Analysis and Prediction of Molecular Interactions

分子相互作用的分析和预测

• 批准号: 10410497
• 负责人: SANDOR VAJDA
• 金额: $ 57.62万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-06 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10410497
• 关键词: Antibodies; Area; Binding; Binding Proteins; Binding Sites; Biological; Biomedical Research; Cells; Communication; Communities; Computer software; Computers; Conflict (Psychology); Data; Docking; Elements; Epitopes; Escherichia coli; Free Energy; Goals; Hot Spot; Human; Ligand Binding; Ligands; Machine Learning; Mass Spectrum Analysis; Methods; Modeling; Molecular Probes; Multiprotein Complexes; Online Systems; Property; Protac; Protein Analysis; Protein Region; Proteins; Research; Sampling; Site; Structure; Surface; Testing; base; biophysical techniques; design; experimental study; flexibility; functional group; improved; interest; macromolecule; molecular recognition; molecular size; novel; pharmacophore; predictive tools; preference; protein degradation; protein metabolite; protein protein interaction; relating to nervous system; simulation; small molecule; tool

Abstract Our research focuses on molecular recognition, with the goal of providing methods and software for solving biomedical problems. The primary areas of interest are protein-protein interactions and the ligand binding properties of proteins. We believe that predictive methods will be substantially improved during the next five years due to the increasing amount of information on sequences, structures, and interactions of molecules in the cell, and the unprecedented availability of computing power. To take advantage of these opportunities we will integrate the use of structural templates, co-evolutionary information, and machine learning into classical biophysical methods. Our rigid body protein docking server ClusPro, which has over 15,000 users, will be combined with our new template based server ClusPro TBM. We also add elements of flexible docking, either by remodeling the regions that cause steric conflicts, or by using a neural net for calculating post-minimization energy values without performing the actual minimization. Several tools will be combined for the structural analysis of protein interaction networks, including a novel method of constructing multi-protein complexes based on pre-calculated tables of interaction energies between pairs of proteins. Examples of applications include the design of PROteolysis TArgeting Chimeras (PROTACs) for modulating a target protein by degradation, the prediction of antibody epitopes, and searching for epitope-specific antibodies. To study the ligand binding properties of proteins we focus on binding hot spots, regions of proteins that are major contributors to the binding free energy. Our FTMap server globally samples the surface of target proteins using fragment sized molecular probes and provides reliable hot spot and pharmacophore information. We will improve the scoring function using neural nets, and expand the set of probes to obtain generalized pharmacophores that identify regions in the protein binding site with preferences for specific functional groups and a number of bound fragments. Since this information can be used to find larger ligands, the goal is to convert FTMap into a fragment based ligand discovery platform. We will also improve our template-based server LigTBM, which docks small molecules to proteins, and will integrate template-based modeling with FTMap. In a collaborative application we will analyze metabolite-protein interaction data obtained by precision mass spectrometry in E. coli and human protein pull-down experiments. FTMap will be used to test whether a target protein has a suitable binding hot spot, and LigTBM will place the metabolite. We are particularly interested in finding metabolites that bind at novel allosteric regulatory sites. A related application will be to study ensembles of structures obtained by dynamic simulations to find potential correlations between FTMap derived binding properties at different regions of proteins, thus exploring potential allosteric communication.

摘要 我们的研究重点是分子识别，目标是提供解决的方法和软件 生物医学问题。主要研究领域是蛋白质-蛋白质相互作用和配体结合 蛋白质的性质。我们相信，预测方法将在未来五年内得到实质性改进。 多年来，由于越来越多的信息序列，结构和相互作用的分子， 以及前所未有的计算能力。为了利用这些机会，我们 将结构模板，共同进化信息和机器学习的使用整合到经典的 生物物理方法。我们的刚体蛋白质对接服务器RightPro拥有超过15，000名用户， 结合我们新的基于模板的服务器ESPRO Pro TBM。我们还增加了灵活对接的元素， 通过重塑引起空间冲突的区域，或者通过使用神经网络来计算后最小化， 能量值，而不执行实际的最小化。几种工具将结合起来， 蛋白质相互作用网络的分析，包括构建多蛋白质复合物的新方法 基于预先计算的蛋白质对之间的相互作用能的表格。应用的示例 包括设计用于通过以下方式调节靶蛋白的蛋白水解靶向嵌合体（PROTAC 降解、抗体表位的预测和寻找表位特异性抗体。研究 蛋白质的配体结合特性，我们专注于结合热点，蛋白质的主要区域 对结合自由能的贡献。我们的FTMap服务器使用以下方法对目标蛋白的表面进行全球采样： 片段大小的分子探针，并提供可靠的热点和药效团信息。我们将 利用神经网络改进评分函数，并扩展探针集，以获得广义的 识别蛋白质结合位点中对特定官能团具有偏好的区域的药效团 和一些结合的碎片由于这些信息可用于寻找更大的配体，因此目标是 将FTMap转换为基于片段的配体发现平台。我们还将改进基于模板的 服务器LigTBM，它将小分子对接到蛋白质上，并将基于模板的建模与 FTMap.在协作应用程序中，我们将分析通过精密度获得的代谢物-蛋白质相互作用数据。 质谱分析在E.大肠杆菌和人蛋白质下拉实验。FTMap将用于测试 靶蛋白具有合适的结合热点，LigTBM将放置代谢物。我们特别 感兴趣的是发现在新的变构调节位点结合的代谢物。一个相关的应用程序将是 研究通过动态模拟获得的结构集合，以发现FTMap之间的潜在相关性 在蛋白质的不同区域衍生的结合特性，从而探索潜在的变构通信。