iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features
Neoantigens recognized by cytotoxic T cells are effective targets for tumor-specific immune responses for personalized cancer immunotherapy. Quite a few neoantigen identification pipelines and computational strategies have been developed to improve the accuracy of the peptide selection process. Howe...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1141535/full |
| _version_ | 1797830936232984576 |
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| author | Yu Zhang Yu Zhang Xingxing Jian Xingxing Jian Linfeng Xu Linfeng Xu Jingjing Zhao Manman Lu Yong Lin Lu Xie |
| author_facet | Yu Zhang Yu Zhang Xingxing Jian Xingxing Jian Linfeng Xu Linfeng Xu Jingjing Zhao Manman Lu Yong Lin Lu Xie |
| author_sort | Yu Zhang |
| collection | DOAJ |
| description | Neoantigens recognized by cytotoxic T cells are effective targets for tumor-specific immune responses for personalized cancer immunotherapy. Quite a few neoantigen identification pipelines and computational strategies have been developed to improve the accuracy of the peptide selection process. However, these methods mainly consider the neoantigen end and ignore the interaction between peptide-TCR and the preference of each residue in TCRs, resulting in the filtered peptides often fail to truly elicit an immune response. Here, we propose a novel encoding approach for peptide-TCR representation. Subsequently, a deep learning framework, namely iTCep, was developed to predict the interactions between peptides and TCRs using fusion features derived from a feature-level fusion strategy. The iTCep achieved high predictive performance with AUC up to 0.96 on the testing dataset and above 0.86 on independent datasets, presenting better prediction performance compared with other predictors. Our results provided strong evidence that model iTCep can be a reliable and robust method for predicting TCR binding specificities of given antigen peptides. One can access the iTCep through a user-friendly web server at http://biostatistics.online/iTCep/, which supports prediction modes of peptide-TCR pairs and peptide-only. A stand-alone software program for T cell epitope prediction is also available for convenient installing at https://github.com/kbvstmd/iTCep/. |
| first_indexed | 2024-04-09T13:44:56Z |
| format | Article |
| id | doaj.art-b614da1956ec4cfb93f6d42db579dd09 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-09T13:44:56Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-b614da1956ec4cfb93f6d42db579dd092023-05-09T05:38:31ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-05-011410.3389/fgene.2023.11415351141535iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion featuresYu Zhang0Yu Zhang1Xingxing Jian2Xingxing Jian3Linfeng Xu4Linfeng Xu5Jingjing Zhao6Manman Lu7Yong Lin8Lu Xie9School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaShanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, ChinaShanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, ChinaBioinformatics Center, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, ChinaShanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, ChinaMinistry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Bio-Diversity Science, School of Life Sciences, Fudan University, Shanghai, ChinaShanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, ChinaShanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, ChinaSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaShanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, ChinaNeoantigens recognized by cytotoxic T cells are effective targets for tumor-specific immune responses for personalized cancer immunotherapy. Quite a few neoantigen identification pipelines and computational strategies have been developed to improve the accuracy of the peptide selection process. However, these methods mainly consider the neoantigen end and ignore the interaction between peptide-TCR and the preference of each residue in TCRs, resulting in the filtered peptides often fail to truly elicit an immune response. Here, we propose a novel encoding approach for peptide-TCR representation. Subsequently, a deep learning framework, namely iTCep, was developed to predict the interactions between peptides and TCRs using fusion features derived from a feature-level fusion strategy. The iTCep achieved high predictive performance with AUC up to 0.96 on the testing dataset and above 0.86 on independent datasets, presenting better prediction performance compared with other predictors. Our results provided strong evidence that model iTCep can be a reliable and robust method for predicting TCR binding specificities of given antigen peptides. One can access the iTCep through a user-friendly web server at http://biostatistics.online/iTCep/, which supports prediction modes of peptide-TCR pairs and peptide-only. A stand-alone software program for T cell epitope prediction is also available for convenient installing at https://github.com/kbvstmd/iTCep/.https://www.frontiersin.org/articles/10.3389/fgene.2023.1141535/fulliTCepT cell epitopespeptide-TCR interactionimmunotherapydeep learning modeling |
| spellingShingle | Yu Zhang Yu Zhang Xingxing Jian Xingxing Jian Linfeng Xu Linfeng Xu Jingjing Zhao Manman Lu Yong Lin Lu Xie iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features Frontiers in Genetics iTCep T cell epitopes peptide-TCR interaction immunotherapy deep learning modeling |
| title | iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features |
| title_full | iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features |
| title_fullStr | iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features |
| title_full_unstemmed | iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features |
| title_short | iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features |
| title_sort | itcep a deep learning framework for identification of t cell epitopes by harnessing fusion features |
| topic | iTCep T cell epitopes peptide-TCR interaction immunotherapy deep learning modeling |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1141535/full |
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