Analysis and prediction of molecular interactions

分子相互作用的分析和预测

• 批准号: 9070917
• 负责人: SANDOR VAJDA
• 金额: $ 32.16万
• 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 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9070917
• 关键词: Address; Algorithms; Area; Binding; Binding Sites; Biophysics; Communities; Complex; Computing Methodologies; Crowding; Docking; Drug Targeting; Free Energy; Goals; Homology Modeling; Hot Spot; Laboratories; Ligands; Maps; Methodology; Methods; Modeling; Molecular Probes; Online Systems; Paper; Peptides; Pharmacologic Substance; Property; Protein Region; Proteins; Proteome; Reporting; Research; Sampling; Side; Solvents; Structure; Surface; System; Testing; Validation; Work; base; cost; experimental analysis; flexibility; follow-up; improved; molecular recognition; novel strategies; protein protein interaction; public health relevance; research study; screening; virtual

 DESCRIPTION (provided by applicant): The research in our lab focuses on molecular recognition using computational methods and follow-up validation experiments. Our primary target areas are (1) protein-protein docking and (2) exploring binding properties of proteins by computational solvent mapping. Protein docking methods are needed because many important interactions occur in weak, transient complexes that are not amenable to direct experimental analysis. We have developed ClusPro, the best docking server currently available. While the server is heavily used, with over 350 research papers reporting models constructed by ClusPro, the methodology has several limitations. First, global docking of relatively rigid proteins usually generates structures within 10Å interface RMSD from the native complex, but selecting and refining the best models frequently fail. Second, the methods are less accurate when docking peptides, proteins with flexible loops or unstructured regions, or homology models. Third, no reliable method is available for determining whether a docked structure represents a stable complex, and for calculating its binding free energy with any reasonable accuracy. Fourth, even these imperfect methods are too slow for proteome-wide analyses. We expect to address and solve all these problems. In addition, a new approach, based on pre-calculated pairwise interactions, will be developed for modeling complex systems, including aggregation and crowding effects. The second application considered in the proposal, computational solvent mapping, globally samples the surface of target proteins using fragment sized molecular probes. The general goals of mapping are determining binding hot spots, i.e., regions of proteins that are major contributors to the binding free energy, and identifying fragments with preferential binding to these hot spots. The main advantage of studying hot spots is that they are more conserved than binding sites are. We will improve the efficiency of flexible mapping by performing side chain search directly within the global mapping algorithm, and extend the algorithm to models with flexible loops and to homology models. The method will also be used for large scale mapping calculations. We will develop an effective combination of computational and experimental methods for the identification of fragments binding to a given hot spot, and working with collaborators attempt to find fragment hits for a number of important drug target proteins. The ultimate goals of this research are developing algorithms for virtual fragment screening in order to reduce the number of fragments that need to be experimentally tested, and expanding the method to provide direct input for fragment based ligand discovery (FBLD), thereby reducing the high costs of the approach and making it more accessible to academic laboratories and small companies.

 描述（由申请人提供）：本实验室的研究重点是使用计算方法和后续验证实验进行分子识别。我们的主要目标领域是（1）蛋白质-蛋白质对接和（2）通过计算溶剂映射探索蛋白质的结合特性。蛋白质对接的方法是必要的，因为许多重要的相互作用发生在弱，短暂的复合物，不适合直接实验分析。我们已经开发了最好的对接服务器，目前可用。虽然该服务器被大量使用，超过350篇研究论文报告了由KNOPRO构建的模型，但该方法有几个局限性。首先，相对刚性的蛋白质的全局对接通常 在10个接口RMSD内从本地复杂生成结构，但选择和优化最佳模型经常失败。其次，当对接肽、具有柔性环或非结构化区域的蛋白质或同源模型时，该方法不太准确。第三，没有可靠的方法可用于确定对接结构是否代表稳定的复合物，以及以任何合理的精度计算其结合自由能。第四，即使这些不完美的方法对于蛋白质组范围的分析来说也太慢了。我们希望处理和解决所有这些问题。此外，一种新的方法，预先计算的成对相互作用的基础上，将开发复杂的系统，包括聚集和拥挤效应建模。第二个应用程序中考虑的建议，计算溶剂映射，全球样本的目标蛋白质的表面使用片段大小的分子探针。映射的一般目标是确定结合热点，即，区域的蛋白质是主要贡献者的结合自由能，并确定片段优先结合这些热点。研究热点的主要优点是它们比结合位点更保守。我们将通过在全局映射算法中直接执行侧链搜索来提高柔性映射的效率，并将该算法扩展到具有柔性环的模型和同源模型。该方法也将用于大规模绘图计算。我们将开发一种有效的计算和实验方法的组合，用于识别与给定热点结合的片段，并与合作者合作，试图找到一些重要的药物靶蛋白的片段命中。这项研究的最终目标是开发虚拟片段筛选算法，以减少需要进行实验测试的片段数量，并扩展该方法，为基于片段的配体发现（FBLD）提供直接输入，从而降低该方法的高成本，使其更容易为学术实验室和小公司所用。