PreprintCited by 50Genomics and Phylogenetic StudiesMachine Learning in Bioinformaticsvaccines and immunoinformatics approaches
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Whole-genome prediction of bacterial pathogenic capacity on novel bacteria using protein language models, with PathogenFinder2
Open accessFerrer Florensa, Alfred, Almagro Armenteros, Jose Juan, Kaas, Rolf Sommer, Conradsen Clausen, Philip Thomas Lanken, Nielsen, Henrik, Rost, Burkhard, Aarestrup, Frank Møller·2025·Bioinformatics
Preprint — published as 10.1093/bioinformatics/btag129 · Paper page
Abstract
Infectious diseases continue to be a leading cause of mortality and pose a significant global health threat. Thus the development of tools for surveillance and early detection of emerging pathogens is needed. In this study, we introduce PathogenFinder2, a novel predictor of bacterial pathogenic capacity in humans, available through an online server ( http://genepi.food.dtu.dk/pathogenfinder2 ), or as a standalone program ( https://github.com/genomicepidemiology/PathogenFinder2 ). The model, using protein language models for whole-genome phenotype prediction, surpasses the performance of previous methods, especially for novel bacterial taxa, while being taxonomy-agnostic and alignment-free. At the same time, it predicts the importance of each protein for the pathogenic capacity. This output might aid in characterizing potential pathogens, it readily identifies new candidates for virulence factors and vaccine targets, and offers insights into infection metabolic pathways. Furthermore, we introduce the Bacterial Pathogenic Landscape, revealing distributions related to the host conditions, antagonist bacteria, infection site, or habitat.
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Metadata
- Authors
- Ferrer Florensa, A., Almagro Armenteros, J. J., Kaas, R. S., Conradsen Clausen, P. T. L., Nielsen, H., Rost, B., & Aarestrup, F. M.
- Affiliations
- Research Group for Genomic Epidemiology, Technical University of Denmark, Kongens Lyngby, Denmark
- Informatics and Predictive Sciences Research, Bristol Myers Squibb Company, Sevilla, Spain
- Technical University of Denmark (DK)
- Technical University of Munich (DE)
- ORCID
- Venue
- Bioinformatics
- Publisher
- openRxiv
- Published
- 2025-04-18
- Volume
- 42
- Issue
- 5
- ISSN
- 1367-4803 (ISSN-L)
- OpenAlex
- W7139978436
- Cited by
- 50
- References
- 1
- Citations by year
- 2026: 41 · 2025: 9
- FWCI
- 84.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- Funders
- European High Performance Computing Joint Undertaking
- Bristol-Myers Squibb
- European Commission (874735, HORIZON2020)
- Novo Nordisk Fonden (NNF26SA0109818)
- National Institute of Allergy and Infectious Diseases (U24AI183840)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- protein language models, novel bacterial taxa, virulence factors, vaccine targets, infection site, microbial antagonism, emerging pathogens
- MeSH terms
- Bacteria, Humans, Software, Genome, Bacterial, Computational Biology, Genomics, Virulence Factors
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