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: 10385733
• 负责人: FREDERICK R BLATTNER
• 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/10385733
• 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; 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.

治疗性单抗与蛋白质的特定区域结合，这种区域称为表位，可诱导细胞 治疗或治愈疾病的反应。传统上，发现治疗性抗体需要费力和 昂贵的筛选实验，所以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用性能的需求很高。基于结构的抗体 设计对现代药物发现和开发过程也很重要。这种方法需要很高的 拆分四元(3D)蛋白质复杂结构，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将产生影响 通过加快构建药物靶点的高置信度结构模型来促进人类健康 生物制药，这将有助于确定新的治疗策略。然而，目前的算法是 它们区分强结合抗体和弱结合抗体的能力有限，这阻止了 发现了治疗学的广泛类别。此外，还需要技术来预测候选抗体是否 将在开发过程中尽早失败。在模拟分子移除方面有所改进 在不损害功能的情况下，计算机辅助抗体设计可以用于降低药物开发 成本，并将试验重点放在最有希望的候选药物上。 在这里，我们建议通过开发高精度的软件工具来推进抗体发现 DNAStar的NovaFold抗体程序用于抗体结构预测的成功，NovaDock用于灵活的 蛋白质-蛋白质对接，用于蛋白质工程的激光蛋白质设计。项目重点的目标1) 关于开发更准确和有效的免疫复合体(相互作用的抗体和抗原)结构 通过对具有挑战性的互补性决定区域(CDR)进行更好的建模进行预测，这在 在抗体亲和力和选择性中的关键作用；以及2)预测减少化学物质的抗体序列 以及事实证明对抗体的制造过程或治疗效果不利的能量负担 有耐心的。特别是，通过结合计算加速，整体预测能力将得到改善 支持对数万个抗体序列进行虚拟筛选的技术。最后，也是第一次 这一项目将开发一种“虚拟免疫系统”，以接近人类抗体的发现，其中抗体 将从生殖系序列中建模，并被选择为最佳识别感兴趣的抗原。整体而言 该项目的目标是提供一种先进的抗体筛查流水线，功能强大，准确，生产速度快 结果，这将通过启用详细和准确的免疫复合体结构来加速抗体发现 高吞吐量规模的预测和基于结构的责任检测。