Surface Shape Based Screening of Large Protein Databases

在结构基因组学时代，需要提取和表示蛋白质结合部位的3D形状， 以便以健壮和简单的方式重复使用信息。这项提议的长期目标是 一套使用局部表面形状的计算算法和数据库的开发 用于预测蛋白质功能和蛋白质-蛋白质对接预测的蛋白质特征。储存 数据库中预先计算的结合位点允许快速筛选和比较。用于识别的算法， 将表示、比较、聚集和对接蛋白质的局部表面形状特征 发展起来的。为了识别结合部位，将使用基于可见性的算法，该算法可以识别两个空洞 和突起区域。为了表示已识别的结合位点，表示的三个层次级别 都被提出了。最简单的表示方案使用捕获全局或局部的特征点 结合部位的最大/最小平均曲率、半径和深度。第二个层次的 制图表达使用基于直方图的方法，捕获 结合部位。最后一种表示采用了体素化方法。结合位点数据库将 使用基于R树的多维索引来实现绑定的实时筛选和聚类 网站。它还计划使用自组织映射进行集群，它允许动态更新 光泽。通过R树或自组织映射预先计算的层次聚类提供了一个框架 用于由可缩放用户界面显示的动态集群。基于几何散列的快速算法 开发了蛋白质-蛋白质对接算法，该算法使用预先计算的结合位点来减少蛋白质对接的 搜索空间。在散列步骤中将使用新的不变基，以降低 从O(N3)到O(N2)的算法。哈希算法采用了一种新的非均匀哈希表，具有较强的容错性 到微小的误差或参数的改变。该方法将得到扩展，以能够处理 可能存在错误的已判定结构。活动站点的识别方法将是APP；IED来预测 结构基因组计划确定的未知功能的蛋白质结构的功能。这个 对接算法将应用于大肠杆菌的蛋白质-蛋白质相互作用数据。

In the structural genomics era, there is a need to extract and represent 3D shapes of protein binding sites, in order to reuse the information in a robust and simple manner. The long term objective of this proposal is the development of a set of computational algorithms and a database for using local surface shape signatures of proteins to predict function of proteins, and for protein-protein docking prediction. Storing precalculated binding sites in a database allows fast screening and comparison. Algorithms for identifying, representing, comparing, clustering, and docking local surface shape signatures of proteins will be developed. To identify binding sites, a visibility based algorithm will be used, which can identify bothcavity and protrusion regions. To represent identified binding sites, three hierarchical levels of representation are proposed. The simplest representation scheme uses feature points which capture global or local maximum/minimum mean curvatures, the radius, and depth of a binding site. The second level of the representation uses a histogram-based method, capturing relative distances between feature points of a binding site. The last representation employs a voxelization method. The database of binding sites will employ R-tree based multidimensional indexes to allow real-time screening and clustering of binding sites. It is also planned to use a Self-Organizing Map for clustering, which allows dynamic updating of lusters. Pre-calculated hierarchical clusters by the R-tree or a Self-Organizing Map provide a framework for dynamic clustering displayed by a zoomable user interface. The fast geometric hashing-based protein-protein docking algorithm is developed, which uses precalculated binding sites to reduce the search space. A new invariant basis will be used in the hashing step to reduce the complexity of the algorithm from O(n3) to O(n2). A novel non-uniform hashing table is used in the hashing, which is tolerant to small errors or changes of parameters. The methodology will be extended to be able to handle aredicted structures with possible errors. The active site identification methods will be app;ied to predict function of protein structures of unknown function determined by structural genomics projects. The docking algorithm will be applied to protein-protein interaction data of ¿ coli.