Quantifying and Designing for Electrostatic Preorganization in Enzymes

在化学学部生命过程化学（CLP）项目的支持下，加州大学洛杉矶分校的Anastassia Alexandrova博士和科罗拉多矿业学院的Mark Eberhart博士正在开发能够设计人工酶的工具和方法。酶以蛋白质为基础，是生物催化的主力。现代化学面临的巨大挑战之一是阐明大多数酶所显示的催化效率的机制，这些酶是生物分子进化的产物，是生物功能的达尔文选择压力。本节目将探讨酶结构远端催化活性位点的作用；也就是活性部位。这种长距离的相互作用被认为是区分天然酶与人工酶的关键特征。这项研究将结合计算化学与实验测试和基准测试的进展。实验方法将用于评估远端酶结构变化对催化效率的影响。新的计算方法将用于确定这些遥远的结构变化如何特异性地改变近端反应位点环境并促进或抑制酶催化。所采取的方法将是首先预测特定结构改变对酶效率的影响，然后是突变的表达，设计的酶，然后是实验测试它们的催化效率。这样，在本研究中，计算与实验之间就形成了一种固有的反馈回路。在这项研究中开发的计算工具将提供给其他研究小组探索计算辅助分子设计。该项目还包括一个外展计划，旨在使K-12学生熟悉分子生物学和化学的进步所带来的巨大挑战和机遇，以及计算方面的同等进步。该项目将开发基于电荷密度分配的新计算工具，以评估酶结构对天然和设计酶的静电预组织的影响。这些工具将被用于研究几种具有代表性的酶类的预组织，并询问伴随自然和实验室酶进化的预组织进展。这些研究的目的是揭示酶结构、活性位点电荷密度、反应屏障、催化效率和特异性之间的关系。如果成功，从这些研究中获得的见解将有助于将预组织纳入酶设计范式。最终，这些预测在实验上是可测试的，并且通过引入有效预测和设计促进所需催化功能的战略性静电环境的工具，这些预测在蛋白质设计领域具有广泛影响的潜力。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

With the support of the Chemistry of Life Processes (CLP) program in the Chemistry Division, Dr. Anastassia Alexandrova from the University of California, Los Angeles, and Dr. Mark Eberhart from the Colorado School of Mines are developing tools and methods to enable the design of artificial enzymes. Enzymes are general protein-based and serve as the workhorses for biological catalysis. One of the great challenges confronting modern chemistry is to illuminate the mechanisms responsible for the catalytic efficiency displayed by most enzymes that is the products of bimolecular evolution are Darwinian selection pressure for biological function. This program will explore the role of enzyme structure distal to the site of catalytic activity; namely, the active site. Such long range interactions are thought to be a key feature distinguishing natural enzymes from their artificial counterparts. This investigation will combine advances in computational chemistry with experimental testing and benchmarking. Experimental approaches will be used to assess the effect of distal changes in enzyme structure upon catalytic efficiency. The novel computational methods will be employed to determine how these distant structural changes specifically alter the proximal reaction site environment and promote or inhibit enzyme catalysis. The approach taken will be to first predict the effects of specific structural alterations to enzyme efficiency, followed by the expression of the mutant, designed enzymes, followed by experimental testing their catalytic efficiency. In this way, there will be an inherent feedback loop between computation and experiment in this research. The computational tools developed in this investigation will be made available to other research groups exploring computationally-aided molecular design. This project also includes an outreach program intended to familiarize K-12 students with the great challenges and opportunities afforded by the advances in molecular biology and chemistry when coupled with equivalent advances in computation. This project will develop new computational tools based on charge density-partitioning to assess the effects of enzyme structure on electrostatic pre-organization in natural and designed enzymes. These tools will be used, and concurrently refined, to study pre-organization in several representative enzyme classes, and to interrogate the progress of pre-organization accompanying natural and laboratory enzyme evolution. The goal of these studies is to uncover relationships between enzymatic structure, active site charge density, reaction barriers, and catalytic efficiency and specificity. If successful, the insight resulting from these investigations will facilitate the inclusion of pre-organization into enzyme design paradigms. Ultimately, such predictions are experimentally testable, and such have the potential for broad impact in the protein design field by introducing tools to effectively predict and design strategic electrostatic environments that promote the desired catalytic function.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.