Slone Jared K, Conev Anja, Rigo Mauricio M, Reuben Alexandre, Kavraki Lydia E
IEEE Trans Comput Biol Bioinform. 2025 Jan-Feb;22(1):171-179. doi: 10.1109/TCBBIO.2024.3504235.
The mapping of T-cell-receptors (TCRs) to their cognate peptides is crucial to improving cancer immunotherapy. Numerous computational methods and machine learning tools have been proposed to aid in the task. Yet, accurately constructing this map computationally remains a difficult problem. Most prior work has sought to predict TCR-peptide-MHC (TCR-pMHC) binding specificity by analyzing the amino acid sequences of the TCRs and peptides. However, recent advancements in crystallography, cryo-EM, and in silico protein modeling have provided researchers with the necessary data to analyze the 3D structures of TCRs, peptides, and MHCs. Current research suggests that information contained in the 3D structure of the TCRs and pMHCs can explain instances of TCR specificity that are not explained by sequence alone. As protein structure data continues to become more accurate and easier to obtain, structure-based methodologies for predicting TCR-pMHC binding will become increasingly important. We present STAG, a novel graph-based machine learning architecture for predicting TCR-pMHC binding specificity using 3D structure data. We show that STAG achieves comparable or better performance than existing methods while utilizing only spatial and physicochemical features from modeled protein structures.
将T细胞受体（TCR）与其同源肽进行匹配对于改善癌症免疫疗法至关重要。人们已经提出了许多计算方法和机器学习工具来辅助这项任务。然而，通过计算准确构建这一匹配图谱仍然是一个难题。大多数先前的工作试图通过分析TCR和肽的氨基酸序列来预测TCR-肽-MHC（TCR-pMHC）结合特异性。然而，晶体学、冷冻电镜和计算机模拟蛋白质建模方面的最新进展为研究人员提供了分析TCR、肽和MHC三维结构所需的数据。当前研究表明，TCR和pMHC三维结构中包含的信息可以解释仅靠序列无法解释的TCR特异性情况。随着蛋白质结构数据不断变得更加准确且易于获取，基于结构的预测TCR-pMHC结合的方法将变得越来越重要。我们提出了STAG，这是一种新颖的基于图的机器学习架构，用于使用三维结构数据预测TCR-pMHC结合特异性。我们表明，STAG在仅利用建模蛋白质结构的空间和物理化学特征的情况下，实现了与现有方法相当或更好的性能。
