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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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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. |
format | Online Article Text |
id | pubmed-10162658 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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