Analysis and Prediction of Molecular Interactions

分子相互作用的分析和预测

• 批准号: 10596186
• 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/10596186
• 关键词: Antibodies; Area; Binding; Binding Proteins; Binding Sites; Biological; Biomedical Research; Cells; Communication; Communities; Computer software; Computers; 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; biophysical techniques; design; experimental study; flexibility; functional group; improved; interest; macromolecule; molecular recognition; molecular size; neural; novel; pharmacophore; predictive tools; preference; protein degradation; protein metabolite; protein protein interaction; simulation; small molecule; tool; Antibodies; Area; Binding; Binding Proteins; Binding Sites; Biological; Biomedical Research; Cells; Communication; Communities; Computer software; Computers; 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; biophysical techniques; design; experimental study; flexibility; functional group; improved; interest; macromolecule; molecular recognition; molecular size; neural; novel; pharmacophore; predictive tools; preference; protein degradation; protein metabolite; protein protein interaction; 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.

抽象的 我们的研究重点是分子识别，目的是提供解决方法和软件 生物医学问题。感兴趣的主要领域是蛋白质 - 蛋白质相互作用和配体结合 蛋白质的特性。我们认为，预测方法将在接下来的五个 由于关于序列，结构和分子相互作用的信息量增加而导致的年份 该单元格以及计算能力的前所未有的可用性。为了利用这些机会 将整合结构模板，共同进化信息和机器学习的使用 生物物理方法。我们的僵硬的身体蛋白质对接服务器Cluspro（拥有15,000多名用户）将是 结合我们基于新模板的服务器ClusPro TBM。我们还添加了灵活对接的元素，要么 通过重塑引起空间冲突的区域，或者使用神经网计算最小化后 能量值，而无需执行实际最小化。几种工具将合并为结构 蛋白质相互作用网络的分析，包括一种构建多蛋白质复合物的新方法 基于对蛋白质对之间相互作用能的预计数表。申请的示例 包括靶向嵌合体（Protac）的蛋白水解设计，以调节靶蛋白 降解，抗体表位的预测以及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，蛋白质的区域是主要的 结合自由能的贡献者。我们的ftmap服务器全球范围 碎片大小的分子探针，并提供可靠的热点和药效团信息。我们将 使用神经网提高评分函数，并扩展一组探针以获得广义 鉴定蛋白质结合位点区域具有特定官能团的偏好的药物团 以及许多界限。由于这些信息可用于查找较大的配体，因此目标是 将FTMAP转换为基于碎片的配体发现平台。我们还将改善基于模板的 服务器ligtbm，将小分子停靠在蛋白质上，并将基于模板的建模集成 ftmap。在协作应用程序中，我们将分析通过精确获得的代谢产物 - 蛋​​白质相互作用数据 大肠杆菌和人蛋白下拉实验中的质谱法。 FTMAP将用于测试是否a 靶蛋白具有合适的结合点，LigTBM将放置代谢物。我们特别是 有兴趣寻找在新型的变构调节位点结合的代谢产物。相关申请将是 通过动态模拟获得的结构的研究集合，以找到FTMAP之间的潜在相关性 蛋白质不同区域的衍生结合特性，从而探索了潜在的变构通信。