MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods
Major histocompatibility complex Class II (MHCII) proteins initiate and regulate immune responses by presentation of antigenic peptides to CD4+ T-cells and self-restriction. The interactions between MHCII and peptides determine the specificity of the immune response and are crucial in immunotherapy...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-03-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1293706/full |
| _version_ | 1797221166079803392 |
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| author | Yaqing Yang Yaqing Yang Zhonghui Wei Zhonghui Wei Gabriel Cia Gabriel Cia Xixi Song Xixi Song Fabrizio Pucci Fabrizio Pucci Marianne Rooman Marianne Rooman Fuzhong Xue Fuzhong Xue Qingzhen Hou Qingzhen Hou |
| author_facet | Yaqing Yang Yaqing Yang Zhonghui Wei Zhonghui Wei Gabriel Cia Gabriel Cia Xixi Song Xixi Song Fabrizio Pucci Fabrizio Pucci Marianne Rooman Marianne Rooman Fuzhong Xue Fuzhong Xue Qingzhen Hou Qingzhen Hou |
| author_sort | Yaqing Yang |
| collection | DOAJ |
| description | Major histocompatibility complex Class II (MHCII) proteins initiate and regulate immune responses by presentation of antigenic peptides to CD4+ T-cells and self-restriction. The interactions between MHCII and peptides determine the specificity of the immune response and are crucial in immunotherapy and cancer vaccine design. With the ever-increasing amount of MHCII-peptide binding data available, many computational approaches have been developed for MHCII-peptide interaction prediction over the last decade. There is thus an urgent need to provide an up-to-date overview and assessment of these newly developed computational methods. To benchmark the prediction performance of these methods, we constructed an independent dataset containing binding and non-binding peptides to 20 human MHCII protein allotypes from the Immune Epitope Database, covering DP, DR and DQ alleles. After collecting 11 known predictors up to January 2022, we evaluated those available through a webserver or standalone packages on this independent dataset. The benchmarking results show that MixMHC2pred and NetMHCIIpan-4.1 achieve the best performance among all predictors. In general, newly developed methods perform better than older ones due to the rapid expansion of data on which they are trained and the development of deep learning algorithms. Our manuscript not only draws a full picture of the state-of-art of MHCII-peptide binding prediction, but also guides researchers in the choice among the different predictors. More importantly, it will inspire biomedical researchers in both academia and industry for the future developments in this field. |
| first_indexed | 2024-04-24T13:01:06Z |
| format | Article |
| id | doaj.art-b3e1da7d4a5945d2b7ba5241f41096db |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-24T13:01:06Z |
| publishDate | 2024-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-b3e1da7d4a5945d2b7ba5241f41096db2024-04-05T10:59:43ZengFrontiers Media S.A.Frontiers in Immunology1664-32242024-03-011510.3389/fimmu.2024.12937061293706MHCII-peptide presentation: an assessment of the state-of-the-art prediction methodsYaqing Yang0Yaqing Yang1Zhonghui Wei2Zhonghui Wei3Gabriel Cia4Gabriel Cia5Xixi Song6Xixi Song7Fabrizio Pucci8Fabrizio Pucci9Marianne Rooman10Marianne Rooman11Fuzhong Xue12Fuzhong Xue13Qingzhen Hou14Qingzhen Hou15Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, ChinaNational Institute of Health Data Science of China, Shandong University, Jinan, ChinaDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, ChinaNational Institute of Health Data Science of China, Shandong University, Jinan, ChinaComputational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, BelgiumInteruniversity Institute of Bioinformatics in Brussels, Brussels, BelgiumDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, ChinaNational Institute of Health Data Science of China, Shandong University, Jinan, ChinaComputational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, BelgiumInteruniversity Institute of Bioinformatics in Brussels, Brussels, BelgiumComputational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, BelgiumInteruniversity Institute of Bioinformatics in Brussels, Brussels, BelgiumDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, ChinaNational Institute of Health Data Science of China, Shandong University, Jinan, ChinaDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, ChinaNational Institute of Health Data Science of China, Shandong University, Jinan, ChinaMajor histocompatibility complex Class II (MHCII) proteins initiate and regulate immune responses by presentation of antigenic peptides to CD4+ T-cells and self-restriction. The interactions between MHCII and peptides determine the specificity of the immune response and are crucial in immunotherapy and cancer vaccine design. With the ever-increasing amount of MHCII-peptide binding data available, many computational approaches have been developed for MHCII-peptide interaction prediction over the last decade. There is thus an urgent need to provide an up-to-date overview and assessment of these newly developed computational methods. To benchmark the prediction performance of these methods, we constructed an independent dataset containing binding and non-binding peptides to 20 human MHCII protein allotypes from the Immune Epitope Database, covering DP, DR and DQ alleles. After collecting 11 known predictors up to January 2022, we evaluated those available through a webserver or standalone packages on this independent dataset. The benchmarking results show that MixMHC2pred and NetMHCIIpan-4.1 achieve the best performance among all predictors. In general, newly developed methods perform better than older ones due to the rapid expansion of data on which they are trained and the development of deep learning algorithms. Our manuscript not only draws a full picture of the state-of-art of MHCII-peptide binding prediction, but also guides researchers in the choice among the different predictors. More importantly, it will inspire biomedical researchers in both academia and industry for the future developments in this field.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1293706/fullMHCIIpeptide binding predictionimmunologybioinformaticswebservermachine learning |
| spellingShingle | Yaqing Yang Yaqing Yang Zhonghui Wei Zhonghui Wei Gabriel Cia Gabriel Cia Xixi Song Xixi Song Fabrizio Pucci Fabrizio Pucci Marianne Rooman Marianne Rooman Fuzhong Xue Fuzhong Xue Qingzhen Hou Qingzhen Hou MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods Frontiers in Immunology MHCII peptide binding prediction immunology bioinformatics webserver machine learning |
| title | MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods |
| title_full | MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods |
| title_fullStr | MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods |
| title_full_unstemmed | MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods |
| title_short | MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods |
| title_sort | mhcii peptide presentation an assessment of the state of the art prediction methods |
| topic | MHCII peptide binding prediction immunology bioinformatics webserver machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1293706/full |
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