Protein Structure/Function Specific Packing Motifs

蛋白质结构/功能特异性包装基序

基本信息

批准号：
7665373
负责人：
Alexander Tropsha
金额：
$ 26.44万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2006
资助国家：
美国
起止时间：
2006-08-01 至 2011-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7665373
关键词：
Active Sites Address Algorithms Amino Acid Sequence Amino Acids Bioinformatics Characteristics Classification Classification Scheme Collaborations Collection Complement Conserved Sequence Databases Development Family Family member Fingerprint Genes Genomics Graph Health High Performance Computing Homology Modeling Human Individual Knowledge Lead Libraries Maps Methodology Methods Mining Neighborhoods Ontology Orphan Pattern Peptide Sequence Determination Pharmacotherapy Protein Analysis Protein Databases Protein Family Protein Structure Databases Proteins Proteome Protocols documentation Quadruplet Multiple Birth Recurrence Research Research Personnel Specific qualifier value Structural Protein Structure Structure-Activity Relationship Techniques base computerized tools data mining novel programs protein function protein structure protein structure function rapid growth structural genomics therapeutic target tool

项目摘要

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.

描述（由申请人提供）：结构基因组学的主要需求之一是自动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，即蛋白质包装的简单邻域分析 (SNAPP)，用于识别蛋白质结构集合中重复出现的序列结构基序。我们建议系统地应用统计几何和几何图案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对已知的 3D 蛋白质结构进行比较和分类。最后，我们的目标是证明其中一些结构模式可以映射到形成序列特异性模式的基础蛋白质序列上，因此也可用于序列注释和分类。我们采用了一种称为 Delaunay 曲面细分的计算几何技术，它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一组自然的基序，这些基序可能是蛋白质结构和功能类别的特征。通过对代表已知结构和功能家族的蛋白质图集合应用频繁的常见子图挖掘方法，可以获得更广泛的基序定义。为了发现结构和功能家族特定基序并将其应用于蛋白质分类和注释，该提案围绕以下具体目标构建：目标 1. 开发新算法，基于蛋白质图家族的频繁公共子图挖掘来识别蛋白质家族特定包装基序;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。该项目受益于四位研究人员的共同努力，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) 方面拥有互补的专业知识。所提出的方法预计既稳健又有效，能够应用于蛋白质结构和序列的大型后基因组规模数据库。拟议的研究将导致发现以前未知的氨基酸残基模式，这些氨基酸残基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了解。由于蛋白质是最典型的治疗靶点，我们旨在更好地理解蛋白质结构与功能关系的研究应有助于发现药物治疗的新靶点，从而有助于改善人类健康。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Functional neighbors: inferring relationships between nonhomologous protein families using family-specific packing motifs.

功能邻居：使用家族特异性包装基序推断非同源蛋白质家族之间的关系。

DOI：
发表时间：
2010-09
期刊：
影响因子：
0
作者：
Bandyopadhyay, Deepak;Huan, Jun;Liu, Jinze;Prins, Jan;Snoeyink, Jack;Wang, Wei;Tropsha, Alexander
通讯作者：
Tropsha, Alexander

Evaluation of the relative stability of liganded versus ligand-free protein conformations using Simplicial Neighborhood Analysis of Protein Packing (SNAPP) method.

使用蛋白质包装的简单邻域分析 (SNAPP) 方法评估配体与无配体蛋白质构象的相对稳定性。