Milighetti Martina, Shawe-Taylor John, Chain Benny
Division of Infection and Immunity, University College London, London, United Kingdom.
Cancer Institute, University College London, London, United Kingdom.
Front Physiol. 2021 Sep 8;12:730908. doi: 10.3389/fphys.2021.730908. eCollection 2021.
The physical interaction between the T cell receptor (TCR) and its cognate antigen causes T cells to activate and participate in the immune response. Understanding this physical interaction is important in predicting TCR binding to a target epitope, as well as potential cross-reactivity. Here, we propose a way of collecting informative features of the binding interface from homology models of T cell receptor-peptide-major histocompatibility complex (TCR-pMHC) complexes. The information collected from these structures is sufficient to discriminate binding from non-binding TCR-pMHC pairs in multiple independent datasets. The classifier is limited by the number of crystal structures available for the homology modelling and by the size of the training set. However, the classifier shows comparable performance to sequence-based classifiers requiring much larger training sets.
T细胞受体（TCR）与其同源抗原之间的物理相互作用会促使T细胞激活并参与免疫反应。了解这种物理相互作用对于预测TCR与靶表位的结合以及潜在的交叉反应性至关重要。在此，我们提出了一种从T细胞受体-肽-主要组织相容性复合体（TCR-pMHC）复合体的同源模型中收集结合界面信息特征的方法。从这些结构中收集到的信息足以在多个独立数据集中区分结合型与非结合型TCR-pMHC对。该分类器受到可用于同源建模的晶体结构数量以及训练集大小的限制。然而，该分类器表现出与需要大得多的训练集的基于序列的分类器相当的性能。
