Prediction of Structure of Therapeutic Antibodies with their Antigens

治疗性抗体结构及其抗原的预测

基本信息

批准号：
8731999
负责人：
JEFFREY J GRAY
金额：
$ 2.11万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-09-01 至 2016-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8731999
关键词：
Address Affinity Age Algorithms Antibodies Antibody Binding Sites Antibody Repertoire Antigen Targeting Antigen-Antibody Complex Antigens Autoimmune Diseases Benchmarking Binding Biological Biological Models Biological Phenomena Bone Marrow Charge Complement component C1s Complementarity Determining Regions Complex Data Disease Docking Electrostatics Enzymes Epitopes Food Hypersensitivity Freedom Genomics Goals Graft Rejection High-Throughput Nucleotide Sequencing Homology Modeling Human Immune system Immunology Individual Inflammatory Internet Knowledge Light Maps Medical Methods Modeling Molecular Conformation Movement Mus Mutation Ovalbumin Patients Pharmaceutical Preparations Plasma Cells Play Point Mutation Positioning Attribute Protein Binding Proteins Research Resolution Resources Role Sampling Side Specificity Structural Models Structure System Testing Therapeutic Therapeutic antibodies Time Transplantation Uncertainty Vaccines Vertebral column Work antigen antibody binding complement system density design flexibility food allergen improved insight kinematics molecular recognition novel polyclonal antibody protein structure prediction research study statistics therapeutic development therapeutic target three dimensional structure vaccine development

项目摘要

DESCRIPTION (provided by applicant): Antibodies play a critical role in the immune system for recognition of foreign intruders. Because of their excellent affinity and specificity, they hav also been exploited as therapeutic molecules and biotechnological components for sensing and assembly. Structures of antibodies in complex with their antigens can yield insight into biological phenomena or drug and disease mechanisms. However, structures of antibodies and antibody-antigen complexes can be difficult, time consuming, and expensive to determine. The proposed research focuses on the computational prediction of the structure of antibodies and antibody-antigen complexes. Computational approaches are particularly important because the repertoire of antibodies in a human patient is far too large for complete structural characterization by experiment. Prior work has isolated the most critical challenges: most of the antibodies in the human repertoire have hypervariable CDR H3 loops longer than that which is predictable using current loop methods; backbone conformational uncertainty and flexibility confound current docking methods; and no current method can quantitatively predict antibody-antigen binding affinities from structure. Thus, the first three aims of the project are to (1) develop new methods to predict the structure of long CDR H3 loops using statistics to identify likely ¿ turns, (2) develop flexible backbone docking routines using an expanded ensemble approach with a conformational web, and (3) develop methods to quantitatively predict protein-protein binding affinity using improved electrostatics treatments. Finally, the fourth aim will be to (4) use existng and proposed methods to predict structures of antibodies and antibody-antigen complexes for entire polyclonal antibody repertoires. Structures will be predicted for antibody repertoires determined from bone marrow plasma cells of mice immunized against ovalbumin (a food allergen) and enzyme C1s (a therapeutic target for autoimmune diseases and transplant tolerance). Ultimately, these studies will yield insights into immunology, molecular recognition, and design of protein-protein interfaces and vaccines.

描述（应用程序提供）：抗体在识别外国入侵者的免疫系统中起关键作用。由于它们具有出色的亲和力和特异性，因此也被探讨为具有敏感性和组装的治疗分子和生物技术成分。与其抗原复杂的抗体结构可以洞悉生物学现象或药物和疾病机制。但是，抗体和抗体 - 抗原络合物的结构可能很困难，耗时且昂贵。拟议的研究重点是抗体和抗体 - 抗原复合物结构的计算预测。计算方法尤其重要，因为人类患者中抗体的曲目太大了，无法通过实验进行完整的结构表征。先前的工作隔离了最关键的挑战：人类曲目中的大多数抗体的CDR H3环比使用当前环路方法可预测的抗体更长。骨干构象不确定性和灵活性混淆了当前的对接方法；并且当前的方法无法定量预测结构的抗体 - 抗原结合亲和力。这是该项目的前三个目标是（1）开发新方法为了使用统计数据来预测长CDR H3回路的结构，以识别可能的转弯，（2）使用构型Web的扩展集合方法开发灵活的骨干对接例程，并且（3）开发方法以使用改进的静电处理来定量预测蛋白质 - 蛋白质结合亲和力。最后，第四个目标是（4）使用现有的和提出的方法来预测整个多克隆抗体库的抗体和抗体 - 抗原复合物的结构。将预测由针对卵蛋白（食物过敏原）和酶C1（自身免疫性疾病和移植耐受性的治疗靶标）免疫的小鼠的骨髓浆细胞确定的抗体。最终，这些研究将产生对免疫学，分子识别以及蛋白质蛋白界面和疫苗设计的见解。