Supplementary files including new predictor and benchmarks - Part of the review process of "Exo-Tox: Identifying Exotoxins from secreted bacterial proteins"

Zitation: Krueger, Tanja und Jimenez Soto, Luisa F.: Supplementary files including new predictor and benchmarks - Part of the review process of "Exo-Tox: Identifying Exotoxins from secreted bacterial proteins". 8. Juli 2025. Open Data LMU. 10.5282/ubm/data.665

Be­schrei­bung

Background: Bacterial exotoxins are secreted proteins able to affect target cells, and associated with diseases. Their accurate identification can enhance drug discovery and ensure the safety of bacteria-based medical applications. How- ever, current toxin predictors prioritize broad coverage by mixing toxins from multiple biological kingdoms and diverse control sets. This general approach has proven sub-optimal for identifying niche toxins, such as bacterial exotoxins. Recent Protein Language Models offer an opportunity to improve toxin prediction by capturing global sequence context and biochemical properties from protein sequences. Results: We introduce Exo-Tox, a specialized predictor trained exclusively on curated datasets of bacterial exotoxins and secreted non-toxic bacterial proteins,represented as embeddings by Protein Language Models. Compared to Basic Local Alignment Search Tool (BLAST)-based methods and generalized toxin predictors, Exo-Tox outperforms across multiple metrics, achieving an Matthews correlation coefficient > 0.9. Notably, Exo-Tox’s performance remains robust regardless of protein length or the presence of signal peptides. We analyze its limited transfer-ability to bacteriophage proteins and non-secreted proteins.

Stichwörter

foldseek, bacterial toxins, exotoxins, embeddings, protein Language Models

Quellenangaben

Krueger, Tanja and Durmaz, Damla A. and Jimenez Soto, Luisa F.: Exo-Tox: Identifying Exotoxins from secreted bacterial proteins. 2025. Open Data LMU. 10.5282/ubm/data.576

• Dokumententyp: Sonstiges
• Name der Kontakt­person: Jimenez-Soto, Luisa F
• E-Mail der Kontaktperson: l.jimenez at lmu.de
• Fächer: Mathematik, Informatik und Statistik ; Biologie
• Dewey Dezimal­klassi­fikation: 500 Naturwissenschaften und Mathematik ; 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften, Biologie
• Eingestellt von: Dr. Luisa F. Jimenez Soto