Emrah SARIYER, Ayşegül SARAL

Evaluation of quercetin as a potential β-lactamase CTX-M-15 inhibitor via the molecular docking, dynamics simulations, and MMGBSA

Antimicrobial resistance (AMR) threatens millions of people around the world and has been declared a global risk by the World Economic Forum. One of the important AMR mechanisms in Enterobacteriaceae is the production of extended-spectrum β-lactamases. The most common ESBL, CTX-M β-lactamases, is spread to the world by CTX-M-15 and CTX-M-14. Sulbactam, clavulanic acid, and tazobactam are first-generation β-lactamase inhibitors and avibactam is a new non-β-lactam β-lactamase inhibitor. We studied that avibactam, sulbactam, clavulanic acid, tazobactam, and quercetin natural flavonoids were docked to target protein CTXM-15. Subsequently, the complexes were simulated using the molecular dynamics simulations method during 100 ns for determining the final binding positions of ligands. Clavulanic acid left CTX-M-15 and other ligands remained in the binding site after the simulation. The estimated binding energies were calculated during 100 ns simulation by the MMGBSA-MMPBSA method. The estimated free binding energies of avibactam, sulbactam, quercetin, tazobactam, and clavulanic acid were sorted as –33.61 kcal/mol, –16.04 kcal/mol, –14 kcal/mol, –12.68 kcal/mol, and –2.95 kcal/mol. As a result of both final binding positions and free binding energy calculations, Quercetin may be evaluated an alternative candidate and a more potent β-lactamases inhibitor for new antimicrobial combinations to CTX-M-15. The results obtained in silico studies are predicted to be a preliminary study for in vitro studies for quercetin and similar bioactive natural compounds. These studies are notable for the discovery of natural compounds that can be used in the treatment of infections caused by β-lactamase-producing pathogens.

PDF

___

1. Prestinaci F, Pezzotti P, Pantosti A. Antimicrobial resistance: a global multifaceted phenomenon. Pathogens and Global Health 2015; 109 (7): 309-318. doi: 10.1179/2047773215Y.0000000030
2. Sukmawinata E, Sato W, Mitoma S, Kanda T, Kusano K et al. Extended-spectrum β-lactamase-producing Escherichia coli isolated from healthy thoroughbred racehorses in Japan. Journal of Equine Science 2019; 30 (3): 47-53. doi: 10.1294/jes.30.47
3. Mrowiec P, Klesiewicz K, Małek M, Skiba-Kurek I, Sowa-Sierant I et al. Antimicrobial susceptibility and prevalence of extended-spectrum beta-lactamases in clinical strains of Klebsiella pneumoniae isolated from pediatric and adult patients of two Polish hospitals. The New Microbiologica 2019; 42 (4).
4. Faheem M, Rehman MT, Danishuddin M, Khan AU. Biochemical characterization of CTX-M-15 from Enterobacter cloacae and designing a novel non-β-lactam-β-lactamase inhibitor. PLoS One 2013; 8 (2). doi: 10.1371/journal.pone.0056926
5. Tehrani KH, Martin NI. β-lactam/β-lactamase inhibitor combinations: an update. MedChemComm 2018; 9 (9): 1439-56. doi: 10.1039/ c8md00342d
6. Bush K, Bradford PA. β-Lactams and β-lactamase inhibitors: an overview. Cold Spring Harbor Perspectives in Medicine 2016; 6 (8): a025247. doi: 10.1101/cshperspect.a025247
7. Ehmann DE, Jahić H, Ross PL, Gu R-F, Hu J et al. Avibactam is a covalent, reversible, non–β-lactam β-lactamase inhibitor. Proceedings of the National Academy of Sciences 2012; 109 (29): 11663-8. doi: 10.1073/pnas.1205073109
8. Faheem M, Rehman MT, Danishuddin M, Khan AU. Biochemical characterization of CTX-M-15 from Enterobacter cloacae and designing a novel non-β-lactam-β-lactamase inhibitor. PLoS One 2013; 8 (2): e56926. doi: 10.1371/journal.pone.0056926
9. Ali A, Danishuddin, Maryam L, Srivastava G, Sharma A et al. Designing of inhibitors against CTX-M-15 type β-lactamase: potential drug candidate against β-lactamases-producing multi-drug-resistant bacteria. Journal of Biomolecular Structure and Dynamics 2018; 36 (7): 1806-1821. doi: 10.1080/07391102.2017.1335434
10. Shirley M. Ceftazidime-avibactam: a review in the treatment of serious gram-negative bacterial infections. Drugs 2018; 78 (6): 675-92. doi: 10.1007/s40265-018-0902-x
11. Ghiglione B, Rodríguez MM, Curto L, Brunetti F, Dropa M et al. Defining substrate specificity in the CTX-M family: the role of Asp240 in ceftazidime hydrolysis. Antimicrobial Agents and Chemotherapy 2018; 62 (6): e00116-18. doi: 10.1128/AAC.00116-18
12. Cheesman MJ, Ilanko A, Blonk B, Cock IE. Developing new antimicrobial therapies: are synergistic combinations of plant extracts/compounds with conventional antibiotics the solution? Pharmacognosy Reviews 2017; 11 (22): 57. doi: 10.4103/phrev.phrev_21_17
13. Farrag HA, Abdallah N, Shehata MM, Awad EM. Natural outer membrane permeabilizers boost antibiotic action against irradiated resistant bacteria. Journal of Biomedical Science 2019; 26 (1): 69. doi: 10.1186/s12929-019-0561-6
14. Rajasekharan SK, Ramesh S. Inhibitory effect of quercetin on beta-lactam-resistant urinary tract pathogens. Minerva Biotecnologica 2016; 28 (4): 228-232.
15. Kim S, Chen J, Cheng T, Gindulyte A, He J et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Research 2018; 47 (D1): 1102-1109. doi: 10.1093/nar/gky1033
16. King DT, King AM, Lal SM, Wright GD, Strynadka NC. Molecular mechanism of avibactam-mediated β-lactamase inhibition. ACS Infectious Diseases 2015; 1 (4): 175-184. doi: 10.1021/acsinfecdis.5b00007
17. Kim S, Thiessen PA, Bolton EE, Chen J, Fu G et al. PubChem substance and compound databases. Nucleic Acids Research 2016; 44 (D1): D1202-D1213. doi: 10.1093/nar/gkv951
18. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK et al. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. Journal of Computational Chemistry 2009; 30 (16): 2785-2791. doi: 10.1002/jcc.21256
19. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE et al. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of Computational Chemistry 1998; 19 (14): 1639-1662. doi: 10.1002/(SICI)1096- 987X(19981115)19:14<1639::AID-JCC10>3.0.CO;2-B
20. Jakalian A, Jack DB, Bayly CI. Fast, efficient generation of high‐quality atomic charges. AM1‐BCC model: II. Parameterization and validation. Journal of Computational Chemistry 2002; 23 (16): 1623-1641. doi: 10.1002/jcc.10128
21. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA. Development and testing of a general amber force field. Journal of Computational Chemistry 2004; 25 (9): 1157-1174. doi: 10.1002/jcc.20035
22. Wang J, Wang W, Kollman PA, Case DA. Automatic atom type and bond type perception in molecular mechanical calculations. Journal of Molecular Graphics and Modelling 2006; 25 (2): 247-260. doi: 10.1016/j.jmgm.2005.12.005
23. Maier JA, Martinez C, Kasavajhala K, Wickstrom L, Hauser KE et al. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. Journal of Chemical Theory and Computation 2015; 11 (8): 3696-3713. doi: 10.1021/acs.jctc.5b00255
24. Mark P, Nilsson L. Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. The Journal of Physical Chemistry A 2001; 105 (43): 9954-9960. doi: 10.1021/jp003020w
25. Fletcher R, Powell MJ. A rapidly convergent descent method for minimization. The Computer Journal 1963; 6 (2): 163-168. doi: 10.1093/ comjnl/6.2.163
26. Møller MF. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 1993; 6 (4): 525-533. doi: 10.1016/S0893- 6080(05)80056-5
27. Osadcha O, Marszaek Z. Comparison of steepest descent method and conjugate gradient method. CEUR Workshop Proceedings, System; 2017.
28. Davidchack RL, Handel R, Tretyakov M. Langevin thermostat for rigid body dynamics. The Journal of Chemical Physics 2009; 130 (23): 234101. doi: 10.1063/1.3149788
29. Ryckaert J-P, Ciccotti G, Berendsen HJ. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. Journal of Computational Physics 1977; 23 (3): 327-341. doi: 10.1016/0021-9991(77)90098-5
30. Berendsen HJ, Postma JV, Van Gunsteren WF, DiNola A, Haak JR. Molecular dynamics with coupling to an external bath. The Journal of Chemical Physics 1984; 81 (8): 3684-3690. doi: 10.1063/1.448118
31. Roe DR, Cheatham III TE. PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. Journal of Chemical Theory and Computation 2013; 9 (7): 3084-3095. doi: 10.1021/ct400341p
32. Onufriev A, Bashford D, Case DA. Exploring protein native states and large‐scale conformational changes with a modified generalized born model. Proteins: Structure, Function, and Bioinformatics 2004; 55 (2): 383-394. doi: 10.1002/prot.20033
33. Ding Y, Fang Y, Moreno J, Ramanujam J, Jarrell M et al. Assessing the similarity of ligand binding conformations with the contact mode score. Computational Biology and Chemistry 2016; 64: 403-413. doi: 10.1016/j.compbiolchem.2016.08.007
34. Martinez L. Automatic identification of mobile and rigid substructures in molecular dynamics simulations and fractional structural fluctuation analysis. PloS One 2015; 10 (3). doi: 10.1371/journal.pone.0119264
35. Schrödinger. Maestro, Schrödinger, LLC, New York, NY, 2020.
36. Bentz AB. A review of quercetin: chemistry, antioxidant properties, and bioavailability. Journal of Young Investigators 2017.
37. Genheden S, Ryde U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opinion on Drug Discovery 2015; 10 (5): 449-461. doi: 10.1517/17460441.2015.1032936
38. Sun H, Duan L, Chen F, Liu H, Wang Z et al. Assessing the performance of MM/PBSA and MM/GBSA methods. 7. Entropy effects on the performance of end-point binding free energy calculation approaches. Physical Chemistry Chemical Physics 2018; 20 (21): 14450-14460. doi: 10.1039/c7cp07623a