Improved Protein Mapping for Fragment-Based Drug Design

描述(由申请人提供)：计算溶剂作图方法将分子探针-小分子或功能基团-放置在蛋白质表面，以确定最有利的结合位置。虽然X射线结晶学和核磁共振显示有机小分子优先聚集在结合部位，但目前的计算方法在蛋白质表面产生数百个能量最小值，并且很难确定哪些是相关的。波士顿大学结构生物信息学小组开发了一种新的映射算法，该算法通常可以消除虚假的局部极小值，并找到与X射线或核磁共振数据很好地吻合的有机小探针分子的结合位置。到目前为止，该方法的主要应用是描绘酶和其他蛋白质的活性部位，以及检测配体结合部位的微小构象变化。本提案的总体目标是将这种高效和高精度的映射算法发展成基于片段的药物设计程序的第一步，并将该方法应用于已知困难的药物靶点，从而为合作协议发展一个科学和商业基础。第一阶段的具体目标如下：(1)根据基于片段的药物设计需要修改作图程序，包括改进探针的初始放置，促进新探针的添加，引入更一般的经验潜力，以及改进作图后对探针分布的评估；(2)通过对药用活性化合物数据库中的分子进行碎片化，然后对所得到的片段进行聚类，来开发最佳片段文库；以及(3)开发具有适当存储、检索和结果分析的高通量自动蛋白质图谱软件包。我们预计，当映射结合或无配体的蛋白质结构时，映射将正确地放置来自已知配体的片段。利用通用片段库和聚焦片段库，我们将探索几个重要的药物靶标的结合部位，包括过氧化物酶体增殖物激活受体(PPPAR)、蛋白酪氨酸磷酸酶IB(FTP1B)、一些蛋白激酶和细胞色素P450。初步结果表明，用设计良好的文库中的片段进行映射将为药物设计提供非常有用的信息。

DESCRIPTION (provided by applicant): Computational solvent mapping methods place molecular probes - small molecules or functional groups - on a protein surface in order to identify the most favorable binding positions. Although X-ray crystallography and NMR show that small organic molecules preferentially cluster in the binding site, current computational methods yield hundreds of energy minima on the surface of the protein, and it is difficult to determine which of the minima are relevant. The Structural Bioinformatics Group at Boston University has developed a novel mapping algorithm that generally eliminates the spurious local minima, and finds the bound positions of small organic probe molecules in good agreement with x-ray or NMR data. The major applications of the method so far have been delineating the active sites of enzymes and other proteins, and detecting minor conformational changes in ligand binding sites. The general goal of the present proposal is to develop this efficient and highly accurate mapping algorithm into the first step of a fragment-based drug design procedure, and apply the method to drug targets that are known to be difficult, thereby developing a scientific and commercial base for collaborative agreements. The Phase I Specific Aims are as follows: (1) modifying the mapping program as required for fragment-based drug design, including improvements in the initial placement of the probes, facilitating the addition of new probes, introducing a more general empirical potential, and improving the evaluation of probe distributions after the mapping; (2) developing optimal fragment libraries by the fragmentation of molecules in databases of pharmaceutically active compounds and then clustering the resulting fragments; and (3) developing a high throughput automated protein mapping software package with appropriate storage, retrieval and analysis of results. We expect that the mapping will correctly place fragments derived from known ligands when mapping either bound or ligand-free protein structures. Using both general and focused fragment libraries, we will explore the binding sites of several important drug targets, including peroxisome proliferator activated receptors (PPPARs), protein tyrosine phosphatase IB (FTP1B), some protein kinases, and cytochrome P450s. Preliminary results suggest that mapping with fragments from well designed libraries will provide very useful information for drug design.