Analysis and prediction of molecular interactions

分子相互作用的分析和预测

基本信息

批准号：
9256506
负责人：
SANDOR VAJDA
金额：
$ 56.89万
依托单位：
BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
依托单位国家：
美国
项目类别：
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/9256506
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 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 base cost experimental analysis experimental study flexibility follow-up improved molecular recognition molecular size novel strategies protein protein interaction public health relevance screening virtual

Address Algorithms Area Binding Binding Sites Biophysics Communities Complex Computing Methodologies Crowding Docking Drug Targeting Free Energy Goals Homology Modeling Hot Spot Laboratories Ligands 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 base cost experimental analysis experimental study flexibility follow-up improved molecular recognition molecular size novel strategies protein protein interaction public health relevance screening virtual

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项目摘要

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（由适用提供）：我们实验室中的研究重点是使用计算方法和后续验证实验的分子识别。我们的主要目标区域是（1）蛋白质 - 蛋白质对接和（2）通过计算溶液映射探索蛋白质的结合特性。需要蛋白质对接方法，因为许多重要的相互作用发生在不适合直接实验分析的弱瞬态复合物中。我们已经开发了ClusPro，这是当前可用的最佳对接服务器。尽管服务器大量使用，但由ClusPro构建的350多个研究论文报告模型，该方法学有多个限制。首先，通常从天然复合物中生成在10Å接口RMSD内的结构，但是选择和完善最佳模型经常失败。其次，当对接Petides，具有柔性环或非结构化区域的蛋白质或同源模型时，这些方法的准确性较差。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，并以任何合理的精度来计算其结合自由能。第四，即使这些不完善的方法对于全蛋白质组分析都太慢了。我们希望解决并解决所有这些问题。此外，将开发一种基于预估算的成对相互作用的新方法，用于建模复杂系统，包括聚集和拥挤效应。提案中考虑的第二个应用，即计算溶剂映射，全球在全球范围内使用碎片大小的分子问题对靶蛋白的表面进行样品。映射的一般目标是确定结合热点，即蛋白质的区域，这些区域是结合自由能的主要促进者，并确定具有优先结合与这些热点的片段。研究热点的主要优点是它们比结合位点更保守。我们将通过直接在全局映射算法中执行侧链搜索来提高灵活映射的效率，并将算法扩展到具有灵活循环和同源模型的模型。该方法还将用于大规模映射计算。我们将开发有效的计算方法和实验方法，以鉴定与给定热点结合的片段，并与合作者合作尝试寻找许多重要的药物靶蛋白的碎片命中。这项研究的最终目标是开发用于虚拟碎片筛选的算法，以减少需要经过实验测试的碎片数量，并扩大方法为基于碎片的配体发现（FBLD）提供直接输入的方法，从而减少方法的高成本并使学术实验室和小型公司更容易获得。