Data from: Protein Set Transformer: A protein-based genome language model to power high diversity viromics

Exponential increases in microbial and viral genomic data demand
transformational advances in scalable, generalizable frameworks for their
interpretation. Standard homology-based functional analyses are hindered
by the rapid divergence of microbial and especially viral genomes and
proteins that significantly decreases the volume of usable data. Here, we
present Protein Set Transformer (PST), a protein-based genome language
model that models genomes as sets of proteins without considering sparsely
available functional labels. Trained on >100k viruses, PST
outperformed other homology- and language model-based approaches for
relating viral genomes based on shared protein content. Further, PST
demonstrated protein structural and functional awareness by clustering
capsid-fold-containing proteins with known capsid proteins and uniquely
clustering late gene proteins within related viruses. Our data establish
PST as a valuable method for diverse viral genomics, ecology, and
evolutionary applications. We posit that the PST framework can be a
foundation model for microbial genomics when trained on suitable data.