Structure-Function Relationships in the Tautomerase Superfamily

互变酶超家族的结构-功能关系

• 批准号: 9767833
• 负责人: CHRISTIAN P. WHITMAN
• 金额: $ 30.92万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9767833
• 关键词: Active Sites; Address; Amino Acids; Area; Autoimmune Diseases; Bacteria; Biochemical; Biological; Biological Models; Biological Process; Carboxy-Lyases; Disease; Drug resistance; Enzymes; Evolution; Family; Genome; Genomics; Goals; Health; Human; Infection; Inflammatory; Length; Malignant Neoplasms; Malonates; Medical; Metagenomics; Methodology; Methods; Migration Inhibitory Factor; Mining; Organism; Phylogenetic Analysis; Proline; Property; Proteins; Reaction; Research; Sepsis; Structure; Structure-Activity Relationship; Subgroup; Technology; Work; cis-3-chloroacrylic acid dehalogenase; cytokine; drug development; drug resistant microorganism; enzyme activity; fungus; genomic data; human migration; insight; member; microorganism; oxalocrotonate

The annotation of proteins with unknown functions discovered in genome projects is a key goal for the effective use of genomic data in the treatment of disease in humans and in other organisms. It remains an extremely difficult problem, but real progress has been made using the concept of functionally diverse superfamilies. The work proposed here uses an integrated computational/experimental strategy to enhance current approaches for the assignment of function in the mechanistically diverse tautomerase superfamily (TSF). Exploitation of key features of the TSF (such as the ease of biochemical characterization) that make it a good model system for the proposed aims lays the groundwork for application of these methods to all other superfamilies. This will lead to a sustained improvement in our ability to use structure-function relationships on a large scale to inform function prediction. The proposed work will address fundamental questions about structure-function relationships in TSF enzymes and in other superfamilies using the TSF as a model system. It will move sequence similarity network (SSN) technology forward in the areas of identifying and exploiting linkers, mining metagenomic sequences, and genome context networks. These methodologies can be applied to all superfamilies. It will characterize the macrophage migration inhibitory factor (MIF) subgroup of the TSF, the subgroup with the most direct medical relevance. The phylogenetic distribution of MIF is the broadest of the 5 subgroups in the TSF. Mammalian MIFs are proinflammatory cytokines, but little is known about the functions in bacteria, fungi, and other organisms. The work will be accomplished in three specific aims. These aims are to: (1) investigate how active site sequences and structures diverge to generate new functions; (2) explore sequence length and oligomer size in the evolution of structure and activity; and (3) examine genomic context and biological function in the TSF. The results will also address our long-term goals, which are to obtain a more comprehensive understanding of the relationship between structure and function in enzyme-catalyzed reactions, and how these features change to create new activities. The results will advance our understanding of the functions of bacterial and parasitic MIFs and their relationships to the mammalian ones, which are implicated in multiple inflammatory disorders, and provide insight into the evolution of enzyme activities, which has implications for the development of drug resistance in microorganisms.

The annotation of proteins with unknown functions discovered in genome projects is a key goal for the effective use of genomic data in the treatment of disease in humans and in other organisms. It remains an extremely difficult problem, but real progress has been made using the concept of functionally diverse superfamilies. The work proposed here uses an integrated computational/experimental strategy to enhance current approaches for the assignment of function in the mechanistically diverse tautomerase superfamily (TSF). Exploitation of key features of the TSF (such as the ease of biochemical characterization) that make it a good model system for the proposed aims lays the groundwork for application of these methods to all other superfamilies. This will lead to a sustained improvement in our ability to use structure-function relationships on a large scale to inform function prediction. The proposed work will address fundamental questions about structure-function relationships in TSF enzymes and in other superfamilies using the TSF as a model system. It will move sequence similarity network (SSN) technology forward in the areas of identifying and exploiting linkers, mining metagenomic sequences, and genome context networks. These methodologies can be applied to all superfamilies. It will characterize the macrophage migration inhibitory factor (MIF) subgroup of the TSF, the subgroup with the most direct medical relevance. The phylogenetic distribution of MIF is the broadest of the 5 subgroups in the TSF. Mammalian MIFs are proinflammatory cytokines, but little is known about the functions in bacteria, fungi, and other organisms. The work will be accomplished in three specific aims. These aims are to: (1) investigate how active site sequences and structures diverge to generate new functions; (2) explore sequence length and oligomer size in the evolution of structure and activity; and (3) examine genomic context and biological function in the TSF. The results will also address our long-term goals, which are to obtain a more comprehensive understanding of the relationship between structure and function in enzyme-catalyzed reactions, and how these features change to create new activities. The results will advance our understanding of the functions of bacterial and parasitic MIFs and their relationships to the mammalian ones, which are implicated in multiple inflammatory disorders, and provide insight into the evolution of enzyme activities, which has implications for the development of drug resistance in microorganisms.