Analysis and prediction of molecular interactions

分子相互作用的分析和预测

• 批准号: 9920157
• 负责人: SANDOR VAJDA
• 金额: $ 57.07万
• 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 2021-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9920157
• 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

 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.