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Prediction of the 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：
8546392
负责人：
JEFFREY J GRAY
金额：
$ 29.9万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2006
资助国家：
美国
起止时间：
2006-09-01 至 2016-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8546392
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 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（由申请方提供）：抗体在免疫系统识别外来入侵者中发挥关键作用。由于其优异的亲和性和特异性，它们还被开发作为治疗分子和用于传感和组装的生物技术组件。抗体与其抗原复合物的结构可以使人们了解生物学特性。现象或药物和疾病机制。然而，确定抗体和抗体-抗原复合物的结构可能是困难、耗时和昂贵的。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。计算方法特别重要，因为人类患者中的抗体库太大，无法通过实验进行完整的结构表征。先前的工作已经分离出最关键的挑战：人类库中的大多数抗体具有比使用当前环方法可预测的更长的高变CDR H3环;主链构象不确定性和灵活性混淆当前对接方法;并且没有当前方法可以从结构定量预测抗体-抗原结合亲和力。因此，该项目的前三个目标是（1）开发新方法使用统计学预测长CDR H3环的结构以鉴定可能的转角，（2）使用具有构象网的扩展系综方法开发灵活的骨架对接程序，以及（3）开发使用改进的静电处理定量预测蛋白质-蛋白质结合亲和力的方法。最后，第四个目标将是（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。将预测从卵清蛋白（食物过敏原）和酶C1（自身免疫性疾病和移植耐受的治疗靶点）免疫小鼠的骨髓浆细胞确定的抗体库的结构。最终，这些研究将产生对免疫学，分子识别和蛋白质-蛋白质界面和疫苗设计的见解。