Quantifying and Designing for Electrostatic Preorganization in Enzymes

在化学部生命过程化学（CLP）项目的支持下，来自加州大学洛杉矶分校的Anastassia Alexandrova博士和来自科罗拉多矿业学院的Mark Eberhart博士正在开发工具和方法，以实现人工酶的设计。酶是一般的蛋白质为基础，并作为生物催化的主力。现代化学面临的最大挑战之一是阐明生物功能的达尔文选择压力是双分子进化产物的大多数酶所表现出的催化效率的机制。 该计划将探讨酶结构远端的催化活性的网站，即活性位点的作用。 这种长程相互作用被认为是天然酶区别于人工酶的关键特征。 这项调查将结合联合收割机在计算化学与实验测试和基准的进展。 实验方法将用于评估酶结构远端变化对催化效率的影响。 新的计算方法将被用来确定这些遥远的结构变化，特别是改变近端反应位点的环境，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，然后是突变体的表达，设计的酶，然后是实验测试其催化效率。 这样，在本研究中，计算和实验之间将存在一个固有的反馈回路。 在这项调查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划分的新计算工具，以评估酶结构对天然和设计酶中静电预组织的影响。 这些工具将被用来，并同时完善，研究预组织在几个代表性的酶类，并询问预组织伴随自然和实验室酶的进化的进展。这些研究的目的是揭示酶的结构，活性位点电荷密度，反应障碍，催化效率和特异性之间的关系。 如果成功的话，从这些调查中得到的见解将有助于将预组织纳入酶设计范式。 最终，这种预测是实验测试，并通过引入工具，有效地预测和设计战略静电环境，促进所需的催化功能，在蛋白质设计领域有广泛的影响潜力。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.