Week 5 HW: Protein Design Part ii

Your challenge:

Background: Design short peptides that bind mutant SOD1 and 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

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

🟢🟤🟡P00441

Here is fully translated superoxide dimutase protein P00441 in uniprot with the initiator methionine included. We need to cleave that M off before we apply our requested mutation to progress with a mature enzyme.

So not this… 1 2 3 4 M A T K

But this.. 1 2 3 4 A T K A

To create our A4V SOD (love the rhyme) mutant… 1 2 3 4 A T K V

The savvy student who fails to cleave the first methionine (M) can intuit the actual amino acid to change without thinking through any of the previous steps, but it’s nice to have a why in all things, since this is biology after all and we have evolution and ChatGPT. Please note that we will not want to use a protein sequence with any sort of truncation or wrapping on the sequence so here are my sequences for PPMLM-650M.

                  Using the PepMLM Colab linked from the HuggingFace PepMLM-650M model card: 

🤗pepmlm650mlink

                  Generate four peptides of length 12 amino acids conditioned on the mutant SOD1 sequence.

To create our A4V SOD (love the rhyme) mutant… 1 2 3 4 A T K V

Mutant A4V SOD for PepMLM-650 There are two options, full protein sequence and a 12-Sequence input which I settled on in later runs.
MATKVVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

colabcode

MATKVVCVLKGD

Within the PepMLM-650 codebase in Google Colab Notebook, there are sliders and input fields to parameterize individual runs. However, these parameters didn’t seem to encode, so I finally hard-coded changes, as I will show below as a series of excerpts pulled from the codebase.

single_sequence = True #@param {type:"boolean"} protein_seq = "MATKVVCVLKGD" #@param {type:"string"} # Initial value for num_binders num_binders = 4 # Initial values for top_k and peptide_length top_k = 3 peptide_length = 12

code_constrained_step

Initial_4in1_SequenceSet

Binder	Pseudo_Perplexity_Score
WVVVLVAGVVGE	35.014933
LTLVVAVGEVGE	25.582245
SVTEEVEDVDPV	21.336863
LPTVVVEGVDPE	17.079494

To your generated list, add the known SOD1-binding peptide FLYRWLPSRRGG for comparison.

Scores above, I think. I feel like I’m in Gulliver’s Travels returning this exercise cold after 2 months so I am going to try and piece together memory using a peers homework. For the next phase I am now going to find the amino acid sequence for my SOD1 sequence. Now I’m not copying information because I just went to uniprot site myself and searched for SOD1 on the splash page and scrolling down on first page I found human sodc but I skipped over that one and found the sod for sheep

P09670 · SODC_SHEEP
MATKAVCVLKG
DGPVQGTIRFE
AKGDKVVVTGS
ITGLTEGDHGF
HVHQFGDNTQG
CTSAGPHFNPL
SKKHGGPKDEE
RHVGDLGNVKA
DKNGVAIVDIV
DPLISLSGEYS
IIGRTMVVHEK
PDDLGRGGNEE
STKTGNAGGRL
ACGVIGIAP

Record the perplexity scores that indicate PepMLM’s confidence in the binders.

Given confusion perplexity scores are likely very high. The model confidence according to UniProt is spot on though, specifically model confidence is very high (pLDDT > 90). This is generated by AlphaFold as a per-residue confidence score (pLDDT) between 0 and 100. Now I am prepared to transition to part 2.

What about my mutants, though? I do not want to disrupt folding randomly, need an appropriate target region for mutation logic so I will leverage the MobiDB website.



Enumerated Amino Acids with position and highlighted for subsequent mutagenesis based on encode segment flexibility, disruptability, and functional consequences

Position	Amino Acid
1	M
2	A
3	T
4	K
5	A
6	V
7	C
8	V
9	L
10	K
11	G
12	D
13	G
14	P
15	V
16	Q
17	G
18	T
19	I
20	R
21	F
22	E
23	A
24	K
25	G
26	D
27	K
28	V
29	V
30	V
31	T
32	G
33	S
34	I
35	T
36	G
37	L
38	T
39	E
40	G
41	D
42	H
43	G
44	F
45	H
46	V
47	H
48	Q
49	F
50	G
51	D
52	N
53	T
54	Q
55	G
56	C
57	T
58	S
59	A
60	G
61	P
62	H
63	F
64	N
65	P
66	L
67	S
68	K
69	K
70	H
71	G
72	G
73	P
74	K
75	D
76	E
77	E
78	R
79	H
80	V
81	G
82	D
83	L
84	G
85	N
86	V
87	K
88	A
89	D
90	K
91	N
92	G
93	V
94	A
95	I
96	V
97	D
98	I
99	V
100	D
101	P
102	L
103	I
104	S
105	L
106	S
107	G
108	E
109	Y
110	S
111	I
112	I
113	G
114	R
115	T
116	M
117	V
118	V
119	H
120	E
121	K
122	P
123	D
124	D
125	L
126	G
127	R
128	G
129	G
130	N
131	E
132	E
133	S
134	T
135	K
136	T
137	G
138	N
139	A
140	G
141	G
142	R
143	L
144	A
145	C
146	G
147	V
148	I
149	G
150	I
151	A
152	P

Amino acids selected for mutation

Position	WT	Candidate	Reason
71	G	A	Reduce flexibility
72	G	A	Flexible glycine region
76	E	P	Potential structural disruption site

Final mutant sequence with three changes

MATKAVCVLKGDGPVQGTIRFEAKGDKVVVTGSITGLTEGDHGFHVHQFGDNTQGCTSAGPHFNPLSKKHAAPKDPERHVGDLGNVKADKNGVAIVDIVDPLISLSGEYSIIGRTMVVHEKPDDLGRGGNEESTKTGNAGGRLACGVIGIAP

Sequence Type	Amino Acid Sequence
G71A/G72A + E76P Mutant



Part 2: Evaluate Binders with AlphaFold3

Navigate to the AlphaFold Server: [alphafoldserver](https://alphafoldserver.com/welcome) For each peptide, submit the mutant SOD1 sequence followed by the peptide sequence as separate chains to model the protein-peptide complex. 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?MATKVVCVLKGDGPVQGTIRFEAKGDKVVVTGSITGLTEGDHGFHVHQFGDNTQGCTSAGPHFNPLSKKHGGPKDEERHVGDLGNVKADKNGVAIVDIVDPLISLSGEYSIIGRTMVVHEKPDDLGRGGNEESTKTGNAGGRLACGVIGIAP

Sequence Type	Amino Acid Sequence
A4V Mutant

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

There were two mutation pathways considered in this section the A4V change resulted in a 0.1 ipTM drop. I can comment on the mutation I engineered above, which introduces a three-residue change (G71A/G72A + E76P). The predicted template modeling (pTM) score and interface predicted template modeling (ipTM) scores are based on the template modeling (TM) score which are all metrics available in the AlphaFold Server visualization. The TM was originally proposed by Zang_&_Skolnick based on the Global Distance Test (GDT) and MaxSub. The scores are evaluated using a statistical association, measured by a correlation coefficient, after adjusting for differences in protein size. An interesting observation in Abramson et al. (2024) methods article they do not resport statistical tests of association due to small n populations paper. The paper describes pTM and ipTM as global ranking variables that can increase rates of disorder in model. In addition chain ranking can be performed with a variation of the pTM metric and pLDDT can be averaged for putative residues.



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:



Paste the peptide sequence. Paste the A4V mutant SOD1 sequence in the target field.

MATKVVCVLKGDGPVQGTIRFEAKGDKVVVTGSITGLTEGDHGFHVHQFGDNTQGCTSAGPHFNPLSKKHGGPKDEERHVGDLGNVKADKNGVAIVDIVDPLISLSGEYSIIGRTMVVHEKPDDLGRGGNEESTKTGNAGGRLACGVIGIAP

Sequence Type	Amino Acid Sequence
A4V Mutant



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.

There can be alignment between the computational prediction scores from AlphaFold Server, specifically ipTM rank score, which are both “significant” because they are larger than 0.80 but the wild type ipTM score is 0.01 larger than the mutant ipTM. In regards to the thermodynamics expressed in the PeptiVerse datasheets. Solubility and penetrance increase in the WT as hydrophobicity declines, compared to the mutant, which has greater hydrophobicity and lower solubility and penetrance. The explanation for the thermodynamic differences between the wild type and the mutants is the exposure of hydrophobic bases to the solution, leading to more water cages forming around them in the mutant than in the wild type.

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.

Open the moPPit Colab linked from the HuggingFace moPPIt model card Make a copy and switch to a GPU runtime. In the notebook: ~Make sure to switch De Novo to enter manual sequence

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. ~Set target for affinity to original WT: MATKAVCVLKGDGPVQGTIRFEAKGDKVVVTGSITGLTEGDHGFHVHQFGDNTQGCTSAGPHFNPLSKKHGGPKDEERHVGDLGNVKADKNGVAIVDIVDPLISLSGEYSIIGRTMVVHEKPDDLGRGGNEESTKTGNAGGRLACGVIGIAP 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? It doesn’t stay connected to the necessary server to run.