Evolutionary study of structure-function relationship

结构-功能关系的进化研究

• 批准号: 6773025
• 负责人: EUGENE I SHAKHNOVICH
• 金额: $ 30.05万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-04-01 至 2008-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6773025
• 关键词: biochemical evolution; protein metabolism; protein structure function; proteomics; statistics /biometry; technology /technique development

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.

描述（由申请人提供）：该提案的主要目的是发展蛋白质结构-功能关系的综合定量进化理论。为此，提出了一种基于图论的研究蛋白质的新方法，即根据结构功能或代谢参与的相似性，将所有蛋白质域的整个宇宙组织成一个图（PDUG）。这提供了特定生物体中所有现有域或整个蛋白质组水平上蛋白质的多维描述。使用图论来分析结构、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。比较不同生物体的蛋白质域宇宙图有助于创建系统发育的新范例，即生命树不是基于特定基因或 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，对蛋白质域宇宙图的稳健特性的分析使得开发可测试的蛋白质进化动态模型成为可能，该模型涵盖从单一突变到生物体进化的一系列进化时间尺度。 该研究计划包含实现这些具体目标的几个关键步骤。首先，将开发蛋白质之间功能和代谢关系的新定量图理论描述。它将基于蛋白质功能和代谢注释的分层描述，并将使用马尔可夫模型来量化功能和代谢空间中的距离，以及通过基于图的代谢物之间的相似性比较来量化酶之间的功能距离。使用这些新的定量描述，将构建多维蛋白质域宇宙图，并且每个图将被聚类成结构、功能和代谢相似蛋白质的不相交簇。这些簇之间的重叠提供了结构-功能关系的程度，并且还将蛋白质的功能与其参与特定的代谢途径联系起来。通过为各种生物体创建多维蛋白质域宇宙图，我们首先将评估不同生物体中各种结构和功能模板的参与程度，并通过比较这些，我们将创建一个全面的生命树，这将揭示重大进化事件。这些发现将用于增强我们预测新型蛋白质的结构和功能的能力，从而产生可能的治疗应用。我们的研究结果将通过 ELISA 数据库提供给科学界。