Semantic search using protein large language models detects class II microcins in bacterial genomes.

ABSTRACT: Class II microcins are antimicrobial peptides that have shown some potential as novel antibiotics. However, to date, only 10 class II microcins have been described, and the discovery of novel microcins has been hampered by their short length and high sequence divergence. Here, we ask if we can use numerical embeddings generated by protein large language models to detect microcins in bacterial genome assemblies and whether this method can outperform sequence-based methods such as BLAST. We find that embeddings detect known class II microcins much more reliably than does BLAST and that any two microcins tend to have a small distance in embedding space even though they typically are highly diverged at the sequence level. In data sets of Escherichia coli, Klebsiella spp., and

SUBMITTER: Kulikova AV 

PROVIDER: S-EPMC11494933 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature