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Accurate modeling of peptide-MHC structures with AlphaFold.

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 precision and class II peptide register prediction. We explore applications of this method towards improving peptide-MHC binding prediction.

SUBMITTER: Mikhaylov V

PROVIDER: S-EPMC10028922 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Accurate modeling of peptide-MHC structures with AlphaFold.

Mikhaylov Victor V Levine Arnold J AJ

bioRxiv : the preprint server for biology 20230308

Major histocompatibility complex (MHC) proteins present peptides on the cell surface for T-cell surveillance. Reliable <i>in silico</i> 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 exist ...[more]

PMID: 36945436

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