Skip to main content
Log in

Inhibition of Monkeypox Virus DNA Polymerase Using Moringa oleifera Phytochemicals: Computational Studies of Drug-Likeness, Molecular Docking, Molecular Dynamics Simulation and Density Functional Theory

  • Original research article
  • Published:
Indian Journal of Microbiology Aims and scope Submit manuscript

Abstract

The emergence of zoonotic monkeypox (MPX) disease, caused by the double-stranded DNA monkeypox virus (MPXV), has become a global threat. Due to unavailability of a specific small molecule drug for MPX, this study investigated Moringa oleifera phytochemicals to find potent and safe inhibitors of DNA Polymerase (DNA Pol), a poxvirus drug target due to its role in the viral life cycle. For that, 146 phytochemicals were screened through drug-likeness and molecular docking analyses. Among these, 136 compounds exhibited drug-like properties, with Gossypetin showing the highest binding affinity (− 7.8 kcal/mol), followed by Riboflavin (− 7.6 kcal/mol) and Ellagic acid (− 7.6 kcal/mol). In comparison, the control drugs Cidofovir and Brincidofovir displayed lower binding affinities, with binding energies of − 6.0 kcal/mol and − 5.1 kcal/mol, respectively. Hydrogen bonds, electrostatic and hydrophobic interactions were the main non-bond interactions between inhibitors and protein active site. The identified compounds were further evaluated using molecular dynamics simulation, density functional theory analysis and ADMET analysis. Molecular dynamics simulations conducted over 200 ns revealed that DNA Pol-Gossypetin complex was not stable, however, Riboflavin and Ellagic acid complexes showed excellent stability indicating them as better DNA Pol inhibitors. The density functional theory analysis exhibited the chemical reactivity of these inhibitor compounds. The ADMET analysis suggested that the top phytochemicals were safe and showed no toxicity. In conclusion, this study has identified Riboflavin and Ellagic acid as potential DNA Pol inhibitors to control MPXV. Further experimental assays and clinical trials are needed to confirm their activity against the disease.

Graphical Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data Availability

All data generated or analyzed during this study are included in this article.

References

  1. Alakunle E, Moens U, Nchinda G, Okeke MI (2020) Monkeypox virus in nigeria: Infection biology, epidemiology, and evolution. Viruses. https://doi.org/10.3390/v12111257

    Article  PubMed  PubMed Central  Google Scholar 

  2. Bunge EM, Hoet B, Chen L et al (2022) The changing epidemiology of human monkeypox—a potential threat? A systematic review. PLoS Negl Trop Dis. https://doi.org/10.1371/journal.pntd.0010141

    Article  PubMed  PubMed Central  Google Scholar 

  3. Kugelman JR, Johnston SC, Mulembakani PM et al (2014) Genomic variability of monkeypox virus among humans, Democratic Republic of the Congo. Emerg Infect Dis 20:232–239. https://doi.org/10.3201/eid2002.130118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. von Magnus P, Andersen EK, Petersen KB, Birch-Andersen A (1959) A Pox-like disease in cynomolgus monkeys. Acta Pathol Microbiol Scand 46:156–176. https://doi.org/10.1111/j.1699-0463.1959.tb00328.x

    Article  Google Scholar 

  5. Brown K, Leggat PA (2016) Human monkeypox: current state of knowledge and implications for the future. Trop Med Infect Dis 1:8. https://doi.org/10.3390/tropicalmed1010008

    Article  PubMed  PubMed Central  Google Scholar 

  6. Ladnyj ID, Ziegler P, Kima E (1972) A human infection caused by monkeypox virus in Basankusu territory, democratic republic of the Congo. Bull World Health Organ 46:593

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Yinka-Ogunleye A, Aruna O, Dalhat M et al (2019) Outbreak of human monkeypox in Nigeria in 2017–18: a clinical and epidemiological report. Lancet Infect Dis 19:872–879. https://doi.org/10.1016/S1473-3099(19)30294-4

    Article  PubMed  PubMed Central  Google Scholar 

  8. Isidro J, Borges V, Pinto M et al (2022) Phylogenomic characterization and signs of microevolution in the 2022 multi-country outbreak of monkeypox virus. Nat Med 2022:1–1. https://doi.org/10.1038/s41591-022-01907-y

    Article  CAS  Google Scholar 

  9. Likos AM, Sammons SA, Olson VA et al (2005) A tale of two clades: monkeypox viruses. J Gen Virol 86:2661–2672

    Article  CAS  PubMed  Google Scholar 

  10. WHO (2022) Multi-country monkeypox outbreak in non-endemic countries. https://www.who.int/emergencies/disease-outbreak-news/item/2022-DON385. Accessed 9 Jul 2022

  11. Alakunle EF, Okeke MI (2022) Monkeypox virus: a neglected zoonotic pathogen spreads globally. Nat Rev Microbiol 20:507–508

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Chaix E, Boni M, Guillier L et al (2022) Risk of Monkeypox virus (MPXV) transmission through the handling and consumption of food. Microb Risk Anal. https://doi.org/10.1016/j.mran.2022.100237

    Article  PubMed  PubMed Central  Google Scholar 

  13. Tiecco G, Degli Antoni M, Storti S et al (2022) Monkeypox, a literature review: what is new and where does this concerning virus come from? Viruses. https://doi.org/10.3390/v14091894

    Article  PubMed  PubMed Central  Google Scholar 

  14. Petersen E, Kantele A, Koopmans M et al (2019) Human monkeypox: epidemiologic and clinical characteristics, diagnosis, and prevention. Infect Dis Clin North Am 33:1027–1043. https://doi.org/10.1016/j.idc.2019.03.001

    Article  PubMed  PubMed Central  Google Scholar 

  15. Kumar N, Acharya A, Gendelman HE, Byrareddy SN (2022) The 2022 outbreak and the pathobiology of the monkeypox virus. J Autoimmun. https://doi.org/10.1016/J.JAUT.2022.102855

    Article  PubMed  PubMed Central  Google Scholar 

  16. Mukherjee AG, Wanjari UR, Kannampuzha S et al (2023) The pathophysiological and immunological background of the monkeypox virus infection: an update. J Med Virol. https://doi.org/10.1002/jmv.28206

    Article  PubMed  Google Scholar 

  17. Okyay RA, Bayrak E, Kaya E et al (2022) Another epidemic in the shadow of Covid 19 pandemic: a review of monkeypox. Eurasian J Med Oncol 6:95–99. https://doi.org/10.14744/ejmo.2022.2022

    Article  Google Scholar 

  18. Adler H, Gould S, Hine P et al (2022) Clinical features and management of human monkeypox: a retrospective observational study in the UK. Lancet Infect Dis. https://doi.org/10.1016/S1473-3099(22)00228-6

    Article  PubMed  PubMed Central  Google Scholar 

  19. Farahat RA, Abdelaal A, Shah J et al (2022) Monkeypox outbreaks during COVID-19 pandemic: are we looking at an independent phenomenon or an overlapping pandemic? Ann Clin Microbiol Antimicrob 21:1–3. https://doi.org/10.1186/S12941-022-00518-2

    Article  Google Scholar 

  20. Gessain A, Nakoune E, Yazdanpanah Y (2022) Monkeypox. N Engl J Med 387:1783–1793. https://doi.org/10.1056/NEJMra2208860

    Article  CAS  PubMed  Google Scholar 

  21. Berdis AJ (2008) DNA polymerases as therapeutic targets. Biochemistry 47:8253–8260. https://doi.org/10.1021/bi801179f

    Article  CAS  PubMed  Google Scholar 

  22. Luczkowiak J, Álvarez M, Sebastián-Martín A, Menéndez-Arias L (2018) DNA-dependent DNA polymerases as drug targets in herpesviruses and poxviruses. In: Gupta SP (ed) Viral polymerases: structures, functions and roles as antiviral drug targets. Elsevier, Amsterdam, pp 95–134

    Google Scholar 

  23. Moss B (2013) Poxvirus DNA replication. Cold Spring Harb Perspect Biol. https://doi.org/10.1101/cshperspect.a010199

    Article  PubMed  PubMed Central  Google Scholar 

  24. Andrei G, De Clercq E, Snoeck R (2009) Viral DNA polymerase inhibitors. Viral genome replication. Springer, Berlin, pp 481–526

    Chapter  Google Scholar 

  25. Tsai C-H, Lee P-Y, Stollar V, Li M-L (2006) Antiviral therapy targeting viral polymerase. Curr Pharm Des 12:1339–1355. https://doi.org/10.2174/138161206776361156

    Article  CAS  PubMed  Google Scholar 

  26. Antoine TE, Park PJ, Shukla D (2013) Glycoprotein targeted therapeutics: a new era of anti-herpes simplex virus-1 therapeutics. Rev Med Virol 23:194–208. https://doi.org/10.1002/rmv.1740

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Cui W, Aouidate A, Wang S et al (2020) Discovering anti-cancer drugs via computational methods. Front Pharmacol. https://doi.org/10.3389/fphar.2020.00733

    Article  PubMed  PubMed Central  Google Scholar 

  28. Li K, Du Y, Li L, Wei D-Q (2019) Bioinformatics approaches for anti-cancer drug discovery. Curr Drug Targets 21:3–17. https://doi.org/10.2174/1389450120666190923162203

    Article  CAS  Google Scholar 

  29. Biswas D, Nandy S, Mukherjee A et al (2020) Moringa oleifera Lam. and derived phytochemicals as promising antiviral agents: a review. South African J Bot 129:272–282. https://doi.org/10.1016/J.SAJB.2019.07.049

    Article  CAS  Google Scholar 

  30. Peng Q, Xie Y, Kuai L et al (2023) Structure of monkeypox virus DNA polymerase holoenzyme. Science 379:100–105. https://doi.org/10.1126/SCIENCE.ADE6360

    Article  CAS  PubMed  Google Scholar 

  31. Burley SK, Berman HM, Christie C et al (2018) RCSB Protein Data Bank: Sustaining a living digital data resource that enables breakthroughs in scientific research and biomedical education. Protein Sci 27:316–330. https://doi.org/10.1002/pro.3331

    Article  CAS  PubMed  Google Scholar 

  32. Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Res 28:235–242. https://doi.org/10.1093/nar/28.1.235

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Morris GM, Huey R, Olson AJ (2008) Using AutoDock for ligand-receptor docking. Curr Protoc Bioinforma. https://doi.org/10.1002/0471250953.bi0814s24

    Article  Google Scholar 

  34. Mohanraj K, Karthikeyan BS, Vivek-Ananth RP et al (2018) IMPPAT: a curated database of Indian medicinal plants, phytochemistry and therapeutics. Sci Rep. https://doi.org/10.1038/s41598-018-22631-z

    Article  PubMed  PubMed Central  Google Scholar 

  35. Kim S, Chen J, Cheng T et al (2019) PubChem 2019 update: improved access to chemical data. Nucleic Acids Res 47:D1102–D1109. https://doi.org/10.1093/nar/gky1033

    Article  PubMed  Google Scholar 

  36. O’Boyle NM, Banck M, James CA et al (2011) Open babel: an open chemical toolbox. J Cheminform. https://doi.org/10.1186/1758-2946-3-33

    Article  PubMed  PubMed Central  Google Scholar 

  37. Dallakyan S, Olson AJ (2015) Small-molecule library screening by docking with PyRx. Methods Mol Biol 1263:243–250. https://doi.org/10.1007/978-1-4939-2269-7_19

    Article  CAS  PubMed  Google Scholar 

  38. Jász Á, Rák Á, Ladjánszki I, Cserey G (2019) Optimized GPU implementation of Merck molecular force field and universal force field. J Mol Struct 1188:227–233. https://doi.org/10.1016/j.molstruc.2019.04.007

    Article  CAS  Google Scholar 

  39. Rappé AK, Casewit CJ, Colwell KS et al (1992) UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J Am Chem Soc 114:10024–10035. https://doi.org/10.1021/ja00051a040

    Article  Google Scholar 

  40. Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. https://doi.org/10.1038/srep42717

    Article  PubMed  PubMed Central  Google Scholar 

  41. Lipinski CA (2004) Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol 1:337–341. https://doi.org/10.1016/j.ddtec.2004.11.007

    Article  CAS  PubMed  Google Scholar 

  42. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2012) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 64:4–17. https://doi.org/10.1016/j.addr.2012.09.019

    Article  Google Scholar 

  43. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25. https://doi.org/10.1016/S0169-409X(96)00423-1

    Article  CAS  Google Scholar 

  44. Trott O, Olson AJ (2010) AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461. https://doi.org/10.1002/JCC.21334

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Reynolds CH, Bembenek SD, Tounge BA (2007) The role of molecular size in ligand efficiency. Bioorganic Med Chem Lett 17:4258–4261. https://doi.org/10.1016/j.bmcl.2007.05.038

    Article  CAS  Google Scholar 

  46. Hopkins AL, Keserü GM, Leeson PD et al (2014) The role of ligand efficiency metrics in drug discovery. Nat Rev Drug Discov 13:105–121. https://doi.org/10.1038/nrd4163

    Article  CAS  PubMed  Google Scholar 

  47. Hopkins AL, Groom CR, Alex A (2004) Ligand efficiency: a useful metric for lead selection. Drug Discov Today 9:430–431. https://doi.org/10.1016/S1359-6446(04)03069-7

    Article  PubMed  Google Scholar 

  48. Edwards MP, Price DA (2010) Role of physicochemical properties and ligand lipophilicity efficiency in addressing drug safety risks. Elsevier, Amsterdam

    Book  Google Scholar 

  49. Murray CW, Erlanson DA, Hopkins AL et al (2014) Validity of ligand efficiency metrics. ACS Med Chem Lett 5:616–618. https://doi.org/10.1021/ml500146d

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. BIOVIA DS (2021) BIOVIA Discovery Studio

  51. Abraham MJ, Murtola T, Schulz R et al (2015) Gromacs: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25. https://doi.org/10.1016/J.SOFTX.2015.06.001

    Article  Google Scholar 

  52. Vanommeslaeghe K, Hatcher E, Acharya C et al (2009) CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem NA-NA. https://doi.org/10.1002/jcc.21367

    Article  Google Scholar 

  53. Lindahl E, Hess B, van der Spoel D (2001) GROMACS 3.0: a package for molecular simulation and trajectory analysis. J Mol Model 7:306–317. https://doi.org/10.1007/s008940100045

    Article  CAS  Google Scholar 

  54. Kavitha R, Karunagaran S, Chandrabose SS et al (2015) Pharmacophore modeling, virtual screening, molecular docking studies and density functional theory approaches to identify novel ketohexokinase (KHK) inhibitors. Biosystems 138:39–52. https://doi.org/10.1016/J.BIOSYSTEMS.2015.10.005

    Article  CAS  PubMed  Google Scholar 

  55. BIOVIA DS (2020) BIOVIA Materials Studio

  56. Tsuneda T, Song JW, Suzuki S, Hirao K (2010) On Koopmans’ theorem in density functional theory. J Chem Phys 133:174101. https://doi.org/10.1063/1.3491272

    Article  CAS  PubMed  Google Scholar 

  57. Curreli F, Do KY, Belov DS et al (2017) Synthesis, antiviral potency, in vitro ADMET, and X-ray structure of potent CD4 mimics as entry inhibitors that target the Phe43 cavity of HIV-1 gp120. J Med Chem 60:3124–3153. https://doi.org/10.1021/ACS.JMEDCHEM.7B00179

    Article  CAS  PubMed  Google Scholar 

  58. Pires DEV, Blundell TL, Ascher DB (2015) pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem 58:4066–4072. https://doi.org/10.1021/acs.jmedchem.5b00104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Banerjee P, Eckert AO, Schrey AK, Preissner R (2018) ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res 46:W257–W263. https://doi.org/10.1093/NAR/GKY318

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Mathialagan S, Piotrowski MA, Tess DA et al (2017) Quantitative prediction of human renal clearance and drug-drug interactions of organic anion transporter substrates using in vitro transport data: a relative activity factor approach. Drug Metab Dispos 45:409–417. https://doi.org/10.1124/DMD.116.074294/-/DC1

    Article  CAS  PubMed  Google Scholar 

  61. Velavan TP, Meyer CG, Thirumalaisamy Velavan CP (2022) Monkeypox 2022 outbreak: an update. Trop Med Int Heal 27:604–605. https://doi.org/10.1111/tmi.13785

    Article  Google Scholar 

  62. Ren S-Y, Li J, Gao R-D (2022) 2022 Monkeypox outbreak: Why is it a public health emergency of international concern? What can we do to control it? World J Clin cases 10:10873–10881. https://doi.org/10.12998/wjcc.v10.i30.10873

    Article  PubMed  PubMed Central  Google Scholar 

  63. Rizk JG, Lippi G, Henry BM et al (2022) Prevention and treatment of monkeypox. Drugs 82:957–963. https://doi.org/10.1007/S40265-022-01742-Y/TABLES/3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Jones EV, Moss B (1984) Mapping of the vaccinia virus DNA polymerase gene by marker rescue and cell-free translation of selected RNA. J Virol 49:72–77. https://doi.org/10.1128/jvi.49.1.72-77.1984

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Prichard MN, Kern ER (2012) Orthopoxvirus targets for the development of new antiviral agents. Antiviral Res 94:111–125. https://doi.org/10.1016/j.antiviral.2012.02.012

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Traktman P, Sridhar P, Condit RC, Roberts BE (1984) Transcriptional mapping of the DNA polymerase gene of vaccinia virus. J Virol 49:125–131. https://doi.org/10.1128/jvi.49.1.125-131.1984

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Ollis DL, Brick P, Hamlin R et al (1985) Structure of large fragment of Escherichia coli DNA polymerase I complexed with dTMP. Nature 313:762–766. https://doi.org/10.1038/313762a0

    Article  CAS  PubMed  Google Scholar 

  68. Patel PH, Loeb LA (2001) Getting a grip on how DNA polymerases function. Nat Struct Biol 8:656–659. https://doi.org/10.1038/90344

    Article  CAS  PubMed  Google Scholar 

  69. Steitz TA (1999) DNA polymerases: structural diversity and common mechanisms. J Biol Chem 274:17395–17398. https://doi.org/10.1074/jbc.274.25.17395

    Article  CAS  PubMed  Google Scholar 

  70. Varma AK, Patil R, Das S et al (2010) Optimized hydrophobic interactions and hydrogen bonding at the target-ligand interface leads the pathways of Drug-Designing. PLoS One. https://doi.org/10.1371/journal.pone.0012029

    Article  PubMed  PubMed Central  Google Scholar 

  71. Alex A, Millan DS, Perez M et al (2011) Intramolecular hydrogen bonding to improve membrane permeability and absorption in beyond rule of five chemical space. Medchemcomm 2:669–674. https://doi.org/10.1039/c1md00093d

    Article  CAS  Google Scholar 

  72. Ferreira De Freitas R, Schapira M (2017) A systematic analysis of atomic protein-ligand interactions in the PDB. Medchemcomm 8:1970–1981. https://doi.org/10.1039/c7md00381a

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Smith RD, Engdahl AL, Dunbar JB, Carlson HA (2012) Biophysical limits of protein-ligand binding. J Chem Inf Model 52:2098–2106. https://doi.org/10.1021/ci200612f

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Nag A, Chowdhury RR (2020) Piperine, an alkaloid of black pepper seeds can effectively inhibit the antiviral enzymes of Dengue and Ebola viruses, an in silico molecular docking study. VirusDisease 31:308–315. https://doi.org/10.1007/s13337-020-00619-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Raj U, Varadwaj PK (2016) Flavonoids as multi-target inhibitors for proteins associated with ebola virus. In silico discovery using virtual screening and molecular docking studies. Interdiscip Sci Comput Life Sci 8:132–141. https://doi.org/10.1007/s12539-015-0109-8

    Article  CAS  Google Scholar 

  76. Eberle RJ, Olivier DS, Amaral MS et al (2022) Riboflavin, a Potent neuroprotective vitamin: focus on flavivirus and alphavirus proteases. Microorganisms. https://doi.org/10.3390/microorganisms10071331

    Article  PubMed  PubMed Central  Google Scholar 

  77. Farah N, Chin VK, Chong PP et al (2022) Riboflavin as a promising antimicrobial agent? A multi-perspective review. Curr Res Microb Sci. https://doi.org/10.1016/j.crmicr.2022.100111

    Article  PubMed  PubMed Central  Google Scholar 

  78. Morosetti G, Criscuolo AA, Santi F, Perno CF, Piccione E, Ciotti M (2017) Ellagic acid and Annona muricata in the chemoprevention of HPV-related pre-neoplastic lesions of the cervix. Oncol Lett 13(3):1880–1884

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Alexova R, Alexandrova S, Dragomanova S et al (2023) Anti-COVID-19 potential of ellagic acid and polyphenols of Punica granatum L. Molecules 28:3772. https://doi.org/10.3390/MOLECULES28093772

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Cui Q, Du R, Anantpadma M et al (2018) Identification of ellagic acid from plant rhodiola rosea l. as an anti-ebola virus entry inhibitor. Viruses. https://doi.org/10.3390/v10040152

    Article  PubMed  PubMed Central  Google Scholar 

  81. Acquadro S, Civra A, Cagliero C et al (2020) Punica granatum Leaf ethanolic extract and ellagic acid as inhibitors of Zika virus infection. Planta Med 86:1363–1374. https://doi.org/10.1055/a-1232-5705

    Article  CAS  PubMed  Google Scholar 

  82. Sargsyan K, Grauffel C, Lim C (2017) How molecular size impacts RMSD applications in molecular dynamics simulations. J Chem Theory Comput 13:1518–1524. https://doi.org/10.1021/acs.jctc.7b00028

    Article  CAS  PubMed  Google Scholar 

  83. Kuzmanic A, Zagrovic B (2010) Determination of ensemble-average pairwise root mean-square deviation from experimental B-factors. Biophys J 98:861–871. https://doi.org/10.1016/j.bpj.2009.11.011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Bornot A, Etchebest C, De Brevern AG (2011) Predicting protein flexibility through the prediction of local structures. Proteins Struct Funct Bioinforma 79:839–852. https://doi.org/10.1002/PROT.22922

    Article  CAS  Google Scholar 

  85. Ghasemi F, Zomorodipour A, Karkhane AA, Khorramizadeh MR (2016) In silico designing of hyper-glycosylated analogs for the human coagulation factor IX. J Mol Graph Model 68:39–47. https://doi.org/10.1016/J.JMGM.2016.05.011

    Article  CAS  PubMed  Google Scholar 

  86. Shahbaaz M, Nkaule A, Christoffels A (2019) Designing novel possible kinase inhibitor derivatives as therapeutics against Mycobacterium tuberculosis: an in silico study. Sci Rep. https://doi.org/10.1038/s41598-019-40621-7

    Article  PubMed  PubMed Central  Google Scholar 

  87. Durham E, Dorr B, Woetzel N et al (2009) Solvent accessible surface area approximations for rapid and accurate protein structure prediction. J Mol Model 15:1093–1108. https://doi.org/10.1007/s00894-009-0454-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Pearson RG (2005) Chemical hardness and density functional theory. J Chem Sci 117:369–377. https://doi.org/10.1007/BF02708340

    Article  CAS  Google Scholar 

  89. Khan SA, Rizwan K, Shahid S et al (2020) Synthesis, DFT, computational exploration of chemical reactivity, molecular docking studies of novel formazan metal complexes and their biological applications. Appl Organomet Chem. https://doi.org/10.1002/aoc.5444

    Article  Google Scholar 

  90. Mumit MA, Pal TK, Alam MA et al (2020) DFT studies on vibrational and electronic spectra, HOMO–LUMO, MEP, HOMA, NBO and molecular docking analysis of benzyl-3-N-(2,4,5-trimethoxyphenylmethylene)hydrazinecarbodithioate. J Mol Struct. https://doi.org/10.1016/j.molstruc.2020.128715

    Article  PubMed  PubMed Central  Google Scholar 

  91. Pearson RG (1988) Electronic spectra and chemical reactivity. J Am Chem Soc 110:2092–2097. https://doi.org/10.1021/ja00215a013

    Article  CAS  Google Scholar 

  92. Ma Y, Tao Y, Qu H et al (2022) Exploration of plant-derived natural polyphenols toward COVID-19 main protease inhibitors: DFT, molecular docking approach, and molecular dynamics simulations. RSC Adv 12:5357–5368. https://doi.org/10.1039/d1ra07364h

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Radchenko EV, Dyabina AS, Palyulin VA (2016) Zefirov NS (2016) Prediction of human intestinal absorption of drug compounds. Russ Chem Bull 652(65):576–580. https://doi.org/10.1007/S11172-016-1340-0

    Article  Google Scholar 

  94. Wadanambi PM, Mannapperuma U (2021) Computational study to discover potent phytochemical inhibitors against drug target, squalene synthase from Leishmania donovani. Heliyon 7:e07178. https://doi.org/10.1016/J.HELIYON.2021.E07178

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Smith DA, Beaumont K, Maurer TS, Di L (2019) Clearance in drug design. J Med Chem 62:2245–2255. https://doi.org/10.1021/ACS.JMEDCHEM.8B01263/ASSET/IMAGES/MEDIUM/JM-2018-01263H_0010.GIF

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Contributions

The authors' contributions to this study are as follows: MAY conceptualized and designed the research; conducted molecular docking, density functional theory and ADMET analysis; and prepared the original draft. SB and SS executed the molecular dynamics simulation.

Corresponding author

Correspondence to Muhammad Abrar Yousaf.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Ethical Approval

Not applicable.

Consent to Participate

All authors agreed to participate in this research.

Consent for Publication

All authors have approved the last version of the manuscript for its submission.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yousaf, M.A., Basheera, S. & Sivanandan, S. Inhibition of Monkeypox Virus DNA Polymerase Using Moringa oleifera Phytochemicals: Computational Studies of Drug-Likeness, Molecular Docking, Molecular Dynamics Simulation and Density Functional Theory. Indian J Microbiol (2024). https://doi.org/10.1007/s12088-024-01244-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12088-024-01244-3

Keywords

Navigation