Accurate modeling of peptide-MHC structures with AlphaFold
Open accessPublished version of a preprint
Abstract
Major histocompatibility complex (MHC) proteins present peptides on the cell surface for T cell surveillance. Reliable in silico prediction of which peptides would be presented and which T cell receptors would recognize them is an important problem in structural immunology. Here, we introduce an AlphaFold-based pipeline for predicting the three-dimensional structures of peptide-MHC complexes for class I and class II MHC molecules. Our method demonstrates high accuracy, outperforming existing tools in class I modeling accuracy and class II peptide register prediction. We validate its performance and utility with new experimental data on a recently described cancer neoantigen/wild-type peptide pair and explore applications toward improving peptide-MHC binding prediction.
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Cites
Highly accurate protein structure prediction with AlphaFold
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The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins
Agarwal, V., & McShan, A. C. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01638-w · Cited by 155 · see citing works
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An in-depth review of AI-powered advancements in cancer drug discovery
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- Authors
- Mikhaylov, V., Brambley, C. A., Keller, G. L. J., Arbuiso, A. G., Weiss, L. I., Baker, B. M., & Levine, A. J.
- Affiliations
- Institute for Advanced Study (US)
- University of Notre Dame (US)
- Corresponding author
- Mikhaylov, Victor
- ORCID
- Venue
- Structure
- Publisher
- Elsevier BV
- Published
- 2024-02
- Volume
- 32
- Issue
- 2
- Pages
- 228-241.e4
- ISSN
- 0969-2126 (ISSN-L)
- PMC
- PMC10872456
- OpenAlex
- W4389920712
- Cited by
- 72
- References
- 6
- Citations by year
- 2026: 24 · 2025: 29 · 2024: 15 · 2023: 4
- FWCI
- 6.42
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 366 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Structure (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- PubMed Central (submittedVersion, cc-by-nd)
- Funders
- U.S. Department of Energy (DE‐ AC02‐06CH11357, AC02‐06CH11357, S10OD021527, 06CH11357, DE-AC02-)
- National Institutes of Health (1s10od021527-01, DE-AC02–06CH11357)
- Office of Science (AC02‐06CH11357, DE-AC02, 06CH11357, DE-AC02-06CH11357)
- National Cancer Institute (P01CA087497, DE-AC02-06CH11357)
- National Institute of General Medical Sciences (DE-AC02-06CH11357, R35GM118166, P30GM124165)
- Office of Research Infrastructure Programs, National Institutes of Health (S10OD021527)
- Argonne National Laboratory (DE- AC02-06-CH11357, DE-AC02, AC02–06CH11357, 06CH11357)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Keywords
- peptide-MHC complex, AlphaFold, MHC class I, MHC class II, T cell receptor recognition, peptide presentation, neoantigens, MHC binding prediction
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