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image of Computational Screening of Novel Nitroimidazole Candidates: Targeting Key Enzymes of Oral Anaerobes for Anti-Parasitic Potential

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 IC50 values. Molecular dynamic simulation indicated that these three compounds remained stable within the active sites of MurA and Topo, with RMSD values consistently below 2Å. 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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2024-11-05
2025-01-12
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References

  1. Dubey P. Mittal N. Periodontal diseases- A brief review. International Journal of Oral Health Dentistry 2020 6 3 177 187 10.18231/j.ijohd.2020.038
    [Google Scholar]
  2. Kinane D.F. Stathopoulou P.G. Papapanou P.N. Periodontal diseases. Nat. Rev. Dis. Primers 2017 3 1 17038 10.1038/nrdp.2017.38 28805207
    [Google Scholar]
  3. Eke P.I. Dye B.A. Wei L. Thornton-Evans G.O. Genco R.J. Prevalence of periodontitis in adults in the United States: 2009 and 2010. J. Dent. Res. 2012 91 10 914 920 10.1177/0022034512457373 22935673
    [Google Scholar]
  4. Hajishengallis G. Periodontitis: From microbial immune subversion to systemic inflammation. Nat. Rev. Immunol. 2015 15 1 30 44 10.1038/nri3785 25534621
    [Google Scholar]
  5. Saito H. Aichelmann-Reidy M.B. Oates T.W. Advances in implant therapy in North America: Improved outcomes and application in the compromised dentition. Periodontol. 2000 2020 82 1 225 237 10.1111/prd.12319 31850626
    [Google Scholar]
  6. Wade W.G. The oral microbiome in health and disease. Pharmacol. Res. 2013 69 1 137 143 10.1016/j.phrs.2012.11.006 23201354
    [Google Scholar]
  7. Hajishengallis G. Lamont R.J. Beyond the red complex and into more complexity: The polymicrobial synergy and dysbiosis (PSD) model of periodontal disease etiology. Mol. Oral Microbiol. 2012 27 6 409 419 10.1111/j.2041‑1014.2012.00663.x 23134607
    [Google Scholar]
  8. Strauss J. Kaplan G.G. Beck P.L. Invasive potential of gut mucosa-derived fusobacterium nucleatum positively correlates with IBD status of the host. Inflamm. Bowel Dis. 2011 17 9 1971 1978 10.1002/ibd.21606 21830275
    [Google Scholar]
  9. Mikelsaar M. Stsepetova J. Hütt P. Intestinal Lactobacillus sp. is associated with some cellular and metabolic characteristics of blood in elderly people. Anaerobe 2010 16 3 240 246 10.1016/j.anaerobe.2010.03.001 20223288
    [Google Scholar]
  10. Frisch M. Trucks G. Schlegel H. Gaussian 16 2016 Available from: https://gaussian.com/gaussian16/
  11. ZEGHEB N. N-ferrocenylmethyl-derivatives as spike glycoprotein inhibitors of SARS-CoV-2 using in silico approaches ChemRxiv 2020 10.26434/chemrxiv.12278078.v1
    [Google Scholar]
  12. Lanez T. Benaicha H. Lanez E. Saidi M. 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. 2017 39 1 76 88 10.1080/17415993.2017.1391811
    [Google Scholar]
  13. Becke A.D. Density-functional thermochemistry. III. The role of exact exchange. J. Chem. Phys. 1993 98 7 5648 5652 10.1063/1.464913
    [Google Scholar]
  14. Benamara H. Lanez T. Lanez E. BSA-binding studies of 2- and 4-ferrocenylbenzonitrile: voltammetric, spectroscopic and molecular docking investigations. Journal of Electrochemical Science and Engineering 2020 10 4 335 346 10.5599/jese.861
    [Google Scholar]
  15. Lanez T. N6,9-bis(ferrocenylmethyl)adenine: synthesis, cyclic voltammetric, spectroscopic characterization, and DFT calculations. St Cerc St CICBIA 2019 20 509 519
    [Google Scholar]
  16. Zegheb N. Boubekri C. Lanez T. 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. 2021 ••• 21 10.2174/1871520621666210624141712 34170810
    [Google Scholar]
  17. Lanez T. Lanez E. A molecular docking study of N-ferrocenylmethylnitroanilines as potential anticancer drugs. Int J Pharmacol Phytochem Ethnomed 2016 2 5 12 10.18052/www.scipress.com/IJPPE.2.5
    [Google Scholar]
  18. Kim S. Chen J. Cheng T. PubChem 2023 update. Nucleic Acids Res. 2023 51 D1 D1373 D1380 10.1093/nar/gkac956 36305812
    [Google Scholar]
  19. Maestro, Schrödinger 2023 Available from: https://www.schrodinger.com/platform/products/maestro/
  20. Friesner R.A. Murphy R.B. Repasky M.P. Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J. Med. Chem. 2006 49 21 6177 6196 10.1021/jm051256o 17034125
    [Google Scholar]
  21. Schüller A. Hنhnke V, Schneider G. SmiLib v2.0: A java‐based tool for rapid combinatorial library enumeration. QSAR Comb. Sci. 2007 26 3 407 410 10.1002/qsar.200630101
    [Google Scholar]
  22. 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 42717 10.1038/srep42717
    [Google Scholar]
  23. Drwal M.N. Banerjee P. Dunkel M. Wettig M.R. Preissner R. ProTox: A web server for the in silico prediction of rodent oral toxicity. Nucleic Acids Res. 2014 42 W1 W53-8 10.1093/nar/gku401 24838562
    [Google Scholar]
  24. Kwong E. Oral Formulation Roadmap from Early Drug Discovery to Development. Wiley 2017 10.1002/9781118907894
    [Google Scholar]
  25. Pliڑka V 1996 10.1002/9783527614998.ch1
  26. Avdeef A. Absorption and Drug Development: Solubility, Permeability, and Charge State. Hoboken, New Jersey, U.S John Wiley & Sons 2012
    [Google Scholar]
  27. Lipinski C.A. Lead- and drug-like compounds: The rule-of-five revolution. Drug Discov. Today. Technol. 2004 1 4 337 341 10.1016/j.ddtec.2004.11.007 24981612
    [Google Scholar]
  28. Ghose A.K. Viswanadhan V.N. Wendoloski J.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. 1999 1 1 55 68 10.1021/cc9800071 10746014
    [Google Scholar]
  29. Veber D.F. Johnson S.R. Cheng H.Y. Smith B.R. Ward K.W. Kopple K.D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 2002 45 12 2615 2623 10.1021/jm020017n 12036371
    [Google Scholar]
  30. Egan W.J. Merz K.M. Jr Baldwin J.J. Prediction of drug absorption using multivariate statistics. J. Med. Chem. 2000 43 21 3867 3877 10.1021/jm000292e 11052792
    [Google Scholar]
  31. Morris G.M. Huey R. Lindstrom W. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem. 2009 30 16 2785 2791 10.1002/jcc.21256 19399780
    [Google Scholar]
  32. O’boyle N.M. Tenderholt A.L. Langner K.M. cclib: A library for package‐independent computational chemistry algorithms. J. Comput. Chem. 2008 29 5 839 845 10.1002/jcc.20823 17849392
    [Google Scholar]
  33. Karrouchi K 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. 2020 1219 128541 10.1016/j.molstruc.2020.128541
    [Google Scholar]
  34. M AQ 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. 2024 1297 136956 10.1016/j.molstruc.2023.136956
    [Google Scholar]
  35. Bateman A. Martin M.J. Orchard S. UniProt: The universal protein knowledgebase in 2023. Nucleic Acids Res. 2023 51 D1 D523 D531 10.1093/nar/gkac1052 36408920
    [Google Scholar]
  36. Harder E. Damm W. Maple J. OPLS3: A force field providing broad coverage of drug-like small molecules and proteins. J. Chem. Theory Comput. 2016 12 1 281 296 10.1021/acs.jctc.5b00864 26584231
    [Google Scholar]
  37. Madhavi Sastry G. Adzhigirey M. Day T. Annabhimoju R. Sherman W. Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des. 2013 27 3 221 234 10.1007/s10822‑013‑9644‑8 23579614
    [Google Scholar]
  38. Venkatesan A. Rambabu M. Jayanthi S. Febin Prabhu Dass J. Pharmacophore feature prediction and molecular docking approach to identify novel anti‐HCV protease inhibitors. J. Cell. Biochem. 2018 119 1 960 966 10.1002/jcb.26262 28691304
    [Google Scholar]
  39. Trott O. Olson A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 2010 31 2 455 461 10.1002/jcc.21334 19499576
    [Google Scholar]
  40. Jones G. Willett P. Glen R.C. Leach A.R. Taylor R. Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol. 1997 267 3 727 748 10.1006/jmbi.1996.0897
    [Google Scholar]
  41. Sherman W. Day T. Jacobson M.P. Friesner R.A. Farid R. Novel procedure for modeling ligand/receptor induced fit effects. J. Med. Chem. 2006 49 2 534 553 10.1021/jm050540c 16420040
    [Google Scholar]
  42. Jorgensen W.L. Chandrasekhar J. Madura J.D. Impey R.W. Klein M.L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 1983 79 2 926 935 10.1063/1.445869
    [Google Scholar]
  43. Parrinello M. Rahman A. Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys. 1981 52 12 7182 7190 10.1063/1.328693
    [Google Scholar]
  44. Berendsen H.J.C. Postma J.P.M. van Gunsteren W.F. DiNola A. Haak J.R. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 1984 81 8 3684 3690 10.1063/1.448118
    [Google Scholar]
  45. Maier J.A. Martinez C. Kasavajhala K. Wickstrom L. Hauser K.E. Simmerling C. ff14SB: Improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput. 2015 11 8 3696 3713 10.1021/acs.jctc.5b00255 26574453
    [Google Scholar]
  46. Hess B. Kutzner C. van der Spoel D. Lindahl E. GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation. J. Chem. Theory Comput. 2008 4 3 435 447 10.1021/ct700301q 26620784
    [Google Scholar]
  47. Bussi G. Donadio D. Parrinello M. Canonical sampling through velocity rescaling. J. Chem. Phys. 2007 126 1 014101 10.1063/1.2408420 17212484
    [Google Scholar]
  48. Huang J. Rauscher S. Nawrocki G. CHARMM36m: An improved force field for folded and intrinsically disordered proteins. Nat. Methods 2017 14 1 71 73 10.1038/nmeth.4067 27819658
    [Google Scholar]
  49. Van Der Spoel D. Lindahl E. Hess B. Groenhof G. Mark A.E. Berendsen H.J.C. GROMACS: Fast, flexible, and free. J. Comput. Chem. 2005 26 16 1701 1718 10.1002/jcc.20291 16211538
    [Google Scholar]
  50. Okuda K. Kimizuka R. Katakura A. Nakagawa T. Ishihara K. Ecological and immunopathological implications of oral bacteria in Helicobacter pylori-infected disease. J. Periodontol. 2003 74 1 123 128 10.1902/jop.2003.74.1.123 12593607
    [Google Scholar]
  51. Hub J.S. de Groot B.L. Detection of functional modes in protein dynamics. PLOS Comput. Biol. 2009 5 8 e1000480 10.1371/journal.pcbi.1000480 19714202
    [Google Scholar]
  52. Antony J. Piquemal J.P. Gresh N. 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. 2005 26 11 1131 1147 10.1002/jcc.20245 15937993
    [Google Scholar]
  53. Pettersen E.F. Goddard T.D. Huang C.C. UCSF Chimera—A visualization system for exploratory research and analysis. J. Comput. Chem. 2004 25 13 1605 1612 10.1002/jcc.20084 15264254
    [Google Scholar]
  54. AlAjmi M.F. Rehman M.T. Hussain A. Celecoxib, Glipizide, Lapatinib, and Sitagliptin as potential suspects of aggravating SARS-CoV-2 (COVID-19) infection: A computational approach. J. Biomol. Struct. Dyn. 2022 40 24 13747 13758 10.1080/07391102.2021.1994013 34709124
    [Google Scholar]
  55. Yan Y. Zhang D. Zhou P. Li B. Huang S.Y. HDOCK: A web server for protein–protein and protein–DNA/RNA docking based on a hybrid strategy. Nucleic Acids Res. 2017 45 W1 W365-73 10.1093/nar/gkx407 28521030
    [Google Scholar]
  56. Gingras J. Smith S. Matson D.J. Global Nav1.7 knockout mice recapitulate the phenotype of human congenital indifference to pain. PLoS One 2014 9 9 e105895 10.1371/journal.pone.0105895 25188265
    [Google Scholar]
  57. McGibbon R.T. Beauchamp K.A. Harrigan M.P. MDTraj: A modern open library for the analysis of molecular dynamics trajectories. Biophys. J. 2015 109 8 1528 1532 10.1016/j.bpj.2015.08.015 26488642
    [Google Scholar]
  58. Spellberg B. Bartlett J.G. Gilbert D.N. The future of antibiotics and resistance. N. Engl. J. Med. 2013 368 4 299 302 10.1056/NEJMp1215093
    [Google Scholar]
  59. Roemer T. Krysan D.J. Antifungal drug development: Challenges, unmet clinical needs, and new approaches. Cold Spring Harb. Perspect. Med. 2014 4 5 a019703 a3 10.1101/cshperspect.a019703 24789878
    [Google Scholar]
  60. Brown D. Antibiotic resistance breakers: Can repurposed drugs fill the antibiotic discovery void? Nat. Rev. Drug Discov. 2015 14 12 821 832 10.1038/nrd4675 26493767
    [Google Scholar]
  61. Socransky S.S. Haffajee A.D. Cugini M.A. Smith C. Kent R.L. Jr Microbial complexes in subgingival plaque. J. Clin. Periodontol. 1998 25 2 134 144 10.1111/j.1600‑051X.1998.tb02419.x 9495612
    [Google Scholar]
  62. Marsh P.D. Are dental diseases examples of ecological catastrophes? Microbiology (Reading) 2003 149 2 279 294 10.1099/mic.0.26082‑0 12624191
    [Google Scholar]
  63. Meng X.Y. Zhang H.X. Mezei M. Cui M. Molecular docking: A powerful approach for structure-based drug discovery. Curr. Computeraided Drug Des. 2011 7 2 146 157 10.2174/157340911795677602 21534921
    [Google Scholar]
  64. Ertl P. Schuffenhauer A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J. Cheminform. 2009 1 1 8 10.1186/1758‑2946‑1‑8 20298526
    [Google Scholar]
  65. Cheng F. Li W. Zhou Y. admetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem. Inf. Model. 2012 52 11 3099 3105 10.1021/ci300367a 23092397
    [Google Scholar]
  66. Eichinger A. Beisel H.G. Jacob U. Crystal structure of gingipain R: An Arg-specific bacterial cysteine proteinase with a caspase-like fold. EMBO J. 1999 18 20 5453 5462 10.1093/emboj/18.20.5453 10523290
    [Google Scholar]
  67. Crow A. Greene N.P. Kaplan E. Koronakis V. Structure and mechanotransmission mechanism of the MacB ABC transporter superfamily. Proc. Natl. Acad. Sci. USA 2017 114 47 12572 12577 10.1073/pnas.1712153114 29109272
    [Google Scholar]
  68. Rice K. Batul K. Whiteside J. The predominance of nucleotidyl activation in bacterial phosphonate biosynthesis. Nat. Commun. 2019 10 1 3698 10.1038/s41467‑019‑11627‑6 31420548
    [Google Scholar]
  69. Gilson M.K. Given J.A. Bush B.L. McCammon J.A. The statistical-thermodynamic basis for computation of binding affinities: A critical review. Biophys. J. 1997 72 3 1047 1069 10.1016/S0006‑3495(97)78756‑3 9138555
    [Google Scholar]
  70. Genheden S. Ryde U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discov. 2015 10 5 449 461 10.1517/17460441.2015.1032936 25835573
    [Google Scholar]
  71. Loferer M.J. Loeffler H.H. Liedl K.R. A QM–MM interface between CHARMM and TURBOMOLE: Implementation and application to systems in bulk phase and biologically active systems. J. Comput. Chem. 2003 24 10 1240 1249 10.1002/jcc.10283 12820132
    [Google Scholar]
  72. Top E.C. Med. Chem. 2013 13 867 7 10.2174/1568026611313070009
    [Google Scholar]
  73. Encyclopedia of Reagents for Organic Synthesis. Wiley 2001 10.1002/047084289X
    [Google Scholar]
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