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CapsNet-MHC predicts peptide-MHC class I binding based on capsule neural networks

The Major Histocompatibility Complex (MHC) binds to the derived peptides from pathogens to present them to killer T cells on the cell surface. Developing computational methods for accurate, fast, and explainable peptide-MHC binding prediction can facilitate immunotherapies and vaccine development. V...

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Detalles Bibliográficos
Autores principales: Kalemati, Mahmood, Darvishi, Saeid, Koohi, Somayyeh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10162658/
https://www.ncbi.nlm.nih.gov/pubmed/37147498
http://dx.doi.org/10.1038/s42003-023-04867-2
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author Kalemati, Mahmood
Darvishi, Saeid
Koohi, Somayyeh
author_facet Kalemati, Mahmood
Darvishi, Saeid
Koohi, Somayyeh
author_sort Kalemati, Mahmood
collection PubMed
description The Major Histocompatibility Complex (MHC) binds to the derived peptides from pathogens to present them to killer T cells on the cell surface. Developing computational methods for accurate, fast, and explainable peptide-MHC binding prediction can facilitate immunotherapies and vaccine development. Various deep learning-based methods rely on separate feature extraction from the peptide and MHC sequences and ignore their pairwise binding information. This paper develops a capsule neural network-based method to efficiently capture the peptide-MHC complex features to predict the peptide-MHC class I binding. Various evaluations confirmed our method outperformance over the alternative methods, while it can provide accurate prediction over less available data. Moreover, for providing precise insights into the results, we explored the essential features that contributed to the prediction. Since the simulation results demonstrated consistency with the experimental studies, we concluded that our method can be utilized for the accurate, rapid, and interpretable peptide-MHC binding prediction to assist biological therapies.
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spelling pubmed-101626582023-05-07 CapsNet-MHC predicts peptide-MHC class I binding based on capsule neural networks Kalemati, Mahmood Darvishi, Saeid Koohi, Somayyeh Commun Biol Article The Major Histocompatibility Complex (MHC) binds to the derived peptides from pathogens to present them to killer T cells on the cell surface. Developing computational methods for accurate, fast, and explainable peptide-MHC binding prediction can facilitate immunotherapies and vaccine development. Various deep learning-based methods rely on separate feature extraction from the peptide and MHC sequences and ignore their pairwise binding information. This paper develops a capsule neural network-based method to efficiently capture the peptide-MHC complex features to predict the peptide-MHC class I binding. Various evaluations confirmed our method outperformance over the alternative methods, while it can provide accurate prediction over less available data. Moreover, for providing precise insights into the results, we explored the essential features that contributed to the prediction. Since the simulation results demonstrated consistency with the experimental studies, we concluded that our method can be utilized for the accurate, rapid, and interpretable peptide-MHC binding prediction to assist biological therapies. Nature Publishing Group UK 2023-05-05 /pmc/articles/PMC10162658/ /pubmed/37147498 http://dx.doi.org/10.1038/s42003-023-04867-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Kalemati, Mahmood
Darvishi, Saeid
Koohi, Somayyeh
CapsNet-MHC predicts peptide-MHC class I binding based on capsule neural networks
title CapsNet-MHC predicts peptide-MHC class I binding based on capsule neural networks
title_full CapsNet-MHC predicts peptide-MHC class I binding based on capsule neural networks
title_fullStr CapsNet-MHC predicts peptide-MHC class I binding based on capsule neural networks
title_full_unstemmed CapsNet-MHC predicts peptide-MHC class I binding based on capsule neural networks
title_short CapsNet-MHC predicts peptide-MHC class I binding based on capsule neural networks
title_sort capsnet-mhc predicts peptide-mhc class i binding based on capsule neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10162658/
https://www.ncbi.nlm.nih.gov/pubmed/37147498
http://dx.doi.org/10.1038/s42003-023-04867-2
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