Algorithmic assignment of probable function to proteins of previously unknown fun

将可能功能分配给先前未知的蛋白质的算法

• 批准号: 8370520
• 负责人: Herbert J. Bernstein
• 金额: $ 10.77万
• 依托单位: DOWLING COLLEGE
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8370520
• 关键词: Active Sites; Algorithms; Atlases; Automation; Biological Process; Catalytic Domain; Charge; Classification; Communities; Computer software; Computing Methodologies; Data; Databases; Elements; Environment; Enzymes; Evaluation; Family; Goals; Home environment; Ligand Binding; Methodology; Molecular; Output; Peroxidases; Plug-in; Process; Protein Family; Protein Structure Initiative; Proteins; Publishing; Reporting; Research Design; Research Methodology; Research Personnel; Resources; Running; Serine Protease; Site; Specificity; Structure; Structure-Activity Relationship; Testing; Time; Vertebral column; Work; base; design; interest; operation; protein function; protein structure; research study; structural genomics; three dimensional structure; tool

DESCRIPTION (provided by applicant): Algorithmic assignment of probable function to proteins of previously unknown function Objectives and Specific Aims: The goal of this project is to extend and apply algorithms that show promise in assigning a probable function for PDB entries of currently unknown function. This should contribute to deriving benefit from the Protein Structure Initiative by "help[ing] researchers illuminate structure-function relationships and thus formulate better hypotheses and design better experiments." Research Design and Methods: New protein structures are being determined at a rate faster than their biological function can be assigned. There are currently 2939 entries in the Protein Data Bank with the classification "Unknown Function". A number of computational methods have been developed to provide rapid, inexpensive means of function prediction for these structures, including those that focus on alignment of entire backbones and others that focus on identification and alignment of active site residues based on the unusual charge distributions in protein structures. We have developed a software plug-in for the PyMOL molecular graphics environment called ProMOL that relies on the geometric relationships conserved in enzyme catalytic sites. Motifs in ProMOL were created from the active site specifications found in the Catalytic Site Atlas (CSA) (http://www.ebi.ac.uk/thornton-srv/databases/CSA/). Our approach explicitly searches for CSA- defined catalytic site residues according to specific atomic geometry, similar in concept to the CSA JESS templates. This dispenses with the need to filter out confounding elements such as conserved folding domains or ligand binding regions. Extensive testing of structural files from the serine protease and peroxidase families confirmed that the geometric relationships of catalytic residues alone are effective and sufficient for function prediction in protein structures. In addition to extensive characterization of serine proteases and peroxidases, we also performed a preliminary study of 39 PDB entries classified as "Structural Genomics, Unknown Function" using the Motif Finder in ProMOL, which contains 22 "native" ProMOL motifs, along with the corresponding CSA JESS C1C2 motifs and CSA Functional Atom motifs. Of the 39 entries studied, 26 (67%) yielded prediction values of 1 (exact match to an existing template). An active site lacking one residue or containing an extra (outlier) residue was identified for 36 (92%) of the structures. No match was reported in only three of the test cases. We will extend the number of motifs in ProMOL's Motif Finder, using both newly created ProMOL motifs and existing JESS motifs to include representatives from the most prominent protein families, increase automation of the process and then evaluate all PDB entries described as having "unknown function". Entries that show positive correlation will then be further explored using sequence and structure alignment tools. Both software and results will be openly released to the community.

描述（由申请人提供）：对先前未知功能的蛋白质进行可能功能的化学分配目标和具体目的：本项目的目标是扩展和应用算法，该算法有望为当前未知功能的PDB条目分配可能功能。这将有助于从蛋白质结构计划中获益，“帮助研究人员阐明结构-功能关系，从而提出更好的假设并设计更好的实验。研究设计和方法：新的蛋白质结构正在以比其生物功能更快的速度被确定。目前在蛋白质数据库中有2939个条目，分类为“未知功能”。已经开发了许多计算方法来为这些结构提供快速，廉价的功能预测手段，包括那些专注于整个骨架的对齐和其他专注于基于蛋白质结构中不寻常的电荷分布的活性位点残基的识别和对齐的方法。我们已经开发了一个软件插件的PyMOL分子图形环境称为ProMOL，依赖于酶催化位点保守的几何关系。ProMOL中的基序是根据催化位点图谱（CSA）（http：//www.ebi.ac.uk/thornton-srv/databases/CSA/）中的活性位点规格创建的。我们的方法显式搜索CSA定义的催化位点残基根据特定的原子几何形状，类似的概念CSA JESS模板。这就不需要过滤掉诸如保守的折叠结构域或配体结合区之类的混杂元件。对来自丝氨酸蛋白酶和过氧化物酶家族的结构文件的广泛测试证实，单独催化残基的几何关系对于蛋白质结构中的功能预测是有效且足够的。除了丝氨酸蛋白酶和过氧化物酶的广泛表征，我们还使用ProMOL中的基序序列对39个PDB条目进行了初步研究，这些条目被归类为“结构基因组学，未知功能”，ProMOL中包含22个“天然”ProMOL基序，沿着相应的CSA JESS C1 C2基序和CSA功能原子基序。在研究的39个条目中，26个（67%）产生的预测值为1（与现有模板完全匹配）。一个活性位点缺乏一个残基或含有额外的（离群值）残基被确定为36（92%）的结构。仅在3个测试用例中报告不匹配。我们将扩展ProMOL的基序库中的基序数量，使用新创建的ProMOL基序和现有的JESS基序，以包括来自最突出的蛋白质家族的代表，增加过程的自动化，然后评估所有被描述为具有“未知功能”的PDB条目。然后将使用序列和结构比对工具进一步探索显示正相关的序列。软件和结果都将向社区公开发布。