Empathi: embedding-based phage protein annotation tool by hierarchical assignment

Alexandre Boulay (), Audrey Leprince, François Enault, Elsa Rousseau and Clovis Galiez ()
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Alexandre Boulay: Université Laval
Audrey Leprince: Université Laval
François Enault: LMGE
Elsa Rousseau: Université Laval
Clovis Galiez: LJK

Nature Communications, 2025, vol. 16, issue 1, 1-9

Abstract: Abstract Bacteriophages, viruses infecting bacteria, are estimated to outnumber their cellular hosts by 10-fold, acting as key players in all microbial ecosystems. Under evolutionary pressure by their host, they evolve rapidly and encode a large diversity of protein sequences. Consequently, the majority of functions carried by phage proteins remain elusive. Current tools to comprehensively identify phage protein functions from their sequence either lack sensitivity (those relying on homology for instance) or specificity (assigning a single coarse grain function to a protein). Here, we introduce Empathi, a protein-embedding-based classifier that assigns functions in a hierarchical manner. New categories were specifically elaborated for phage protein functions and organized such that molecular-level functions are respected in each category, making them well suited for training machine learning classifiers based on protein embeddings. Empathi outperforms homology-based methods on a dataset of cultured phage genomes, tripling the number of annotated homologous groups. On the EnVhogDB database, the most recent and extensive database of metagenomically-sourced phage proteins, Empathi doubled the annotated fraction of protein families from 16% to 33%. Having a more global view of the repertoire of functions a phage possesses will assuredly help to understand them and their interactions with bacteria better.

Date: 2025
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DOI: 10.1038/s41467-025-64177-5

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