Independent mutation effects enable design of combinatorial protein binding mutants

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 (R2 [~] 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 20N) of combinatorial variant effects with high accuracy (Pearson r > 0.8). We hypothesized that the local 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 Combinatorial Variant Effects from Structure. 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.