Active Chemistry Cells, Biochemistry & Physiology

How enzymes flatten the free energy landscape of inherently slow reactions

In plain English

AI plain-English summary

Enzymes that speed up naturally sluggish chemical reactions rely on a hidden thermodynamic trick—stored "entropy"—to keep working efficiently, and this project will reveal exactly how that trick operates. The problem is that scientists cannot yet design artificial enzymes that match the performance of natural ones, especially for reactions that are inherently slow. Current designer enzymes often stall after a single use because their products get stuck inside. This project targets a specific gap: how natural enzymes use large-scale movements—opening and closing like a clamshell—to flush out products and reset for the next reaction, powered by what the researchers call entropy stores. If this fundamental science succeeds, it will provide the mechanistic blueprint needed to build better artificial enzymes from scratch. That could transform manufacturing processes that rely on precise chemical reactions—producing specialist chemicals, pharmaceuticals, or even breaking down plastics—with lower energy use and less reliance on fossil fuels. The work is curiosity-driven, but the knowledge it generates is a prerequisite for AI-driven enzyme design to become genuinely accurate, much as understanding protein folding was necessary before AI could predict structures.

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Context The exploitation of enzyme catalysis both in vitro and in vivo is key to UK life science strategies, specifically to aspirations to engineer biology to deliver essential products with reduced energy usage and lower reliance on non-renewable sources. It also promises the delivery of exquisite positional and stereochemical control of chemical reactions that is so vital to the production of specialist chemicals. This study relates directly to these aspirations, and thereby sections 5.1 (Transformative technologies) and 5.3 (Advanced manufacturing and clean growth) of the current BBSRC Strategic Delivery Plan. While there have been many advances in the understanding of enzyme activity and their evolution using high-throughput methods, the state-of-the-art for the design of new catalysts remains seriously limited by gaps in understanding. Commonly, the outcome is low catalytic efficiency, single turnover enzymes, hampered by product inhibition. This is particularly apparent for enzymes that accelerate slow uncatalyzed reactions, which present the greatest challenge for the design of new enzymes. This vital gap in knowledge is starkly demonstrated by there being no designed enzymes to-date with significant domain opening/closing, a common feature of such enzymes. Challenge The overall challenge is to ensure that the knowledge base relating to the fundamentals of enzyme catalysis is sufficiently broad and robust that the design of new enzymes becomes an efficient reality. Without a detailed understanding of all mechanisms in play, future AI approaches, like those that have made a step change in protein structure prediction, will be slow to develop the necessary accuracy. Our specific challenge is to address questions relating to how and why some native enzymes use large-scale domain opening/closing, and why some distal mutations greatly diminish catalysis and yet bind transition state analogues more tightly than the parent enzyme. We will achieve this by quantitively analysing how enzymes exploit conformational entropy stores, first identified by our preliminary studies. These stores represent in-built thermodynamic helpers that prevent enzymes being inefficient, single turnover catalysts and likely assist domain opening/closing. Aims and Objectives Our overall aim is to make a crucial step change in the knowledge base as to how enzymes deliver enormous catalytic efficiency for very slow uncatalyzed reactions. Specifically, we will examine how entropy stores are used in tandem with the domain opening/closing process that is characteristic of such enzymes, thereby enabling unhindered multiple turnovers by circumventing crippling levels of substrate/product inhibition. To achieve our aims, we will target three objectives that address key and general questions relating to how entropy stores are utilised in tandem with the domain opening/closing process. While we will focus on an archetypal enzyme that catalyses an inherently very slow phosphoryl transfer reaction, we will determine the extent to which such mechanisms are utilised in another phosphoryl transfer enzyme, and an enzyme for an unrelated reaction. Specifically, the three questions that we will address are: (i) How does an enzyme for a very slow uncatalyzed reaction utilise entropy stores to assist the chemical step? (ii) How does an enzyme ensure efficient opening/closing to enable optimum activity and efficient product release? (iii) To what level is this approach retained in enzymes for faster uncatalyzed reactions? Potential applications and benefits The potential applications of our results span the enormous spectrum of scenarios in which improved enzyme catalysis plays a role, from specialist chemicals production to recycling plastics.

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Researchers

Andrew Almond (Co-Investigator)Jonathan Waltho (Principal Investigator)Matthew Cliff (Co-Investigator)

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Original classification

Research and Innovation

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