Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia.

Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, Moscow, Russia.

The search for the relationships between CDR3 TCR sequences and epitopes or MHC types is a challenging task in modern immunology. We propose a new approach to develop the classification models of structure–activity relationships (SAR) using molecular fragment descriptors MNA (Multilevel Neighbourhoods of Atoms) to represent CDR3 TCR sequences and the naïve Bayes classifier algorithm. We have created the freely available TCR-Pred web application (http://way2drug.com/TCR-pred/) to predict the interactions between α chain CDR3 TCR sequences and 116 epitopes or 25 MHC types, as well as the interactions between β chain CDR3 TCR sequences and 202 epitopes or 28 MHC types. The TCR-Pred web application is based on the data (more 250 000 unique CDR3 TCR sequences) from VDJdb, McPAS-TCR, and IEDB databases and the proposed approach. The average AUC values of the prediction accuracy calculated using a 20-fold cross-validation procedure varies from 0.857 to 0.884. The created web application may be useful in studies related with T-cell profiling based on CDR3 TCR sequences. 中文翻译： 寻找 CDR3 TCR 序列与表位或 MHC 类型之间的关系是现代免疫学中的一项具有挑战性的任务。我们提出了一种新方法来开发构效关系（SAR）的分类模型，使用分子片段描述符 MNA（原子多级邻域）来表示 CDR3 TCR 序列和朴素贝叶斯分类器算法。我们创建了免费的 TCR-Pred 网络应用程序 (http://way2drug.com/TCR-pred/) 来预测 α 链 CDR3 TCR 序列与 116 个表位或 25 个 MHC 类型之间的相互作用，以及 β 链之间的相互作用链 CDR3 TCR 序列和 202 个表位或 28 个 MHC 类型。TCR-Pred Web 应用程序基于来自 VDJdb、McPAS-TCR 和 IEDB 数据库的数据（超过 250 000 个独特的 CDR3 TCR 序列）以及所提出的方法。使用 20 倍交叉验证程序计算的预测准确度的平均 AUC 值在 0.857 到 0.884 之间变化。创建的 Web 应用程序可能有助于基于 CDR3 TCR 序列的 T 细胞分析相关研究。