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

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，目的是提供解决问题的方法和软件 生物医学问题。主要感兴趣的领域是蛋白质-蛋白质相互作用和配体结合。 蛋白质的性质。我们相信，在接下来的五年中，预测方法将得到实质性的改进 由于关于分子序列、结构和相互作用的信息量不断增加， 细胞，以及前所未有的计算能力。为了利用这些机会，我们 将把结构模板、协同进化信息和机器学习的使用集成到经典 生物物理学方法。我们的刚体蛋白质对接服务器ClusPro拥有超过15,000名用户，将是 与我们新的基于模板的服务器ClusPro TBM相结合。我们还添加了灵活对接的元素， 通过重塑导致空间冲突的区域，或者使用神经网络来计算后最小化 能量值，而不执行实际最小化。几个工具将组合在一起用于结构 蛋白质相互作用网络分析，包括一种构建多蛋白质复合体的新方法 基于预先计算的蛋白质对之间相互作用能的表格。应用程序示例 包括针对嵌合体的蛋白水解酶(PROTACs)的设计，通过 降解，抗体表位的预测，以及寻找表位特异性抗体。为了研究 蛋白质的配体结合特性我们主要关注结合热点，即蛋白质的主要区域 束缚自由能的贡献者。我们的FTMap服务器使用以下技术对目标蛋白质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。我们会 利用神经网络改进评分函数，并扩展探针集，得到泛化的 用特定官能团的偏好来识别蛋白质结合部位区域的药效团 以及一些绑定的碎片。由于这些信息可以用来寻找更大的配体，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，它将小分子与蛋白质对接，并将基于模板的建模与 FTMap。在一个协作应用程序中，我们将分析由Precision获得的代谢物-蛋白质相互作用数据 大肠杆菌和人体蛋白质下拉实验中的质谱学。FTMap将用于测试是否存在 靶蛋白有一个合适的结合热点，LigTBM会放置代谢产物。我们特别是 对寻找结合在新的变构调节位点的代谢物感兴趣。一个相关的申请将是 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 衍生出蛋白质不同区域的结合特性，从而探索潜在的变构通讯。