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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-9966851 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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