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- W4308238093 abstract "Recent developments in protein design have adapted large neural networks with up to 100s of millions of parameters to learn complex sequence-function mappings. However, it is unclear which dependencies between residues are critical for determining protein function, and a better empirical understanding could enable high quality models that are also more data- and resource-efficient. Here, we observe that the per residue amino acid preferences - without considering interactions between mutations - are sufficient to explain much, and sometimes virtually all of the combinatorial mutation effects across 7 datasets (R 2 ~ 78-98%), including one generated here. These preference parameters (20*N, where N is the number of mutated residues) can be learned from as few as ~5*20*N observations to predict a much larger number (potentially up to 20 N ) of combinatorial variant effects with high accuracy (Pearson r > 0.8). We hypothesized that the structural dependencies surrounding a residue could be sufficient to learn these required mutation preferences, and developed an unsupervised design approach, which we term CoVES for Co mbinatorial V ariant E ffects from S tructure. We show that CoVES outperforms not just model-free sampling approaches but also complicated, high-capacity autoregressive neural networks in generating functional and diverse sequence variants for two example proteins. This simple, biologically-rooted model can be an effective alternative to high-capacity, out of domain models for the design of functional proteins." @default.
- W4308238093 created "2022-11-09" @default.
- W4308238093 creator A5067942150 @default.
- W4308238093 date "2022-11-01" @default.
- W4308238093 modified "2023-10-17" @default.
- W4308238093 title "Independent mutation effects enable design of combinatorial protein binding mutants" @default.
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- W4308238093 doi "https://doi.org/10.1101/2022.10.31.514613" @default.
- W4308238093 hasPublicationYear "2022" @default.
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