Protein language models trained on multiple sequence alignments learn phylogenetic relationships

TL;DR

This paper shows that protein language models trained on multiple sequence alignments inherently learn phylogenetic relationships, which helps distinguish functional signals from evolutionary history in protein analysis.

Contribution

It reveals that MSA Transformer encodes phylogenetic relationships through column attention and improves robustness of contact prediction against phylogenetic noise.

Findings

Column attentions correlate with sequence Hamming distances.

Models can separate coevolutionary signals from phylogenetic correlations.

MSA Transformer outperforms Potts models in noisy, phylogeny-influenced data.

Abstract

Self-supervised neural language models with attention have recently been applied to biological sequence data, advancing structure, function and mutational effect prediction. Some protein language models, including MSA Transformer and AlphaFold's EvoFormer, take multiple sequence alignments (MSAs) of evolutionarily related proteins as inputs. Simple combinations of MSA Transformer's row attentions have led to state-of-the-art unsupervised structural contact prediction. We demonstrate that similarly simple, and universal, combinations of MSA Transformer's column attentions strongly correlate with Hamming distances between sequences in MSAs. Therefore, MSA-based language models encode detailed phylogenetic relationships. We further show that these models can separate coevolutionary signals encoding functional and structural constraints from phylogenetic correlations reflecting historical contingency. To assess this, we generate synthetic MSAs, either without or with phylogeny, from Potts models trained on natural MSAs. We find that unsupervised contact prediction is substantially more resilient to phylogenetic noise when using MSA Transformer versus inferred Potts models.