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

项目摘要/摘要 酶是细胞中的主要功能分子，提供巨大的速率提升， 对生命所必需的各种化学反应的特异性和调节。酶，就像所有生物一样 大分子，是进化的产物：所有的酶都进化成在复合体内运作 特定环境中的有机体/细胞的环境(S)。因此，对酶的功能有一个理解 而进化是生物学的基础。酶在医学上也有巨大的潜力(例如，作为靶标 用于抗癌、抗微生物和抗病毒药物以及作为代谢紊乱的治疗药物)和工业(如： 以制造重要的商品化学品并用作生物修复的催化剂)。我们的中心前提是 定量地、机械地理解酶的功能及其与生物体适应性的关系是至关重要的 需要精确地操纵酶并深入了解生物学。 为了达到这种程度的理解，我们需要：(1)定量、化学和物理知识 以及(2)描述这些物理原理如何以及何时起作用的机械数据 酶在酶活动的复杂环境中发挥作用。加深了对 蛋白质序列、蛋白质功能和细胞/组织适应性之间的关系将具有深远的意义 对生物学和医学的影响，从提高我们预测突变如何影响 通过准确预测提高精准医学对人类病原体的毒力和药物敏感性 等位基因变异的后果，使下一代蛋白质和细胞疗法的设计成为可能。 实现这一理解需要新的工具和新的概念范式。酶的含量很高 它们相互关联，功能多方面，蜂窝环境复杂。传统型 生物化学非常强大，允许在体外对几种单独的酶进行密集的研究(10秒)。 并提供了它们的化学机制的详细知识。但识别出许多重要的残留物 对于酶的功能，需要研究活性部位以外的残基，其规模远远超过 传统生物化学。此外，这些生化信息还需要转化为生物体 以一种定量的方式在体内适应。在这里，我们将克服这些挑战。我们将首先使用进化 序列信息，用于指导序列空间的功能重要区域的酶变异体设计。 我们将采用高通量微流控技术来定量测量生化特性(例如， KCAT、KM、KI和∆GFold)该文库的104个体外酶变体(目标1)。然后我们将确定每个人 利用混合竞争和条形码测序分析，这些变体中的哪一个会影响体内的生物体适应性 (目标2)。最后，我们将使用这个序列-功能-适应度图来测试生物化学和生物化学中的长期模型 生物进化和揭示对工业和医学非常重要的健康的生物化学决定因素(目标3)。是这样的 生化功能与健康之间的全面和定量映射从未实现过。

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.