Survey of Protein Sequence Embedding Models.

ABSTRACT: Derived from the natural language processing (NLP) algorithms, protein language models enable the encoding of protein sequences, which are widely diverse in length and amino acid composition, in fixed-size numerical vectors (embeddings). We surveyed representative embedding models such as Esm, Esm1b, ProtT5, and SeqVec, along with their derivatives (GoPredSim and PLAST), to conduct the following tasks in computational biology: embedding the Saccharomyces cerevisiae proteome, gene ontology (GO) annotation of the uncharacterized proteins of this organism, relating variants of human proteins to disease status, correlating mutants of beta-lactamase TEM-1 from Escherichia coli with experimentally measured antimicrobial resistance, and analyzing diverse fungal mating factors. We di

SUBMITTER: Tran C 

PROVIDER: S-EPMC9963412 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature