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What I know about SOD1 and its mutation:

Challenge of this week: Design short peptides that bind mutant SOD1 & then decide which ones are worth advancing toward therapy.
To generate binders using the suggested program, it’s necessary to have the original sequence and check for the A4V mutation at that position.
Original sequence Uniprot (P00441)
MATKAVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ
A4V Mutation sequence:
MATKVVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ
After having the sequence modified, we use the Colab notebook: Important to know that it’s to make sure you select the number 4 of binders in the input, select the length of peptides, and then run it
Input

Binders and Peptide Length

Table 1. Peptides predicted
| Index | Length | Binder | Pseudo Perplexity (score)* |
|---|---|---|---|
| Pep 0 | 12 | WRYPAVGARWKX | 10.660527 |
| Pep 1 | 12 | WRYPVAAVELKX | 10.027294 |
| Pep 2 | 12 | WLYYPAGAAHWX | 11.046032 |
| Pep 3 | 12 | KRSYVVGVEWGX | 17.759518 |
| Control** | 12 | FLYRWLPSRRGG | —————— |
Description: (*) Pseudo perplexity is an adaptation of the perplexity metric used in masked language models. The model masks each amino acid in the peptide one at a time and estimates the probability of correctly recovering it given the surrounding residues and the target protein sequence. Lower value → model assigns a higher probability to the peptide sequence and high confidence. High value → Less confidence model of sequence for the peptide. (**) Control is a known SOD1-binding peptide.
Based on the results in Table 1, the candidates are in the top 2 positions. And less confidence with the last position (index 3).
To evaluate the generated binders, AlphaFold3 was used to model protein–peptide complexes.