Protein Structure/Function Specific Packing Motifs

描述(由申请人提供)：结构基因组学的主要需求之一是一种用于蛋白质结构自动比较和分类的方法。早些时候，我们已经开发了一个工具，蛋白质包装的单纯邻域分析(SNAPP)，用于识别蛋白质结构集合中的重复序列结构基序。我们提出了系统地应用统计几何和几何模式匹配技术来识别蛋白质家族特定的包装模式(家族特征)。我们进一步建议使用这些特征来比较和分类已知的3D蛋白质结构。最后，我们的目标是证明这些结构模式中的一些可以被映射到潜在的蛋白质序列上，形成序列特定的模式，因此也用于序列注释和分类。我们使用了一种称为Delaunay镶嵌的计算几何技术，它将蛋白质结构划分为独特的四重接触集。这种考虑将三级结构简化为一组可能是蛋白质结构和功能类别特征的自然基序集。通过将频繁的公共子图挖掘方法应用于代表已知结构和功能家族的蛋白质图集合，可以获得更广泛的基序定义。为了发现结构和功能家族特有的基序并将其应用于蛋白质分类和注释，该建议围绕以下具体目标构建：目的1.基于蛋白质图家族的频繁公共子图挖掘，开发新的算法来识别蛋白质家族特有的包装基序；目标2：识别不同蛋白质家族中的特定氨基酸包装基序，并将其定义为序列特有的特征；目标3：建立基于家族特有的包装基序的蛋白质标注方法。这个项目得益于四名研究人员的合作努力，他们在结构生物信息学(Tropasha)、计算几何(Snoeyink)、数据挖掘(Wang)和高性能计算(PRINS)方面拥有互补的专业知识。预计所提出的方法将是稳健和有效的，以便将其应用于蛋白质结构和序列的大型、后基因组规模的数据库。拟议的研究将导致发现对蛋白质结构和功能重要的以前未知的氨基酸残基模式。孤儿蛋白的功能注释将扩大我们对人类蛋白质组的了解。由于蛋白质是最典型的治疗靶点，我们的研究旨在更好地了解蛋白质结构与功能的关系，应该有助于发现药物治疗的新靶点，从而为改善人类健康做出贡献。

DESCRIPTION (provided by applicant): One of the principal needs for structural genomics is a methodology for automated protein structure comparison and classification. Earlier, we have developed a tool, Simplicial Neighborhood Analysis of Protein Packing (SNAPP) for the identification of recurrent sequence-structure motifs in a collection of protein structures. We propose systematic application of statistical geometry and geometric pattern matching techniques for the identification of protein family specific packing patterns (family signatures). We further propose to use these signatures for comparison and classification of known 3D protein structures. Finally, we aim to demonstrate that some of these structural patterns can be mapped onto underlying protein sequences forming sequence specific pattern and therefore used also for sequence annotation and classification. We employ a computational geometry technique known as Delaunay tessellation, which partitions protein structures into unique sets of quadruplet contacts. This consideration reduces tertiary structure to a natural basis set of motifs that may be characteristic of protein structural and functional classes. A broader definition of motifs can be obtained by applying frequent common subgraph mining approaches to the collections of protein graphs representing known structural and functional families. To discover structural and functional family specific motifs and apply them towards protein classification and annotation, this proposal is structured around the following Specific Aims: Aim 1. Develop novel algorithms to identify protein family specific packing motifs based on frequent common subgraph mining of protein graph families; Aim 2: Identify specific amino acid packing motifs in diverse protein families and define them as sequence specific signatures; Aim 3: Develop methodologies for protein annotation based on family-specific packing motifs. This project benefits from collaborative efforts of four investigators with complimentary expertise in structural bioinformatics (Tropsha), computational geometry (Snoeyink), data mining (Wang), and high-performance computing (Prins). The proposed methodologies are expected to be both robust and efficient to afford their application to large, post-genomic scale databases of protein structures and sequences. The proposed studies shall lead to the discovery of previously unknown patterns of amino acid residues that are important for protein structure and function. Functional annotation of orphan proteins will expand our knowledge of the human proteome. Since proteins are the most typical therapeutic targets, our research aimed at bettering our understanding of the protein structure-function relationships should facilitate the discovery of novel targets for drug therapy thereby contributing to the improvement of human health.