OnmiMHC: a machine learning solution for UCEC tumor vaccine development through enhanced peptide-MHC binding prediction
The key roles of Major Histocompatibility Complex (MHC) Class I and II molecules in the immune system are well established. This study aims to develop a novel machine learning framework for predicting antigen peptide presentation by MHC Class I and II molecules. By integrating large-scale mass spect...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1550252/full |
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| author | Fangfang Jian Haihua Cai Qushuo Chen Xiaoyong Pan Weiwei Feng Ye Yuan Ye Yuan |
| author_facet | Fangfang Jian Haihua Cai Qushuo Chen Xiaoyong Pan Weiwei Feng Ye Yuan Ye Yuan |
| author_sort | Fangfang Jian |
| collection | DOAJ |
| description | The key roles of Major Histocompatibility Complex (MHC) Class I and II molecules in the immune system are well established. This study aims to develop a novel machine learning framework for predicting antigen peptide presentation by MHC Class I and II molecules. By integrating large-scale mass spectrometry data and other relevant data types, we present a prediction model OnmiMHC based on deep learning. We rigorously assessed its performance using an independent test set, OnmiMHC achieves a PR-AUC score of 0.854 and a TOP20%-PPV of 0.934 in the MHC-I task, which outperforms existing methods. Likewise, in the domain of MHC-II prediction, our model OnmiMHC exhibits a PR-AUC score of 0.606 and a TOP20%-PPV of 0.690, outperforming other baseline methods. These results demonstrate the superiority of our model OnmiMHC in accurately predicting peptide-MHC binding affinities across both MHC-I and MHC-II molecules. With its superior accuracy and predictive capability, our model not only excels in general predictive tasks but also achieves significant results in the prediction of neoantigens for specific cancer types. Particularly for Uterine Corpus Endometrial Carcinoma (UCEC), our model has successfully predicted neoantigens with a high binding probability to common human alleles. This discovery is of great significance for the development of personalized tumor vaccines targeting UCEC. |
| format | Article |
| id | doaj-art-acd2eb230ea346e79c206477ee6366a9 |
| institution | DOAJ |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj-art-acd2eb230ea346e79c206477ee6366a92025-08-20T02:54:59ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-02-011610.3389/fimmu.2025.15502521550252OnmiMHC: a machine learning solution for UCEC tumor vaccine development through enhanced peptide-MHC binding predictionFangfang Jian0Haihua Cai1Qushuo Chen2Xiaoyong Pan3Weiwei Feng4Ye Yuan5Ye Yuan6Department of Obstetrics and Gynecology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDigitalGene, Ltd, Shanghai, ChinaDigitalGene, Ltd, Shanghai, ChinaInstitute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, ChinaDepartment of Obstetrics and Gynecology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaKey Laboratory of Biopharmaceutical Preparation and Delivery, Chinese Academy of Sciences, Beijing, ChinaState Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, ChinaThe key roles of Major Histocompatibility Complex (MHC) Class I and II molecules in the immune system are well established. This study aims to develop a novel machine learning framework for predicting antigen peptide presentation by MHC Class I and II molecules. By integrating large-scale mass spectrometry data and other relevant data types, we present a prediction model OnmiMHC based on deep learning. We rigorously assessed its performance using an independent test set, OnmiMHC achieves a PR-AUC score of 0.854 and a TOP20%-PPV of 0.934 in the MHC-I task, which outperforms existing methods. Likewise, in the domain of MHC-II prediction, our model OnmiMHC exhibits a PR-AUC score of 0.606 and a TOP20%-PPV of 0.690, outperforming other baseline methods. These results demonstrate the superiority of our model OnmiMHC in accurately predicting peptide-MHC binding affinities across both MHC-I and MHC-II molecules. With its superior accuracy and predictive capability, our model not only excels in general predictive tasks but also achieves significant results in the prediction of neoantigens for specific cancer types. Particularly for Uterine Corpus Endometrial Carcinoma (UCEC), our model has successfully predicted neoantigens with a high binding probability to common human alleles. This discovery is of great significance for the development of personalized tumor vaccines targeting UCEC.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1550252/fullpeptide-MHC bindingMHC I and IIdeep learninguterine corpus endometrial carcinomaneoantigen |
| spellingShingle | Fangfang Jian Haihua Cai Qushuo Chen Xiaoyong Pan Weiwei Feng Ye Yuan Ye Yuan OnmiMHC: a machine learning solution for UCEC tumor vaccine development through enhanced peptide-MHC binding prediction Frontiers in Immunology peptide-MHC binding MHC I and II deep learning uterine corpus endometrial carcinoma neoantigen |
| title | OnmiMHC: a machine learning solution for UCEC tumor vaccine development through enhanced peptide-MHC binding prediction |
| title_full | OnmiMHC: a machine learning solution for UCEC tumor vaccine development through enhanced peptide-MHC binding prediction |
| title_fullStr | OnmiMHC: a machine learning solution for UCEC tumor vaccine development through enhanced peptide-MHC binding prediction |
| title_full_unstemmed | OnmiMHC: a machine learning solution for UCEC tumor vaccine development through enhanced peptide-MHC binding prediction |
| title_short | OnmiMHC: a machine learning solution for UCEC tumor vaccine development through enhanced peptide-MHC binding prediction |
| title_sort | onmimhc a machine learning solution for ucec tumor vaccine development through enhanced peptide mhc binding prediction |
| topic | peptide-MHC binding MHC I and II deep learning uterine corpus endometrial carcinoma neoantigen |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1550252/full |
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