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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10203616/ https://www.ncbi.nlm.nih.gov/pubmed/37229205 http://dx.doi.org/10.3389/fgene.2023.1141535 |
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author | Zhang, Yu Jian, Xingxing Xu, Linfeng Zhao, Jingjing Lu, Manman Lin, Yong Xie, Lu |
author_facet | Zhang, Yu Jian, Xingxing Xu, Linfeng Zhao, Jingjing Lu, Manman Lin, Yong Xie, Lu |
author_sort | Zhang, Yu |
collection | PubMed |
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/. |
format | Online Article Text |
id | pubmed-10203616 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102036162023-05-24 iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features Zhang, Yu Jian, Xingxing Xu, Linfeng Zhao, Jingjing Lu, Manman Lin, Yong Xie, Lu Front Genet Genetics 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/. Frontiers Media S.A. 2023-05-09 /pmc/articles/PMC10203616/ /pubmed/37229205 http://dx.doi.org/10.3389/fgene.2023.1141535 Text en Copyright © 2023 Zhang, Jian, Xu, Zhao, Lu, Lin and Xie. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Zhang, Yu Jian, Xingxing Xu, Linfeng Zhao, Jingjing Lu, Manman Lin, Yong Xie, Lu iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features |
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 | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10203616/ https://www.ncbi.nlm.nih.gov/pubmed/37229205 http://dx.doi.org/10.3389/fgene.2023.1141535 |
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