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DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction
MOTIVATION: Computationally predicting major histocompatibility complex class I (MHC-I) peptide binding affinity is an important problem in immunological bioinformatics, which is also crucial for the identification of neoantigens for personalized therapeutic cancer vaccines. Recent cutting-edge deep...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516514/ https://www.ncbi.nlm.nih.gov/pubmed/37669154 http://dx.doi.org/10.1093/bioinformatics/btad551 |
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author | Qu, Wei You, Ronghui Mamitsuka, Hiroshi Zhu, Shanfeng |
author_facet | Qu, Wei You, Ronghui Mamitsuka, Hiroshi Zhu, Shanfeng |
author_sort | Qu, Wei |
collection | PubMed |
description | MOTIVATION: Computationally predicting major histocompatibility complex class I (MHC-I) peptide binding affinity is an important problem in immunological bioinformatics, which is also crucial for the identification of neoantigens for personalized therapeutic cancer vaccines. Recent cutting-edge deep learning-based methods for this problem cannot achieve satisfactory performance, especially for non-9-mer peptides. This is because such methods generate the input by simply concatenating the two given sequences: a peptide and (the pseudo sequence of) an MHC class I molecule, which cannot precisely capture the anchor positions of the MHC binding motif for the peptides with variable lengths. We thus developed an anchor position-aware and high-performance deep model, DeepMHCI, with a position-wise gated layer and a residual binding interaction convolution layer. This allows the model to control the information flow in peptides to be aware of anchor positions and model the interactions between peptides and the MHC pseudo (binding) sequence directly with multiple convolutional kernels. RESULTS: The performance of DeepMHCI has been thoroughly validated by extensive experiments on four benchmark datasets under various settings, such as 5-fold cross-validation, validation with the independent testing set, external HPV vaccine identification, and external CD8+ epitope identification. Experimental results with visualization of binding motifs demonstrate that DeepMHCI outperformed all competing methods, especially on non-9-mer peptides binding prediction. AVAILABILITY AND IMPLEMENTATION: DeepMHCI is publicly available at https://github.com/ZhuLab-Fudan/DeepMHCI. |
format | Online Article Text |
id | pubmed-10516514 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-105165142023-09-23 DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction Qu, Wei You, Ronghui Mamitsuka, Hiroshi Zhu, Shanfeng Bioinformatics Original Paper MOTIVATION: Computationally predicting major histocompatibility complex class I (MHC-I) peptide binding affinity is an important problem in immunological bioinformatics, which is also crucial for the identification of neoantigens for personalized therapeutic cancer vaccines. Recent cutting-edge deep learning-based methods for this problem cannot achieve satisfactory performance, especially for non-9-mer peptides. This is because such methods generate the input by simply concatenating the two given sequences: a peptide and (the pseudo sequence of) an MHC class I molecule, which cannot precisely capture the anchor positions of the MHC binding motif for the peptides with variable lengths. We thus developed an anchor position-aware and high-performance deep model, DeepMHCI, with a position-wise gated layer and a residual binding interaction convolution layer. This allows the model to control the information flow in peptides to be aware of anchor positions and model the interactions between peptides and the MHC pseudo (binding) sequence directly with multiple convolutional kernels. RESULTS: The performance of DeepMHCI has been thoroughly validated by extensive experiments on four benchmark datasets under various settings, such as 5-fold cross-validation, validation with the independent testing set, external HPV vaccine identification, and external CD8+ epitope identification. Experimental results with visualization of binding motifs demonstrate that DeepMHCI outperformed all competing methods, especially on non-9-mer peptides binding prediction. AVAILABILITY AND IMPLEMENTATION: DeepMHCI is publicly available at https://github.com/ZhuLab-Fudan/DeepMHCI. Oxford University Press 2023-09-05 /pmc/articles/PMC10516514/ /pubmed/37669154 http://dx.doi.org/10.1093/bioinformatics/btad551 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Paper Qu, Wei You, Ronghui Mamitsuka, Hiroshi Zhu, Shanfeng DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction |
title | DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction |
title_full | DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction |
title_fullStr | DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction |
title_full_unstemmed | DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction |
title_short | DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction |
title_sort | deepmhci: an anchor position-aware deep interaction model for accurate mhc-i peptide binding affinity prediction |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516514/ https://www.ncbi.nlm.nih.gov/pubmed/37669154 http://dx.doi.org/10.1093/bioinformatics/btad551 |
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