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Infectious Disorders - Drug Targets

Editor-in-Chief

ISSN (Print): 1871-5265
ISSN (Online): 2212-3989

Research Article

Identification of a Chemical Inhibitor with a Novel Scaffold Targeting Decaprenylphosphoryl-β-D-Ribose Oxidase (DprE1)

Author(s): Tatsuki Matsunaga, Kohei Monobe and Shunsuke Aoki*

Volume 23, Issue 5, 2023

Published on: 17 April, 2023

Article ID: e090323214508 Pages: 7

DOI: 10.2174/1871526523666230309110705

Price: $65

Abstract

Background: Tuberculosis is the second leading cause of death from infectious diseases worldwide. Multidrug-resistant Mycobacterium tuberculosis is spreading throughout the world, creating a crisis. Hence, there is a need to develop anti-tuberculosis drugs with novel structures and versatile mechanisms of action.

Objective: In this study, we identified antimicrobial compounds with a novel skeleton that inhibits mycobacterium decaprenylphosphoryl-β-D-ribose oxidase (DprE1).

Methods: A multi-step, in silico, structure-based drug screening identified potential DprE1 inhibitors from a library of 154,118 compounds. We experimentally verified the growth inhibitory effects of the eight selected candidate compounds against Mycobacterium smegmatis. Molecular dynamics simulations were performed to understand the mechanism of molecular interactions between DprE1 and ompound 4.

Results: Eight compounds were selected through in silico screening. Compound 4 showed strong growth inhibition against M. smegmatis. Molecular dynamics simulation (50 ns) predicted direct and stable binding of Compound 4 to the active site of DprE1.

Conclusion: The structural analysis of the novel scaffold in Compound 4 can pave way for antituberculosis drug development and discovery.

Keywords: Dpre1, tuberculosis, inhibitors, virtual screening, molecular docking, molecular dynamics.

Graphical Abstract
[1]
Harding E. WHO global progress report on tuberculosis elimination. Lancet Respir Med 2020; 8(1): 19.
[http://dx.doi.org/10.1016/S2213-2600(19)30418-7] [PMID: 31706931]
[2]
WHO Global Tuberculosis Report. 2019. Available from : [https://www.who.int/teams/global-tuberculosis-programme/tb-reports
[3]
Alene KA, Wangdi K, Clements ACA. Impact of the COVID-19 pandemic on tuberculosis control: An overview. Trop Med Infect Dis 2020; 5(3): 123.
[http://dx.doi.org/10.3390/tropicalmed5030123] [PMID: 32722014]
[4]
Hogan AB, Jewell BL, Sherrard-Smith E, et al. Potential impact of the COVID-19 pandemic on HIV, tuberculosis, and malaria in low-income and middle-income countries: A modelling study. Lancet Glob Health 2020; 8(9): e1132-41.
[http://dx.doi.org/10.1016/S2214-109X(20)30288-6] [PMID: 32673577]
[5]
Conradie F, Everitt D, Crook AM. Treatment of highly drug-resistant pulmonary tuberculosis. N Engl J Med 2020; 382(24): 2377.
[PMID: 32521143]
[6]
Ignatius EH, Dooley KE. New drugs for the treatment of tuberculosis. Clin Chest Med 2019; 40(4): 811-27.
[http://dx.doi.org/10.1016/j.ccm.2019.08.001] [PMID: 31731986]
[7]
Walker TM, Miotto P, Köser CU, et al. The 2021 WHO catalogue of Mycobacterium tuberculosis complex mutations associated with drug resistance: A genotypic analysis. Lancet Microbe 2022; 3(4): e265-73.
[http://dx.doi.org/10.1016/S2666-5247(21)00301-3] [PMID: 35373160]
[8]
Makarov V, Manina G, Mikusova K, et al. Benzothiazinones kill Mycobacterium tuberculosis by blocking arabinan synthesis. Science 2009; 324(5928): 801-4.
[http://dx.doi.org/10.1126/science.1171583] [PMID: 19299584]
[9]
Huang H, Scherman MS, D’Haeze W, et al. Identification and active expression of the Mycobacterium tuberculosis gene encoding 5-phospho-alpha-d-ribose-1-diphosphate: decaprenyl-phosphate 5-phosphoribosyltransferase, the first enzyme committed to decaprenylphosphoryl-d-arabinose synthesis. J Biol Chem 2005; 280(26): 24539-43.
[http://dx.doi.org/10.1074/jbc.M504068200] [PMID: 15878857]
[10]
Mikušová K, Huang H, Yagi T, et al. Decaprenylphosphoryl arabinofuranose, the donor of the D-arabinofuranosyl residues of mycobacterial arabinan, is formed via a two-step epimerization of decaprenylphosphoryl ribose. J Bacteriol 2005; 187(23): 8020-5.
[http://dx.doi.org/10.1128/JB.187.23.8020-8025.2005] [PMID: 16291675]
[11]
Chikhale RV, Barmade MA, Murumkar PR, Yadav MR. Overview of the development of dpre1 inhibitors for combating the menace of tuberculosis. J Med Chem 2018; 61(19): 8563-93.
[http://dx.doi.org/10.1021/acs.jmedchem.8b00281] [PMID: 29851474]
[12]
Berman HM, Westbrook J, Feng Z, et al. The protein data bank. Nucleic Acids Res 2000; 28(1): 235-42.
[http://dx.doi.org/10.1093/nar/28.1.235] [PMID: 10592235]
[13]
Vilar S, Cozza G, Moro S. Medicinal chemistry and the molecular operating environment (MOE): Application of QSAR and molecular docking to drug discovery. Curr Top Med Chem 2008; 8(18): 1555-72.
[http://dx.doi.org/10.2174/156802608786786624] [PMID: 19075767]
[14]
The Ressource Parisienne en ioinformatioque Structurale (RPBS). Available From: . http://bioserv.rpbs.jussieu.fr/RPBS/cgibin/Ressource.cgi?chzn_lg=an&chzn_rsrc=Collections(accessed: June 10, 2012).
[15]
Lipinski CA. Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods 2000; 44(1): 235-49.
[http://dx.doi.org/10.1016/S1056-8719(00)00107-6] [PMID: 11274893]
[16]
Labute P. LowModeMD--implicit low-mode velocity filtering applied to conformational search of macrocycles and protein loops. J Chem Inf Model 2010; 50(5): 792-800.
[http://dx.doi.org/10.1021/ci900508k] [PMID: 20429574]
[17]
Rogacki MK, Pitta E, Balabon O, et al. Identification and profiling of hydantoins—a novel class of potent antimycobacterial DprE1 inhibitors. J Med Chem 2018; 61(24): 11221-49.
[http://dx.doi.org/10.1021/acs.jmedchem.8b01356] [PMID: 30500189]
[18]
Balabon O, Pitta E, Rogacki MK, et al. Optimization of hydantoins as potent antimycobacterial decaprenylphosphoryl-β- d -ribose oxidase (DprE1) inhibitors. J Med Chem 2020; 63(10): 5367-86.
[http://dx.doi.org/10.1021/acs.jmedchem.0c00107] [PMID: 32342688]
[19]
Lang PT, Brozell SR, Mukherjee S, et al. DOCK 6: Combining techniques to model RNA–small molecule complexes. RNA 2009; 15(6): 1219-30.
[http://dx.doi.org/10.1261/rna.1563609] [PMID: 19369428]
[20]
Jones G, Willett P, Glen RC, Leach AR, Taylor R. Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen. J Mol Biol 1997; 267(3): 727-48.
[http://dx.doi.org/10.1006/jmbi.1996.0897] [PMID: 9126849]
[21]
Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010; 31(2): 455-61.
[PMID: 19499576]
[22]
Ruiz-Carmona S, Alvarez-Garcia D, Foloppe N, et al. rDock: A fast, versatile and open source program for docking ligands to proteins and nucleic acids. PLOS Comput Biol 2014; 10(4): e1003571.
[http://dx.doi.org/10.1371/journal.pcbi.1003571] [PMID: 24722481]
[23]
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 2001; 46(1-3): 3-26.
[http://dx.doi.org/10.1016/S0169-409X(00)00129-0] [PMID: 11259830]
[24]
Jo S, Kim T, Iyer VG, Im W. CHARMM-GUI: A web-based graphical user interface for CHARMM. J Comput Chem 2008; 29(11): 1859-65.
[http://dx.doi.org/10.1002/jcc.20945] [PMID: 18351591]
[25]
Brooks BR, Brooks CL III, Mackerell AD Jr, et al. CHARMM: The biomolecular simulation program. J Comput Chem 2009; 30(10): 1545-614.
[http://dx.doi.org/10.1002/jcc.21287] [PMID: 19444816]
[26]
Lee J, Cheng X, Swails JM, et al. CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J Chem Theory Comput 2016; 12(1): 405-13.
[http://dx.doi.org/10.1021/acs.jctc.5b00935] [PMID: 26631602]
[27]
Abraham MJ, Murtola T, Schulz R, et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015; 1-2: 19-25.
[http://dx.doi.org/10.1016/j.softx.2015.06.001]
[28]
Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC. GROMACS: Fast, flexible, and free. J Comput Chem 2005; 26(16): 1701-18.
[http://dx.doi.org/10.1002/jcc.20291] [PMID: 16211538]
[29]
Hess B, Bekker H, Berendsen HJC, Fraaije JGEM. LINCS: A linear constraint solver for molecular simulations. J Comput Chem 1997; 18(12): 1463-72.
[http://dx.doi.org/10.1002/(SICI)1096-987X(199709)18:12<1463:AID-JCC4>3.0.CO;2-H]
[30]
Darden T, York D, Pedersen L. Particle mesh Ewald: An N ⋅log(N) method for Ewald sums in large systems. J Chem Phys 1993; 98(12): 10089-92.
[http://dx.doi.org/10.1063/1.464397]
[31]
Salentin S, Schreiber S, Haupt VJ, Adasme MF, Schroeder M. PLIP: Fully automated protein–ligand interaction profiler. Nucleic Acids Res 2015; 43(W1): W443-7.
[http://dx.doi.org/10.1093/nar/gkv315] [PMID: 25873628]
[32]
Adasme MF, Linnemann KL, Bolz SN, et al. PLIP 2021: expanding the scope of the protein–ligand interaction profiler to DNA and RNA. Nucleic Acids Res 2021; 49(W1): W530-4.
[http://dx.doi.org/10.1093/nar/gkab294] [PMID: 33950214]
[33]
Piton J, Foo CSY, Cole ST. Structural studies of Mycobacterium tuberculosis DprE1 interacting with its inhibitors. Drug Discov Today 2017; 22(3): 526-33.
[http://dx.doi.org/10.1016/j.drudis.2016.09.014] [PMID: 27666194]
[34]
Daina A, Michielin O, Zoete V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 2017; 7(1): 42717.
[http://dx.doi.org/10.1038/srep42717] [PMID: 28256516]
[35]
Ghose AK, Viswanadhan VN, Wendoloski JJ. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J Comb Chem 1999; 1(1): 55-68.
[http://dx.doi.org/10.1021/cc9800071] [PMID: 10746014]
[36]
Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 2002; 45(12): 2615-23.
[http://dx.doi.org/10.1021/jm020017n] [PMID: 12036371]
[37]
Egan WJ, Merz KM Jr, Baldwin JJ. Prediction of drug absorption using multivariate statistics. J Med Chem 2000; 43(21): 3867-77.
[http://dx.doi.org/10.1021/jm000292e] [PMID: 11052792]
[38]
Muegge I, Heald SL, Brittelli D. Simple selection criteria for drug-like chemical matter. J Med Chem 2001; 44(12): 1841-6.
[http://dx.doi.org/10.1021/jm015507e] [PMID: 11384230]
[39]
Martin YC. A bioavailability score. J Med Chem 2005; 48(9): 3164-70.
[http://dx.doi.org/10.1021/jm0492002] [PMID: 15857122]
[40]
Banerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Res 2018; 46(W1): W257-63.
[http://dx.doi.org/10.1093/nar/gky318] [PMID: 29718510]
[41]
Drwal MN, Banerjee P, Dunkel M, Wettig MR, Preissner R. ProTox: A web server for the in silico prediction of rodent oral toxicityNucleic Acids Res 2014; 42(Web Server issue): W53-8

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