Evolutionary study of structure-function relationship

结构-功能关系的进化研究

• 批准号: 6874497
• 负责人: EUGENE I SHAKHNOVICH
• 金额: $ 26.34万
• 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/6874497
• 关键词: biochemical evolution; protein metabolism; protein structure function; proteomics; statistics /biometry; technology /technique development; 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开发蛋白质中结构功能关系的全面定量进化论。为此，提出了一种基于图理论的新方法研究蛋白质，从而基于结构功能或代谢参与的相似性，将所有蛋白质结构域的整个宇宙组织成图（PDUG）。这提供了在特定生物体中所有现有域或整个蛋白质水平上蛋白质的多维描述。使用图理论分析结构，功能和代谢蛋白领域宇宙使得有可能对蛋白质的进化起源及其多样性的原因进行独特的见解。比较来自不同生物体的蛋白质结构域宇宙图有助于创建一个新的系统发育中的范式，生命树是基于特定基因或RNA分子的，而是基于在结构，功能和代谢关系的多维空间中所采用的整个蛋白质。此外，对稳健性质蛋白结构域宇宙图的分析使得可以开发可测试的蛋白质演化动态模型，该模型涵盖了从单个突变到生物体演变的一系列进化时间尺度。 该研究计划包括实现这些特定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层次描述，并将使用Markov模型来量化功能和代谢空间的距离，并通过基于图的相似性比较其代谢物之间的酶之间的功能距离来量化酶之间的功能距离。使用这些新的定量描述，将构建多维蛋白质结构域宇宙图，并将每个蛋白质界图聚类为结构，功能和代谢上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，还将将蛋白质的功能与特定代谢途径的参与相关联。通过为各种生物创建多维蛋白质结构域宇宙图，我们首先将评估各种结构和功能模板在不同生物体中的参与程度，并且通过比较这些模板，我们将创建一种全面的生命树，以揭示重大进化事件。这些发现将用于增强我们预测新蛋白质结构和功能的能力，从而导致可能的治疗应用。我们的发现将通过ELISA数据库提供给科学界。