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位点的3D形状， 以便以稳健和简单的方式重新使用信息。这项建议的长远目标是 开发了一套计算算法和数据库，用于使用局部表面形状 蛋白质的特征以预测蛋白质的功能，以及用于蛋白质-蛋白质对接预测。存储 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， 表示、比较、聚类和对接蛋白质的局部表面形状特征将是 开发为了识别结合位点，将使用基于可见性的算法，该算法可以识别两个腔 和突起区域。为了表示识别的结合位点，三个分层表示水平 被提议。最简单的表示方案使用捕获全局或局部的特征点 结合位点的最大/最小平均曲率、半径和深度。第二层 表示使用基于直方图的方法，捕获 结合位点最后一种表示采用体素化方法。结合位点数据库将 采用基于R树的多维索引，以允许对绑定进行实时筛选和聚类 网站.还计划使用自组织映射进行聚类，这允许动态更新 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聚类。基于快速几何散列的 蛋白质-蛋白质对接算法的发展，它使用预先计算的结合位点，以减少 搜索空间在散列步骤中将使用新的不变基以降低 时间复杂度为O（n2）该算法采用了一种新的非均匀哈希表，具有较好的容错性 小的错误或参数的变化。该方法将被扩展到能够处理 预测可能存在错误的结构。活性部位识别方法将被应用于预测 结构基因组学计划确定的未知功能的蛋白质结构的功能。的 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.