Decoding the interactions between T cell receptors and peptide-MHC

项目摘要 T细胞受体（TCR）对同源肽-主要组织相容性复合物（pMHC）的识别是免疫应答的核心。 适应性免疫识别这种相互作用的某些特征是很好理解的，包括许多规则 控制肽与MHC的结合。然而，我们对TCR：pMHC三元复合物的建模能力仍然有限 有三个主要原因：（1）数据可用性;（2）结合;和（3）交叉反应性。在阐明规则时， TCR：pMHC接口操作的，这些努力站在解决的核心基本问题， 适应性免疫识别，具有重要的理论和实践意义，包括潜在的 具有选定特异性的新型受体的正向设计，最近流入的TCR的“解码”， 特异性抗原靶点的测序数据，以及对交叉反应潜力的理解。 保留曲目。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-一个简单的 和有效的距离度量来比较TCR序列。TCRdist可用于将抗原特异性 TCR序列，并且可以并入能够正确分配TCR序列的基于距离的分类器中。 将先前未观察到的TCR转化为具有稳健灵敏度和特异性的特征库。综合起来看， 这些实验的结果和TCRdist算法的普遍成功提供了令人信服的证据 对于这个建议的中心前提：给定足够数量的实验验证的表位- 特异性TCR序列，TCR的表位特异性可从其序列预测; 此外，表位特异性TCR序列数据的产生，结合结构上的 知情的计算分析，为建立TCR：pMHC的预测模型提供了路线图 互动虽然我们在这方面的调查取得了重大进展，但最大的障碍是 库内明显的广泛交叉反应性。为了充分阐明复杂的网络， 为了了解TCR和pMHC之间的相互作用，我们必须解决的问题是： 有什么共同点吗不同的pMHCs被相同的TCR所看到， 普通吗这些研究的最终结果，除了直接的生物学应用之外，将是 协助开发下一代分析工具，用于TCR：pMHC相互作用的建模， 从而导致用于该基本接口的真正“解码器”的最终目标。

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.