Generic placeholder image

Current Drug Discovery Technologies

Editor-in-Chief

ISSN (Print): 1570-1638
ISSN (Online): 1875-6220

Research Article

Analysis of Inhibitor Binding Combined with Reactivity Studies to Discover the Potentially Inhibiting Phytochemicals Targeting Chikungunya Viral Replication

Author(s): Nouman Rasool*, Afreen Bakht and Waqar Hussain

Volume 18, Issue 3, 2021

Published on: 12 March, 2020

Page: [437 - 450] Pages: 14

DOI: 10.2174/1570163817666200312102659

Price: $65

Abstract

Background: Chikungunya fever is a challenging threat to human health in various parts of the world nowadays. Many attempts have been made for developing an effective drug against this viral disease and no effective antiviral treatment has been developed to control the spread of the Chikungunya virus (CHIKV) in humans.

Objective: This research is aimed at the discovery of potential inhibitors against this virus by employing computational techniques to study the interactions between non-structural proteins of Chikungunya virus and phytochemicals from plants.

Methods: Four non-structural proteins were docked with 2035 phytochemicals from various plants. The ligands having binding energies ≥ -8.0 kcal/mol were considered as potential inhibitors for these proteins. ADMET studies were also performed to analyze different pharmacological properties of these docked compounds and to further analyze the reactivity of these phytochemicals against CHIKV, DFT analysis was carried out based on HOMO and LUMO energies.

Results: By analyzing the binding energies, Ki, ADMET properties and band energy gaps, it was observed that 13 phytochemicals passed all the criteria to be a potent inhibitor against CHIKV in humans.

Conclusion: A total of 13 phytochemicals were identified as potent inhibiting candidates, which can be used against the Chikungunya virus.

Keywords: CHIKV, non-structural proteins, phytochemicals, molecular docking, ADMET, DFT.

Graphical Abstract
[1]
Álvarez-Argüelles ME, Alba SR, Pérez MR. In Current topics in neglected tropical diseases. IntechOpen 2019.
[2]
Aubry M, Kama M, Henderson AD, et al. Low chikungunya virus seroprevalence two years after emergence in Fiji. Int J Infect Dis 2020; 90: 223-5.
[http://dx.doi.org/10.1016/j.ijid.2019.10.040] [PMID: 31689529]
[3]
Hussain W, Amir A, Rasool N. Computer-aided study of selective flavonoids against chikungunya virus replication using molecular docking and DFT-based approach. Struct Chem 2020.
[http://dx.doi.org/10.1007/s11224-020-01507-x]
[4]
Kajeguka DC, Msonga M, Schiøler KL, et al. Individual and environmental risk factors for dengue and chikungunya seropositivity in North-Eastern Tanzania. Infect Dis Health 2017; 22(2): 65-76.
[http://dx.doi.org/10.1016/j.idh.2017.04.005]
[5]
Parashar D, Cherian S. Antiviral perspectives for chikungunya virus. BioMed Research International 2014 2014.
[http://dx.doi.org/10.1155/2014/631642]
[6]
Sourisseau M, Schilte C, Casartelli N, et al. Characterization of reemerging chikungunya virus. PLoS Pathog 2007; 3(6)e89
[http://dx.doi.org/10.1371/journal.ppat.0030089] [PMID: 17604450]
[7]
Voss JE, Vaney M-C, Duquerroy S, et al. Glycoprotein organization of Chikungunya virus particles revealed by X-ray crystallography. Nature 2010; 468(7324): 709-12.
[http://dx.doi.org/10.1038/nature09555] [PMID: 21124458]
[8]
Rashad AA, Mahalingam S, Keller PA. Chikungunya virus: emerging targets and new opportunities for medicinal chemistry. J Med Chem 2014; 57(4): 1147-66.
[http://dx.doi.org/10.1021/jm400460d] [PMID: 24079775]
[9]
Kaur P, Thiruchelvan M, Lee RCH, et al. Inhibition of chikungunya virus replication by harringtonine, a novel antiviral that suppresses viral protein expression. Antimicrob Agents Chemother 2013; 57(1): 155-67.
[http://dx.doi.org/10.1128/AAC.01467-12] [PMID: 23275491]
[10]
Abu Bakar F, Ng LFP. Nonstructural Proteins of Alphavirus-Potential Targets for Drug Development. Viruses 2018; 10(2): 71.
[http://dx.doi.org/10.3390/v10020071] [PMID: 29425115]
[11]
Lokireddy S, Vemula S, Vadde R. Connective tissue metabolism in chikungunya patients. Virol J 2008; 5(1): 31.
[http://dx.doi.org/10.1186/1743-422X-5-31] [PMID: 18302795]
[12]
Parola P, Simon F, Oliver M. Tenosynovitis and vascular disorders associated with Chikungunya virus-related rheumatism. Clin Infect Dis 2007; 45(6): 801-2.
[http://dx.doi.org/10.1086/521171] [PMID: 17712768]
[13]
Powers AM, Brault AC, Tesh RB, Weaver SC. Re-emergence of Chikungunya and O’nyong-nyong viruses: evidence for distinct geographical lineages and distant evolutionary relationships. J Gen Virol 2000; 81(Pt 2): 471-9.
[http://dx.doi.org/10.1099/0022-1317-81-2-471] [PMID: 10644846]
[14]
Mishra K, Sharma N, Diwaker D, Ganju L, Singh S. Plant derived antivirals: a potential source of drug development. J Virol Antivir Res 2013; 2: 2-9.
[15]
Gopal Samy B, Xavier L. Molecular docking studies on antiviral drugs for SARS. Int J 2015; 5(3)
[16]
Stark JL. Powers, RNMR of proteins and small biomolecules. Springer 2011; pp. 1-34.
[http://dx.doi.org/10.1007/128_2011_213]
[17]
Akhtar A, Amir A, Hussain W, Ghaffar A, Rasool N. In Silico Computations Of Selective Phytochemicals As Potential Inhibitors Against Major Biological Targets Of Diabetes Mellitus. Curr Comput Aided Drug Des 2019.
[http://dx.doi.org/10.2174/1573409915666190130164923]
[18]
Akhtar A, Hussain W, Rasool N. Probing the Pharmacological Binding Properties, and Reactivity of Selective Phytochemicals as Potential HIV-1 protease Inhibitors. Univ Sci 2019; 24(3): 441-64.
[http://dx.doi.org/10.11144/Javeriana.SC24-3.artf]
[19]
Amjad H, Hussain W, Rasool N. Molecular Simulation Investigation of Prolyl Oligopeptidase from Pyrobaculum Calidifontis and In Silico Docking With Substrates and Inhibitors. Open Access Journal Of Biomedical Engineering And Biosciences 2018; 2(4): 185-94.
[20]
Arif N, Subhani A, Hussain W, et al. In Silico Inhibition of BACE 1 by Selective Phytochemicals as Novel Potential Inhibitors: Molecular Docking and DFT Studies. Curr Drug Discov Technol 2019. E-pub Ahead of Print
[http://dx.doi.org/10.2174/1570163816666190214161825]
[21]
Hussain W, Ali M, Sohail Afzal M, Rasool N. Penta-1,4-Diene-3-One Oxime Derivatives Strongly Inhibit the Replicase Domain of Tobacco Mosaic Virus: Elucidation Through Molecular Docking and Density Functional Theory Mechanistic Computations. J Antivir Antiretrovir 2018; 10(3)
[http://dx.doi.org/10.4172/1948-5964.1000177]
[22]
Hussain W, Qaddir I, Mahmood S, Rasool N. In silico targeting of non-structural 4B protein from dengue virus 4 with spiropyrazolopyridone: study of molecular dynamics simulation, ADMET and virtual screening. Virusdisease 2018; 29(2): 147-56.
[http://dx.doi.org/10.1007/s13337-018-0446-4] [PMID: 29911147]
[23]
Qaddir I, Rasool N, Hussain W, Mahmood S. Computer-aided analysis of phytochemicals as potential dengue virus inhibitors based on molecular docking, ADMET and DFT studies. J Vector Borne Dis 2017; 54(3): 255-62.
[http://dx.doi.org/10.4103/0972-9062.217617] [PMID: 29097641]
[24]
Rasool N, Ashraf A, Waseem M, Hussain W, Mahmood S. Computational exploration of antiviral activity of phytochemicals against NS2B/NS3 proteases from dengue virus. Turkish Journal of Biochemistry. 2019.
[25]
N Powers C. N Setzer, W Setzer WN. An in-silico investigation of phytochemicals as antiviral agents against dengue fever. Comb Chem High Throughput Screen 2016; 19(7): 516-36.
[http://dx.doi.org/10.2174/1386207319666160506123715] [PMID: 27151482]
[26]
Seyedi SS, Shukri M, Hassandarvish P, et al. Computational approach towards exploring potential anti-chikungunya activity of selected flavonoids. Sci Rep 2016; 6: 24027.
[http://dx.doi.org/10.1038/srep24027] [PMID: 27071308]
[27]
Eswar N, Eramian D, Webb B, et al. In Structural proteomics. Springer 2008; pp. 145-59.
[http://dx.doi.org/10.1007/978-1-60327-058-8_8]
[28]
Söding J, Biegert A, Lupas AN. The HHpred interactive server for protein homology detection and structure prediction. Nucleic acids research 2005; 33(suppl_2): W244-8.
[http://dx.doi.org/10.1093/nar/gki408]
[29]
Huang B. MetaPocket: a meta approach to improve protein ligand binding site prediction. OMICS 2009; 13(4): 325-30.
[http://dx.doi.org/10.1089/omi.2009.0045] [PMID: 19645590]
[30]
Morris GM, Huey R, Lindstrom W, et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 2009; 30(16): 2785-91.
[http://dx.doi.org/10.1002/jcc.21256] [PMID: 19399780]
[31]
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]
[32]
Rasool N, Jalal A, Amjad A, Hussain W. Probing the Pharmacological Parameters, Molecular Docking and Quantum Computations of Plant Derived Compounds Exhibiting Strong Inhibitory Potential Against NS5 from Zika Virus. Braz Arch Biol Technol 2018; 61(0)
[http://dx.doi.org/10.1590/1678-4324-2018180004]
[33]
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.
[http://dx.doi.org/10.1038/srep42717] [PMID: 28256516]
[34]
Lee S, Lee I, Kim H, Chang G, Chung J, No K. The PreADME Approach: Web-based program for rapid prediction of physicochemical, drug absorption and drug-like properties. EuroQSAR 2002 Designing Drugs and Crop Protectants: processes, problems and solutions 2003; 2003: 418-20.
[35]
Neese F, Wennmohs F, Hansen A, Becker U. Efficient, approximate and parallel Hartree–Fock and hybrid DFT calculations. A ‘chain-of-spheres’ algorithm for the Hartree–Fock exchange. Chem Phys 2009; 356(1-3): 98-109.
[http://dx.doi.org/10.1016/j.chemphys.2008.10.036]
[36]
Gill PM, Johnson BG, Pople JA, Frisch MJ. The performance of the Becke—Lee—Yang—Parr (B—LYP) density functional theory with various basis sets. Chem Phys Lett 1992; 197(4-5): 499-505.
[http://dx.doi.org/10.1016/0009-2614(92)85807-M]
[37]
Neese F. The ORCA program system. Wiley Interdiscip Rev Comput Mol Sci 2012; 2(1): 73-8.
[http://dx.doi.org/10.1002/wcms.81]
[38]
Rasool N, Husssain W, Khan YDJCb. chemistry, Revelation of Enzyme Activity of Mutant Pyrazinamidases from Mycobacterium Tuberculosis upon Binding with Various Metals using Quantum Mechanical Approach 2019; 107108.
[39]
Rasool N, Iftikhar S, Amir A. Hussain WJJoMG.. Modelling, Structural and quantum mechanical computations to elucidate the altered binding mechanism of metal and drug with pyrazinamidase from Mycobacterium tuberculosis due to mutagenicity 2018; 80: 126-31.
[40]
Ahmadi A, Hassandarvish P, Lani R, et al. Inhibition of chikungunya virus replication by hesperetin and naringenin. RSC Advances 2016; 6(73): 69421-30.
[http://dx.doi.org/10.1039/C6RA16640G]
[41]
Rauf M. Fatima-Tuz-Zahra, Manzoor S, Mehmood A, Bhatti S. Outbreak of chikungunya in Pakistan. Lancet Infect Dis 2017; 17(3): 258.
[http://dx.doi.org/10.1016/S1473-3099(17)30074-9] [PMID: 28244384]
[42]
Bhakat S, Soliman ME. Chikungunya virus (CHIKV) inhibitors from natural sources: a medicinal chemistry perspective. J Nat Med 2015; 69(4): 451-62.
[http://dx.doi.org/10.1007/s11418-015-0910-z] [PMID: 25921858]
[43]
Leonti M, Casu L. Traditional medicines and globalization: current and future perspectives in ethnopharmacology. Front Pharmacol 2013; 4: 92.
[http://dx.doi.org/10.3389/fphar.2013.00092] [PMID: 23898296]
[44]
Nguyen PT, Yu H, Keller PA. Discovery of in silico hits targeting the nsP3 macro domain of chikungunya virus. J Mol Model 2014; 20(5): 2216.
[http://dx.doi.org/10.1007/s00894-014-2216-6] [PMID: 24756552]
[45]
Nguyen PT, Yu H, Keller PA. Identification of chikungunya virus nsP2 protease inhibitors using structure-base approaches. J Mol Graph Model 2015; 57: 1-8.
[http://dx.doi.org/10.1016/j.jmgm.2015.01.001] [PMID: 25622129]
[46]
Delang L, Li C, Tas A, et al. The viral capping enzyme nsP1: a novel target for the inhibition of chikungunya virus infection. Sci Rep 2016; 6: 31819.
[http://dx.doi.org/10.1038/srep31819] [PMID: 27545976]
[47]
Oo A, Hassandarvish P, Chin SP, Lee VS, Abu Bakar S, Zandi K. In silico study on anti-Chikungunya virus activity of hesperetin. PeerJ 2016.4e2602
[http://dx.doi.org/10.7717/peerj.2602] [PMID: 27812412]
[48]
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]
[49]
Delaney JS. ESOL: estimating aqueous solubility directly from molecular structure. J Chem Inf Comput Sci 2004; 44(3): 1000-5.
[http://dx.doi.org/10.1021/ci034243x] [PMID: 15154768]
[50]
Tran N. Blood-brain barrier.Encyclopedia of Clinical Neuropsychology. 2011; pp. 426-6.
[51]
Kimura T, Higaki K. Gastrointestinal transit and drug absorption. Biol Pharm Bull 2002; 25(2): 149-64.
[http://dx.doi.org/10.1248/bpb.25.149] [PMID: 11853157]
[52]
Malik R, Mehta P, Srivastava S, Choudhary BS, Sharma M. Structure-based screening, ADMET profiling, and molecular dynamic studies on mGlu2 receptor for identification of newer antiepileptic agents. J Biomol Struct Dyn 2017; 35(16): 3433-48.
[http://dx.doi.org/10.1080/07391102.2016.1257440] [PMID: 27822979]
[53]
Ritter J. Wiley Handbook of current and emerging drug therapies. Br J Clin Pharmacol 2008; 65(3): 449.
[http://dx.doi.org/10.1111/j.1365-2125.2007.03054.x] [PMID: 18304209]
[54]
Szymański P, Markowicz M, Mikiciuk-Olasik E. Adaptation of high-throughput screening in drug discovery-toxicological screening tests. Int J Mol Sci 2012; 13(1): 427-52.
[http://dx.doi.org/10.3390/ijms13010427] [PMID: 22312262]
[55]
Eroglu E, Türkmen H. A DFT-based quantum theoretic QSAR study of aromatic and heterocyclic sulfonamides as carbonic anhydrase inhibitors against isozyme, CA-II. J Mol Graph Model 2007; 26(4): 701-8.
[http://dx.doi.org/10.1016/j.jmgm.2007.03.015] [PMID: 17493855]
[56]
Fang J, Yang R, Gao L, et al. Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery. J Chem Inf Model 2013; 53(11): 3009-20.
[http://dx.doi.org/10.1021/ci400331p] [PMID: 24144102]
[57]
Gogoi D, Baruah VJ, Chaliha AK, Kakoti BB, Sarma D, Buragohain AK. Identification of novel human renin inhibitors through a combined approach of pharmacophore modelling, molecular DFT analysis and in silico screening. Comput Biol Chem 2017; 69: 28-40.
[http://dx.doi.org/10.1016/j.compbiolchem.2017.04.005] [PMID: 28552695]
[58]
Kavitha R, Karunagaran S, Chandrabose SS, Lee KW, Meganathan C. Pharmacophore modeling, virtual screening, molecular docking studies and density functional theory approaches to identify novel ketohexokinase (KHK) inhibitors. Biosystems 2015; 138: 39-52.
[http://dx.doi.org/10.1016/j.biosystems.2015.10.005] [PMID: 26521124]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy