Alignment-free estimation of sequence conservation for identifying functional sites using protein sequence embeddings.

ABSTRACT: Protein language modeling is a fast-emerging deep learning method in bioinformatics with diverse applications such as structure prediction and protein design. However, application toward estimating sequence conservation for functional site prediction has not been systematically explored. Here, we present a method for the alignment-free estimation of sequence conservation using sequence embeddings generated from protein language models. Comprehensive benchmarks across publicly available protein language models reveal that ESM2 models provide the best performance to computational cost ratio for conservation estimation. Applying our method to full-length protein sequences, we demonstrate that embedding-based methods are not sensitive to the order of conserved elements-conservation scores can

SUBMITTER: Yeung W 

PROVIDER: S-EPMC9851297 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature