Energetic basis of excited-state enzyme design and function

Chi-Yun Lin, M. Romei, I. Mathews, S. Boxer
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引用次数: 2

Abstract

The last decades have witnessed an explosion of de novo protein designs with a remarkable range of scaffolds. It remains challenging, however, to design catalytic functions that are competitive with naturally occurring counterparts as well as biomimetic or non-biological catalysts. Although directed evolution often offers efficient solutions, the fitness landscape remains opaque. Green fluorescent protein (GFP), which has revolutionized biological imaging and assays, is one of the most re-designed proteins. While not an enzyme in the conventional sense, GFPs feature competing excited-state decay pathways with the same steric and electrostatic origins as conventional ground-state catalysts, and they exert exquisite control over multiple reaction outcomes through the same principles. Thus, GFP is an “excited-state enzyme”. Herein we show that rationally designed mutants and hybrids that contain environmental mutations and substituted chromophores provide the basis for a quantitative model and prediction that describes the influence of sterics and electrostatics on excited-state catalysis of GFPs. As both perturbations can selectively bias photoisomerization pathways, GFPs with fluorescence quantum yields (FQYs) and photoswitching characteristics tailored for specific applications could be predicted and then demonstrated. The underlying energetic landscape, readily accessible via spectroscopy for GFPs, offers an important missing link in the design of protein function that is generalizable to catalyst design.
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激发态酶设计和功能的能量基础
在过去的几十年里,我们见证了一系列支架的全新蛋白质设计的爆炸式发展。然而,设计与天然存在的对应物以及仿生或非生物催化剂具有竞争力的催化功能仍然具有挑战性。尽管定向进化通常提供有效的解决方案,但适应度仍然不透明。绿色荧光蛋白(GFP)是重新设计最多的蛋白质之一,它已经彻底改变了生物成像和检测。虽然不是传统意义上的酶,但GFP具有与传统基态催化剂具有相同空间和静电起源的竞争激发态衰变途径,并且它们通过相同的原理对多种反应结果进行精细控制。因此,GFP是一种“激发态酶”。在此,我们表明,合理设计的含有环境突变和取代发色团的突变体和杂交种为描述空间学和静电学对GFP激发态催化的影响的定量模型和预测提供了基础。由于这两种扰动都可以选择性地偏置光异构化途径,因此可以预测并证明具有荧光量子产率(FQY)和针对特定应用定制的光开关特性的GFP。通过GFP的光谱学很容易获得潜在的能量景观,这在蛋白质功能的设计中提供了一个重要的缺失环节,可推广到催化剂设计中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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