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Trans-Allelic Model for Prediction of Peptide:MHC-II Interactions
Major histocompatibility complex class two (MHC-II) molecules are trans-membrane proteins and key components of the cellular immune system. Upon recognition of foreign peptides expressed on the MHC-II binding groove, CD4(+) T cells mount an immune response against invading pathogens. Therefore, mech...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6026802/ https://www.ncbi.nlm.nih.gov/pubmed/29988560 http://dx.doi.org/10.3389/fimmu.2018.01410 |
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author | Degoot, Abdoelnaser M. Chirove, Faraimunashe Ndifon, Wilfred |
author_facet | Degoot, Abdoelnaser M. Chirove, Faraimunashe Ndifon, Wilfred |
author_sort | Degoot, Abdoelnaser M. |
collection | PubMed |
description | Major histocompatibility complex class two (MHC-II) molecules are trans-membrane proteins and key components of the cellular immune system. Upon recognition of foreign peptides expressed on the MHC-II binding groove, CD4(+) T cells mount an immune response against invading pathogens. Therefore, mechanistic identification and knowledge of physicochemical features that govern interactions between peptides and MHC-II molecules is useful for the design of effective epitope-based vaccines, as well as for understanding of immune responses. In this article, we present a comprehensive trans-allelic prediction model, a generalized version of our previous biophysical model, that can predict peptide interactions for all three human MHC-II loci (HLA-DR, HLA-DP, and HLA-DQ), using both peptide sequence data and structural information of MHC-II molecules. The advantage of this approach over other machine learning models is that it offers a simple and plausible physical explanation for peptide–MHC-II interactions. We train the model using a benchmark experimental dataset and measure its predictive performance using novel data. Despite its relative simplicity, we find that the model has comparable performance to the state-of-the-art method, the NetMHCIIpan method. Focusing on the physical basis of peptide–MHC binding, we find support for previous theoretical predictions about the contributions of certain binding pockets to the binding energy. In addition, we find that binding pocket P5 of HLA-DP, which was not previously considered as a primary anchor, does make strong contribution to the binding energy. Together, the results indicate that our model can serve as a useful complement to alternative approaches to predicting peptide–MHC interactions. |
format | Online Article Text |
id | pubmed-6026802 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-60268022018-07-09 Trans-Allelic Model for Prediction of Peptide:MHC-II Interactions Degoot, Abdoelnaser M. Chirove, Faraimunashe Ndifon, Wilfred Front Immunol Immunology Major histocompatibility complex class two (MHC-II) molecules are trans-membrane proteins and key components of the cellular immune system. Upon recognition of foreign peptides expressed on the MHC-II binding groove, CD4(+) T cells mount an immune response against invading pathogens. Therefore, mechanistic identification and knowledge of physicochemical features that govern interactions between peptides and MHC-II molecules is useful for the design of effective epitope-based vaccines, as well as for understanding of immune responses. In this article, we present a comprehensive trans-allelic prediction model, a generalized version of our previous biophysical model, that can predict peptide interactions for all three human MHC-II loci (HLA-DR, HLA-DP, and HLA-DQ), using both peptide sequence data and structural information of MHC-II molecules. The advantage of this approach over other machine learning models is that it offers a simple and plausible physical explanation for peptide–MHC-II interactions. We train the model using a benchmark experimental dataset and measure its predictive performance using novel data. Despite its relative simplicity, we find that the model has comparable performance to the state-of-the-art method, the NetMHCIIpan method. Focusing on the physical basis of peptide–MHC binding, we find support for previous theoretical predictions about the contributions of certain binding pockets to the binding energy. In addition, we find that binding pocket P5 of HLA-DP, which was not previously considered as a primary anchor, does make strong contribution to the binding energy. Together, the results indicate that our model can serve as a useful complement to alternative approaches to predicting peptide–MHC interactions. Frontiers Media S.A. 2018-06-20 /pmc/articles/PMC6026802/ /pubmed/29988560 http://dx.doi.org/10.3389/fimmu.2018.01410 Text en Copyright © 2018 Degoot, Chirove and Ndifon. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Degoot, Abdoelnaser M. Chirove, Faraimunashe Ndifon, Wilfred Trans-Allelic Model for Prediction of Peptide:MHC-II Interactions |
title | Trans-Allelic Model for Prediction of Peptide:MHC-II Interactions |
title_full | Trans-Allelic Model for Prediction of Peptide:MHC-II Interactions |
title_fullStr | Trans-Allelic Model for Prediction of Peptide:MHC-II Interactions |
title_full_unstemmed | Trans-Allelic Model for Prediction of Peptide:MHC-II Interactions |
title_short | Trans-Allelic Model for Prediction of Peptide:MHC-II Interactions |
title_sort | trans-allelic model for prediction of peptide:mhc-ii interactions |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6026802/ https://www.ncbi.nlm.nih.gov/pubmed/29988560 http://dx.doi.org/10.3389/fimmu.2018.01410 |
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