Contribution of T Cell Receptor Alpha and Beta CDR3, MHC Typing, V and J Genes to Peptide Binding Prediction
IntroductionPredicting the binding specificity of T Cell Receptors (TCR) to MHC-peptide complexes (pMHCs) is essential for the development of repertoire-based biomarkers. This affinity may be affected by different components of the TCR, the peptide, and the MHC allele. Historically, the main element...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-04-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2021.664514/full |
| _version_ | 1818716084262404096 |
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| author | Ido Springer Nili Tickotsky Yoram Louzoun |
| author_facet | Ido Springer Nili Tickotsky Yoram Louzoun |
| author_sort | Ido Springer |
| collection | DOAJ |
| description | IntroductionPredicting the binding specificity of T Cell Receptors (TCR) to MHC-peptide complexes (pMHCs) is essential for the development of repertoire-based biomarkers. This affinity may be affected by different components of the TCR, the peptide, and the MHC allele. Historically, the main element used in TCR-peptide binding prediction was the Complementarity Determining Region 3 (CDR3) of the beta chain. However, recently the contribution of other components, such as the alpha chain and the other V gene CDRs has been suggested. We use a highly accurate novel deep learning-based TCR-peptide binding predictor to assess the contribution of each component to the binding.MethodsWe have previously developed ERGO-I (pEptide tcR matchinG predictiOn), a sequence-based T-cell receptor (TCR)-peptide binding predictor that employs natural language processing (NLP) -based methods. We improved it to create ERGO-II by adding the CDR3 alpha segment, the MHC typing, V and J genes, and T cell type (CD4+ or CD8+) as to the predictor. We then estimate the contribution of each component to the prediction.Results and DiscussionERGO-II provides for the first time high accuracy prediction of TCR-peptide for previously unseen peptides. For most tested peptides and all measures of binding prediction accuracy, the main contribution was from the beta chain CDR3 sequence, followed by the beta chain V and J and the alpha chain, in that order. The MHC allele was the least contributing component. ERGO-II is accessible as a webserver at http://tcr2.cs.biu.ac.il/ and as a standalone code at https://github.com/IdoSpringer/ERGO-II. |
| first_indexed | 2024-12-17T19:13:38Z |
| format | Article |
| id | doaj.art-cd05af4dcb5c40868fb9260b2550efc2 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-12-17T19:13:38Z |
| publishDate | 2021-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-cd05af4dcb5c40868fb9260b2550efc22022-12-21T21:35:48ZengFrontiers Media S.A.Frontiers in Immunology1664-32242021-04-011210.3389/fimmu.2021.664514664514Contribution of T Cell Receptor Alpha and Beta CDR3, MHC Typing, V and J Genes to Peptide Binding PredictionIdo Springer0Nili Tickotsky1Yoram Louzoun2Department of Mathematics, Bar-Ilan University, Ramat Gan, IsraelFaculty of Life Science, Bar-Ilan University, Ramat Gan, IsraelDepartment of Mathematics, Bar-Ilan University, Ramat Gan, IsraelIntroductionPredicting the binding specificity of T Cell Receptors (TCR) to MHC-peptide complexes (pMHCs) is essential for the development of repertoire-based biomarkers. This affinity may be affected by different components of the TCR, the peptide, and the MHC allele. Historically, the main element used in TCR-peptide binding prediction was the Complementarity Determining Region 3 (CDR3) of the beta chain. However, recently the contribution of other components, such as the alpha chain and the other V gene CDRs has been suggested. We use a highly accurate novel deep learning-based TCR-peptide binding predictor to assess the contribution of each component to the binding.MethodsWe have previously developed ERGO-I (pEptide tcR matchinG predictiOn), a sequence-based T-cell receptor (TCR)-peptide binding predictor that employs natural language processing (NLP) -based methods. We improved it to create ERGO-II by adding the CDR3 alpha segment, the MHC typing, V and J genes, and T cell type (CD4+ or CD8+) as to the predictor. We then estimate the contribution of each component to the prediction.Results and DiscussionERGO-II provides for the first time high accuracy prediction of TCR-peptide for previously unseen peptides. For most tested peptides and all measures of binding prediction accuracy, the main contribution was from the beta chain CDR3 sequence, followed by the beta chain V and J and the alpha chain, in that order. The MHC allele was the least contributing component. ERGO-II is accessible as a webserver at http://tcr2.cs.biu.ac.il/ and as a standalone code at https://github.com/IdoSpringer/ERGO-II.https://www.frontiersin.org/articles/10.3389/fimmu.2021.664514/fullTCR - T cell receptorTCR repertoire analysispeptide bindingepitope specificitymachine learningdeep learning |
| spellingShingle | Ido Springer Nili Tickotsky Yoram Louzoun Contribution of T Cell Receptor Alpha and Beta CDR3, MHC Typing, V and J Genes to Peptide Binding Prediction Frontiers in Immunology TCR - T cell receptor TCR repertoire analysis peptide binding epitope specificity machine learning deep learning |
| title | Contribution of T Cell Receptor Alpha and Beta CDR3, MHC Typing, V and J Genes to Peptide Binding Prediction |
| title_full | Contribution of T Cell Receptor Alpha and Beta CDR3, MHC Typing, V and J Genes to Peptide Binding Prediction |
| title_fullStr | Contribution of T Cell Receptor Alpha and Beta CDR3, MHC Typing, V and J Genes to Peptide Binding Prediction |
| title_full_unstemmed | Contribution of T Cell Receptor Alpha and Beta CDR3, MHC Typing, V and J Genes to Peptide Binding Prediction |
| title_short | Contribution of T Cell Receptor Alpha and Beta CDR3, MHC Typing, V and J Genes to Peptide Binding Prediction |
| title_sort | contribution of t cell receptor alpha and beta cdr3 mhc typing v and j genes to peptide binding prediction |
| topic | TCR - T cell receptor TCR repertoire analysis peptide binding epitope specificity machine learning deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2021.664514/full |
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