576 18 3676 2 14271 document 3676 Exo-Tox.zip application/zip b0c3e6d62021ba280c4fdb231a7adba4 MD5 1532027314 2025-03-07 15:00:30 https://data.ub.uni-muenchen.de/576/1/Exo-Tox.zip 576 1 1 application/zip other Complete files used for the creation and test of the Exo-Tox predictor en public

Exo-Tox.zip

other 3681 3 14307 document 3681 README_Exo-Tox.md text/plain 82da3ed93a844caf377f2588dd73d8e4 MD5 9667 2025-03-18 12:34:16 https://data.ub.uni-muenchen.de/576/2/README_Exo-Tox.md 576 2 2 text/plain other ReadMe-File to Exo-Tox.zip de public

README_Exo-Tox.md

metadata archive 856 disk0/00/00/05/76 2025-03-18 12:37:08 2026-03-18 13:01:37 2025-03-18 12:37:08 other show 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. These Datasets are part of the article published in BioData Mining : Krueger, T., Durmaz, D. & Jimenez-Soto, L. Exo-Tox: Identifying Exotoxins from secreted bacterial proteins. BioData Mining 18, 52 (2025). https://doi.org/10.1186/s13040-025-00469-2. Use the following link to access: https://rdcu.be/eAeOs Data to: Jiménez Soto, Luisa Fernanda, SSTDBB: Do You Speak Toxin? The Message of Bacteriophages; Subproject Translation. Research Report. ReNaTe (2026-03-12) https://doi.org/10.34657/31485 eng Krueger Tanja Durmaz Damla A. Jimenez Soto Luisa F. 2025 500 570 10.5282/ubm/data.576 https://doi.org/10.5282/ubm/data.576 public Predictor bacterial toxins bacteriophages en cc-by-sa Jimenez-Soto Luisa F fak07 fak16 fak19