High resolution modeling and design of immune recognition

免疫识别的高分辨率建模和设计

• 批准号: 10543798
• 负责人: Brian G. Pierce
• 金额: $ 34.41万
• 依托单位: UNIV OF MARYLAND, COLLEGE PARK
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10543798
• 关键词: Address; Affinity; Algorithms; Antibodies; Antibody Affinity; Antibody Specificity; Antigen Targeting; Antigens; Area; Benchmarking; Code; Collaborations; Communities; Complex; Computational Biology; Data; Data Set; Databases; Disease; Docking; Immune; Immune response; Immunoglobulins; Immunologic Receptors; Immunological Models; Laboratories; Lead; Medical; Methods; Modeling; Molecular Conformation; Peptide/MHC Complex; Resolution; Specificity; Structure; T-Cell Receptor; T-cell diversity; Therapeutic; Update; Virus; Work; antigen binding; deep learning; design; flexibility; improved; interest; knowledge base; learning strategy; machine learning method; prediction algorithm; tool; web server

Project Summary: Accurate modeling of immune receptors and their recognition is a major challenge in computational biology, of direct relevance to many diseases and therapeutics. While they share common heterodimeric immunoglobulin folds, the immense sequence diversities of T cell receptors (TCRs) and antibodies lead to an astounding range of antigen binding modes and specificities. Current docking approaches are largely incapable of producing near-native models of these complexes in the set of top-ranked predictions, and conformational flexibility of TCR and antibody loops pose a major barrier to predictive algorithms. My laboratory has had a longstanding interest in developing and applying algorithms to better model and design TCRs and antibodies. We recently developed an algorithm and web server to model TCRs from sequence (TCRmodel), a database of TCR structures and sequences (TCR3d), and we have assembled an updated docking benchmark, which is being used to develop improvements to our TCR docking algorithm. We have also recently developed an updated antibody-antigen docking and affinity benchmark, which more than doubles the size of the previous benchmark release; we have performed docking and affinity prediction assessment on these cases, giving us a rich dataset of models and scores. During the next five years, we plan to expand and capitalize on these datasets to develop advanced knowledge-based tools and algorithms, including geometric deep learning methods, to address major challenges in this area: reliable modeling of CDR3 loop structures, accurate predictive antibody- antigen and TCR-peptide-MHC docking, and design of TCR and antibody targeting. This will result in the ability to model TCR and antibody interaction structures from sequence, precise control of TCR and antibody affinity and specificity, and the design of new interactions to target antigens of interest. We will release our methods and results to the community as web servers, databases, and code. This work will be enhanced by collaborations with leading laboratories, through which we will have access to new experimental structural, dynamic, and affinity data which will be used to develop, apply, and validate our algorithms.

项目概要： 免疫受体的准确建模及其识别是计算生物学的主要挑战 与许多疾病和治疗直接相关。虽然它们有共同的异二聚体免疫球蛋白 T 细胞受体 (TCR) 和抗体的巨大序列多样性导致了令人震惊的范围 抗原结合模式和特异性。目前的对接方法基本上无法产生 这些复合物在排名靠前的预测集中的近乎天然模型，以及构象灵活性 TCR 和抗体环对预测算法构成了主要障碍。我的实验室有着悠久的历史 对开发和应用算法来更好地建模和设计 TCR 和抗体感兴趣。我们最近 开发了一种算法和网络服务器来根据序列对 TCR 进行建模（TCRmodel），TCR 数据库 结构和序列（TCR3d），我们已经组装了一个更新的对接基准，正在 用于开发我们的 TCR 对接算法的改进。我们最近还开发了一个更新的 抗体-抗原对接和亲和基准，其大小是之前基准的两倍多 发布;我们对这些案例进行了对接和亲和力预测评估，为我们提供了丰富的参考资料 模型和分数的数据集。在接下来的五年中，我们计划扩展和利用这些数据集 开发先进的基于知识的工具和算法，包括几何深度学习方法， 解决该领域的主要挑战：CDR3环结构的可靠建模、准确的预测抗体- 抗原与TCR-肽-MHC对接，以及TCR和抗体靶向设计。这将导致能力 从序列中模拟 TCR 和抗体相互作用结构，精​​确控制 TCR 和抗体亲和力 和特异性，以及针对感兴趣的目标抗原的新相互作用的设计。我们将发布我们的方法 并将结果以 Web 服务器、数据库和代码的形式发布给社区。这项工作将得到加强 与领先实验室的合作，通过这些我们将获得新的实验结构， 动态和亲和力数据将用于开发、应用和验证我们的算法。