ArticleNature communications2025
Protein Set Transformer: a protein-based genome language model to power high-diversity viromics.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Foundation Models for Microbiome Research: From Sequence Semantics to Community Dynamics and Multimodal World Models.Advanced genetics (Hoboken, N.J.) · 2026Review
- Decoding viral protein sequences by large language models.Briefings in bioinformatics · 2026Review
- Predicting phage-host specificity.Nature reviews. Microbiology · 2026Article
- DposFinder: an interpretable transformer model for predicting phage-derived polysaccharide depolymerases and their host capsular serotypes.Genome medicine · 2026Article
- Extending protein language models to a viral genomic scale using biologically induced sparse attention.GigaScience · 2026Article
- vir2vec: A Viral Genome-Wide Viral Embedding.bioRxiv : the preprint server for biology · 2025Article
- Viral Dark Matter: Illuminating Protein Function, Ecology, and Biotechnological Promises.Biochemistry · 2025Review
- Viral Dark Matter: Illuminating Protein Function, Ecology, and Biotechnological Promises.ArXiv · 2025Article
- Fine-tuning protein language models unlocks the potential of underrepresented viral proteomes.PeerJ · 2025Article
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Abstract
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 outperforms other homology- and language model-based approaches for relating viral genomes based on shared protein content. Further, PST demonstrates 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.