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

基本信息

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
项目类别：
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

项目摘要

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（由申请人提供）：治疗性单克隆抗体与称为表位的蛋白质的特定区域结合，这会引起细胞反应。传统的抗体发现过程需要费力且昂贵的筛选实验，因此预测表位和加速抗体发现的计算方法需求很高。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的第四纪（3D）蛋白质复合结构，其实验性确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助于通过加快药物靶标和生物药物的高信心结构模型来影响人类健康，这将有助于鉴定新的治疗策略。但是，当前的算法在预测高分辨率抗体模型的能力方面非常有限，这阻止了发现广泛的治疗剂类别。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效。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用于降低药物开发成本，并将重点实验放在最有希望的候选药物上。 DNASTAR最近发布了Novafold，这是世界领先的I-Tasser蛋白质折叠算法（Yang Zhang，U. Michigan）在云计算平台上运行的世界。我们的前瞻性蛋白质相互作用建模产品Novadock将使用相同的云基础架构使用相同的云基础架构。事实证明，诺维福对分子生物学界有用。但是，它不适用于建模抗体之类的蛋白质复合物。此外，诺瓦福德和诺瓦多目前并未对抗体识别至关重要的结构波动的类型进行建模。这些增强功能可以极大地提高程序的预测准确性。我们建议创建一种自动软件管道，该管道可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。以前，在第一阶段，我们通过结合蛋白质序列信息和源自实验和高分辨率预测蛋白质抗原结构的蛋白质序列信息和结构特征，成功地创建了用于预测表位的最准确模型。在此II阶段项目中，我们将将我们的FIEL领先的表位预测模型与对Novafold和Novadock的改进相结合，这些预测模型将发现抗体及其抗原之间的更好，较低的能量结合布置。这些改进将通过扩大预测过程的构象多样性来更准确地对抗体的结构可塑性进行建模。在这项工作结束时，我们将提供基于云的软件产品，具有合适的精度，以显着提高选择明确识别所需治疗靶标的抗体的速率。