High Resolution Modeling and Design of T-Cell Receptors

T 细胞受体的高分辨率建模和设计

• 批准号: 9759968
• 负责人: Brian G. Pierce
• 金额: $ 32.18万
• 依托单位: UNIV OF MARYLAND, COLLEGE PARK
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9759968
• 关键词: Address; Affinity; Algorithm Design; Algorithms; Antibodies; Antigens; Area; Behavior; Benchmarking; Binding; Biophysics; Code; Collaborations; Complementarity Determining Regions; Complex; Computational Biology; Data; Data Set; Development; Disease; Docking; Engineering; Epitopes; Exhibits; Goals; Human; Immune; Immune response; Immunity; Immunologic Receptors; Immunology; Individual; Informatics; Laboratories; Libraries; Light; Lighting; Machine Learning; Major Histocompatibility Complex; Measurement; Mediating; Medical; Methods; Modeling; Molecular Conformation; Motion; Nuclear Magnetic Resonance; Peptides; Performance; Protein Engineering; Protocols documentation; Receptor Cell; Resolution; Roentgen Rays; Sampling; Specificity; Structural Models; Structure; System; T-Cell Receptor; Testing; Therapeutic; Training; Update; Validation; Virus; Virus Diseases; Work; X-Ray Crystallography; antigen binding; antigen-specific T cells; base; clinically relevant; deep sequencing; design; engineered T cells; experimental study; flexibility; immunoglobulin structure; improved; interest; knowledge base; model design; molecular recognition; mutant; novel; receptor; response; tool

Accurate modeling of the structure and recognition of adaptive immune receptors is a major challenge in computational biology. Despite a shared immunoglobulin structural framework, highly variable antigen binding loop sequences and structures, with intrinsic dynamics and binding conformational changes, are often not accurately represented or correctly modeled using current algorithms. There is an even greater need to address this challenge due to the rapidly growing field of immune sequencing, which often results in thousands of sequences of antigen-specific immune receptors from the repertoire of a single individual per experiment. In the absence of reliable modeling tools, the observed shared sequence motifs and areas of divergence lack a structural and mechanistic explanation, given that experimental structural characterization is not practical or feasible for more than a handful of molecules. The focus of this application is on T cell receptors (TCRs), which recognize antigenic peptides by the major histocompatibility complex (MHC), leading to the cellular immune response. We will develop advanced modeling and design algorithms to address the challenges of flexible loop modeling through informatics and knowledge-based developments to help unravel their recognition code. This will entail the development of algorithms to reliably model TCR structures from sequence (Aim 1), model TCR recognition of peptide-MHCs through docking (Aim 2), and design TCR recognition through loop engineering (Aim 3). These Aims will be accomplished through validation against existing experimental structural and affinity data, as well as close partnership with experimental laboratories that will provide sequence, structural, dynamic, and binding measurements of TCRs, and validate affinity and structure of designed receptors. Collectively, these developments will allow the illumination of the mechanistics underpinning recognition by specific and repertoire-level TCRs from sequence, improved loop modeling and docking algorithms, and the capability to effectively control and engineer TCR recognition through structure-based design.

适应性免疫受体的结构和识别的准确建模是免疫领域的主要挑战 计算生物学。尽管有共同的免疫球蛋白结构框架，但抗原结合高度可变 具有内在动力学和结合构象变化的环序列和结构通常不是 使用当前算法准确表示或正确建模。更需要 由于免疫测序领域的快速发展，通常会导致数千 每个实验中单个个体的抗原特异性免疫受体序列。在 由于缺乏可靠的建模工具，观察到的共享序列基序和分歧区域缺乏 结构和机械解释，考虑到实验结构表征不切实际或 对于多个分子来说都是可行的。该应用的重点是 T 细胞受体 (TCR)，它 通过主要组织相容性复合物 (MHC) 识别抗原肽，从而产生细胞免疫 回复。我们将开发先进的建模和设计算法来应对柔性循环的挑战 通过信息学和基于知识的开发进行建模，以帮助解开他们的识别代码。这 将需要开发算法来根据序列可靠地模拟 TCR 结构（目标 1）、模型 TCR 通过对接识别肽-MHC（目标2），并通过环工程设计TCR识别 （目标 3）。这些目标将通过验证现有的实验结构和 亲和力数据，以及与实验实验室的密切合作，提供序列、结构、 TCR 的动态和结合测量，并验证设计受体的亲和力和结构。 总的来说，这些进展将阐明支撑认识的机制 来自序列的特定和库级 TCR、改进的循环建模和对接算法以及 通过基于结构的设计有效控制和设计 TCR 识别的能力。