Integrating Experiment and Theory to Characterize Diagnostic Antibody Specificity

结合实验和理论来表征诊断抗体特异性

• 批准号: 8136201
• 负责人: ROBERT J WOODS
• 金额: $ 33.08万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8136201
• 关键词: Antibodies; Antibody Specificity; Binding; Carbohydrates; Cell surface; Communities; Complex; Data; Data Set; Development; Diagnostic; Diagnostic Reagent; Diagnostic Specificity; Disease; Disease Marker; Docking; Goals; Knowledge; Ligands; Methods; Molecular; Online Systems; Polysaccharides; Potential Energy; Proteins; Reagent; Screening procedure; Specificity; Staging; Techniques; Work; base; computerized tools; flexibility; innovation; protein complex; protein structure; public health relevance; research study; sugar; theories; three dimensional structure; three-dimensional modeling; tool

DESCRIPTION (provided by applicant): Diagnostic agents for many diseases are based on the ability of antibody reagents to bind tightly and specifically to sugars on cell surfaces that are markers for the disease. Knowledge of the 3D structure of these interactions helps to optimize the reagent and aids in confirming its specificity, however traditional experimental methods are often poorly suited to working with large flexible sugars. Consequently, alternative high- throughput techniques, such as screening the protein or antibody reagent against immobilized arrays of glycans have become popular for identifying the binding partners. Array screening does not however provide any information on the 3D structure, and can be prone to false negative or non-specific results. The principle aim of this proposal is to develop a robust high-throughput computational platform for predicting the 3D structure of carbohydrate-antibody (or protein in general) complexes, which will integrate data from glycan microarray screening to define the origin of the observed binding specificity, with the ultimate goal of aiding in the development of high-specificity diagnostic agents. This work will proceed through the following stages 1) The development of automated tools for docking the minimal glycan binding motifs, superimposing each of the glycans from the experimental screening data set that share the motif, and determining experimentally- consistent 3D poses 2) The development of a computational scoring function that is tuned for use with carbohydrates, and that incorporates the potential energy functions from the GLYCAM carbohydrate force field 3) The creation of an annual competition to predict glycan-protein 3D structures involving the theoretical and experimental glycoscience communities 4) The creation of a web-based set of tools that allows users to upload their protein structure and glycan array data and perform Carbohydrate Threading to generate an experimentally-consistent 3D model for their carbohydrate-protein complex This work is highly innovative in integrating high-throughput computational and experimental techniques to generate experimentally-consistent 3D models for otherwise hard to characterize molecular complexes. Notably the approach builds on the strengths of each method and concurrently aids in identifying the errors commonly found in array screening and theoretical ligand docking. PUBLIC HEALTH RELEVANCE: The development of highly specific diagnostic agents would benefit greatly from the ability to study their 3D structures; however traditional experimental methods are often poorly suited to working with some typical disease-associated markers, such as large flexible sugars. Consequently, high-throughput experimental techniques, such as screening the diagnostic reagent against immobilized arrays of complex sugars (glycans) have become popular for identifying the binding partners; but array screening alone does not provide any 3D information. The principle aim of this proposal is to develop and integrate theoretical 3D structure prediction methods with data from glycan array screening to generate computational tools to aid in the development of high-specificity diagnostic agents.

描述（由申请人提供）：许多疾病的诊断试剂基于抗体试剂与细胞表面糖（疾病标志物）紧密特异性结合的能力。这些相互作用的3D结构的知识有助于优化试剂并有助于确认其特异性，然而传统的实验方法通常不适合与大的柔性糖一起工作。因此，替代的高通量技术，例如针对固定化的聚糖阵列筛选蛋白质或抗体试剂，已经变得流行用于鉴定结合配偶体。然而，阵列筛选不提供关于3D结构的任何信息，并且可能倾向于假阴性或非特异性结果。该提案的主要目的是开发一个强大的高通量计算平台，用于预测碳水化合物-抗体（或一般蛋白质）复合物的3D结构，该平台将整合来自聚糖微阵列筛选的数据，以定义所观察到的结合特异性的起源，最终目标是帮助开发高特异性诊断剂。这项工作将通过以下阶段进行：1）开发用于对接最小聚糖结合基序的自动化工具，叠加来自共享基序的实验筛选数据集的每个聚糖，并确定实验一致的3D姿态2）开发被调整用于碳水化合物的计算评分函数，并结合了来自GLYCAM碳水化合物力场的势能函数3）创建一个年度竞赛，以预测聚糖-蛋白质3D结构，涉及理论和实验糖科学社区4）创建一个网络-一套基于的工具，允许用户上传他们的蛋白质结构和聚糖阵列数据，并执行碳水化合物线程，以生成他们的碳水化合物-蛋白质复合物的实验一致的3D模型这项工作在整合高通量计算和实验技术方面具有高度创新性，一致的3D模型，否则难以表征分子复合物。值得注意的是，该方法建立在每种方法的优势上，同时有助于识别阵列筛选和理论配体对接中常见的错误。 公共卫生相关性：高度特异性诊断试剂的开发将极大地受益于研究其3D结构的能力;然而，传统的实验方法通常不适合与一些典型的疾病相关标志物，如大的柔性糖。因此，高通量的实验技术，如筛选诊断试剂对固定阵列的复杂糖（聚糖）已成为流行的识别结合伙伴，但阵列筛选单独不提供任何3D信息。该提案的主要目的是开发和整合理论3D结构预测方法与聚糖阵列筛选数据，以生成计算工具，以帮助开发高特异性诊断试剂。