ArticleNature microbiology2024
Large language models improve annotation of prokaryotic viral proteins.
Article in Nature microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed, 78 citations in OpenAlex.
- Phage bioinformatics tools: a review of computational approaches for bacteriophage research.Briefings in bioinformatics · 2026Review
- Protein-DNA Binding Sites Prediction via Integrating Pretrained Large Language Models and Contrastive Learning.Interdisciplinary sciences, computational life sciences · 2026Article
- ViralMap: predicting features in viral proteins from primary sequence.Journal of virology · 2026Article
- Article
- Decoding viral protein sequences by large language models.Briefings in bioinformatics · 2026Review
- Tutorial: annotation of animal genomes.Nature protocols · 2026Review
- AI-empowered human microbiome research.Gut · 2026Review
- Computational prediction resolves thousands of homooligomeric phage protein structures.bioRxiv : the preprint server for biology · 2026Article
- A tri-modal contrastive learning framework for protein representation learning.Cell reports methods · 2026Article
- Distinct prokaryotic gut microbiome and proviral-immune axes of pathophysiology in Sickle Cell Disease.bioRxiv : the preprint server for biology · 2026Article
- Microbial diversity as a foundation for biological AI : Learning biology from evolution's largest dataset.EMBO reports · 2026Article
- Compressing the collective knowledge of ESM into a single protein language model.Nature methods · 2026Article
- MicrobeDiscover: A Knowledge Graph-Enabled AI Framework for Identifying Microbes for Inorganic Nanomaterial Biosynthesis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Unveiling the biodiversity of large DNA viruses in intertidal mudflats via metagenomics.Nature communications · 2026Article
- High-resolution phage-host assignment through key proteins using large language models.Nature communications · 2026Article
- PhaLP 2.0: extending the community-oriented phage lysin database with a SUBLYME pipeline for metagenomic discovery.Database : the journal of biological databases and curation · 2026Article
- Protein structure-informed bacteriophage genome annotation with Phold.Nucleic acids research · 2026Article
- ADAPT: a programme for the advanced detection of AI-enabled pathogenic threats.Frontiers in bioengineering and biotechnology · 2026Article
- Viromics approaches for the study of viral diversity and ecology in microbiomes.Nature reviews. Genetics · 2026Review
- DeepVIC: modular prediction and classification of bacterial virulence factors using protein language model embeddings.Bioinformatics advances · 2026Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
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 globally widespread viral elements. Furthermore, improved high-level functional annotation provides a means to characterize similarities in genomic organization among diverse viral sequences. Protein language models thus enhance remote homology detection of viral proteins, serving as a useful complement to existing approaches.
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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.