Complete computational design of high-efficiency Kemp elimination enzymes

This paper presents a fully computational approach to designing enzymes that efficiently catalyze a model chemical reaction, demonstrating performance comparable to natural enzymes without experimental optimization. It highlights a practical step forward in enzyme design for broader applications in science and industry.

What is this paper about?

The researchers developed a fully computational method, eliminating test-tube tinkering and large mutant screens, to design enzymes that catalyze a non-natural reaction called the Kemp elimination. This serves as a model for creating brand‑new enzymatic functions.

Historically, computer‑designed enzymes were far less efficient than natural ones, and improving them required laborious lab work in the form of screening the computer-generated enzymes. This study eliminates that barrier. The team created three successful designs with efficiency over 2,000 M⁻¹·s⁻¹. One design, drastically different from any natural protein (with over 140 mutations and a novel active site), achieved an efficiency of 12,700 M⁻¹·s⁻¹ and a catalytic rate of 2.8 s⁻¹, approximately 100 times better than previous efforts.
Pushing further, adding a single engineered residue raised the efficiency above 10⁵ M⁻¹·s⁻¹ and the rate to 30 s⁻¹, matching natural enzyme performance. All accomplished without any physical screening, just smart computational design.

Why is it important?

• Efficiency of enzyme design skyrockets: Now, researchers can move beyond trial‑and‑error. They can program enzymes entirely in silico and still achieve near‑natural results.
• Broader applications: Custom enzymes like these have practical potential in sustainable chemistry, medicine (drug synthesis), environmental cleanup, and industrial bioprocessing.
• Challenge accepted and met: The study fundamentally changes assumptions about computational enzyme design, demonstrating it’s possible to match natural enzyme efficiency without experimental tweaks.

Abstract: 

Until now, computationally designed enzymes exhibited low catalytic rates and required intensive experimental optimization to reach activity levels observed in comparable natural enzymes. These results exposed limitations in design methodology and suggested critical gaps in our understanding of the fundamentals of biocatalysis. We present a fully computational workflow for designing efficient enzymes in TIM-barrel folds using backbone fragments from natural proteins and without requiring optimization by mutant-library screening. Three Kemp eliminase designs exhibit efficiencies greater than 2,000 M−1 s−1. The most efficient shows more than 140 mutations from any natural protein, including a novel active site. It exhibits high stability (greater than 85 °C) and remarkable catalytic efficiency (12,700 M−1 s−1) and rate (2.8 s−1), surpassing previous computational designs by two orders of magnitude. Furthermore, designing a residue considered essential in all previous Kemp eliminase designs increases efficiency to more than 105 M−1 s−1 and rate to 30 s−1, achieving catalytic parameters comparable to natural enzymes and challenging fundamental biocatalytic assumptions. By overcoming limitations in design methodology, our strategy enables programming stable, high-efficiency, new-to-nature enzymes through a minimal experimental effort.