Leveraging protein language models for accurate multiple sequence alignments

Abstract

Multiple sequence alignment is a critical step in the study of protein sequence and function. Typically, multiple sequence alignment algorithms progressively align pairs of sequences and combine these alignments with the aid of a guide tree. These alignment algorithms use scoring systems based on substitution matrices to measure amino acid similarities. While successful, standard methods struggle on sets of proteins with low sequence identity - the so-called twilight zone of protein alignment. For these difficult cases, another source of information is needed. Protein language models are a powerful new approach that leverage massive sequence datasets to produce high-dimensional contextual embeddings for each amino acid in a sequence. These embeddings have been shown to reflect physicochemical and higher-order structural and functional attributes of amino acids within proteins. Here, we present a novel approach to multiple sequence alignment, based on clustering and ordering amino acid contextual embeddings. Our method for aligning semantically consistent groups of proteins circumvents the need for many standard components of multiple sequence alignment algorithms, avoiding initial guide tree construction, intermediate pairwise alignments, gap penalties, and substitution matrices. The added information from contextual embeddings leads to higher accuracy alignments for structurally similar proteins with low amino acid similarity. We anticipate that protein language models will become a fundamental component of the next generation of algorithms for generating MSAs.