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2000
Volume 22, Issue 4
  • ISSN: 1570-1638
  • E-ISSN: 1875-6220

Abstract

Background

The study focuses on evaluating the parasitic potential of novel metronidazole analogs using computational methods. Specifically, it aims to target key enzymes of oral anaerobes, including UDP-N-acetylglucosamine 1-carboxyvinyltransferase (MurA) of Fusobacterium nucleatum and DNA topoisomerase (Topo) of Prevotella intermedia.

Objective

The objective is to assess the pharmacokinetic and toxicity properties of 368 novel nitroimidazole candidates through virtual screening. Additionally, the study aims to determine the binding affinity of the most promising candidates with the target proteins through molecular docking analyses.

Methods

A combinatorial library of nitroimidazole candidates was constructed, and virtual screening was performed. Molecular docking analyses were conducted to evaluate the binding affinity of selected compounds with MurA and Topo. Further investigation involved molecular dynamic simulation to assess the stability of the compounds within the active sites of MurA and Topo.

Results

All selected compounds exhibited activity against both MurA and Topo. Among them, Mnz11, Mnz12, and Mnz15 demonstrated the lowest binding free energies and IC values. Molecular dynamic simulation indicated that these three compounds remained stable within the active sites of MurA and Topo, with RMSD values consistently below 2 Å. Additionally, the antibacterial potential of the most potent compound, Mnz15, was evaluated against a series of oral microbes.

Conclusion

The study concludes that the newly identified nitroimidazole candidates show promise as anti-parasitic agents, based on their activity against key enzymes of oral anaerobes and their pharmacokinetic properties evaluated through computational methods.

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References

  1. DubeyP. MittalN. Periodontal diseases- A brief review.Int. J. Oral Health Dent.20206317718710.18231/j.ijohd.2020.038
    [Google Scholar]
  2. KinaneD.F. StathopoulouP.G. PapapanouP.N. Periodontal diseases.Nat. Rev. Dis. Primers2017311703810.1038/nrdp.2017.38 28805207
    [Google Scholar]
  3. EkeP.I. DyeB.A. WeiL. Thornton-EvansG.O. GencoR.J. Prevalence of periodontitis in adults in the United States: 2009 and 2010.J. Dent. Res.2012911091492010.1177/0022034512457373 22935673
    [Google Scholar]
  4. HajishengallisG. Periodontitis: From microbial immune subversion to systemic inflammation.Nat. Rev. Immunol.2015151304410.1038/nri3785 25534621
    [Google Scholar]
  5. SaitoH. Aichelmann-ReidyM.B. OatesT.W. Advances in implant therapy in North America: Improved outcomes and application in the compromised dentition.Periodontol. 2000202082122523710.1111/prd.12319 31850626
    [Google Scholar]
  6. WadeW.G. The oral microbiome in health and disease.Pharmacol. Res.201369113714310.1016/j.phrs.2012.11.006 23201354
    [Google Scholar]
  7. HajishengallisG. LamontR.J. Beyond the red complex and into more complexity: The polymicrobial synergy and dysbiosis (PSD) model of periodontal disease etiology.Mol. Oral Microbiol.201227640941910.1111/j.2041‑1014.2012.00663.x 23134607
    [Google Scholar]
  8. StraussJ. KaplanG.G. BeckP.L. Invasive potential of gut mucosa-derived fusobacterium nucleatum positively correlates with IBD status of the host.Inflamm. Bowel Dis.20111791971197810.1002/ibd.21606 21830275
    [Google Scholar]
  9. MikelsaarM. StsepetovaJ. HüttP. Intestinal Lactobacillus sp. is associated with some cellular and metabolic characteristics of blood in elderly people.Anaerobe201016324024610.1016/j.anaerobe.2010.03.001 20223288
    [Google Scholar]
  10. FrischM. TrucksG. SchlegelH. Gaussian 16.Available from2016https://gaussian.com/gaussian16/
    [Google Scholar]
  11. ZeghebN. N-ferrocenylmethyl-derivatives as spike glycoprotein inhibitors of SARS-CoV-2 using in silico approachesChemRxiv202010.26434/chemrxiv.12278078.v1
    [Google Scholar]
  12. LanezT. BenaichaH. LanezE. SaidiM. Electrochemical, spectroscopic and molecular docking studies of 4-methyl-5-((phenylimino)methyl)-3H- and 5-(4-fluorophenyl)-3H-1,2-dithiole-3-thione interacting with DNA.J. Sulfur Chem.2017391768810.1080/17415993.2017.1391811
    [Google Scholar]
  13. BeckeA.D. Density-functional thermochemistry. III. The role of exact exchange.J. Chem. Phys.19939875648565210.1063/1.464913
    [Google Scholar]
  14. BenamaraH. LanezT. LanezE. BSA-binding studies of 2- and 4-ferrocenylbenzonitrile: voltammetric, spectroscopic and molecular docking investigations.J Electrochem Sci Eng202010433534610.5599/jese.861
    [Google Scholar]
  15. LanezT. N6,9-bis(ferrocenylmethyl)adenine: synthesis, cyclic voltammetric, spectroscopic characterization, and DFT calculations.St Cerc St CICBIA201920509519
    [Google Scholar]
  16. ZeghebN. BoubekriC. LanezT. In vitro and in silico determination of some N-ferrocenylmethylaniline derivatives as anti-proliferative agents against MCF-7 human breast cancer cell lines.Anticancer. Agents Med. Chem.20212110.2174/1871520621666210624141712 34170810
    [Google Scholar]
  17. LanezT. LanezE. A molecular docking study of N-ferrocenylmethylnitroanilines as potential anticancer drugs.Int J Pharmacol Phytochem Ethnomed2016251210.18052/www.scipress.com/IJPPE.2.5
    [Google Scholar]
  18. KimS. ChenJ. ChengT. PubChem 2023 update.Nucleic Acids Res.202351D1D1373D138010.1093/nar/gkac956 36305812
    [Google Scholar]
  19. Maestro, Schrödinger 2023. Available from:https://www.schrodinger.com/platform/products/maestro/
  20. FriesnerR.A. MurphyR.B. RepaskyM.P. Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes.J. Med. Chem.200649216177619610.1021/jm051256o 17034125
    [Google Scholar]
  21. SchüllerA. HähnkeV. SchneiderG. SmiLib v2.0: A java‐based tool for rapid combinatorial library enumeration.QSAR Comb. Sci.200726340741010.1002/qsar.200630101
    [Google Scholar]
  22. DainaA. MichielinO. ZoeteV. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules.Sci. Rep.201774271710.1038/srep42717
    [Google Scholar]
  23. DrwalM.N. BanerjeeP. DunkelM. WettigM.R. PreissnerR. ProTox: A web server for the in silico prediction of rodent oral toxicity.Nucleic Acids Res.201442W1W53-810.1093/nar/gku401 24838562
    [Google Scholar]
  24. KwongE. Oral formulation roadmap from early drug discovery to development.Wiley201710.1002/9781118907894
    [Google Scholar]
  25. PliškaV. TestaB. van de Waterbeemd H. Lipophilicity: The empirical tool and the fundamental objective. An introduction In: Methods and Principles in Medicinal Chemistry.199610.1002/9783527614998.ch1
    [Google Scholar]
  26. AvdeefA. Absorption and drug development: solubility, permeability, and charge state. hoboken.New Jersey, U.SJohn Wiley & Sons2012
    [Google Scholar]
  27. LipinskiC.A. Lead- and drug-like compounds: The rule-of-five revolution.Drug Discov. Today. Technol.20041433734110.1016/j.ddtec.2004.11.007 24981612
    [Google Scholar]
  28. GhoseA.K. ViswanadhanV.N. WendoloskiJ.J. 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.199911556810.1021/cc9800071 10746014
    [Google Scholar]
  29. VeberD.F. JohnsonS.R. ChengH.Y. SmithB.R. WardK.W. KoppleK.D. Molecular properties that influence the oral bioavailability of drug candidates.J. Med. Chem.200245122615262310.1021/jm020017n 12036371
    [Google Scholar]
  30. EganW.J. MerzK.M.Jr BaldwinJ.J. Prediction of drug absorption using multivariate statistics.J. Med. Chem.200043213867387710.1021/jm000292e 11052792
    [Google Scholar]
  31. MorrisG.M. HueyR. LindstromW. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility.J. Comput. Chem.200930162785279110.1002/jcc.21256 19399780
    [Google Scholar]
  32. O’boyleN.M. TenderholtA.L. LangnerK.M. cclib: A library for package‐independent computational chemistry algorithms.J. Comput. Chem.200829583984510.1002/jcc.20823 17849392
    [Google Scholar]
  33. KarrouchiK. BrandánS.A. SertY. Synthesis, X-ray structure, vibrational spectroscopy, DFT, biological evaluation and molecular docking studies of (E)-N′-(4-(dimethylamino)benzylidene)-5-methyl-1H-pyrazole-3-carbohydrazide.J. Mol. Struct.2020121912854110.1016/j.molstruc.2020.128541
    [Google Scholar]
  34. M AQ, Tahir MN, Munawar KS, et al. One-dimensional polymer of copper with salicylic acid and pyridine linkers: Synthesis, characterizations, solid state assembly investigation by hirshfeld surface analysis, and computational studies.J. Mol. Struct.2024129713695610.1016/j.molstruc.2023.136956
    [Google Scholar]
  35. BatemanA. MartinM.J. OrchardS. UniProt: The universal protein knowledgebase in 2023.Nucleic Acids Res.202351D1D523D53110.1093/nar/gkac1052 36408920
    [Google Scholar]
  36. HarderE. DammW. MapleJ. OPLS3: A force field providing broad coverage of drug-like small molecules and proteins.J. Chem. Theory Comput.201612128129610.1021/acs.jctc.5b00864 26584231
    [Google Scholar]
  37. Madhavi SastryG. AdzhigireyM. DayT. AnnabhimojuR. ShermanW. Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments.J. Comput. Aided Mol. Des.201327322123410.1007/s10822‑013‑9644‑8 23579614
    [Google Scholar]
  38. VenkatesanA. RambabuM. JayanthiS. Febin Prabhu DassJ. Pharmacophore feature prediction and molecular docking approach to identify novel anti‐HCV protease inhibitors.J. Cell. Biochem.2018119196096610.1002/jcb.26262 28691304
    [Google Scholar]
  39. TrottO. OlsonA.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.J. Comput. Chem.201031245546110.1002/jcc.21334 19499576
    [Google Scholar]
  40. JonesG. WillettP. GlenR.C. LeachA.R. TaylorR. Development and validation of a genetic algorithm for flexible docking.J. Mol. Biol.1997267372774810.1006/jmbi.1996.0897
    [Google Scholar]
  41. ShermanW. DayT. JacobsonM.P. FriesnerR.A. FaridR. Novel procedure for modeling ligand/receptor induced fit effects.J. Med. Chem.200649253455310.1021/jm050540c 16420040
    [Google Scholar]
  42. JorgensenW.L. ChandrasekharJ. MaduraJ.D. ImpeyR.W. KleinM.L. Comparison of simple potential functions for simulating liquid water.J. Chem. Phys.198379292693510.1063/1.445869
    [Google Scholar]
  43. ParrinelloM. RahmanA. Polymorphic transitions in single crystals: A new molecular dynamics method.J. Appl. Phys.198152127182719010.1063/1.328693
    [Google Scholar]
  44. BerendsenH.J.C. PostmaJ.P.M. van GunsterenW.F. DiNolaA. HaakJ.R. Molecular dynamics with coupling to an external bath.J. Chem. Phys.19848183684369010.1063/1.448118
    [Google Scholar]
  45. MaierJ.A. MartinezC. KasavajhalaK. WickstromL. HauserK.E. SimmerlingC. ff14SB: Improving the accuracy of protein side chain and backbone parameters from ff99SB.J. Chem. Theory Comput.20151183696371310.1021/acs.jctc.5b00255 26574453
    [Google Scholar]
  46. HessB. KutznerC. van der SpoelD. LindahlE. GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation.J. Chem. Theory Comput.20084343544710.1021/ct700301q 26620784
    [Google Scholar]
  47. BussiG. DonadioD. ParrinelloM. Canonical sampling through velocity rescaling.J. Chem. Phys.2007126101410110.1063/1.2408420 17212484
    [Google Scholar]
  48. HuangJ. RauscherS. NawrockiG. CHARMM36m: An improved force field for folded and intrinsically disordered proteins.Nat. Methods2017141717310.1038/nmeth.4067 27819658
    [Google Scholar]
  49. Van Der SpoelD. LindahlE. HessB. GroenhofG. MarkA.E. BerendsenH.J.C. GROMACS: Fast, flexible, and free.J. Comput. Chem.200526161701171810.1002/jcc.20291 16211538
    [Google Scholar]
  50. OkudaK. KimizukaR. KatakuraA. NakagawaT. IshiharaK. Ecological and immunopathological implications of oral bacteria in Helicobacter pylori-infected disease.J. Periodontol.200374112312810.1902/jop.2003.74.1.123 12593607
    [Google Scholar]
  51. HubJ.S. de GrootB.L. Detection of functional modes in protein dynamics.PLOS Comput. Biol.200958e100048010.1371/journal.pcbi.1000480 19714202
    [Google Scholar]
  52. AntonyJ. PiquemalJ.P. GreshN. Complexes of thiomandelate and captopril mercaptocarboxylate inhibitors to metallo‐β‐lactamase by polarizable molecular mechanics. Validation on model binding sites by quantum chemistry.J. Comput. Chem.200526111131114710.1002/jcc.20245 15937993
    [Google Scholar]
  53. PettersenE.F. GoddardT.D. HuangC.C. UCSF Chimera—A visualization system for exploratory research and analysis.J. Comput. Chem.200425131605161210.1002/jcc.20084 15264254
    [Google Scholar]
  54. AlAjmiM.F. RehmanM.T. HussainA. Celecoxib, Glipizide, Lapatinib, and Sitagliptin as potential suspects of aggravating SARS-CoV-2 (COVID-19) infection: A computational approach.J. Biomol. Struct. Dyn.20224024137471375810.1080/07391102.2021.1994013 34709124
    [Google Scholar]
  55. YanY. ZhangD. ZhouP. LiB. HuangS.Y. HDOCK: A web server for protein–protein and protein–DNA/RNA docking based on a hybrid strategy.Nucleic Acids Res.201745W1W365-7310.1093/nar/gkx407 28521030
    [Google Scholar]
  56. GingrasJ. SmithS. MatsonD.J. Global Nav1.7 knockout mice recapitulate the phenotype of human congenital indifference to pain.PLoS One201499e10589510.1371/journal.pone.0105895 25188265
    [Google Scholar]
  57. McGibbonR.T. BeauchampK.A. HarriganM.P. MDTraj: A modern open library for the analysis of molecular dynamics trajectories.Biophys. J.201510981528153210.1016/j.bpj.2015.08.015 26488642
    [Google Scholar]
  58. SpellbergB. BartlettJ.G. GilbertD.N. The future of antibiotics and resistance.N. Engl. J. Med.2013368429930210.1056/NEJMp1215093
    [Google Scholar]
  59. RoemerT. KrysanD.J. Antifungal drug development: Challenges, unmet clinical needs, and new approaches.Cold Spring Harb. Perspect. Med.201445a019703a310.1101/cshperspect.a019703 24789878
    [Google Scholar]
  60. BrownD. Antibiotic resistance breakers: Can repurposed drugs fill the antibiotic discovery void?Nat. Rev. Drug Discov.2015141282183210.1038/nrd4675 26493767
    [Google Scholar]
  61. SocranskyS.S. HaffajeeA.D. CuginiM.A. SmithC. KentR.L.Jr Microbial complexes in subgingival plaque.J. Clin. Periodontol.199825213414410.1111/j.1600‑051X.1998.tb02419.x 9495612
    [Google Scholar]
  62. MarshP.D. Are dental diseases examples of ecological catastrophes?Microbiology (Reading)2003149227929410.1099/mic.0.26082‑0 12624191
    [Google Scholar]
  63. MengX.Y. ZhangH.X. MezeiM. CuiM. Molecular docking: A powerful approach for structure-based drug discovery.Curr. Computeraided Drug Des.20117214615710.2174/157340911795677602 21534921
    [Google Scholar]
  64. ErtlP. SchuffenhauerA. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions.J. Cheminform.200911810.1186/1758‑2946‑1‑8 20298526
    [Google Scholar]
  65. ChengF. LiW. ZhouY. admetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties.J. Chem. Inf. Model.201252113099310510.1021/ci300367a 23092397
    [Google Scholar]
  66. EichingerA. BeiselH.G. JacobU. Crystal structure of gingipain R: An Arg-specific bacterial cysteine proteinase with a caspase-like fold.EMBO J.199918205453546210.1093/emboj/18.20.5453 10523290
    [Google Scholar]
  67. CrowA. GreeneN.P. KaplanE. KoronakisV. Structure and mechanotransmission mechanism of the MacB ABC transporter superfamily.Proc. Natl. Acad. Sci. USA201711447125721257710.1073/pnas.1712153114 29109272
    [Google Scholar]
  68. RiceK. BatulK. WhitesideJ. The predominance of nucleotidyl activation in bacterial phosphonate biosynthesis.Nat. Commun.2019101369810.1038/s41467‑019‑11627‑6 31420548
    [Google Scholar]
  69. GilsonM.K. GivenJ.A. BushB.L. McCammonJ.A. The statistical-thermodynamic basis for computation of binding affinities: A critical review.Biophys. J.19977231047106910.1016/S0006‑3495(97)78756‑3 9138555
    [Google Scholar]
  70. GenhedenS. RydeU. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities.Expert Opin. Drug Discov.201510544946110.1517/17460441.2015.1032936 25835573
    [Google Scholar]
  71. LofererM.J. LoefflerH.H. LiedlK.R. A QM–MM interface between CHARMM and TURBOMOLE: Implementation and application to systems in bulk phase and biologically active systems.J. Comput. Chem.200324101240124910.1002/jcc.10283 12820132
    [Google Scholar]
  72. CampelloRS Alves-WagnerAB LucasTF tor 1 agonist PPT stimulates Slc2a4 gene expression and improves insulin-induced glucose uptake in adipocytes. Curr Topics Med Chem20121920596910.2174/1568026611313070009 23167795
  73. Encyclopedia of Reagents for Organic Synthesis. Wiley.Available from200110.1002/047084289X
    [Google Scholar]
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