Prediction of the Structure of Therapeutic Antibodies with their Antigens

用抗原预测治疗性抗体的结构 项目摘要 抗体在识别外来入侵者方面发挥着关键作用。由于其高亲和力和特异性， 已经被开发作为用于传感和组装的治疗分子和生物技术组件。 最近的高通量测序和纳米流体技术已经阐明了大组（103-104）的DNA序列。 幼稚和抗原暴露的抗体序列，现在可以确定一套完整的病毒 一个人所遇到的抗体除了他们的生物学，医学， 由于技术的重要性，关于抗体的广泛知识使其成为理想的模型系统， 研究蛋白质结合和识别。 研究蛋白质结合和识别的可靠工具包是完全解开邦蒂富尔的 抗体和抗原库中的信息。先前的工作证明了预测抗体的成功 结构，这个建议的重点是对接问题。虽然对接算法对于本地 搜索和小的构象变化，在搜索大的抗原以 鉴定表位和确定正确的结合方向，当存在骨架柔性时， 同源建模起始结构的不确定性。绑定引起的主干的核算 构象变化仍然是蛋白质-蛋白质对接领域的中心难点，主要是由于 取样限制。另一个挑战是许多病毒外壳和细菌蛋白是糖基化的。 聚糖是很好的水合和灵活的，这些修改通常被完全忽略对接 算法 准确预测抗体和抗体-抗原的结构是本研究的长期目标 复合物，使得它们可用于解码生物学机制和设计改进的治疗剂。 因此，当前项目的前两个目标是：（1）开发快速、积极、灵活的骨干网对接 方法，和（2）扩展对接以包括糖基化抗原。最后，第三个目标是（3）应用 抗体建模和对接，以确定用于乳糜泻和肺部疾病的生物标志物和治疗剂 高血压

Prediction of the Structure of Therapeutic Antibodies with their Antigens PROJECT SUMMARY Antibodies play a critical role for recognition of foreign intruders. Due to their high affinity and specificity, they have been exploited as therapeutic molecules and biotechnological components for sensing and assembly. Recent high-throughput sequencing and nanofluidics technologies have elucidated large sets (103–104) of naïve and antigen-exposed antibody sequences, and it is now possible to determine a complete set of viruses that an individual has encountered based on one’s antibodies. In addition to their biological, medical, and technological importance, the extensive knowledge about antibodies makes them an ideal model system for studying protein binding and recognition. A reliable toolkit to study protein binding and recognition is the missing link to fully unlock the bountiful information in antibody and antigen repertoires. Prior work demonstrated success in predicting antibody structures, and this proposal focuses on the docking problem. While docking algorithms are reliable for local searches and small conformational changes, significant challenges remain in searching large antigens to identify epitopes and in determining the correct binding orientation when there is backbone flexibility or uncertainty in the homology-modeled starting structures. Accounting for binding-induced backbone conformational changes remains the central difficulty in the protein–protein docking field, primarily due to sampling limitations. An additional challenge is that many viral coat and bacterial proteins are glycosylated. Glycans are well hydrated and can be flexible; these modifications are typically ignored entirely by docking algorithms. The long-term goal of this research is the accurate prediction of structures of antibodies and antibody–antigen complexes such that they are useful to decode biological mechanisms and engineer improved therapeutics. Thus, the first two aims of the current project are to (1) develop fast, aggressive, flexible backbone docking approaches, and (2) extend docking to include glycosylated antigens. Finally, the third aim will be to (3) apply antibody modeling and docking to determine biomarkers and therapeutics for celiac disease and pulmonary hypertension.