Geometric deep learning improves generalizability of MHC-bound peptide predictions
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 genera...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-024-07292-1 |
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| author | Dario F. Marzella Giulia Crocioni Tadija Radusinović Daniil Lepikhov Heleen Severin Dani L. Bodor Daniel T. Rademaker ChiaYu Lin Sonja Georgievska Nicolas Renaud Amy L. Kessler Pablo Lopez-Tarifa Sonja I. Buschow Erik Bekkers Li C. Xue |
| author_facet | Dario F. Marzella Giulia Crocioni Tadija Radusinović Daniil Lepikhov Heleen Severin Dani L. Bodor Daniel T. Rademaker ChiaYu Lin Sonja Georgievska Nicolas Renaud Amy L. Kessler Pablo Lopez-Tarifa Sonja I. Buschow Erik Bekkers Li C. Xue |
| author_sort | Dario F. Marzella |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-74a496029f184ad6b454f6b29b4a4566 |
| institution | Kabale University |
| issn | 2399-3642 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj-art-74a496029f184ad6b454f6b29b4a45662024-12-22T12:41:59ZengNature PortfolioCommunications Biology2399-36422024-12-017111210.1038/s42003-024-07292-1Geometric deep learning improves generalizability of MHC-bound peptide predictionsDario F. Marzella0Giulia Crocioni1Tadija Radusinović2Daniil Lepikhov3Heleen Severin4Dani L. Bodor5Daniel T. Rademaker6ChiaYu Lin7Sonja Georgievska8Nicolas Renaud9Amy L. Kessler10Pablo Lopez-Tarifa11Sonja I. Buschow12Erik Bekkers13Li C. Xue14Medical BioSciences department, Radboudumc, Radboud University Medical CenterNetherlands eScience CenterUniversity of AmsterdamMedical BioSciences department, Radboudumc, Radboud University Medical CenterMedical BioSciences department, Radboudumc, Radboud University Medical CenterNetherlands eScience CenterMedical BioSciences department, Radboudumc, Radboud University Medical CenterNetherlands eScience CenterNetherlands eScience CenterNetherlands eScience CenterDepartment of Gastroenterology and Hepatology, Erasmus MC, University Medical Center RotterdamNetherlands eScience CenterDepartment of Gastroenterology and Hepatology, Erasmus MC, University Medical Center RotterdamUniversity of AmsterdamMedical BioSciences department, Radboudumc, Radboud University Medical CenterAbstract 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.https://doi.org/10.1038/s42003-024-07292-1 |
| spellingShingle | Dario F. Marzella Giulia Crocioni Tadija Radusinović Daniil Lepikhov Heleen Severin Dani L. Bodor Daniel T. Rademaker ChiaYu Lin Sonja Georgievska Nicolas Renaud Amy L. Kessler Pablo Lopez-Tarifa Sonja I. Buschow Erik Bekkers Li C. Xue Geometric deep learning improves generalizability of MHC-bound peptide predictions Communications Biology |
| title | Geometric deep learning improves generalizability of MHC-bound peptide predictions |
| title_full | Geometric deep learning improves generalizability of MHC-bound peptide predictions |
| title_fullStr | Geometric deep learning improves generalizability of MHC-bound peptide predictions |
| title_full_unstemmed | Geometric deep learning improves generalizability of MHC-bound peptide predictions |
| title_short | Geometric deep learning improves generalizability of MHC-bound peptide predictions |
| title_sort | geometric deep learning improves generalizability of mhc bound peptide predictions |
| url | https://doi.org/10.1038/s42003-024-07292-1 |
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