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Dynamical responses predict a distal site that modulates activity in an antibiotic resistance enzyme

Michael Beer, Ana Sofia F Oliveira, Catherine L Tooke, Philip Hinchliffe, Angie Tsz Yan Li, Balazs Balega, James Spencer, Adrian J Mulholland

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Abstract

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-2 G89D 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.

Original languageEnglish
Pages (from-to)17232-17244
Number of pages13
JournalChemical Science
Volume15
Issue number41
Early online date30 Sept 2024
DOIs
Publication statusPublished - 7 Nov 2024

Data Availability Statement

All raw MD simulations data (including equilibrium and non-equilibrium simulations) will be made freely available at the University of Bristol Research Data Repository (https://data.bris.ac.uk/). Analysis scripts will be made available upon request.

Funding

MB was supported by the BBSRC-funded South West Biosciences Doctoral Training Partnership [BB/T008741/10]. ASFO thanks the Biotechnology and Biological Sciences Research Council for support through BBSRC grants BB/W003449/1 and BB/X009831/1. C. L. T., J. S. and A. J. M. thank the Medical Research Council for support through the grant MR/T016035/1. This work is part of a project that has received funding from the European Research Council under the European Horizon 2020 research and innovation program (PREDACTED Advanced Grant Agreement no. 101021207) to A. J. M. and J. S. The authors thank Marc W. van der Kamp for discussion about the work and providing feedback. All simulations were conducted using the facilities of the Advanced Computing Research Centre at the University of Bristol (https://www.bris.ac.uk/acrc/). The authors thank the Diamond Light Source (beamline I03, proposals 23269 and 31440) and the beamline scientists for their service that enabled the X-ray diffraction data presented here to be collected. The authors would like to thank Prof. Shozeb Haider for useful discussions on the manuscript and suggesting the inclusion of ref. 72.

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