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?