Geometric deep learning improves generalizability of MHC-bound peptide predictions

The interaction between peptides and major histocompatibility complex (MHC) molecules is pivotal in autoimmunity, pathogen recognition and tumor immunity. Recent advances in cancer immunotherapies demand for more accurate computational prediction of MHC-bound peptides. We address the generalizability challenge of MHC-bound peptide predictions, revealing limitations in current sequence-based approaches. Our structure-based methods leveraging geometric deep learning (GDL) demonstrate promising improvement in generalizability across unseen MHC alleles. Further, we tackle data efficiency by introducing a self-supervised learning approach on structures (3D-SSL). Without being exposed to any binding affinity data, our 3D-SSL outperforms sequence-based methods trained on ~90 times more data points. Finally, we demonstrate the resilience of structure-based GDL methods to biases in binding data on an Hepatitis B virus vaccine immunopeptidomics case study. This proof-of-concept study highlights structure-based methods' potential to enhance generalizability and data efficiency, with possible implications for data-intensive fields like T-cell receptor specificity predictions.

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Publication Details

Journal
Communications Biology
Published
2024-12-19
DOI
https://doi.org/10.1038/s42003-024-07292-1
Citations
14
Primary Topic
vaccines and immunoinformatics approaches
Type
article
Field-Weighted Citation Impact
3.27

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Geometric deep learning improves generalizability of MHC-bound peptide predictions

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Communications Biology
vaccines and immunoinformatics approaches
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Geometric deep learning improves generalizability of MHC-bound peptide predictions

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article en
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Abstract

The interaction between peptides and major histocompatibility complex (MHC) molecules is pivotal in autoimmunity, pathogen recognition and tumor immunity. Recent advances in cancer immunotherapies demand for more accurate computational prediction of MHC-bound peptides. We address the generalizability challenge of MHC-bound peptide predictions, revealing limitations in current sequence-based approaches. Our structure-based methods leveraging geometric deep learning (GDL) demonstrate promising improvement in generalizability across unseen MHC alleles. Further, we tackle data efficiency by introducing a self-supervised learning approach on structures (3D-SSL). Without being exposed to any binding affinity data, our 3D-SSL outperforms sequence-based methods trained on ~90 times more data points. Finally, we demonstrate the resilience of structure-based GDL methods to biases in binding data on an Hepatitis B virus vaccine immunopeptidomics case study. This proof-of-concept study highlights structure-based methods' potential to enhance generalizability and data efficiency, with possible implications for data-intensive fields like T-cell receptor specificity predictions.

Communications BiologyVol. 7(1)
Radboud University Nijmegen (NL), Erasmus MC (NL), University Medical Center (US), Radboud University Medical Center (NL), Netherlands eScience Center (NL), Radboud Institute for Molecular Life Sciences (NL), University of Amsterdam (NL)
Nvidia, Netherlands eScience Center, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Radboud Universitair Medisch Centrum, Stichting Kinderen Kankervrij
Good health and well-being
Openalex Percentile: Top 7%
vaccines and immunoinformatics approaches
3.27
DOIOpen Access PDFOpenAlex
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