Decoding the interactions between T cell receptors and peptide-MHC

项目总结 T细胞受体(TCR)对同源肽-主要组织相容性复合体(PMHC)的识别是 适应性免疫识别。这种互动的某些特征是众所周知的，包括许多规则 与MHC结合的控制肽。然而，我们对三元TCR：pMHC络合物进行建模的能力仍然有限 主要原因有三：(1)数据可用性；(2)绑定；(3)交叉反应。通过以下方式阐明规则 由TCR：pMHC接口操作，这些努力将解决核心问题 适应性免疫识别，具有重要的理论和实践意义，包括 具有特定特异性的新型受体的正向设计，对最近涌入的TCR的“解码” 对特定抗原靶标的测序数据，以及对 曲目。以前，我们开发了新的方法，为构建 预测TCR特异性的各个方面的算法(1)，包括我们称为TCRdist的算法-简单 和有效距离度量来比较TCR序列。TCRdist可用于聚集特异性抗原 TCR序列，并可以结合到基于距离的分类器中，能够正确地分配 以前未观察到的TCR具有强大的敏感性和特异性。加在一起， 这些实验的结果和TCRdist算法的总体成功提供了令人信服的证据 对于这项提议的中心前提：假设有足够数量的经实验验证的表位- 特定的TCR序列，根据ITS序列可以预测TCR的表位特异性； 此外，表位特异性TCR序列数据的生成与结构上的结合 知情的计算分析，为建立TCR的预测模型提供了路线图：pMHC 互动。虽然我们在这方面取得了重大进展，但仍然存在的最大障碍是 曲目中明显存在广泛的交叉反应。为了充分阐明生物多样性的复杂网络 TCR和PMHC之间的相互作用，那么我们必须解决的问题是：不同的TCR看到什么 相同的pMHC有共同之处吗？同样的TCR看到的不同的PMHC在 普通吗？这些研究的最终结果，除了它们直接的生物学应用外，将是 协助开发下一代分析工具，用于TCR的建模：pMHC相互作用， 最终目标是为这个基本接口提供一个真正的“解码器”。

PROJECT SUMMARY T cell receptor (TCR) recognition of a cognate peptide-major histocompatibility complex (pMHC) is central to adaptive immune recognition. Certain features of this interaction are well-understood, including many of the rules governing peptide binding to MHC. However, our ability to model the ternary TCR:pMHC complex remains limited for three primary reasons: (1) Data availability; (2) Binding; and (3) Cross-reactivity. In elucidating the rules by which the TCR:pMHC interface operates, these efforts stand to address fundamental questions at the heart of adaptive immune recognition, with important theoretical and practical implications that include the potential for the forward design of novel receptors with selected specificities, the “decoding” of the recent influx of TCR sequencing data for specific antigenic targets, and an understanding of the cross-reactive potential of the repertoire. Previously, we developed novel approaches that provided training data for the construction of algorithms that predict various aspects of TCR specificity (1), including an algorithm we call TCRdist - a simple and effective distance measure to compare TCR sequences. TCRdist can be used to cluster antigen-specific TCR sequences and can be incorporated into a distance-based classifier capable of correctly assigning previously unobserved TCRs to characterized repertoires with robust sensitivity and specificity. Taken together, the results of these experiments and the general success of the TCRdist algorithm provide compelling evidence for the central premises of this proposal: Given a sufficient number of experimentally verified epitope- specific TCR sequences, the epitope specificity of a TCR can be predicted from its sequence; furthermore, the generation of epitope-specific TCR sequence data, in combination with structurally informed computational analysis, provides a roadmap for building a predictive model of the TCR:pMHC interaction. While we have made significant progress in this line of inquiry, the largest remaining hurdle is the apparent broad cross-reactivity within the repertoire. In order to fully elucidate the complex network of interactions among TCRs and pMHCs, the questions we must address then are: what do diverse TCRs that see the same pMHC have in common? And what do diverse pMHCs that are seen by the same TCRs have in common? The ultimate consequence of these studies, beyond their immediate biological applications, will be to assist in the development of the next generation of analytical tools for the modeling of TCR:pMHC interaction, leading to the ultimate goal of a true “decoder” for this essential interface.