Department of Pharmacology and Pharmaceutical Sciences, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, USA.
Titus Family Department of Clinical Pharmacy, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, USA.
Methods Mol Biol. 2023;2673:273-287. doi: 10.1007/978-1-0716-3239-0_19.
Formation of major histocompatibility (MHC)-peptide-T cell receptor (TCR) complexes is central to initiation of an adaptive immune response. These complexes form through initial stabilization of the MHC fold via binding of a short peptide, and subsequent interaction of the TCR to form a ternary complex, with contacts made predominantly through the complementarity-determining region (CDR) loops of the TCR. Stimulation of an immune response is central to cancer immunotherapy. This approach depends on identification of the appropriate combinations of MHC molecules, peptides, and TCRs to elicit an antitumor immune response. This prediction is a current challenge in computational biochemistry. In this chapter, we introduce a predictive method that involves generation of multiple peptides and TCR CDR 3 loop conformations, solvation of these conformers in the context of the MHC-peptide-TCR ternary complex, extraction of parameters from the generated complexes, and use of an AI model to evaluate the potential for the assembled ternary complex to support an immune response.
主要组织相容性复合体(MHC)-肽-T 细胞受体(TCR)复合物的形成是启动适应性免疫反应的核心。这些复合物通过 MHC 折叠的初始稳定形成,通过结合短肽,随后 TCR 的相互作用形成三元复合物,主要通过 TCR 的互补决定区(CDR)环形成接触。免疫反应的刺激是癌症免疫治疗的核心。这种方法取决于识别适当的 MHC 分子、肽和 TCR 组合,以引发抗肿瘤免疫反应。这种预测是计算生物化学中的一个当前挑战。在本章中,我们介绍了一种预测方法,该方法涉及生成多种肽和 TCR CDR3 环构象,在 MHC-肽-TCR 三元复合物的背景下对这些构象进行溶剂化处理,从生成的复合物中提取参数,并使用人工智能模型评估组装的三元复合物支持免疫反应的潜力。