Rapid structure-based software to enhance antibody affinity and developability for high-throughput screening

基于快速结构的软件可增强抗体亲和力和高通量筛选的可开发性

• 批准号: 10155411
• 负责人: Steven Joseph Darnell
• 金额: $ 99.87万
• 依托单位: DNASTAR, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10155411
• 关键词: 3-Dimensional; Acceleration; Address; Affinity; Algorithms; Antibodies; Antibody Affinity; Antibody Therapy; Antigen-Antibody Complex; Antigens; Area; Autoimmune Diseases; Binding; Binding Proteins; Biological; Biological Products; Biological Response Modifier Therapy; Biotechnology; Businesses; Chemical Structure; Chemicals; Clinic; Clinical; Cloud Computing; Complementarity Determining Regions; Complex; Computer Assisted; Computer software; Computers; Consumption; Deamination; Dependence; Detection; Development; Diagnosis; Disease; Docking; Drug Targeting; Effectiveness; Epitopes; Excision; Freedom; Goals; Health; Histocompatibility Testing; Hormones; Human; Immune system; Immunological Models; Ligands; Light; Malignant Neoplasms; Modeling; Modernization; Molecular; Molecular Conformation; Monoclonal Antibodies; Patients; Phage Display; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Play; Potential Energy; Process; Property; Protein Engineering; Protein Region; Proteins; Research Contracts; Resolution; Rewards; Role; Software Tools; Speed; Structural Models; Structure; Surface; Techniques; Technology; Testing; Therapeutic; Therapeutic Effect; Therapeutic Monoclonal Antibodies; Therapeutic antibodies; Thermodynamics; Time; Toxin; V(D)J Recombination; Variant; Virus Diseases; Work; antigen binding; base; combinatorial; cost; design; drug candidate; drug development; drug discovery; experimental study; flexibility; glycosylation; high throughput screening; human disease; improved; innovation; interest; manufacturing process; novel strategies; novel therapeutic intervention; organ transplant rejection; pathogen; pre-B cell receptor; prediction algorithm; prevent; programs; protein complex; protein structure; protein structure prediction; response; scale up; screening; side effect; simulation; structural biology; success; three dimensional structure; three-dimensional modeling; tool; virtual; virtual screening

Therapeutic monoclonal antibodies bind to specific regions of proteins called epitopes, which elicit cellular responses that treat or cure disease. Discovering therapeutic antibodies traditionally requires laborious and expensive screening experiments, so computational approaches that select which antibodies bind an epitope best and have the most desirable pharmaceutical properties are in high demand. Structure-based antibody design is also important to the modern drug discovery and development process. This approach requires a high- resolution quaternary (3D) protein complex structure, whose experimental determination is often a slow process that is not always successful. Protein structure and binding interface prediction algorithms are poised to impact human health by accelerating the construction of high-confidence structural models of drug targets and biopharmaceuticals, which will help identify new therapeutic strategies. However, the current algorithms are limited in their ability to distinguish stronger-binding antibodies from weaker ones, which is preventing the discovery of broad classes of therapeutics. In addition, technologies are needed to predict if a candidate antibody will fail as early as possible in the development process. With improvements in simulating removal of molecular liabilities without damaging function, computer-aided antibody design can be used to lower drug development costs and focus experiments on the most promising drug candidates. Here we propose to advance antibody discovery by developing highly accurate software tools built on the success of DNASTAR’s NovaFold Antibody program for antibody structure prediction, NovaDock for flexible protein-protein docking, and Lasergene Protein Design for protein engineering. The aims of the project focus 1) on developing more accurate and effective immune complex (an interacting antibody and antigen) structure predictions through better modeling of the challenging complementarity-determining regions (CDR), which play a critical role in antibody affinity and selectivity; and 2) on predicting antibody sequences that reduce chemical and energetic liabilities that prove detrimental to an antibody’s manufacturing process or therapeutic effect in a patient. In particular, overall predictive capability will be improved by incorporating computational acceleration techniques to support the virtual screening of tens of thousands of antibody sequences. Finally, and for the first time, this project will develop a “virtual immune system” to approach human antibody discovery, where antibodies will be modeled from germline sequences and selected for best recognizing an antigen of interest. The overall project goal is to deliver an advanced antibody screening pipeline that is powerful, accurate, and produces fast results, which will accelerate antibody discovery by enabling detailed and accurate immune complex structure predictions and structure-based liability detection at a high-throughput scale.

治疗性单克隆抗体与称为表位的蛋白质的特定区域结合， 治疗或治愈疾病的反应发现治疗性抗体传统上需要费力且 昂贵的筛选实验，因此选择哪些抗体结合表位的计算方法 最好的和具有最理想的药物性能的药物是高需求的。结构抗体 设计对现代药物发现和开发过程也很重要。这种方法需要高- 四元（3D）蛋白质复合物结构的分辨率，其实验测定通常是一个缓慢的过程 这并不总是成功的。蛋白质结构和结合界面预测算法有望影响 通过加快构建药物靶点的高置信度结构模型， 这将有助于确定新的治疗策略。然而，目前的算法是 它们区分较强结合抗体和较弱结合抗体的能力有限，这阻碍了它们的免疫反应。 发现了广泛的治疗方法。此外，还需要技术来预测候选抗体是否 在开发过程中尽早失败。随着模拟去除分子的改进， 在不破坏功能的情况下，计算机辅助抗体设计可用于降低药物开发 成本和重点实验最有前途的候选药物。 在这里，我们建议通过开发基于 DNASTAR的NovaFold Antibody项目成功预测抗体结构，NovaDock项目成功预测柔性 蛋白质-蛋白质对接和蛋白质工程的激光基因蛋白质设计。项目重点1） 开发更准确和有效的免疫复合物（相互作用的抗体和抗原）结构 通过对具有挑战性的互补决定区（CDR）进行更好的建模， 在抗体亲和力和选择性中发挥关键作用;以及2）预测减少化学物质的抗体序列 以及被证明对抗体的制造过程或治疗效果有害的精力充沛的责任 病人特别是，通过结合计算加速，将提高整体预测能力 技术来支持数以万计的抗体序列的虚拟筛选。最后，也是第一次 该项目将开发一个“虚拟免疫系统”，以接近人类抗体的发现，其中抗体 将从生殖系序列建模，并选择最佳识别感兴趣的抗原。整体 项目的目标是提供一个先进的抗体筛选管道，是强大的，准确的，并产生快速 结果，这将通过实现详细和准确的免疫复合物结构来加速抗体发现 预测和基于结构的可靠性检测。