Whole-genome prediction of bacterial pathogenic capacity on novel bacteria using protein language models with PathogenFinder2
Open accessPublished version of a preprint
Abstract
Motivation: 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. Results: We introduce PathogenFinder2, a novel, alignment-free, taxonomy-agnostic model for predicting bacterial pathogenic capacity in humans using protein language models. It outperforms previous methods, particularly for novel taxa, and provides interpretable outputs by highlighting proteins most relevant to pathogenic potential. These insights aid the identification of virulence factors, vaccine targets, and infection-related metabolic pathways. Furthermore, we introduce the Bacterial Pathogenic Capacity Landscape, which reveals patterns linked to host condition, infection site, microbial antagonism, and environmental origin. Availability: The model is freely available online at https://genepi.dk/pathogenfinder2, or as a standalone program (https://github.com/genomicepidemiology/PathogenFinder2).
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Cites
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- Authors
- Ferrer Florensa, A., Almagro Armenteros, J. J., Kaas, R. S., Clausen, P. T. L. C., Nielsen, H., Rost, B., & Aarestrup, F. M.
- Affiliations
- Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark , Kongens Lyngby, 2800,
- Informatics and Predictive Sciences Research, Bristol Myers Squibb Company , Sevilla, 41092,
- Technical University of Denmark (DK)
- Technical University of Munich (DE)
- ORCID
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2026-05
- Volume
- 42
- Issue
- 5
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W7139978436
- Cited by
- 50
- References
- 9
- 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 Union’s Horizon 2020 (874735)
- National Institute Of Allergy And Infectious Diseases of the National Institutes of Health (U24AI183840)
- Novo Nordisk Foundation (NNF26SA0109818)
- 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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