Le Hoa Nhu, de Freitas Martiela Vaz, Antunes Dinler Amaral
University of Houston, Departments of Biology and Biochemistry, Houston, 77204, TX, USA.
Comput Struct Biotechnol J. 2024 Jul 1;23:2938-2948. doi: 10.1016/j.csbj.2024.06.028. eCollection 2024 Dec.
Cellular immunity relies on the ability of a T-cell receptor (TCR) to recognize a peptide (p) presented by a class I major histocompatibility complex (MHC) receptor on the surface of a cell. The TCR-peptide-MHC (TCRpMHC) interaction is a crucial step in activating T-cells, and the structural characteristics of these molecules play a significant role in determining the specificity and affinity of this interaction. Hence, obtaining 3D structures of TCRpMHC complexes offers valuable insights into various aspects of cellular immunity and can facilitate the development of T-cell-based immunotherapies. Here, we aimed to compare three popular web servers for modeling the structures of TCRpMHC complexes, namely ImmuneScape (IS), TCRpMHCmodels, and TCRmodel2, to examine their strengths and limitations. Each method employs a different modeling strategy, including docking, homology modeling, and deep learning. The accuracy of each method was evaluated by reproducing the 3D structures of a dataset of 87 TCRpMHC complexes with experimentally determined crystal structures available on the Protein Data Bank (PDB). All selected structures were limited to human MHC alleles, presenting a diverse set of peptide ligands. A detailed analysis of produced models was conducted using multiple metrics, including Root Mean Square Deviation (RMSD) and standardized assessments from CAPRI and DockQ. Special attention was given to the complementarity-determining region (CDR) loops of the TCRs and to the peptide ligands, which define most of the unique features and specificity of a given TCRpMHC interaction. Our study provides an optimistic view of the current state-of-the-art for TCRpMHC modeling but highlights some remaining challenges that must be addressed in order to support the future application of these tools for TCR engineering and computer-aided design of TCR-based immunotherapies.
细胞免疫依赖于T细胞受体（TCR）识别细胞表面I类主要组织相容性复合体（MHC）受体呈递的肽段（p）的能力。TCR-肽-MHC（TCRpMHC）相互作用是激活T细胞的关键步骤，这些分子的结构特征在决定这种相互作用的特异性和亲和力方面起着重要作用。因此，获得TCRpMHC复合物的三维结构有助于深入了解细胞免疫的各个方面，并促进基于T细胞的免疫疗法的开发。在此，我们旨在比较三种用于模拟TCRpMHC复合物结构的流行网络服务器，即ImmuneScape（IS）、TCRpMHCmodels和TCRmodel2，以研究它们的优势和局限性。每种方法采用不同的建模策略，包括对接、同源建模和深度学习。通过重现蛋白质数据库（PDB）上有实验确定晶体结构的87个TCRpMHC复合物数据集的三维结构，评估了每种方法的准确性。所有选定的结构均限于人类MHC等位基因，呈现出多种肽配体。使用多种指标对生成的模型进行了详细分析，包括均方根偏差（RMSD）以及来自CAPRI和DockQ的标准化评估。特别关注了TCR的互补决定区（CDR）环和肽配体，它们定义了给定TCRpMHC相互作用的大部分独特特征和特异性。我们的研究对当前TCRpMHC建模的技术水平给出了乐观的看法，但也强调了一些仍需解决的挑战，以便支持这些工具未来在TCR工程和基于TCR的免疫疗法的计算机辅助设计中的应用。
