Large language models improve annotation of prokaryotic viral proteins.

ABSTRACT: Viral genomes are poorly annotated in metagenomic samples, representing an obstacle to understanding viral diversity and function. Current annotation approaches rely on alignment-based sequence homology methods, which are limited by the paucity of characterized viral proteins and divergence among viral sequences. Here we show that protein language models can capture prokaryotic viral protein function, enabling new portions of viral sequence space to be assigned biologically meaningful labels. When applied to global ocean virome data, our classifier expanded the annotated fraction of viral protein families by 29%. Among previously unannotated sequences, we highlight the identification of an integrase defining a mobile element in marine picocyanobacteria and a capsid protein that anchors glo

SUBMITTER: Flamholz ZN 

PROVIDER: S-EPMC11311208 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature