Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, United States.
Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, United States.
Mol Immunol. 2021 Nov;139:76-86. doi: 10.1016/j.molimm.2021.07.020. Epub 2021 Aug 26.
The activation of T cells is triggered by the interactions of T cell receptors (TCRs) with their epitopes, which are peptides presented by major histocompatibility complex (MHC) on the surfaces of antigen presenting cells (APC). While each TCR can only recognize a specific subset from a large repertoire of peptide-MHC (pMHC) complexes, it is very often that peptides in this subset share little sequence similarity. This is known as the specificity and cross-reactivity of T cells, respectively. The binding affinities between different types of TCRs and pMHC are the major driving force to shape this specificity and cross-reactivity in T cell recognition. The binding affinities, furthermore, are determined by the sequence and structural properties at the interfaces between TCRs and pMHC. Fortunately, a wealth of data on binding and structures of TCR-pMHC interactions becomes publicly accessible in online resources, which offers us the opportunity to develop a random forest classifier for predicting the binding affinities between TCR and pMHC based on the structure of their complexes. Specifically, the structure and sequence of a given complex were projected onto a high-dimensional feature space as the input of the classifier, which was then trained by a large-scale benchmark dataset. Based on the cross-validation results, we found that our machine learning model can predict if the binding affinity of a given TCR-pMHC complex is stronger or weaker than a predefined threshold with an overall accuracy approximately around 75 %. The significance of our prediction was estimated by statistical analysis. Moreover, more than 60 % of binding affinities in the ATLAS database can be successfully classified into groups within the range of 2 kcal/mol. Additionally, we show that TCR-pMHC complexes with strong binding affinity prefer hydrophobic interactions between amino acids with large aromatic rings instead of electrostatic interactions. Our results therefore provide insights to design engineered TCRs which enhance the specificity for their targeted epitopes. Taken together, this method can serve as a useful addition to a suite of existing approaches which study binding between TCR and pMHC.
T 细胞的激活是由 T 细胞受体 (TCR) 与它们的表位相互作用触发的，这些表位是主要组织相容性复合体 (MHC) 在抗原呈递细胞 (APC) 表面呈现的肽。虽然每个 TCR 只能识别来自大量肽-MHC (pMHC) 复合物的特定亚群，但该亚群中的肽通常很少有序列相似性。这分别被称为 T 细胞的特异性和交叉反应性。不同类型的 TCR 和 pMHC 之间的结合亲和力是塑造 T 细胞识别的特异性和交叉反应性的主要驱动力。此外，结合亲和力取决于 TCR 和 pMHC 之间界面的序列和结构特性。幸运的是，大量关于 TCR-pMHC 相互作用的结合和结构数据在在线资源中变得可公开访问，这为我们提供了机会，基于它们复合物的结构，开发一种随机森林分类器来预测 TCR 和 pMHC 之间的结合亲和力。具体来说，给定复合物的结构和序列被投影到高维特征空间作为分类器的输入，然后通过大规模基准数据集进行训练。基于交叉验证结果，我们发现我们的机器学习模型可以预测给定的 TCR-pMHC 复合物的结合亲和力是否强于或弱于预定阈值，总体准确率约为 75%。我们的预测意义通过统计分析进行估计。此外，ATLAS 数据库中超过 60%的结合亲和力可以成功地分为 2 kcal/mol 范围内的组。此外，我们还表明，具有强结合亲和力的 TCR-pMHC 复合物更喜欢具有大芳香环的氨基酸之间的疏水相互作用，而不是静电相互作用。因此，我们的结果为设计增强其靶向表位特异性的工程化 TCR 提供了思路。总的来说，这种方法可以作为研究 TCR 和 pMHC 之间结合的现有方法套件的有用补充。
