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: 10330807
• 负责人: Brian G. Pierce
• 金额: $ 20.87万
• 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/10330807
• 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; Five-Year Plans; 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; interest; knowledge base; learning strategy; machine learning method; model design; 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.

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