Improved prediction of MHC-peptide binding using protein language models
Open accessPublished version of a preprint
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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- Authors
- Hashemi, N., Hao, B., Ignatov, M., Paschalidis, I. C., Vakili, P., Vajda, S., & Kozakov, D.
- Affiliations
- Boston University (US)
- Stony Brook University (US)
- Corresponding author
- Hashemi, Nasser
- Kozakov, Dima
- Venue
- Frontiers in Bioinformatics
- Publisher
- Frontiers Media SA
- Published
- 2023-08-17
- Volume
- 3
- Pages
- 1207380-1207380
- ISSN
- 2673-7647 (ISSN-L)
- PMC
- PMC10469926
- OpenAlex
- W4385982832
- Cited by
- 25
- References
- 11
- Citations by year
- 2026: 10 · 2025: 3 · 2024: 8 · 2023: 3 · 2022: 1
- FWCI
- 2.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OpenBU (Boston University) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (1645681, 2054251, 1664644, RM1135136, 1914792)
- Georgia Clinical and Translational Science Alliance (UL54 TR004130)
- National Institutes of Health (UL54 TR004130, RM1135136)
- Office of Naval Research (N00014-19-1-2571, N00014-19, N00014)
- National Institute of General Medical Sciences (R01GM135930, R01GM140098, R35GM118078)
- National Center for Advancing Translational Sciences (UL54 TR004130)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Keywords
- MHC-peptide binding prediction, MHC class I, protein language models, NetMHCpan, antigen presentation, T cell recognition, binary classification, deep learning
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