Table_1_Contribution of T Cell Receptor Alpha and Beta CDR3, MHC Typing, V and J Genes to Peptide Binding Prediction.docx

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.

引言
预测T细胞受体(T Cell Receptors, TCR)与MHC肽复合物(MHC-peptide complexes, pMHCs)的结合特异性，对于基于免疫组库的生物标志物开发至关重要。该结合亲和力可能受TCR、肽段以及MHC等位基因的不同组分影响。历史上，TCR-肽段结合预测中常用的核心元素为β链的互补决定区3(Complementarity Determining Region 3, CDR3)。但近期有研究提出，α链以及其他V基因CDR等组分同样对结合过程具有贡献。本研究采用一款高精度的新型基于深度学习的TCR-肽段结合预测模型，以评估各组分对结合的贡献。
方法
我们此前开发了ERGO-I（全称为pEptide tcR matchinG predictiOn），这是一款基于序列、采用自然语言处理(Natural Language Processing, NLP)方法的T细胞受体(TCR)-肽段结合预测模型。我们对其进行改进，新增了α链CDR3区段、MHC分型、V及J基因以及T细胞类型(CD4+或CD8+)等特征，得到ERGO-II，并借此评估各组分在预测任务中的贡献度。
结果与讨论
ERGO-II首次实现了针对未见肽段的高精度TCR-肽段结合预测。针对多数测试肽段以及所有结合预测精度指标而言，贡献度最高的组分均为β链CDR3序列，其次依次为β链V、J基因以及α链，MHC等位基因的贡献度最低。研究团队已将ERGO-II部署为网页服务器，访问地址为http://tcr2.cs.biu.ac.il/，同时提供独立开源代码，仓库地址为https://github.com/IdoSpringer/ERGO-II。