Structural bioinformatics software for epitope selection and antibody engineering

用于表位选择和抗体工程的结构生物信息学软件

• 批准号: 9009304
• 负责人: Steven Joseph Darnell
• 金额: $ 2.5万
• 依托单位: DNASTAR, INC.
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-15 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9009304
• 关键词: Algorithms; Amino Acid Sequence; Animals; Antibodies; Antibody Binding Sites; Antibody Formation; Antigen-Antibody Complex; Antigens; Area; Autoimmune Diseases; B-Lymphocyte Epitopes; Binding; Binding Sites; Bioinformatics; Biological; Biological Products; Cloud Computing; Communities; Complementarity Determining Regions; Computer software; Computers; Coupled; Data; Development; Diagnosis; Docking; Drug Targeting; Epitope Mapping; Epitopes; Frequencies; Guidelines; Health; Human; Immunoglobulin Variable Region; Libraries; Light; Machine Learning; Malignant Neoplasms; Manufacturer Name; Maps; Marketing; Measures; Methods; Michigan; Modeling; Molecular Biology; Molecular Conformation; Monoclonal Antibodies; Nature; Peptide Sequence Determination; Peptides; Performance; Phase; Post-Translational Protein Processing; Process; Protein Binding; Protein Dynamics; Protein Region; Proteins; Protocols documentation; Receiver Operating Characteristics; Research; Resolution; Running; Sampling; Scientist; Seminal; Site; Software Tools; Structural Models; Structure; Surface Antigens; Techniques; Technology; Testing; Therapeutic; Therapeutic Monoclonal Antibodies; Time; Work; Yang; antibody engineering; anticancer research; antigen binding; base; cloud based; cost; design; drug candidate; drug development; drug discovery; flexibility; glycosylation; graphical user interface; human disease; improved; in vivo; innovation; kinematics; knowledge base; member; novel therapeutics; prevent; programs; prospective; protein complex; protein folding; protein structure; public health relevance; research study; response; restraint; screening; simulation; structural biology; therapeutic target; therapy development; three dimensional structure; three-dimensional modeling; tool; usability

DESCRIPTION (provided by applicant): Therapeutic monoclonal antibodies bind to specific regions of proteins called epitopes, which elicits cellular responses. Traditional antibody discovery processes require laborious and expensive screening experiments, so computational approaches that predict epitopes and accelerate antibody discovery 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 very limited in their ability to predict high-resolution antibody-antige models, 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 accuracy and usability, computational antibody structure and epitope prediction methods can be used to lower drug development costs and focus experiments on the most promising drug candidates. DNASTAR recently released NovaFold, a commercial version of the world leading I-TASSER protein folding algorithm (Yang Zhang, U. Michigan) running on a cloud computing platform. NovaDock, our prospective protein interaction modeling product based on the up-and-coming SwarmDock algorithm (Paul Bates, Cancer Research UK), will use the same cloud infrastructure. NovaFold is proving useful to the molecular biology community; however, it is not adapted to model protein complexes like antibodies. Also, NovaFold and NovaDock currently do not model the type of structural fluctuations that are critical for antibody recognition. These enhancements could dramatically improve the predictive accuracy of the programs. We propose to create an automatic software pipeline that predicts the highest frequency of high-resolution antibody-antigen structures that are suitable for antibody screening and biopharmaceutical design projects. Previously in Phase I, we successfully created the most accurate models for predicting epitopes by incorporating both protein sequence information and structural features derived from experimental and high- resolution predicted protein antigen structures. In this Phase II project, we will combine our fiel-leading epitope prediction models with improvements to NovaFold and NovaDock that will discover better, lower energy binding arrangements between an antibody and its antigen. The improvements will more accurately model the structural plasticity of an antibody by broadening the conformational diversity of the prediction process. At the conclusion of this work, we will deliver a cloud-based software product of suitable accuracy to dramatically increase the rate of selecting antibodies that specifically recognize a desired therapeutic target.

描述(申请人提供)：治疗性单抗与蛋白质的特定区域结合，称为表位，引起细胞反应。传统的抗体发现过程需要费力且昂贵的筛选实验，因此对预测表位和加速抗体发现的计算方法的需求很高。基于结构的抗体设计在现代药物发现和开发过程中也很重要。这种方法需要高分辨率的四元(3D)蛋白质复杂结构，其实验测定通常是一个缓慢的过程，并不总是成功的。蛋白质结构和结合界面预测算法有望通过加速构建药物靶点和生物制药的高置信度结构模型来影响人类健康，这将有助于确定新的治疗策略。然而，目前的算法在预测高分辨率抗体-抗病毒模型方面的能力非常有限，这阻碍了广泛类别的治疗药物的发现。此外，还需要技术来预测候选抗体是否会在开发过程中尽早失败。随着准确性和可用性的提高，计算抗体结构和表位预测方法可以用于降低药物开发成本，并将实验集中在最有前途的候选药物上。DNAStar最近发布了NovaFold，这是在云计算平台上运行的世界领先的i-tasser蛋白质折叠算法的商业版本(杨章，密歇根大学)。我们未来的蛋白质相互作用建模产品NovaDock将使用相同的云基础设施，该产品基于新兴的SarmDock算法(Paul Bates，英国癌症研究机构)。NovaFold被证明对分子生物界很有用；然而，它不适合于对抗体等蛋白质复合体进行建模。此外，NovaFold和NovaDock目前还没有对抗体识别至关重要的结构波动类型进行建模。这些增强可以极大地提高程序的预测准确性。我们建议创建一个自动软件流水线，预测适合抗体筛选和生物制药设计项目的高分辨率抗体-抗原结构的最高频率。在之前的第一阶段，我们成功地创建了最准确的预测表位的模型，通过结合来自实验和高分辨率预测的蛋白质抗原结构的蛋白质序列信息和结构特征。在这个第二阶段的项目中，我们将把我们的Fiel领先的表位预测模型与对NovaFold和NovaDock的改进相结合，这将发现抗体与其抗原之间更好、更低能量的结合安排。这些改进将通过扩大预测过程的构象多样性来更准确地模拟抗体的结构可塑性。在这项工作结束时，我们将提供一种基于云的软件产品，具有适当的准确性，以显著提高选择特定识别所需治疗目标的抗体的速度。