Prediction of the Structure of Therapeutic Antibodies with their Antigens

说明(申请人提供)：抗体在识别外来入侵者的免疫系统中起着关键作用。由于它们良好的亲和力和特异性，它们还被用作治疗分子和用于传感和组装的生物技术组件。抗体及其抗原复合体的结构可以提供对生物学的洞察 现象或药物和疾病机制。然而，确定抗体和抗体-抗原复合体的结构可能是困难、耗时和昂贵的。建议的研究重点是抗体和抗体-抗原复合体的结构的计算预测。计算方法特别重要，因为人类患者体内的抗体库太大了，无法通过实验来完整地描述结构。以前的工作已经隔离了最关键的挑战：人类谱系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量CDR H3环；骨架构象的不确定性和灵活性混淆了当前的对接方法；并且没有当前的方法可以根据结构定量预测抗体与抗原的结合亲和力。因此，该项目的前三个目标是(1)开发新的方法 为了预测CDR H3长环的结构，使用统计学来确定可能的转折，(2)使用具有构象网络的扩展系综法开发灵活的骨架对接程序，以及(3)使用改进的静电处理开发定量预测蛋白质-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-抗原复合体的结构。将预测由卵清蛋白(一种食物过敏原)和酶C1s(自身免疫性疾病和移植耐受的治疗靶标)免疫的小鼠骨髓浆细胞确定的抗体库的结构。最终，这些研究将对免疫学、分子识别以及蛋白质-蛋白质界面和疫苗的设计产生深刻的影响。

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.