Hierarchical progressive learning for zero-shot peptide-HLA binding prediction and automated antigenic peptide design
Summary: Predicting the binding of peptides to human leukocyte antigen (HLA) alleles is critical for identifying epitopes initiating immune responses in vaccine development and immunotherapies. Despite the progress in predicting binding peptides of 1% HLA class-I alleles with in silico prediction to...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Cell Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211124725005340 |
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| author | Xinyuan Zhu Jiadong Lu Xinting Hu Tengchuan Jin Shan Lu Fuli Feng |
| author_facet | Xinyuan Zhu Jiadong Lu Xinting Hu Tengchuan Jin Shan Lu Fuli Feng |
| author_sort | Xinyuan Zhu |
| collection | DOAJ |
| description | Summary: Predicting the binding of peptides to human leukocyte antigen (HLA) alleles is critical for identifying epitopes initiating immune responses in vaccine development and immunotherapies. Despite the progress in predicting binding peptides of 1% HLA class-I alleles with in silico prediction tools, it remains challenging to predict binding peptides for alleles lacking epitopes. To achieve the binding prediction on those zero-shot alleles, we developed a hierarchical progressive learning (HPL) framework that progressively learns sequence patterns of specific peptide-HLA complexes. HPL models improve the prediction performance for zero-shot HLA class-I alleles by 60.8% (1,414.0% for non-classical ones). We further developed an automated peptide mutation search program, which automatically identifies binding peptides for any target HLA class-I allele and mutates weak-binding or non-binding peptides to enhance binding affinity under the guidance of HPL models. Our program generated high-affinity binding candidates of any target allele with limited mutations in more than 38.1% of testing cases. |
| format | Article |
| id | doaj-art-770cc9803dfb44fd937f9dc775bc4b1f |
| institution | OA Journals |
| issn | 2211-1247 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cell Reports |
| spelling | doaj-art-770cc9803dfb44fd937f9dc775bc4b1f2025-08-20T02:17:19ZengElsevierCell Reports2211-12472025-06-0144611576310.1016/j.celrep.2025.115763Hierarchical progressive learning for zero-shot peptide-HLA binding prediction and automated antigenic peptide designXinyuan Zhu0Jiadong Lu1Xinting Hu2Tengchuan Jin3Shan Lu4Fuli Feng5School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui, ChinaSchool of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei, Anhui, ChinaSchool of Computer Science and Engineering, Nanyang Technological University, Singapore, SingaporeDivision of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, ChinaDepartment of Cellular and Molecular Medicine, University of California, San Diego, San Diego, CA, USA; Corresponding authorSchool of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei, Anhui, China; Corresponding authorSummary: Predicting the binding of peptides to human leukocyte antigen (HLA) alleles is critical for identifying epitopes initiating immune responses in vaccine development and immunotherapies. Despite the progress in predicting binding peptides of 1% HLA class-I alleles with in silico prediction tools, it remains challenging to predict binding peptides for alleles lacking epitopes. To achieve the binding prediction on those zero-shot alleles, we developed a hierarchical progressive learning (HPL) framework that progressively learns sequence patterns of specific peptide-HLA complexes. HPL models improve the prediction performance for zero-shot HLA class-I alleles by 60.8% (1,414.0% for non-classical ones). We further developed an automated peptide mutation search program, which automatically identifies binding peptides for any target HLA class-I allele and mutates weak-binding or non-binding peptides to enhance binding affinity under the guidance of HPL models. Our program generated high-affinity binding candidates of any target allele with limited mutations in more than 38.1% of testing cases.http://www.sciencedirect.com/science/article/pii/S2211124725005340CP: Immunology |
| spellingShingle | Xinyuan Zhu Jiadong Lu Xinting Hu Tengchuan Jin Shan Lu Fuli Feng Hierarchical progressive learning for zero-shot peptide-HLA binding prediction and automated antigenic peptide design Cell Reports CP: Immunology |
| title | Hierarchical progressive learning for zero-shot peptide-HLA binding prediction and automated antigenic peptide design |
| title_full | Hierarchical progressive learning for zero-shot peptide-HLA binding prediction and automated antigenic peptide design |
| title_fullStr | Hierarchical progressive learning for zero-shot peptide-HLA binding prediction and automated antigenic peptide design |
| title_full_unstemmed | Hierarchical progressive learning for zero-shot peptide-HLA binding prediction and automated antigenic peptide design |
| title_short | Hierarchical progressive learning for zero-shot peptide-HLA binding prediction and automated antigenic peptide design |
| title_sort | hierarchical progressive learning for zero shot peptide hla binding prediction and automated antigenic peptide design |
| topic | CP: Immunology |
| url | http://www.sciencedirect.com/science/article/pii/S2211124725005340 |
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