Algorithmic assignment of probable function to proteins of previously unknown fun

描述(由申请人提供)：将可能的功能分配给先前未知功能目标和特定目的的蛋白质的算法：该项目的目标是扩展和应用在为当前未知功能的PDB条目分配可能功能方面显示前景的算法。这将有助于“帮助研究人员阐明结构与功能的关系，从而形成更好的假设和设计更好的实验”，从而从蛋白质结构倡议中获益。研究设计与方法：新的蛋白质结构被确定的速度快于其生物学功能被分配的速度。目前蛋白质数据库中有2939个条目被归类为“未知功能”。已经发展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比对的方法，以及其他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比对的方法。我们已经开发了一个用于PyMOL分子图形环境的软件插件ProMOL，它依赖于酶催化位置中保守的几何关系。PromoL中的基序是根据催化中心图谱(CsA)(http://www.ebi.ac.uk/thornton-srv/databases/CSA/).中的活性中心规范创建的我们的方法根据特定的原子几何结构显式搜索CSA定义的催化位点残基，在概念上类似于CSA Jess模板。这就不需要过滤掉混杂的元件，例如保守的折叠结构域或配体结合区。对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，仅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的和足够的。除了对丝氨酸蛋白酶和过氧化物酶的广泛表征外，我们还使用ProMOL中的Motif Finder对39个被归类为“结构基因组学，未知功能”的PDB条目进行了初步研究，其中包含22个“天然”ProMOL基序，以及相应的CSA Jess C1C2基序和CSA功能原子基序。在所研究的39个条目中，26个(67%)的预测值为1(与现有模板完全匹配)。在36个(92%)的结构中，鉴定出缺少一个残基或含有额外(异常值)残基的活性部位。只有三个测试案例没有匹配的报告。我们将扩展ProMOL基序查找器中的基序数量，使用新创建的ProMOL基序和现有的Jess基序，以包括来自最著名的蛋白质家族的代表，提高过程的自动化，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性的条目。软件和结果都将向社区公开发布。 与公共健康相关：对以前未知功能相关的蛋白质进行可能功能的算法分配：蛋白质结构倡议(PSI)的一个预期好处是，结构描述将帮助研究人员阐明结构-功能关系，从而制定更好的假设和设计更好的实验；然而，即使在获得蛋白质的三维结构后，该蛋白质的一个或多个功能也不总是明显的。将已知功能的蛋白质的显著结构特征与功能未知的PSI靶标中的相似特征进行比较的算法可以为那些靶标分配可能的功能提供有用的指导，本项目的目的是使用这种算法将可能的功能分配给未知功能的PSI靶标的重要子集，从而帮助更好地理解结构-功能关系。

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. PUBLIC HEALTH RELEVANCE: Algorithmic assignment of probable function to proteins of previously unknown function Relevance: One expected benefit of the Protein Structure Initiative (PSI) is that structural descriptions will help researchers illuminate structure-function relationships and thus formulate better hypotheses and design better experiments; however, even after a three dimensional structure of a protein has been obtained the function or functions of that protein are not always apparent. Algorithms that compare salient structural features of proteins of known function to similar features in PSI targets for which the function is not yet known can provide helpful guidance in assigning probable functions to those targets and the aim of this project is to use such algorithms to assign probable functions to a significant subset of the PSI targets of unknown function and thereby help in better understanding structure-function relationships.