Rapid structure-based software to enhance antibody affinity and developability for high-throughput screening: Aiming toward total in silico design of antibodies

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：旨在实现抗体的全面计算机设计

• 批准号: 10603473
• 负责人: FREDERICK R BLATTNER
• 金额: $ 100万
• 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 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10603473
• 关键词: 2019-nCoV; Acceleration; Address; Affinity; Algebra; Algorithms; Antibodies; Antibody Affinity; Antibody Binding Sites; Antigen Targeting; Antigen-Antibody Complex; Antigens; Artificial Intelligence; Autoimmune Diseases; Avidity; B-Lymphocyte Epitopes; Binding; Bioinformatics; Biotechnology; Carrier Proteins; Charge; Chemicals; Chinese Hamster Ovary Cell; Client; Cloud Computing; Computer Assisted; Computer software; Consumption; Critiques; DNA; Data; Detection; Development; Diagnosis; Disease; Dissociation; Docking; Drug Industry; Epitopes; Event; G-Protein-Coupled Receptors; Genetic Recombination; Goals; Health; Histocompatibility Testing; Hormones; Human; Immune system; Immunoglobulin Fragments; In Vitro; Individual; Interferometry; Kinetics; Laboratories; Libraries; Malignant Neoplasms; Marketing; Mathematics; Measures; Medical; Methods; Minor; Modeling; Molecular; Molecular Conformation; Monoclonal Antibodies; Mutation; Parents; Pharmacologic Substance; Phase; Physics; Process; Property; Protein Engineering; Protein Region; Proteins; Recording of previous events; Research Contracts; Research Personnel; Running; SARS-CoV-2 spike protein; Screening Result; Services; Shapes; Site; Specificity; Speed; Structure; Surface; System; Techniques; Testing; Therapeutic; Therapeutic Monoclonal Antibodies; Therapeutic antibodies; Time; Toxin; Training; Translating; Variant; Virus Diseases; Visualization; Western Blotting; Work; algorithm development; blind; cost; cross reacting material 197; design; drug development; drug discovery; drug testing; experimental study; flexibility; high throughput screening; human disease; improved; in silico; kinematics; mathematical methods; mechanical force; molecular mechanics; nanomolar; novel therapeutics; organ transplant rejection; particle; pathogen; predictive modeling; protein structure prediction; response; screening; simulation; success; tool; virtual; virtual screening; wasting

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 costly and labor- intensive, laboratory-based screening experiments. Computational approaches that select antibodies with the most desirable pharmaceutical properties are thus poised to improve health by accelerating the development of new drugs. Unfortunately, current algorithms are often unable to distinguish stronger-binding antibodies from weaker ones. Improvements to structure prediction and molecular visualization will lower costs and increase the speed with which new drugs are developed by allowing researchers to focus on the most promising candidates as early in the process as possible. DNASTAR’s goals are to increase the speed of predicting the structure of antibody-antigen interactions using superior mathematical methods and to transform antibodies with micromolar binding affinity into those with improved nanomolar affinity using new computer-aided antibody design techniques. This will accelerate antibody discovery by enabling detailed and accurate immune complex structure predictions and structure-based chemical liability detection at a high-throughput scale. In Phase II, we first created an in silico human germline sequence library and used it to simulate the natural V(D)J and VJ recombination events of the immune system, generating a new library of assembled antibody sequences. To select antibody candidates that bound a chosen target, we developed a simulation algorithm in which antibody candidates were docked against a chosen target protein. The 24 candidates with the best predicted binding energy were converted to single-chain antibodies and propagated in CHO cells. Three candidates were found to bind the target using native Western blots. The binding affinity and kinetics of these three candidates were then measured by bio-layer interferometry. The tightest binding candidate was then subjected to a form of simulated affinity maturation where individual site-directed mutations were ranked by their predicted ability to enhance affinity for the antigen. Four out of five tested variants showed improved binding over its parent using bio-layer interferometry. The goal of our Phase IIB proposal is to build upon this success and further improve predictive capability by incorporating unequaled algebraic mathematics and computational acceleration techniques to support the virtual screening of tens of thousands of antibody sequences. For the first time in history, this will enable antibodies to be selected for development by first modeling them from germline sequences using a “virtual immune system.” Our ultimate intent is to deliver a complete antibody discovery pipeline that is powerful, accurate, produces fast results, and yields lab-scale quantities of DNA and protein materials for the selected antibodies.

治疗性单克隆抗体与称为表位的蛋白质的特定区域结合，这些区域引起细胞 治疗或治愈疾病的反应。传统上发现治疗抗体需要昂贵和劳动力 - 基于实验室的密集筛查实验。选择抗体的计算方法 因此 新药。不幸的是，当前的算法通常无法将更强的结合抗体与 较弱的。改进结构预测和分子可视化将降低成本并增加 通过允许研究人员专注于最有前途的候选人来开发新药的速度 在此过程的早期。 DNASTAR的目标是提高使用使用抗体 - 抗原相互作用的结构的速度 上级数学方法并将具有微摩尔结合亲和力转化为具有的抗体 使用新的计算机辅助抗体设计技术改善了纳摩尔亲和力。这将加速抗体 通过启用详细且准确的免疫复杂结构预测和基于结构 高通量量表的化学责任检测。 在第二阶段，我们首先创建了一个硅的人类种系序列库，并使用它来模拟自然 v（d）J和VJ的重组事件的免疫系统，生成一个新的组装抗体库 序列。为了选择绑定所选靶标的抗体候选物，我们开发了一种模拟算法 哪些抗体候选物是针对选定的靶蛋白对接的。最好的24名候选人 预测的结合能转化为单链抗体，并在CHO细胞中传播。三 发现候选者使用天然蛋白质印迹结合目标。这些的绑定亲和力和动力学 然后通过生物层干扰测量三个候选者。那时最紧的约束候选人是 经过一种模拟亲和力成熟的形式，在单个位置定向的突变被其排名 预测增强对抗原亲和力的能力。在五个测试的变体中，有四个显示了改进的结合 其父母使用生物层干扰。 我们阶段IIB提议的目标是建立这一成功，并进一步提高预测能力 合并无等的代数数学和计算加速技术来支持虚拟 筛选成千上万的抗体序列。这是历史上的第一次，这将使抗体能够 首先使用“虚拟免疫系统”从种系序列中对其进行建模，以进行开发。 我们的最终目的是提供一条完整的抗体发现管道，该管道强大，准确，可以快速产生 结果，并产生用于选定抗体的DNA和蛋白质材料的实验室规模量。