T cells have the ability to eliminate infected and cancer cells and play an essential role in cancer immunotherapy. T cell activation is elicited by the binding of the T cell receptor (TCR) to epitopes displayed on MHC molecules, and the TCR specificity is determined by the sequence of its α and β chains. Here, we collect and curate a dataset of 17,715 αβTCRs interacting with dozens of class I and class II epitopes. We use this curated data to develop MixTCRpred, an epitope-specific TCR-epitope interaction predictor. MixTCRpred accurately predicts TCRs recognizing several viral and cancer epitopes. MixTCRpred further provides a useful quality control tool for multiplexed single-cell TCR sequencing assays of epitope-specific T cells and pinpoints a substantial fraction of putative contaminants in public databases. Analysis of epitope-specific dual α T cells demonstrates that MixTCRpred can identify α chains mediating epitope recognition. Applying MixTCRpred to TCR repertoires from COVID-19 patients reveals enrichment of clonotypes predicted to bind an immunodominant SARS-CoV-2 epitope. Overall, MixTCRpred provides a robust tool to predict TCRs interacting with specific epitopes and interpret TCR-sequencing data from both bulk and epitope-specific T cells.

T 细胞具有消除感染细胞和癌细胞的能力，在癌症免疫治疗中发挥着重要作用。 T 细胞激活是由 T 细胞受体 (TCR) 与 MHC 分子上显示的表位结合引起的，TCR 特异性由其 α 和 β 链的序列决定。在这里，我们收集并整理了 17,715 个 αβTCR 的数据集，这些 αβTCR 与数十个 I 类和 II 类表位相互作用。我们使用这些精选数据开发 MixTCRpred，一种表位特异性 TCR-表位相互作用预测器。 MixTCRpred 准确预测可识别多种病毒和癌症表位的 TCR。 MixTCRpred 进一步为表位特异性 T 细胞的多重单细胞 TCR 测序分析提供了有用的质量控制工具，并查明公共数据库中大部分假定的污染物。表位特异性双 α T 细胞的分析表明 MixTCRpred 可以识别介导表位识别的 α 链。将 MixTCRpred 应用到来自 COVID-19 患者的 TCR 库中，揭示了预测与免疫显性 SARS-CoV-2 表位结合的克隆型的富集。总体而言，MixTCRpred 提供了一个强大的工具来预测 TCR 与特定表位相互作用，并解释来自批量 T 细胞和表位特异性 T 细胞的 TCR 测序数据。