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自动化蛋白质结构比较和分类的方法。早些时候，我们开发了一种工具，简单邻域分析蛋白质包装（SNAPP），用于识别蛋白质结构集合中的重复序列结构基序。我们建议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（家族签名）。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，我们的目标是证明其中一些结构模式可以映射到潜在的蛋白质序列上，形成序列特定模式，因此也用于序列注释和分类。我们采用了一种称为德劳内镶嵌的计算几何技术，该技术将蛋白质结构划分为独特的四联体接触集。这种考虑将三级结构简化为可能是蛋白质结构和功能类特征的基序的自然基础集。通过对表示已知结构和功能家族的蛋白质图集合应用频繁的公共子图挖掘方法，可以获得更广泛的基序定义。为了发现结构和功能家族特异性基序，并将其应用于蛋白质分类和注释，本提案围绕以下具体目标进行：基于蛋白质图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定的包装基序；目的2：鉴定不同蛋白质家族中特定的氨基酸包装基序，并将其定义为序列特异性特征；目标3：开发基于家族特异性包装基序的蛋白质注释方法。该项目得益于四位研究人员的合作努力，他们在结构生物信息学（Tropsha）、计算几何（Snoeyink）、数据挖掘（Wang）和高性能计算（Prins）方面具有互补的专业知识。所提出的方法有望既稳健又有效，以提供它们的应用，蛋白质结构和序列的大，后基因组规模的数据库。拟议的研究将导致发现以前未知的氨基酸残基模式，这些模式对蛋白质结构和功能很重要。孤儿蛋白的功能注释将扩展我们对人类蛋白质组的认识。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的结构-功能关系，这将有助于发现新的药物治疗靶点，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.