9HVB image
Deposition Date 2024-12-26
Release Date 2025-06-11
Last Version Date 2025-08-13
Entry Detail
PDB ID:
9HVB
Keywords:
Title:
High-efficiency Kemp eliminases by complete computational design
Biological Source:
Source Organism:
Escherichia coli (Taxon ID: 562)
Host Organism:
Method Details:
Experimental Method:
Resolution:
2.00 Å
R-Value Free:
0.24
R-Value Work:
0.19
R-Value Observed:
0.19
Space Group:
P 61
Macromolecular Entities
Structures with similar UniProt ID
Protein Blast
Polymer Type:polypeptide(L)
Molecule:Kemp eliminase
Chain IDs:A
Chain Length:256
Number of Molecules:1
Biological Source:Escherichia coli
Ligand Molecules
Primary Citation
Complete computational design of high-efficiency Kemp elimination enzymes.
Nature 643 1421 1427 (2025)
PMID: 40533551 DOI: 10.1038/s41586-025-09136-2

Abstact

Until now, computationally designed enzymes exhibited low catalytic rates1-5 and required intensive experimental optimization to reach activity levels observed in comparable natural enzymes5-9. These results exposed limitations in design methodology and suggested critical gaps in our understanding of the fundamentals of biocatalysis10,11. 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 magnitude1-5. 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 methodology11, our strategy enables programming stable, high-efficiency, new-to-nature enzymes through a minimal experimental effort.

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Primary Citation of related structures