Evolutionary study of structure-function relationship

描述（由申请人提供）：本提案的主要目的是发展蛋白质结构-功能关系的全面定量进化理论。为此，提出了一种基于图论的研究蛋白质的新方法，即基于结构功能或代谢参与相似性，将所有蛋白质结构域的整个宇宙组织成一个图（PDUG）。这提供了在特定生物体中所有现有结构域或整个蛋白质组的水平上对蛋白质的多维描述。利用图论分析结构、功能和代谢蛋白质域宇宙，可以对蛋白质的进化起源及其多样性的原因获得独特的见解。比较来自不同生物体的蛋白质结构域宇宙图有助于在系统发育中创建一个新的范例，即生命之树不是基于特定的基因或RNA分子，而是基于结构、功能和代谢关系的多维空间中的整个蛋白质组。此外，对蛋白质结构域宇宙图的鲁棒性分析使得开发可测试的蛋白质进化动态模型成为可能，该模型涵盖了从单个突变到生物体进化的一系列进化时间尺度。

DESCRIPTION (provided by applicant): The main aim of this proposal is to develop a comprehensive quantitative evolutionary theory of structure-function relationship in proteins. To this end, a novel approach to study proteins is proposed based on graph theory, whereby the whole universe of all protein domains is organized into a graph (PDUG), based on structural functional or metaboilic participation similarities. This provides a multidimensional description of proteins at the level of all existing domains or whole proteoms in specific organisms. Using graph theory to analyze structural, functional and metabolic protein domain universes makes it possible to get unique insights into the evolutionary origin of proteins and the cause of their diversity. Comparing protein domain universe graphs from different organisms helps to create a new paradigm in phylogeny, whereby the tree of life is built based, not on specific genes or RNA molecules, but on whole proteoms taken in multidimensional space of structural, functional and metabolic relationships. Furthermore, the analysis of robust properties protein domain universe graphs makes it possible to develop testable dynamic models of protein evolution that encompass a range of evolutionary time scales from single mutations to the evolution of organisms. The research plan encompasses several crucial steps to achieve these specific aims. First, a new quantitative graph theoretical description of functional and metabolic relationships between proteins will be developed. It will be based on hierarchical description of functional and metabolic annotation of proteins, and will use markov models to quantify the distances in functional and metabolic spaces, as well as to quantify functional distances between enzymes via graph based similarity comparisons between their metabolites. Using these new quantitative descriptions, multidimentional protein domain universe graphs will be constructed and each will be clustered into disjoint clusters of structurally, functionally and metabolically similar proteins. Overlap between these clusters provides the extent of structure-function relationship and will also relate functions of proteins with their participation in particular metabolic pathways. By creating multidimensional protein domain universe graphs for various organisms, we first will evaluate the degree of participation of various structural and functional templates in different organisms, and by comparing those, we will create a comprehensive tree of life that will shed light on major evolutionary events. These findings will be applied to enhance our ability to predict structure and function of novel proteins leading to possible therapeutical applications. Our findings will be available to the scientific community via the ELISA database.