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Identification of potential inhibitors of Zika virus targeting NS3 helicase using molecular dynamics simulations and DFT studies

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Abstract

Despite the various research efforts towards the drug discovery program for Zika virus treatment, no antiviral drugs or vaccines have yet been discovered. The spread of the mosquito vector and ZIKV infection exposure is expected to accelerate globally due to continuing global travel. The NS3-Hel is a non-structural protein part and involved in different functions such as polyprotein processing, genome replication, etc. It makes an NS3-Hel protein an attractive target for designing novel drugs for ZIKV treatment. This investigation identifies the novel, potent ZIKV inhibitors by virtual screening and elucidates the binding pattern using molecular docking and molecular dynamics simulation studies. The molecular dynamics simulation results indicate dynamic stability between protein and ligand complexes, and the structures keep significantly unchanged at the binding site during the simulation period. All inhibitors found within the acceptable range having drug-likeness properties. The synthetic feasibility score suggests that all screened inhibitors can be easily synthesizable. Therefore, possible inhibitors obtained from this study can be considered a potential inhibitor for NS3 Hel, and further, it could be provided as a lead for drug development.

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References

  1. Marin MS, Zanotto PDA, Gritsun TS, Gould EA (1995) Phylogeny of TYU, SRE, and CFA virus: different evolutionary rates in the genus flavivirus. Virology 206(2):1133–1139. https://doi.org/10.1006/viro.1995.1038

    Article  CAS  PubMed  Google Scholar 

  2. Paixão ES, Barreto F, da Glória Teixeira M, da Conceição N, Costa M, Rodrigues LC (2016) History, epidemiology, and clinical manifestations of Zika: a systematic review. Am J Public Health 106(4):606–612. https://doi.org/10.2105/ajph.2016.303112

    Article  PubMed  PubMed Central  Google Scholar 

  3. Campos GS, Bandeira AC, Sardi SI (2015) Zika virus outbreak, bahia, brazil. Emerg Infect Dis 21(10):1885. https://doi.org/10.3201/eid2110.150847

    Article  PubMed  PubMed Central  Google Scholar 

  4. Arzuza-Ortega L, Polo A, Pérez-Tatis G, López-García H, Parra E, Pardo-Herrera LC, Rodríguez-Morales AJ (2016) Fatal sickle cell disease and Zika virus infection in girl from Colombia. Emerg Infect Dis 22(5):925. https://doi.org/10.3201/eid2205.151934

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Azevedo RS, Araujo MT, Martins Filho AJ, Oliveira CS, Nunes BT, Cruz AC, Vasconcelos PF (2016) Zika virus epidemic in Brazil. I. Fatal disease in adults: clinical and laboratorial aspects. J Clin Virol 85:56–64. https://doi.org/10.1016/j.jcv.2016.10.024

    Article  PubMed  PubMed Central  Google Scholar 

  6. Fernandez-Garcia MD, Mazzon M, Jacobs M, Amara A (2009) Pathogenesis of flavivirus infections: using and abusing the host cell. Cell Host Microbe 5(4):318–328. https://doi.org/10.1016/j.chom.2009.04.001

    Article  CAS  PubMed  Google Scholar 

  7. Salonen ANNE, Ahola TERO, Kääriäinen LEEVI (2004) Viral RNA replication in association with cellular membranes. Membr Traffick Viral Replication. https://doi.org/10.1007/3-540-26764-6_5

    Article  Google Scholar 

  8. Yamashita T, Unno H, Mori Y, Tani H, Moriishi K, Takamizawa A, Matsuura Y (2008) Crystal structure of the catalytic domain of Japanese encephalitis virus NS3 helicase/nucleoside triphosphatase at a resolution of 1.8 Å. Virology 373(2):426–436. https://doi.org/10.1016/j.virol.2007.12.018

    Article  CAS  PubMed  Google Scholar 

  9. Fang J, Jing X, Lu G, Xu Y, Gong P (2019) Crystallographic snapshots of the Zika Virus NS3 helicase help visualize the reactant water replenishment. ACS Infectious Dis 5(2):177–183. https://doi.org/10.1021/acsinfecdis.8b00214

    Article  CAS  Google Scholar 

  10. Luo D, Xu T, Watson RP, Scherer-Becker D, Sampath A, Jahnke W, Lescar J (2008) Insights into RNA unwinding and ATP hydrolysis by the flavivirus NS3 protein. EMBO J 27(23):3209–3219. https://doi.org/10.1038/emboj.2008.232

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Singleton MR, Dillingham MS, Wigley DB (2007) Structure and mechanism of helicases and nucleic acid translocases. Annu Rev Biochem 76:23–50. https://doi.org/10.1146/annurev.biochem.76.052305.115300

    Article  CAS  PubMed  Google Scholar 

  12. Jain R, Coloma J, García-Sastre A, Aggarwal AK (2016) Structure of the NS3 helicase from Zika virus. Nat Struct Mol Biol 23(8):752–754. https://doi.org/10.1038/nsmb.3258

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Matusan AE, Pryor MJ, Davidson AD, Wright PJ (2001) Mutagenesis of the Dengue virus type 2 NS3 protein within and outside helicase motifs: effects on enzyme activity and virus replication. J Virol 75(20):9633–9643. https://doi.org/10.1128/jvi.75.20.9633-9643.2001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Sampath A, Xu T, Chao A, Luo D, Lescar J, Vasudevan SG (2006) Structure-based mutational analysis of the NS3 helicase from dengue virus. J Virol 80(13):6686–6690. https://doi.org/10.1128/jvi.02215-05

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Yuan S, Chan JFW, den-Haan H, Chik KKH, Zhang AJ, Chan CCS, Yuen KY (2017) Structure-based discovery of clinically approved drugs as Zika virus NS2B-NS3 protease inhibitors that potently inhibit Zika virus infection in vitro and in vivo. Antivir Res 145:33–43. https://doi.org/10.1016/j.antiviral.2017.07.007

    Article  CAS  PubMed  Google Scholar 

  16. Devillers J (2018) Repurposing drugs for use against Zika virus infection. SAR QSAR Environ Res 29(2):103–115. https://doi.org/10.1080/1062936x.2017.1411642

    Article  CAS  PubMed  Google Scholar 

  17. Xu M, Lee EM, Wen Z, Cheng Y, Huang WK, Qian X, Tang H (2016) Identification of small-molecule inhibitors of Zika virus infection and induced neural cell death via a drug repurposing screen. Nat Med 22(10):1101–1107. https://doi.org/10.1038/nm.4184

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Kumar D, Aarthy M, Kumar P, Singh SK, Uversky VN, Giri R (2020) Targeting the NTPase site of Zika virus NS3 helicase for inhibitor discovery. J Biomol Struct Dyn 38(16):4827–4837. https://doi.org/10.1080/07391102.2019.1689851

    Article  CAS  PubMed  Google Scholar 

  19. Retallack H, Di Lullo E, Arias C, Knopp KA, Laurie MT, Sandoval-Espinosa C, DeRisi JL (2016) Zika virus cell tropism in the developing human brain and inhibition by azithromycin. Proc Natl Acad Sci USA 113(50):14408–14413. https://doi.org/10.1073/pnas.1618029113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Kumar N, Mishra SS, Sharma CS, Singh HP, Kalra S (2018) In silico binding mechanism prediction of benzimidazole based corticotropin releasing factor-1 receptor antagonists by quantitative structure activity relationship, molecular docking and pharmacokinetic parameters calculation. J Biomol Struct Dyn 36(7):1691–1712. https://doi.org/10.1080/07391102.2017.1332688

    Article  CAS  PubMed  Google Scholar 

  21. Azam MA, Jupudi S, Saha N, Paul RK (2019) Combining molecular docking and molecular dynamics studies for modelling Staphylococcus aureus MurD inhibitory activity. SAR QSAR Environ Res 30(1):1–20. https://doi.org/10.1080/1062936x.2018.1539034

    Article  CAS  PubMed  Google Scholar 

  22. Tian H, Ji X, Yang X, Xie W, Yang K, Chen C, Yang H (2016) The crystal structure of Zika virus helicase: basis for antiviral drug design. Protein Cell 7(6):450–454. https://doi.org/10.1007/s13238-016-0275-4

    Article  PubMed  PubMed Central  Google Scholar 

  23. Berman HM, Battistuz T, Bhat TN, Bluhm WF, Bourne PE, Burkhardt K, Zardecki C (2002) The protein data bank. Acta Crystallogr D 58(6):899–907. https://doi.org/10.1107/s0907444902003451

    Article  PubMed  Google Scholar 

  24. Hajduk PJ, Huth JR, Tse C (2005) Predicting protein druggability. Drug Discov Today 10(23–24):1675–1682. https://doi.org/10.1016/s1359-6446(05)03624-x

    Article  CAS  PubMed  Google Scholar 

  25. Halgren T (2007) New method for fast and accurate binding-site identification and analysis. Chem Biol Drug Des 69(2):146–148. https://doi.org/10.1111/j.1747-0285.2007.00483.x

    Article  CAS  PubMed  Google Scholar 

  26. Grinter SZ, Zou X (2014) Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design. Molecules 19(7):10150–10176. https://doi.org/10.3390/molecules190710150

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Athar M, Lone MY, Khedkar VM, Jha PC (2016) Pharmacophore model prediction, 3D-QSAR and molecular docking studies on vinyl sulfones targeting Nrf2-mediated gene transcription intended for anti-Parkinson drug design. J Biomol Struct Dyn 34(6):1282–1297. https://doi.org/10.1080/07391102.2015.1077343

    Article  CAS  PubMed  Google Scholar 

  28. Korkmaz S, Zararsiz G, Goksuluk D (2014) Drug/nondrug classification using support vector machines with various feature selection strategies. Comput Methods Programs Biomed 117(2):51–60. https://doi.org/10.1016/j.cmpb.2014.08.009

    Article  PubMed  Google Scholar 

  29. Rowley JD (1973) A new consistent chromosomal abnormality in chronic myelogenous leukaemia identified by quinacrine fluorescence and Giemsa staining. Nature 243(5405):290–293. https://doi.org/10.1038/243290a0

    Article  CAS  PubMed  Google Scholar 

  30. Kumar H, Raj U, Gupta S, Varadwaj PK (2016) In-silico identification of inhibitors against mutated BCR-ABL protein of chronic myeloid leukemia: a virtual screening and molecular dynamics simulation study. J Biomol Struct Dyn 34(10):2171–2183. https://doi.org/10.1080/07391102.2015.1110046

    Article  CAS  PubMed  Google Scholar 

  31. Genheden S, Kuhn O, Mikulskis P, Hoffmann D, Ryde U (2012) The normal-mode entropy in the MM/GBSA method: effect of system truncation, buffer region, and dielectric constant. J Chem Inf Model 52(8):2079–2088. https://doi.org/10.1021/ci3001919

    Article  CAS  PubMed  Google Scholar 

  32. Bowers KJ, Chow DE, Xu H, Dror RO, Eastwood MP, Gregersen BA, Shaw DE (2006) Scalable algorithms for molecular dynamics simulations on commodity clusters. In SC’06: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing (pp. 43–43). IEEE. https://doi.org/10.1145/1188455.1188544

  33. Guo Z, Mohanty U, Noehre J, Sawyer TK, Sherman W, Krilov G (2010) Probing the α-helical structural stability of stapled p53 peptides: molecular dynamics simulations and analysis. Chem Biol Drug Des 75(4):348–359. https://doi.org/10.1111/j.1747-0285.2010.00951.x

    Article  CAS  PubMed  Google Scholar 

  34. Shekhar MS, Venkatachalam T, Sharma CS, Singh HP, Kalra S, Kumar N (2018) Computational investigation of binding mechanism of substituted pyrazinones targeting corticotropin releasing factor-1 receptor deliberated for anti-depressant drug design. J Biomol Struct Dyn. https://doi.org/10.1080/07391102.2018.1513379

    Article  PubMed  Google Scholar 

  35. Mishra SS, Ranjan S, Sharma CS, Singh HP, Kalra S, Kumar N (2021) Computational investigation of potential inhibitors of novel coronavirus 2019 through structure-based virtual screening, molecular dynamics and density functional theory studies. J Biomol Struct Dyn 39(12):4449–4461. https://doi.org/10.1080/07391102.2020.1791957

    Article  CAS  PubMed  Google Scholar 

  36. Chandrashekhar M, Nayak VL, Ramakrishna S, Mallavadhani UV (2016) Novel triazole hybrids of myrrhanone C, a natural polypodane triterpene: synthesis, cytotoxic activity and cell based studies. Eur J Med Chem 114:293–307

    Article  CAS  PubMed  Google Scholar 

  37. Madasu C, Karri S, Sangaraju R, Sistla R, Uppuluri MV (2020) Synthesis and biological evaluation of some novel 1,2,3-triazole hybrids of myrrhanone B isolated from Commiphora mukul gum resin: identification of potent antiproliferative leads active against prostate cancer cells (PC-3). Eur J Med Chem 188:111974. https://doi.org/10.1016/j.ejmech.2019.111974

    Article  CAS  PubMed  Google Scholar 

  38. 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 7(1):1–13. https://doi.org/10.1038/srep42717

    Article  Google Scholar 

  39. Kumar D, Sharma N, Aarthy M, Singh SK, Giri R (2020) Mechanistic insights into Zika virus NS3 helicase inhibition by Epigallocatechin-3-gallate. ACS Omega 5(19):11217–11226. https://doi.org/10.1021/acsomega.0c01353

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Ertl P, Schuffenhauer A (2009) Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J Cheminform 1(1):1–11. https://doi.org/10.1186/1758-2946-1-8

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Department of Pharmaceutical Chemistry, School of Pharmaceutical & Populations Health Informatics, DIT University, Dehradun, 248009, India

    Shashank Shekher Mishra

  2. Department of Pharmaceutical Chemistry, Bhupal Nobles’ College of Pharmacy, Bhupal Nobles’ University, Udaipur, 313001, India

    Neeraj Kumar & C. S. Sharma

  3. Department of Pharmaceutical Sciences, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Guntur, 522213, India

    Bidhu Bhusan Karkara

  4. National Institute of Pharmaceutical Education & Research, Mohali, Punjab, India

    Sourav Kalra

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Mishra, S.S., Kumar, N., Karkara, B.B. et al. Identification of potential inhibitors of Zika virus targeting NS3 helicase using molecular dynamics simulations and DFT studies. Mol Divers 27, 1689–1701 (2023). https://doi.org/10.1007/s11030-022-10522-5

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