Learning Drug Specifity in Protein Families by Docking

描述(由申请人提供)： 其目标是提供一套强大的基于对接的理性药物发现工具，以利用基因组学和结构基因组学努力产生的关于蛋白质家族序列和结构的日益丰富的信息。这些工具将通过利用关于蛋白质靶标家族的所有可用信息，包括文献中描述的比对序列、可用结构、共结晶配体和活性化合物，来提高当前虚拟筛选方法的可靠性。这些工具还将特别允许在设计以家庭为中心的组合文库时，针对家庭中的任何成员，以家庭活跃部位的共同区域为目标，具有高活动率。最后，这些工具将允许目标活性部位的独特区域指导对特定目标具有高选择性的化合物的设计。这套工具的一个关键组成部分是对配体与蛋白质表面相互作用的新颖描述，称为足迹。示意图用作集群、过滤和学习方法的输入，以分析停靠屏幕的结果并比较目标系列成员之间的停靠结果。将对四个蛋白质家族进行大型化合物文库的虚拟筛选，对数据进行分析，并将发现新的有希望的组合文库。 总之，本赠款申请中所述工具的成功开发和应用可以： 1.显著提高虚拟屏幕的成功率； 2.构建针对蛋白质家族的高效聚焦组合文库； 3.在困难的靶标家族中产生特定靶标的导联，如激酶； 4.指导构建高度聚焦的体外实验筛选体系。

DESCRIPTION (provided by applicant): The goal is to provide a set of powerful docking-based rational drug discovery tools to take advantage of the increasingly rich amount of information on protein families sequence and structure emerging from genomics and structural genomics efforts. The tools will improve the reliability of current virtual screening methods by taking advantage of all available information on a protein target's family, including aligned sequences, available structures, co-crystalized ligands, and active compounds described in the literature. The tools will also specifically allow common regions of family active sites to be targeted in the design of family-focused combinatorial libraries with high activity rates against any member of the family. Lastly the tools will allow unique regions of the target active site to guide the design of compounds with high selectivity for a specific target. A key component of this suite of tools is a novel description of the interaction of a ligand with the surface of a protein called a footprint. Footprints are used as input to clustering, filtering, and learning methods to analyze the results of docking screens and to compare docking results across members of the target family. Virtual screening of large libraries of chemical compounds will be performed on four protein families, the data analyzed, and new promising scaffolds for future focused combinatorial libraries will be detected. In summary the successful development and application of the tools described in this grant request can: 1. Significantly increase the success rate of virtual screens; 2. Generate highly effective focused combinatorial libraries to protein families; 3. Generate target-specific leads in difficult target families such as kinases; 4. Guide the construction of highly focused in-vitro experimental screening.