Improved prediction of MHC-peptide binding using protein language models
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Date
2023
Authors
Hashemi, Nasser
Hao, Boran
Ignatov, Mikhail
Paschalidis, Ioannis Ch.
Vakili, Pirooz
Vajda, Sandor
Kozakov, Dima
Version
Published version
OA Version
Citation
N. Hashemi, B. Hao, M. Ignatov, I.C. Paschalidis, P. Vakili, S. Vajda, D. Kozakov. 2023. "Improved prediction of MHC-peptide binding using protein language models." Front Bioinform, Volume 3, pp.1207380-. https://doi.org/10.3389/fbinf.2023.1207380
Abstract
Major histocompatibility complex Class I (MHC-I) molecules bind to peptides derived from intracellular antigens and present them on the surface of cells, allowing the immune system (T cells) to detect them. Elucidating the process of this presentation is essential for regulation and potential manipulation of the cellular immune system. Predicting whether a given peptide binds to an MHC molecule is an important step in the above process and has motivated the introduction of many computational approaches to address this problem. NetMHCPan, a pan-specific model for predicting binding of peptides to any MHC molecule, is one of the most widely used methods which focuses on solving this binary classification problem using shallow neural networks. The recent successful results of Deep Learning (DL) methods, especially Natural Language Processing (NLP-based) pretrained models in various applications, including protein structure determination, motivated us to explore their use in this problem. Specifically, we consider the application of deep learning models pretrained on large datasets of protein sequences to predict MHC Class I-peptide binding. Using the standard performance metrics in this area, and the same training and test sets, we show that our models outperform NetMHCpan4.1, currently considered as the-state-of-the-art.
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License
Attribution 4.0 International
© 2023 Hashemi, Hao, Ignatov, Paschalidis, Vakili, Vajda and Kozakov. 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(s) 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.
© 2023 Hashemi, Hao, Ignatov, Paschalidis, Vakili, Vajda and Kozakov. 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(s) 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.