Multiple Protein Structures in Computational Drug Design

描述(申请人提供)：提出了一种使用多个 蛋白质结构在计算机药物设计中的应用。这样做的最初目标是 建议是提供一种结合蛋白质灵活性的可靠技术 转化为基于结构的药物发现(SBDD)。建模社区需要 方法来解决这个长期存在的问题，并且该任务提供了一个 在解决以下问题之前衡量使用MPS的优缺点 结构基因组学面临更大的挑战。 NIGMS最近资助了七个研究中心作为其蛋白质的一部分 结构倡议。这次爆炸将给药物发现带来革命性的变化 未来的信息，但前提是有工具将许多相关的 以对SBDD有用的方式研究蛋白质结构。这样做的长期目标是 工作是通过开发出更好的计算机辅助药物设计 更准确地对目标蛋白质进行建模并开发大量 来自蛋白质组学的可用信息。更好的药物设计方法将 加快发现先导化合物和新的药物疗法。 该项目的具体目标集中在(1)优化协议以实现 使用来自计算机模拟和实验源的MPS，(2)外推 MPS在发育过程中开发同源蛋白亚家族的应用 以及(3)将该方法应用于以下系统 生物医学的重要性。MPS将被用来创建基于受体的药效团 几种酶系统的模型。将通过搜索一个 文献中活性和非活性抑制化合物的数据库。 成功的模型将呈现很少的假阳性，并将识别大多数 数据库中的活性化合物。与酶学家的合作是 建议在后期阶段协助开发新的抑制剂 项目。

DESCRIPTION (provided by applicant): A method is presented for using multiple protein structures (MPS) in computational drug design. The initial goal of this proposal is to provide a solid technique for incorporating protein flexibility into structure-based drug discovery (SBDD). The modeling community needs methods that address this long-standing problem, and the task provides a measure of the strengths and weaknesses of using MPS before tackling the greater challenge of structural genomics. NIGMS has recently funded seven research centers as part of its Protein Structure Initiative. Drug discovery will be revolutionized with the explosion of information to come, but only if the tools exist to combine many related protein structures in a way that is useful for SBDD. The long-term goal of this work is to improve the field of computer-aided drug design by developing methods that more accurately model target proteins and exploit the vast amount of information available from proteomics. Better methods for drug design will speed the discovery of lead compounds and new pharmaceutical therapies. The specific aims for this project focus on (1) optimizing the protocol for using MPS from computer simulations and experimental sources, (2) extrapolating the use of MPS to exploit a subfamily of homologous proteins in the development of broad-spectrum therapeutics, and (3) applying the method to systems of biomedical importance. MPS will be used to create receptor-based pharmacophore models for several enzymatic systems. The models will be judged by searching a database of active and inactive inhibitory compounds from the literature. Successful models will present few false positives and will identify the most active compounds in the database. Collaborations with enzymologists are proposed to aid the development of novel inhibitors in the later stages of the project.