Energetic basis of excited-state enzyme design and function

22 November 2021, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

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.

Keywords

protein engineering
green fluorescent protein
fluorescence quantum yield
color
electrostatics
sterics
catalysis
excited state
photochemistry

Supplementary materials

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Description
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Supporting Information
Description
Materials and methods, further discussions on the use of electrostatic models for fluorescent protein FQY tuning, supporting figures, and supporting tables.
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