Week 4 HW: Protein Design Part I

Part A. Conceptual Questions

Answer any NINE of the following questions from Shuguang Zhang:

  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?

The natural amino acids are determined by codons, which are determined by three nucleotides (of which can be adenine, uracil, guanine, cytosine). This gives 4 x 4 x 4 = 64 total codons, but redundancy among codons produces only 20 unique amino acids.

  1. Can you make other non-natural amino acids? Design some new amino acids.
  2. Where did amino acids come from before enzymes that make them, and before life started?
  3. If you make an α-helix using D-amino acids, what handedness (right or left) would you expect?
  4. Can you discover additional helices in proteins?
  5. Why are most molecular helices right-handed?
  6. Why do β-sheets tend to aggregate?
    • What is the driving force for β-sheet aggregation?
  7. Why do many amyloid diseases form β-sheets?
    • Can you use amyloid β-sheets as materials?
  8. Design a β-sheet motif that forms a well-ordered structure.

Part B: Protein Analysis and Visualization

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

I picked crystallin, which is a protein in the eye responsible for the movement of your iris as you focus. It’s notably transparent, being part of the eye lens, and water-soluble, which was a callback to our lecture. I picked the protein because I was interested in how cataracts were formed.

The specific protein I went with for the following questions is P02511, or Alpha-crystallin B (in humans).

  1. Identify the amino acid sequence of your protein.

The AA sequence from UnitProt is

sp|P02511|CRYAB_HUMAN Alpha-crystallin B chain OS=Homo sapiens OX=9606 GN=CRYAB PE=1 SV=2 MDIAIHHPWIRRPFFPFHSPSRLFDQFFGEHLLESDLFPTSTSLSPFYLRPPSFLRAPSW FDTGLSEMRLEKDRFSVNLDVKHFSPEELKVKVLGDVIEVHGKHEERQDEHGFISREFHR KYRIPADVDPLTITSSLSSDGVLTVNGPRKQVSGPERTIPITREEKPAVTAAPKK

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

Using the Colab notebook, the protein is 175 amino acids long with the most common amino acid being P (and appearing 17 times).

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

According to UniProt, it’s part of the small heat shock protein (HSP20) family, along with all other Alpha-crystallin B proteins. However, according to the Transporter Classification Database, it’s part of the α-Crystallin Chaperone (CryA) family (where other Alpha-crystallin B proteins don’t appear).

  • Does your protein belong to any protein family?

Homology refers to protein sequences that likely have a common ancestor (identified through having similarities in sequence/structure?). Using the BLAST software gives 250 results for similar proteins, with results primarily appearing to be Alpha-crystallin B in different animals.

  1. Identify the structure page of your protein in RCSB

This step was particularly difficult for me, as I didn’t always understand how to get to the answer based on what I had on the screen

  • When was the structure solved? Is it a good quality structure?

The structure seems to be initially solved in 2009 but has increased members up until 2025. Some particularly high resolution structures were identified in 2012 and 2014 through X-ray diffraction, with a resolution of 1.0 - 1.5 Å.

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

I’m not entirely sure how to identify this…

  • Does your protein belong to any structure classification family?

Using the Structural Classification website, it belongs to the “Alpha crystallin-like” family, further within the “Hsp20 chaperone-like” family.

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

I chose to use PyMol to open my structure, getting the structure below.

  • Visualize the protein as “cartoon”, “ribbon” and “ball and stick”.
Opened structure on PyMol Opened structure on PyMol
  • Color the protein by secondary structure. Does it have more helices or sheets?

The protein seems to mostly be composed of sheets with some helices. Secondary structure colored protein Secondary structure colored protein

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

I colored hydrophobic residues in red and hydrophillic residues in green.

  • Hydrophobic (in red): glycine (Gly), alanine (Ala), valine (Val), leucine (Leu), isoleucine (Ile), proline (Pro), phenylalanine (Phe), methionine (Met), tryptophan (Trp)
    • PyMol code: select hydrophobic, resn Gly resn Ala resn Val resn Leu resn Ile resn Pro resn Phe resn Met resn Trp
  • Hydrophilic (in green): serine (Ser), threonine (Thr), asparagine (Asn), glutamine (Gln), cysteine (Cys), glycine (Gly)
    • PyMol code: select hydrophillic, resn Ser resn Thr resn Asn resn Gln resn Cys resn Gly

Residue-colored protein Residue-colored protein I had to switch to a spheres visualization to better see how molecules were interacting. It was a little hard for me to see a significant pattern, but I do feel like the hydrophilic residues have more “open” facing areas, whereas the hydrophobic residues were more clumped (both together and with neighboring residues).

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

Visualizing the protein as a surface was really helpful! I could easily find a couple areas that could be binding pockets. It’s a little difficult to show it accurately in a photo, but I indicated potential areas below:

Surface visualization of protein Surface visualization of protein

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:

C1. Protein Language Modeling

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

C2. Protein Folding

Folding a protein

  1. Fold your protein with ESMFold. Do the predicted coordinates match your original structure?
  2. 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

  1. Analyze the predicted sequence probabilities and compare the predicted sequence vs the original one.
  2. Input this sequence into ESMFold and compare the predicted structure to your original.

Part D. Group Brainstorm on Bacteriophage Engineering

  1. Find a group of ~3–4 students
  2. Read through the Phage Reading material listed under “Reading & Resources” below.
  3. Review the Bacteriophage Final Project Goals for engineering the L Protein:
    • Increased stability (easiest)
    • Higher titers (medium)
    • Higher toxicity of lysis protein (hard)
  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