Learning Drug Specificity in Protein Families by Docking

通过对接学习蛋白质家族中的药物特异性

• 批准号: 6692482
• 负责人: RICHARD Masten FINE
• 金额: $ 50.18万
• 依托单位: BIOCOMPUTING GROUP, INC.
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-08-01 至 2005-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6692482
• 关键词: drug /agent; ligands; method development; protein sequence; protein structure; structural biology

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.

描述（由申请人提供）： 目标是提供一套强大的基于对接的合理药物发现工具，以利用基因组学和结构基因组学工作中出现的越来越丰富的蛋白质家族序列和结构信息。这些工具将通过利用蛋白质靶标家族的所有可用信息（包括比对序列、可用结构、共结晶配体和文献中描述的活性化合物）来提高当前虚拟筛选方法的可靠性。这些工具还特别允许在设计以家族为中心的组合文库时，以家族活性位点的共同区域为目标，对家族的任何成员都具有高活性率。最后，这些工具将允许目标活性位点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称为足迹的蛋白质表面相互作用的新颖描述。足迹用作聚类、过滤和学习方法的输入，以分析对接屏幕的结果并比较目标家族成员之间的对接结果。将对四个蛋白质家族进行大型化合物库的虚拟筛选，分析数据，并将检测到未来重点组合库的新的有前途的支架。 总之，本拨款请求中描述的工具的成功开发和应用可以： 1.大幅提升虚拟屏幕成功率； 2. 生成高效的蛋白质家族集中组合文库； 3. 在激酶等困难靶标家族中生成靶标特异性先导化合物； 4.指导高度聚焦的体外实验筛选建设。