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

基本信息

批准号：
10080587
负责人：
Steven Joseph Darnell
金额：
$ 100万
依托单位：
DNASTAR, INC.
依托单位国家：
美国
项目类别：
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/10080587
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

项目摘要

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，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实验室和昂贵的筛选实验，因此选择哪种抗体结合表位的计算方法最好，并且具有最理想的药物特性的需求量很高。基于结构的抗体设计对于现代药物发现和开发过程也很重要。这种方法需要高分辨率第四纪（3D）蛋白质复合结构，其实验确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被中毒通过加速构建药物目标的高信任结构模型，并生物制药将有助于确定新的治疗策略。但是，当前的算法是限制了将较强抗体与较弱的抗体区分开的能力，这阻止了发现广泛的理论类别。此外，还需要技术来预测候选抗体是否在开发过程中将尽早失败。随着模拟分子去除的改进负债没有损害功能，计算机辅助抗体设计可用于降低药物开发对最有前途的候选药物进行成本和重点实验。在这里，我们建议通过开发建立在 DNASTAR的NOVAFOLD抗体结构预测计划的成功，Novadock的灵活性蛋白质 - 蛋白质对接和用于蛋白质工程的重度蛋白质设计。项目焦点的目的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）结构通过更好地建模挑战者完成区（CDR）来预测在抗体亲和力和选择性中的关键作用； 2）预测减少化学的抗体序列以及对抗体的制造过程或治疗效果有害的能力负债病人。特别是，通过合并计算加速度将提高总体预测能力支持数以万计的抗体序列的虚拟筛选技术。最后，第一个时间，该项目将开发一个“虚拟免疫系统”来接近人类抗体发现，抗体将根据种系序列进行建模，并选择以最佳识别感兴趣的抗原。总体项目目标是提供强大，准确的高级抗体筛选管道，并产生快速结果，通过启用详细且准确的免疫复杂结构来加速抗体发现高通量量表的预测和基于结构的责任检测。