From single-sequences to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2

TL;DR

This paper shows that the protein language model ESM-2 can predict the effects of mutations in SARS-CoV-2 proteins without needing multiple sequence alignments.

Contribution

The study demonstrates that ESM-2 can capture evolutionary constraints and variant effects directly from single sequences.

Findings

ESM-2 captures evolutionary constraints from single sequences, matching results from multiple sequence alignments.

ESM-2 representations encode evolutionary history and distinguish variants of concern based on receptor binding and antigenicity.

ESM-2 likelihoods identify epistatic interactions among sites in the protein.

Abstract

Protein language models (PLMs) capture features of protein three-dimensional structure from amino acid sequences alone, without requiring multiple sequence alignments (MSA). The concepts of grammar and semantics from natural language have been suggested to have the potential to capture functional properties of proteins. Here, we investigate how these representations enable assessment of variation due to mutation. Applied to the SARS-CoV-2 spike protein via in silico deep mutational scanning (DMS), the PLM ESM-2 captures evolutionary constraints directly from sequence context, recapitulating what normally requires MSA data. Unlike other state-of-the-art methods which require protein structures or multiple sequences for training, we show what can be accomplished using an unmodified pretrained PLM. Applied to SARS-CoV-2 variants across the pandemic, we demonstrate that ESM-2 representations encode the evolutionary history between variants, as well as the distinct nature of variants of concern upon their emergence, associated with shifts in receptor binding and antigenicity. ESM-2 likelihoods can also identify epistatic interactions among sites in the protein. Our results here affirm that PLMs like ESM-2 are broadly useful for variant-effect prediction, including unobserved changes, and can be applied to understand novel viral pathogens with the potential to be applied to any protein sequence, pathogen or otherwise.

Here, the authors report that the protein language model ESM-2 is broadly useful for variant effect prediction, including unobserved changes, and can be applied to understand novel viral pathogens with the potential to be applied to any protein sequence, pathogen or otherwise.

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