Leveraging Adaptive Evolution and High-Throughput Techniques to Dissect the Link Between Biochemical Function and Fitness

利用适应性进化和高通量技术来剖析生化功能与健康之间的联系

• 批准号: 10704076
• 负责人: Margaux Pinney
• 金额: $ 40.38万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-13 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10704076
• 关键词: Active Sites; Address; Affect; Amino Acid Sequence; Antiviral Agents; Appointment; Area; Award; Bar Codes; Basic Science; Benchmarking; Biochemical; Biochemistry; Biological; Biological Assay; Biological Markers; Biology; Biophysics; Bioremediations; Cell Therapy; Cells; Chemicals; Chemistry; Collaborations; Communities; Complex; Data; Data Set; Dedications; Discipline; Doctor of Philosophy; Environment; Enzymes; Evolution; Faculty; Future; Goals; HRK gene; In Vitro; Individual; Industry; Institution; Investigation; Knowledge; Laws; Lead; Learning; Libraries; Life; Link; Maps; Measurement; Measures; Medicine; Mentors; Mentorship; Metabolic Diseases; Methods; Modeling; Molecular; Molecular Evolution; Mutation; Organism; Pathogenesis; Pharmaceutical Preparations; Physics; Positioning Attribute; Postdoctoral Fellow; Predisposition; Property; Proteins; Publications; Regulation; Research; Research Proposals; Specific qualifier value; Specificity; Structure; Techniques; Technology; Temperature; Testing; Therapeutic; Translating; Translational Research; Variant; Virulence; Work; anti-cancer; antimicrobial; biochemical evolution; catalyst; chemical reaction; computing resources; design; enzyme mechanism; experience; faculty mentor; fitness; frontier; genetic variant; graduate student; high throughput technology; human pathogen; improved; in vivo; interest; macromolecule; microfluidic technology; next generation; novel; precision medicine; pressure; protein function; recruit; response; senior faculty; tool; undergraduate student

PROJECT SUMMARY/ABSTRACT Enzymes are the primary functional molecules in cells, providing enormous rate enhancements, specificity and regulation to the diverse chemical reactions that are necessary for life. Enzymes, like all biological macromolecules, are the products of evolution: all enzymes have evolved to operate within the complex environment of the organism/cell in specific environmental niches(s). Thus, an understanding of enzyme function and evolution is fundamental to biology. Enzymes also have tremendous potential in medicine (e.g., as targets for anti-cancer, antimicrobial and antiviral drugs and as therapeutics for metabolic disorders) and in industry (e.g. to make important commodity chemicals and as catalysts for bioremediation). Our central premise is that a quantitative, mechanistic understanding of enzyme function and its relationship to organism fitness is critically needed to precisely manipulate enzymes and to deeply understand biology. To generate this level of understanding, we need: (1) a quantitative, chemical, and physical knowledge of enzyme function, and (2) mechanistic data describing how and when these physical principles contribute to enzyme function within the complex environments where enzymes operate. An enhanced understanding of the relationships between protein sequence, protein function and cellular/organismal fitness will have profound impacts across biology and medicine, from improving our ability to predict how mutations will influence the virulence and drug susceptibility of human pathogens, to enhancing precision medicine by accurately predicting the consequences of allelic variants, to enabling the design of next-generation protein and cellular therapeutics. Achieving this understanding requires new tools and a new conceptual paradigm. Enzymes are highly interconnected, their functions are multifaceted, and their cellular environments are complex. Traditional biochemistry is enormously powerful, allowing for the intensive study of a few individual enzymes in vitro (10s) and providing detailed knowledge of their chemical mechanisms. But identifying the many residues that matter for enzyme function requires investigation of residues beyond the active site at a scale far beyond that of traditional biochemistry. Furthermore, this biochemical information then needs to be translated to organism fitness in vivo in a quantitative manner. Here we will overcome these challenges. We will first use evolutionary sequence information to direct enzyme variant design towards functionally important areas of sequence space. We will adapt high-throughput microfluidic technologies to quantitively measure the biochemical properties (e.g., kcat, Km, Ki, and ∆GFold) of this library of 104 enzyme variants in vitro (Aim 1). Then we will determine how each of these variants affects organismal fitness in vivo using pooled competition and barcode sequencing assays (Aim 2). Finally, we will use this sequence-function-fitness map to test long-standing models in biochemistry and evolution and reveal the biochemical determinants of fitness important for industry and medicine (Aim 3). Such a comprehensive and quantitative mapping of biochemical function to fitness has never been achieved.

PROJECT SUMMARY/ABSTRACT Enzymes are the primary functional molecules in cells, providing enormous rate enhancements, specificity and regulation to the diverse chemical reactions that are necessary for life. Enzymes, like all biological macromolecules, are the products of evolution: all enzymes have evolved to operate within the complex environment of the organism/cell in specific environmental niches(s). Thus, an understanding of enzyme function and evolution is fundamental to biology. Enzymes also have tremendous potential in medicine (e.g., as targets for anti-cancer, antimicrobial and antiviral drugs and as therapeutics for metabolic disorders) and in industry (e.g. to make important commodity chemicals and as catalysts for bioremediation). Our central premise is that a quantitative, mechanistic understanding of enzyme function and its relationship to organism fitness is critically needed to precisely manipulate enzymes and to deeply understand biology. To generate this level of understanding, we need: (1) a quantitative, chemical, and physical knowledge of enzyme function, and (2) mechanistic data describing how and when these physical principles contribute to enzyme function within the complex environments where enzymes operate. An enhanced understanding of the relationships between protein sequence, protein function and cellular/organismal fitness will have profound impacts across biology and medicine, from improving our ability to predict how mutations will influence the virulence and drug susceptibility of human pathogens, to enhancing precision medicine by accurately predicting the consequences of allelic variants, to enabling the design of next-generation protein and cellular therapeutics. Achieving this understanding requires new tools and a new conceptual paradigm. Enzymes are highly interconnected, their functions are multifaceted, and their cellular environments are complex. Traditional biochemistry is enormously powerful, allowing for the intensive study of a few individual enzymes in vitro (10s) and providing detailed knowledge of their chemical mechanisms. But identifying the many residues that matter for enzyme function requires investigation of residues beyond the active site at a scale far beyond that of traditional biochemistry. Furthermore, this biochemical information then needs to be translated to organism fitness in vivo in a quantitative manner. Here we will overcome these challenges. We will first use evolutionary sequence information to direct enzyme variant design towards functionally important areas of sequence space. We will adapt high-throughput microfluidic technologies to quantitively measure the biochemical properties (e.g., kcat, Km, Ki, and ∆GFold) of this library of 104 enzyme variants in vitro (Aim 1). Then we will determine how each of these variants affects organismal fitness in vivo using pooled competition and barcode sequencing assays (Aim 2). Finally, we will use this sequence-function-fitness map to test long-standing models in biochemistry and evolution and reveal the biochemical determinants of fitness important for industry and medicine (Aim 3). Such a comprehensive and quantitative mapping of biochemical function to fitness has never been achieved.