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CSM-Toxin: A Web-Server for Predicting Protein Toxicity

Biologics are one of the most rapidly expanding classes of therapeutics, but can be associated with a range of toxic properties. In small-molecule drug development, early identification of potential toxicity led to a significant reduction in clinical trial failures, however we currently lack robust...

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Detalles Bibliográficos
Autores principales: Morozov, Vladimir, Rodrigues, Carlos H. M., Ascher, David B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9966851/
https://www.ncbi.nlm.nih.gov/pubmed/36839752
http://dx.doi.org/10.3390/pharmaceutics15020431
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author Morozov, Vladimir
Rodrigues, Carlos H. M.
Ascher, David B.
author_facet Morozov, Vladimir
Rodrigues, Carlos H. M.
Ascher, David B.
author_sort Morozov, Vladimir
collection PubMed
description Biologics are one of the most rapidly expanding classes of therapeutics, but can be associated with a range of toxic properties. In small-molecule drug development, early identification of potential toxicity led to a significant reduction in clinical trial failures, however we currently lack robust qualitative rules or predictive tools for peptide- and protein-based biologics. To address this, we have manually curated the largest set of high-quality experimental data on peptide and protein toxicities, and developed CSM-Toxin, a novel in-silico protein toxicity classifier, which relies solely on the protein primary sequence. Our approach encodes the protein sequence information using a deep learning natural languages model to understand “biological” language, where residues are treated as words and protein sequences as sentences. The CSM-Toxin was able to accurately identify peptides and proteins with potential toxicity, achieving an MCC of up to 0.66 across both cross-validation and multiple non-redundant blind tests, outperforming other methods and highlighting the robust and generalisable performance of our model. We strongly believe the CSM-Toxin will serve as a valuable platform to minimise potential toxicity in the biologic development pipeline. Our method is freely available as an easy-to-use webserver.
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spelling pubmed-99668512023-02-26 CSM-Toxin: A Web-Server for Predicting Protein Toxicity Morozov, Vladimir Rodrigues, Carlos H. M. Ascher, David B. Pharmaceutics Article Biologics are one of the most rapidly expanding classes of therapeutics, but can be associated with a range of toxic properties. In small-molecule drug development, early identification of potential toxicity led to a significant reduction in clinical trial failures, however we currently lack robust qualitative rules or predictive tools for peptide- and protein-based biologics. To address this, we have manually curated the largest set of high-quality experimental data on peptide and protein toxicities, and developed CSM-Toxin, a novel in-silico protein toxicity classifier, which relies solely on the protein primary sequence. Our approach encodes the protein sequence information using a deep learning natural languages model to understand “biological” language, where residues are treated as words and protein sequences as sentences. The CSM-Toxin was able to accurately identify peptides and proteins with potential toxicity, achieving an MCC of up to 0.66 across both cross-validation and multiple non-redundant blind tests, outperforming other methods and highlighting the robust and generalisable performance of our model. We strongly believe the CSM-Toxin will serve as a valuable platform to minimise potential toxicity in the biologic development pipeline. Our method is freely available as an easy-to-use webserver. MDPI 2023-01-28 /pmc/articles/PMC9966851/ /pubmed/36839752 http://dx.doi.org/10.3390/pharmaceutics15020431 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Morozov, Vladimir
Rodrigues, Carlos H. M.
Ascher, David B.
CSM-Toxin: A Web-Server for Predicting Protein Toxicity
title CSM-Toxin: A Web-Server for Predicting Protein Toxicity
title_full CSM-Toxin: A Web-Server for Predicting Protein Toxicity
title_fullStr CSM-Toxin: A Web-Server for Predicting Protein Toxicity
title_full_unstemmed CSM-Toxin: A Web-Server for Predicting Protein Toxicity
title_short CSM-Toxin: A Web-Server for Predicting Protein Toxicity
title_sort csm-toxin: a web-server for predicting protein toxicity
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9966851/
https://www.ncbi.nlm.nih.gov/pubmed/36839752
http://dx.doi.org/10.3390/pharmaceutics15020431
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