🧬 Week 5: SOD1 A4V Peptide Binders

Part A1: PepMLM Generation

SOD1 A4V sequence (154 aa): MATKAVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTS AGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVV HEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

A4V mutation: Alanine → Valine at position 4

Generated peptides (12-mers) via PepMLM-650M:

Lower perplexity = higher model confidence in binding

4 Generated peptides (12-mers):

1. RDGEGELLENRR (2.34) ✅ BEST
2. WKLRHYSPQVMK (2.87)
3. FQVTSGDKPLRI (3.12)
4. HESLWRQPGKNT (3.45)

Known: FLYRWLPSRRGG (2.98)

Part A2: AlphaFold3 Structural Evaluation

All 4 peptides + known binder submitted to AlphaFold Server (alphafoldserver.com) as separate chains with mutant SOD1 A4V.

Summary: RDGEGELLENRR (ipTM=0.78) outperforms the known binder (ipTM=0.65) and localizes near the A4V mutation site at the N-terminus — the most therapeutically relevant region. Higher ipTM scores indicate greater structural confidence in the predicted protein-peptide complex.

Part A3: PeptiVerse Therapeutic Properties

All peptides evaluated in PeptiVerse with SOD1 A4V as target sequence.

Summary: RDGEGELLENRR shows the strongest predicted binding affinity (-8.2 kcal/mol), good solubility, and low hemolysis risk — making it the best candidate for therapeutic advancement. The known binder FLYRWLPSRRGG shows moderate hemolysis risk, which is a therapeutic liability.

Selected peptide to advance: RDGEGELLENRR Rationale: Best ipTM (0.78), strongest binding affinity (-8.2 kcal/mol), good solubility, low hemolysis risk, and localizes near the A4V mutation site.

Part 4: moPPIt — Optimized Peptide Design

Used moPPIt (Multi-Objective Guided Discrete Flow Matching) to design peptides targeting specific residues near A4V (position 4) on SOD1.

Settings:

• Target: SOD1 A4V mutant sequence
• Residue indices: 1-8 (N-terminus region near A4V mutation)
• Peptide length: 12 amino acids
• Guidance: motif + affinity + solubility

Generated moPPIt peptides:

Comparison vs PepMLM:

• moPPIt peptides show stronger predicted affinity (-8.9 vs -8.2 kcal/mol)
• PepMLM samples broadly from sequence space; moPPIt steers toward specific residues and optimizes multiple objectives simultaneously
• moPPIt peptides require same validation pipeline before clinical use: AlphaFold3 structural validation → PeptiVerse therapeutic screening → in vitro binding assay → cell toxicity testing → animal models

Part C: Final Project — L-Protein Mutants

Objective: Improve stability and auto-folding of the lysis protein of MS2 phage to better understand antibiotic-resistance mechanisms.

Selected goal: Increased stability (easiest)

Computational Pipeline

Step 1: Baseline structure

• Retrieved MS2 L-protein sequence from UniProt (P03609)
• 75 amino acids; forms transmembrane topology in E. coli membrane
• PDB reference: MS2 phage genome structure

Step 2: Deep Mutational Scan (ESM2)

• Used ESM2 language model to score all single-point mutations
• Identified stabilizing mutations at positions with low conservation (high mutation tolerance)
• Key candidates: L→V at position 23, A→G at position 41

Step 3: AlphaFold3 validation

• Submitted wild-type and mutant sequences to AlphaFold3
• Compared predicted structures — mutations maintain transmembrane helix integrity
• ipTM scores comparable between WT and mutants (>0.7)

Step 4: ProteinMPNN inverse folding

• Used WT backbone to generate alternative sequences maintaining fold
• Generated 10 sequence variants with >60% identity to WT
• Top variant: 8 mutations, predicted stability improvement

Pipeline Schematic

MS2 L-protein sequence
        ↓
ESM2 deep mutational scan
        ↓
Select stabilizing mutations
        ↓
AlphaFold3 structure prediction
        ↓
ProteinMPNN inverse folding
        ↓
Top candidates for experimental validation

Potential Pitfalls

• Limited experimental data on phage-bacteria interactions for training ESM2
• Transmembrane proteins are difficult to fold accurately with AlphaFold3
• In silico stability predictions may not translate to in vivo function

Group Collaboration

As a Global Committed Listener working independently, this proposal was developed based on the Week 4-5 computational tools learned during HTGAA 2026.