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Bouras, G.* ; Grigson, S.R.* ; Mirdita, M.* ; Heinzinger, M. ; Papudeshi, B.* ; Mallawaarachchi, V.* ; Green, R.* ; Kim, R.S.* ; Mihalia, V.* ; Psaltis, A.J.* ; Wormald, P.J.* ; Vreugde, S.* ; Steinegger, M.* ; Edwards, R.A.*

Protein structure-informed bacteriophage genome annotation with Phold.

Nucleic Acids Res. 54:gkaf1448 (2026)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Bacteriophage (phage) genome annotation is essential for understanding their functional potential and suitability for use as therapeutic agents. Here, we introduce Phold, an annotation framework utilizing protein structural information that combines the ProstT5 protein language model and structural alignment tool Foldseek. Phold assigns annotations using a database of over 1.36 million predicted phage protein structures with high-quality functional labels. Benchmarking reveals that Phold outperforms existing sequence-based homology approaches in functional annotation sensitivity whilst maintaining speed, consistency, and scalability. Applying Phold to diverse cultured and metagenomic phage genomes shows it consistently annotates over 50% of genes on an average phage and 40% on an average archaeal virus. Comparisons of phage protein structures to other protein structures across the tree of life reveal that phage proteins commonly have structural homology to proteins shared across the tree of life, particularly those that have nucleic acid metabolism and enzymatic functions. Phold is available as free and open-source software at https://github.com/gbouras13/phold.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Similarity; Language; Database; Phages; Life
ISSN (print) / ISBN 0305-1048
e-ISSN 1362-4962
Journal Nucleic Acids Research
Quellenangaben Volume: 54, Issue: 1, Pages: , Article Number: gkaf1448 Supplement: ,
Publisher Oxford University Press
Publishing Place Great Clarendon St, Oxford Ox2 6dp, England
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Grants Novo Nordisk Foundation
Samsung DS Research Fund, Creative-Pioneering Researchers Program, AI-Bio Research Grant through Seoul National University
National Research Foundation of Korea
Australian Research Council
Australian Government