Protein design using structure-based residue preferences

David Ding (), Ada Y. Shaw, Sam Sinai, Nathan Rollins, Noam Prywes, David F. Savage, Michael T. Laub and Debora S. Marks ()
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David Ding: University of California
Ada Y. Shaw: Harvard Medical School
Sam Sinai: Dyno Therapeutics
Nathan Rollins: Lab Central
Noam Prywes: University of California
David F. Savage: University of California
Michael T. Laub: Massachusetts Institute of Technology
Debora S. Marks: Harvard Medical School

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract Recent developments in protein design rely on large neural networks with up to 100s of millions of parameters, yet it is unclear which residue dependencies are critical for determining protein function. Here, we show that amino acid preferences at individual residues—without accounting for mutation interactions—explain much and sometimes virtually all of the combinatorial mutation effects across 8 datasets (R2 ~ 78-98%). Hence, few observations (~100 times the number of mutated residues) enable accurate prediction of held-out variant effects (Pearson r > 0.80). We hypothesized that the local structural contexts around a residue could be sufficient to predict mutation preferences, and develop an unsupervised approach termed CoVES (Combinatorial Variant Effects from Structure). Our results suggest that CoVES outperforms not just model-free methods but also similarly to complex models for creating functional and diverse protein variants. CoVES offers an effective alternative to complicated models for identifying functional protein mutations.

Date: 2024
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DOI: 10.1038/s41467-024-45621-4

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