use this Colab notebook to count the frequency of amino acids.
- How many protein sequence homologs are there for your protein? Hint: Use Uniprot’s BLAST tool to search for homologs.
- Does your protein belong to any protein family?
- Identify the structure page of your protein in RCSB
- 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)
- Are there any other molecules in the solved structure apart from protein?
- Does your protein belong to any structure classification family?
- 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”.
- Color the protein by secondary structure. Does it have more helices or sheets?
- Color the protein by residue type. What can you tell about the distribution of hydrophobic vs hydrophilic residues?
- Visualize the surface of the protein. Does it have any “holes” (aka binding pockets)?
Part C. Using ML-Based Protein Design Tools
Assignees for the following sections
| 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
- 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.
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
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 the following sections
| 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