Week 5 HW: Protein Design Part II

Part A: SOD1 Binder Peptide Design

Superoxide dismutase 1 (SOD1) is a cytosolic 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 (Ala to Val 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:

• Design short peptides that bind mutant SOD1.
• Then decide which ones are worth advancing toward therapy.

Part 1: Generate Binders with PepMLM

1. Begin by retrieving the human SOD1 sequence from UniProt (P00441) and introducing the A4V mutation.

Fig 1. The human SOD1 protein sequence.

Fig 2. The human SOD1 variant carrying an 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.

Table 1. Results of generating 4 new peptides and the perplexity values against the SOD1_A4V by PepMLM. The known binder is shown in the bottom row for comparison.

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 B-barrel region or approach the dimer interface?

• Does it appear surface-bound or partially buried?

Table 2. Modeling a peptide-protein complex. Interface predicted template modeling (ipTM) scores were obtained from AlphaFold analysis.

(A) peptide_1	(B) peptide_2

(C) peptide_3	(D) peptide_4

(E) known binder peptide

Fig 3. AlphaFold modeling of pepMLM peptides with SOD1_A4V visualized by PyMol. SOD1_A4V monomer (blue) and binder peptides (orange). Highlights: A4V residue with sidechain in stick (red) and dimer interface residues (green) 50–53, 114, 148, and 150–153, as referenced in Hough et al. 2004.

4. In a short paragraph, describe the ipTM values you observe and whether any PepMLM-generated matches or exceed the known binder.

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

• Predicted binding affinity
• Solubility
• Hemolysis probability
• Net charge (pH 7)
• Molecular weight

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.

Table 4. Summary of the PeptiVerse analysis.

I’ve chosen peptide_2 for the therapeutic advancement with the following reasons:

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,

• Paste your A4V mutant SOD1 sequence.
• 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).
• Set peptide length to 12 amino acids.
• Enable motif and affinity guidance (and solubility/hemolysis guidance if available). Generate peptides.
• After generation, briefly describe how these moPPIt peptides differ from your PepLMM peptides. How would you evaluate these peptides before advancing them to clinical studies?

Table 5. moPPIt-generated binder peptides and their in-silico attributes. The following weight adjustments were made for targeting position 4, specifically: Hemolysis=1, Solubility=1, Affinity=1, Motif=1.

Table 6. MoPPIt-generated binder peptides and their in-silico attributes, testing more stringent weight adjustment for optimal binder generation. The following weight adjustments were made, targeting position 4, specifically: Hemolysis=10, Solubility=5, Affinity=5, Motif=10.

(A) m2_peptide_1	(B) m2_peptide_2

(C) m2_peptide_3	(D) m2_peptide_4

(E) m2_peptide_5	(F) m2_peptide_6

(G) m2_peptide_7	(H) m2_peptide_8

Fig 4. AlphaFold modeling of moPPIt peptides with SOD1_A4V visualized by PyMol. SOD1_A4V monomer (blue) and binder peptides (orange). Highlights: A4V residue with sidechain in stick (red), dimer interface residues (green) 50-53, 114, 148, and 150-153, and sidechains of residues highlighted in peptide binders possibly interacting with A4V in stick (orange): m2_peptide_1, residue 4C; m2_peptide_3, residue 10V; m2_peptide_4, residue 11C; m2_peptide_5, residue 4C; m2_peptide_7, residue 10P, and m2_peptide_8, residue 7C. Based on the best-scoring models of m2_peptide_2 and m2_peptide_6, these binders did not interact with residue A4V.

Part B: BRD4 Drug Discovery Platform Tutorial (Optional)

Part C: Final Project: L-Protein Mutants

High-level summary: The objective of this assignment is to improve the stability and auto-folding of the lysis protein of a MS2-phage. This mechanism is to key to the understanding of how phages can potentially solve antibiotic resistance.

The objective is to improve the stability and autofolding of the lysis protein. More specifically, we want to engineer the lysis protein to increase the ability of MS2 to overcome a common E. coli resistance mechanism: a single point mutation in DnaJ prevents the binding of the lysis protein. We can attempt this by mutating the lysis protein to change its properties. Together, we aim for finding mutations that change the lysis protein one of the following ways: (1) an independence of lysis protein processing from DnaJ or other bacterial chaperones or (2) a faster or more efficient killing of E. coli to reduce the window in which the host can acquire resistance (3) higher lysis protein expression. In the course of this class, we will proceed through the following stages to create and test new MS2 phage mutants:

In this subset:

Stage 1: Engineer novel L-protein mutants using protein design tools

Stage 2: Synthesize the L-protein mutant gene via Twist

Stage 3: Clone the L-protein mutant gene into a plasmid using Gibson Assembly

Stage 4: Test the L-protein mutant’s structural integrity using the Nuclera system

Stage 5: Test the L-protein in E. coli with plaque assays