Abstract
Class A β-lactamases are known for being able to rapidly gain broad spectrum catalytic efficiency against most β-lactamase inhibitor combinations as a result of elusively minor point mutations. The evolution in class A β-lactamases occurs through optimisation of their dynamic phenotypes at different timescales. At long-timescales, certain conformations are more catalytically permissive than others while at the short timescales, fine-grained optimisation of free energy barriers can improve efficiency in ligand processing by the active site. Free energy barriers, which define all coordinated movements, depend on the flexibility of the secondary structural elements. The most highly conserved residues in class A β-lactamases are hydrophobic nodes that stabilize the core. To assess how the stable hydrophobic core is linked to the structural dynamics of the active site, we carried out adaptively sampled molecular dynamics (MD) simulations in four representative class A β-lactamases (KPC-2, SME-1, TEM-1, and SHV-1). Using Markov State Models (MSM) and unsupervised deep learning, we show that the dynamics of the hydrophobic nodes is used as a metastable relay of kinetic information within the core and is coupled with the catalytically permissive conformation of the active site environment. Our results collectively demonstrate that the class A enzymes described here, share several important dynamic similarities and the hydrophobic nodes comprise of an informative set of dynamic variables in representative class A β-lactamases.
| Original language | English |
|---|---|
| Article number | 720991 |
| Journal | Frontiers in Microbiology |
| Volume | 12 |
| DOIs | |
| State | Published - Sep 21 2021 |
Funding
SH and RB acknowledge a grant from the National Institutes of Health United States under the award number RO1AI063517. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under contract number DEAC05-00OR22725. This research is sponsored in part by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U.S. Department of Energy. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract no. DE-AC05-00OR22725. 10.1128/AAC.02097-16 Shimamura, T., Ibuka, A., Fushinobu, S., Wakagi, T., Ishiguro, M., Ishii, Y., et al. (2002). Acyl-intermediate structures of the extended-spectrum class A β-lactamase, Toho-1, in complex with Cefotaxime, Cephalothin, and Benzylpenicillin. J. Biol. Chem. 277, 46601–46608. doi: 10.1074/jbc. M207884200 Stoesser, N., Sheppard, A. E., Peirano, G., Anson, L. W., Pankhurst, L., Sebra, R., et al. (2017). Genomic epidemiology of global Klebsiella pneumoniae carbapenemase (KPC)-producing Escherichia coli. Sci. Rep. 7:5917. doi: 10.1038/s41598-017-06256-2 Tomasello, G., Armenia, I., and Molla, G. (2020). The protein imager: a full-featured online molecular viewer interface with server-side HQ-rendering capabilities. Bioinformatics 36, 2909–2911. doi: 10.1093/bioinformatics/ btaa009 Tooke, C. L., Hinchliffe, P., Bragginton, E. C., Colenso, C. K., Hirvonen, V. H. A., Takebayashi, Y., et al. (2019). β-Lactamases and β-lactamase inhibitors in the 21st century. J. Mol. Biol. 431, 3472–3500. doi: 10.1016/j.jmb.2019.04.002 Tooke, C. L., Hinchliffe, P., Krajnc, A., Mulholland, A. J., Brem, J., Schofield, C. J., et al. (2020). Cyclic boronates as versatile scaffolds for KPC-2 β-lactamase inhibition. RSC Med. Chem. 11, 491–496. doi: 10.1039/C9MD00557A Towns, J., Cockerill, T., Dahan, M., Foster, I., Gaither, K., Grimshaw, A., et al. (2014). XSEDE: accelerating scientific discovery. Comput. Sci. Eng. 16, 62–74. doi: 10.1109/MCSE.2014.80 Wang, X., Minasov, G., and Shoichet, B. K. (2002). The structural bases of antibiotic resistance in the clinically derived mutant beta-lactamases TEM-30, TEM-32, and TEM-34. J. Biol. Chem. 277, 32149–32156. doi: 10.1074/jbc. M204212200 Wells, B. A., and Chaffee, A. L. (2015). Ewald summation for molecular simulations. J. Chem. Theory Comput. 11, 3684–3695. doi: 10.1021/acs. jctc.5b00093 Zafaralla, G., and Mobashery, S. (1992). Facilitation of the. DELTA.2.Fwdarw. DELTA.1 pyrroline tautomerization of carbapenem antibiotics by the highly conserved arginine-244 of class A. Beta.-lactamases during the course of turnover. J. Am. Chem. Soc. 114, 1505–1506. doi: 10.1021/ja00030a070 Licenses and Permissions: This manuscript has been co-authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the United States Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paidup, irrevocable, world-wide license to publish, or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
Keywords
- Markov state model
- class A
- deep learning
- hydrophobic nodes
- molecular dynamics
- β-lactamase