Analysis and prediction of molecular interactions

 描述(申请人提供)：我们实验室的研究重点是使用计算方法和后续验证实验的分子识别。我们的主要目标领域是(1)蛋白质-蛋白质对接和(2)通过计算溶剂映射来探索蛋白质的结合性质。蛋白质对接方法是必要的，因为许多重要的相互作用发生在不能直接进行实验分析的弱的、瞬时的复合体中。我们已经开发了ClusPro，这是目前可用的最好的对接服务器。虽然服务器的使用率很高，ClusPro构建了350多篇研究论文报告模型，但这种方法有几个局限性。首先，相对刚性蛋白质的全球对接通常 从本地复合体生成10？接口RMSD内的结构，但选择和改进最佳模型经常失败。其次，当对接多肽、具有柔性环或非结构化区域的蛋白质或同源模型时，这些方法的精确度较低。第三，没有可靠的方法来确定对接结构是否代表稳定的络合物，并以任何合理的精度计算其结合自由能。第四，即使是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。我们希望解决和解决所有这些问题。此外，将开发一种新的方法，基于预先计算的成对相互作用，对包括聚集和拥挤效应在内的复杂系统进行建模。提案中考虑的第二个应用，计算溶剂映射，使用片段大小的分子探针对目标蛋白质的表面进行全局采样。作图的总体目标是确定结合热点，即蛋白质中主要贡献结合自由能的区域，并识别优先与这些热点结合的片段。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。该方法还将用于大规模地图计算。我们将开发一种有效的计算和实验方法相结合的方法来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。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昂成本，并使其更容易为学术实验室和小公司所使用。

 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.