8RWR image
Deposition Date 2024-02-05
Release Date 2025-01-22
Last Version Date 2025-01-22
Entry Detail
PDB ID:
8RWR
Title:
KPC-2 G89D/E166Q Mutant in Complex with Imipenem
Biological Source:
Source Organism:
Method Details:
Experimental Method:
Resolution:
1.03 Å
R-Value Free:
0.17
R-Value Work:
0.15
R-Value Observed:
0.15
Space Group:
P 21 21 2
Macromolecular Entities
Polymer Type:polypeptide(L)
Molecule:Carbapenem-hydrolyzing beta-lactamase KPC
Gene (Uniprot):KPC-2
Chain IDs:A
Chain Length:290
Number of Molecules:1
Biological Source:Klebsiella pneumoniae
Primary Citation
Dynamical responses predict a distal site that modulates activity in an antibiotic resistance enzyme.
Chem Sci 15 17232 17244 (2024)
PMID: 39364073 DOI: 10.1039/d4sc03295k

Abstact

β-Lactamases, which hydrolyse β-lactam antibiotics, are key determinants of antibiotic resistance. Predicting the sites and effects of distal mutations in enzymes is challenging. For β-lactamases, the ability to make such predictions would contribute to understanding activity against, and development of, antibiotics and inhibitors to combat resistance. Here, using dynamical non-equilibrium molecular dynamics (D-NEMD) simulations combined with experiments, we demonstrate that intramolecular communication networks differ in three class A SulpHydryl Variant (SHV)-type β-lactamases. Differences in network architecture and correlated motions link to catalytic efficiency and β-lactam substrate spectrum. Further, the simulations identify a distal residue at position 89 in the clinically important Klebsiella pneumoniae carbapenemase 2 (KPC-2), as a participant in similar networks, suggesting that mutation at this position would modulate enzyme activity. Experimental kinetic, biophysical and structural characterisation of the naturally occurring, but previously biochemically uncharacterised, KPC-2G89D mutant with several antibiotics and inhibitors reveals significant changes in hydrolytic spectrum, specifically reducing activity towards carbapenems without effecting major structural or stability changes. These results show that D-NEMD simulations can predict distal sites where mutation affects enzyme activity. This approach could have broad application in understanding enzyme evolution, and in engineering of natural and de novo enzymes.

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