Computational Mapping of Proteins for Binding of Ligands

配体结合的蛋白质计算图谱

• 批准号: 7818904
• 负责人: SANDOR VAJDA
• 金额: $ 48.75万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7818904
• 关键词: Accounting; Algorithms; Amino Acids; Applications Grants; Binding; Binding Proteins; Binding Sites; Biochemical; Biological Assay; Chemistry; Complex; Computing Methodologies; Docking; Drug Delivery Systems; Drug Design; Environment; Free Energy; Funding; Future; Generations; Goals; Grant; Hot Spot; Interleukin 2 Receptor; Interleukin-2; Libraries; Ligand Binding; Ligands; Location; Maps; Measures; Membrane Proteins; Methods; Molecular; Molecular Conformation; Molecular Probes; Nature; Parents; Peptide Fragments; Pharmaceutical Preparations; Pharmacologic Substance; Positioning Attribute; Protein Binding; Protein Region; Protein-Protein Interaction Map; Proteins; Recovery; Reporting; Research; Roentgen Rays; Side; Site; Structure; Surface; United States National Institutes of Health; Work; X-Ray Crystallography; base; cytokine; design; flexibility; functional group; improved; inhibitor/antagonist; interleukin 2 inhibitor; numb protein; protein protein interaction; protein structure; public health relevance; receptor; small molecule

DESCRIPTION (provided by applicant): This proposal is the competitive revision (Notice NOT-OD-09-58, NIH Announces the Availability of Recovery Act Funds for Competitive Revision Applications) of the grant "Computational Mapping of Proteins for the Binding of Ligands". Mapping methods place molecular probes - small molecules or functional groups - on the surface of proteins in order to identify the most favorable binding positions. Since regions of the protein surface that are major contributors to the binding free energy in drug-protein interactions also bind a variety of small organic molecules, mapping can identify such "hot spots" and the number of probe molecules bound is a good predictor of druggability. The parent proposal focused on "traditional" drug targets that naturally bind small molecular ligands. The general goal of this revision is to extend the analysis to the identification and characterization of druggable sites in protein-protein interfaces. Such analysis facilitates the discovery of small molecules that can inhibit or modulate the association of two proteins, an important emerging problem in pharmaceutical research. Application of mapping to a number of protein-protein interaction (PPI) targets has shown that the method always identifies at least some fraction of the site which can bind small molecular inhibitors within the protein-protein interface region, even when starting from the structure of a ligand-free protein. In Aim 1 we will further study the generality of this observation by mapping a variety of PPI targets on which structural and biochemical information is available. We will also study the interactions between fragments and their protein environments in the binding site by using target-specific probe libraries based on the known ligands of each target. Since binding of small molecules frequently requires conformational changes to form appropriate pockets in a relatively flat protein-protein interface, in Aim 2 we develop a method to account for side chain flexibility prior to mapping. The algorithm combines statistical analysis and energy minimization to identify "moveable" side chains and their potential conformational states in the vicinity of the "hot spot" identified by the initial mapping. Protein structures are generated by combining the potential conformations of moveable side chains. The re-mapping of such adjusted structures generally agrees well with results obtained for the ligand-bound proteins, and hence substantially improves the predictive power of the method. Aim 3 is the theoretical and experimental characterization of hot spots that enable the binding of small molecular inhibitors in the binding interface of IL-2 with IL-2R1, an exemplary PPI target. The binding energies that different portions of the known inhibitors derive from their interactions with the protein have not been systematically elucidated. We will measure experimental binding energies and binding orientations for different molecular fragments derived from these known IL-2 inhibitors, using quantitative biochemical and biophysical assays as well as X-ray crystallography, and will compare the results with those obtained computationally using these same fragments as probes. The results will provide new information on the physicochemical and structural features that render a difficult PPI site druggable, which we will use to further refine our computational method. PUBLIC HEALTH RELEVANCE: Mapping methods place molecular probes - small molecules or functional groups - on the surface of proteins in order to identify the most favorable binding positions, and provide information on the druggability of such site. We focus on the identification and characterization of druggable sites capable of binding small molecular inhibitors of protein-protein interactions, an important emerging problem in pharmaceutical research.

描述（由申请人提供）：本提案是“配体结合蛋白质计算图谱”拨款的竞争性修订（通知no - od -09-58， NIH宣布为竞争性修订申请提供恢复法案资金）。定位方法将分子探针（小分子或官能团）放置在蛋白质表面，以确定最有利的结合位置。由于蛋白质表面的区域是药物-蛋白质相互作用中结合自由能的主要贡献者，也结合了各种小有机分子，因此作图可以识别这样的“热点”，结合的探针分子的数量是一个很好的药物性预测指标。母体提案的重点是自然结合小分子配体的“传统”药物靶标。本修订的总体目标是将分析扩展到蛋白质-蛋白质界面中可药物位点的鉴定和表征。这种分析有助于发现可以抑制或调节两种蛋白质结合的小分子，这是药物研究中一个重要的新问题。对许多蛋白-蛋白相互作用（PPI）靶标的定位应用表明，即使从无配体蛋白的结构开始，该方法也总能识别出至少一部分可以结合蛋白-蛋白界面区域内小分子抑制剂的位点。在Aim 1中，我们将通过绘制各种可获得结构和生化信息的PPI靶点来进一步研究这一观察结果的普遍性。我们还将利用基于每个靶标的已知配体的靶标特异性探针文库，研究片段与其结合位点的蛋白质环境之间的相互作用。由于小分子的结合经常需要构象变化才能在相对平坦的蛋白质-蛋白质界面中形成适当的口袋，因此在Aim 2中，我们开发了一种方法，在绘制之前考虑侧链的灵活性。该算法将统计分析和能量最小化相结合，在初始映射确定的“热点”附近识别“可移动”侧链及其潜在构象状态。蛋白质结构是通过结合可移动侧链的潜在构象而产生的。这种调整后的结构的重新映射通常与配体结合蛋白的结果一致，因此大大提高了该方法的预测能力。目的3是理论和实验表征热点，使IL-2与IL-2R1结合界面中的小分子抑制剂能够结合，IL-2R1是典型的PPI靶点。已知抑制剂的不同部分从它们与蛋白质的相互作用中获得的结合能还没有被系统地阐明。我们将使用定量生化和生物物理分析以及x射线晶体学来测量来自这些已知IL-2抑制剂的不同分子片段的实验结合能和结合方向，并将结果与使用这些相同片段作为探针获得的计算结果进行比较。这些结果将提供新的物理化学信息和结构特征，使一个困难的PPI位点具有可药物性，我们将使用这些信息进一步完善我们的计算方法。