Exo-Tox: Identifying Exotoxins from secreted bacterial proteins
Exo-Tox reliably identifies bacterial exotoxins, filling a niche overlooked by generalized predictors. Our findings highlight the importance of domain-specific training data and emphasize that specialized predictors are necessary for accurate classification. We provide open access to the model, training data, and usage guidelines via the LMU Munich Open Data repository.
Authors
- Todd S. Krueger (ORCID: https://orcid.org/0009-0007-4575-4534)
- Luisa F. Jiménez‐Soto (ORCID: https://orcid.org/0000-0001-8551-5019)
- Damla Ayse Durmaz (ORCID: https://orcid.org/0009-0006-8693-1616)
Institutions
- Technical University of Munich (DE)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- BioData Mining
- Published
- 2025-08-08
- DOI
- https://doi.org/10.1186/s13040-025-00469-2
- Citations
- 2
- Primary Topic
- Biochemical and Structural Characterization
- Type
- article
- Field-Weighted Citation Impact
- 1.61
Funders
- Bundesministerium für Bildung und Forschung
- Ludwig-Maximilians-Universität München