Surface Shape Based Screening of Large Protein Databases

描述（由申请人提供）：在结构基因组学时代，需要提取和表示蛋白质结合位点的三维形状，以便以鲁棒和简单的方式重用信息。本提案的长期目标是开发一套计算算法和数据库，用于使用蛋白质的局部表面形状特征来预测蛋白质的功能，并用于蛋白质-蛋白质对接预测。将预先计算的结合位点存储在数据库中可以快速筛选和比较。将开发用于识别、表示、比较、聚类和对接蛋白质局部表面形状特征的算法。为了识别结合位点，将使用基于可见性的算法，该算法可以识别空洞和突起区域。为了表示已识别的结合位点，提出了三个层次的表示。最简单的表示方案使用特征点来捕获全局或局部最大/最小平均曲率、半径和绑定位点的深度。第二层表示使用基于直方图的方法，捕获结合位点特征点之间的相对距离。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，实现结合位点的实时筛选和聚类。它还计划为集群使用自组织映射，这允许动态更新集群。通过r树或自组织映射预先计算的分层集群为动态集群提供了一个框架，该框架由可缩放的用户界面显示。提出了一种基于几何哈希的蛋白质-蛋白质快速对接算法，该算法利用预先计算的结合位点来减少搜索空间。在哈希步骤中将使用一个新的不变基，以将算法的复杂度从O（n3）降低到O（n2）。在散列中使用了一种新颖的非均匀散列表，它可以容忍小的误差或参数的变化。该方法将被扩展到能够处理可能存在误差的预测结构。活性位点鉴定方法将应用于结构基因组学项目确定的未知功能蛋白结构的功能预测。该对接算法将应用于大肠杆菌蛋白-蛋白相互作用数据。

DESCRIPTION (provided by applicant): 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 both cavity 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 clusters. 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 predicted structures with possible errors. The active site identification methods will be applied 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 E.coli.