Virtual Screening Reveals Potential Anti-Parasitic Drugs Inhibiting the Receptor Binding Domain of SARS-CoV-2 Spike protein
The 2019's COVID-19 outbreak which spread to over 200 countries across the globe had its origin from the 2002's SARS-CoV-1 epidemic. The corona viruses are single stranded positive sense RNA viruses with 4 structural proteins such as spike(S), membrane(M), envelope(E) and nucleocapsid(N) proteins and 16 non-structural proteins (NSPs). The spike(S) protein is a homo-trimer protruding from the viral surface comprising 2 subunits namely, the S1 and S2 where the S1 subunit consists of the receptor binding domain (RBD) and the S2 subunit consists of the fusion peptide. The spike glycoprotein is considered as the most desired pharmacological target for drug designing, thus blocking the viral entry into the host. Computer-Aided Drug Designing significantly reduces the cost and time in drug discovery compared to the in-vitro methods. Hence in our study, we have performed a virtual screening of the complete set of anti-parasitic drugs using the popular molecular docking tool, Autodock vina with an aim to repurpose the potential hits for the SARS-CoV-2 infection. The repurposed drugs are advantageous for their easy and immediate access owing to their already proven safety records in toxicity and hence are better than novel drugs. Our results revealed 32 anti-parasitic compounds crossing our threshold binding affinity with selamectin, ivermectin, artefenomel, moxidectin, posaconazole, imidocarb, piperaquine, cepharantine, betulinic acid and atovaquone at the top of the list and occupying the three different electrostatic regions in the RBD. Further optimization strategies and in-vitro trials could make our potential anti-parasitic hits, a potential cure for the SARS-CoV-2 infection.
Conflict of Interest The authors declare no conflict of interest.
Top Department of Biotechnology, Anna University, Chennai--600025
Adedeji, Severson, Jonsson, Singh, Weiss, Novel Inhibitors of Severe Acute Respiratory Syndrome Coronavirus Entry That Act by Three Distinct Mechanisms, J Virol, doi:10.1128/JVI.00998-13
Baig, Khaleeq, Syeda, Docking Prediction of Amantadine in the Receptor Binding Domain of Spike Protein of SARS-CoV-2, ACS Pharmacol. Transl Sci, doi:10.1021/acsptsci.0c00172
Basu, Sarkar, Maulik, Molecular docking study of potential phytochemicals and their effects on the complex of SARS-CoV2 spike protein and human ACE2, Sci Rep, doi:10.1038/s41598-020-74715-4.
Batiha, Alqahtani, Ilesanmi, Saati, El-Mleeh, A vermectin derivatives, pharmacokinetics, therapeutic and toxic dosages, mechanism of action, and their biological effects, Pharmaceuticals, doi:10.3390/ph13080196
Boyle, Banck, James, Morley, Vandermeersch, Open Babel: An open chemical toolbox
Drożdżal, Rosik, Lechowicz, Machaj, Kotfis, FDA approved drugs with pharmacotherapeutic potential for SARS-CoV-2 (COVID-19) therapy, Drug Resist Updat, doi:10.1016/j.drup.2020.100719
Fan, Wang, Liu, An, Liu, Repurposing of clinically approved drugs for treatment of coronavirus disease 2019 in a 2019-novel coronavirus-related coronavirus model, Chin Med J, doi:10.1097/CM9.0000000000000797
Goodsell, Olson, Automated docking of substrates to proteins by simulated annealing, Proteins Struct. Funct Bioinforma, doi:10.1002/prot.340080302.
Goswami, Bagchi, Molecular Docking study of Receptor Binding Domain of SARS-CoV-2 Spike Glycoprotein with Saikosaponin. a Triterpenoid Natural Product, doi:10.26434/chemrxiv.12033774.
Heimfarth, Serafini, Martins-Filho, Pr, Quintans et al., Drug repurposing and cytokine management in response to COVID-19: A review, Int Immunopharmacol, doi:10.1016/j.intimp.2020.106947.
Khuroo, Chloroquine and hydroxychloroquine in coronavirus disease 2019 (COVID-19). Facts, fiction and the hype: a critical appraisal, Int J Antimicrob Agents, doi:10.1016/j.ijantimicag.2020.106101
Kim, Chen, Cheng, Gindulyte, He, PubChem 2019 update: Improved access to chemical data, Nucleic Acids Res, doi:10.1093/nar/gky1033.
Li, Hu, Zhang, Yu, ResPRE: High-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks, Bioinformatics, doi:10.1093/bioinformatics/btz291.
Li, Zhang, Bell, Yu, Zhang, Ensembling multiple raw coevolutionary features with deep residual neural networks for contact-map prediction in CASP13, Proteins Struct. Funct Bioinforma, doi:10.1002/prot.25798
Li, Zhao, Zhan, Quantitative proteomics reveals a broadspectrum antiviral property of ivermectin, benefiting for COVID-19 treatment, J Cell Physiol, doi:10.1002/jcp.30055
Liu, Xiao, Chen, He, Niu, Interaction between heptad repeat 1 and 2 regions in spike protein of SARS-associated coronavirus: Implications for virus fusogenic mechanism and identification of fusion inhibitors, Lancet, doi:10.1016/S0140-6736(04)15788-7
Mahmoud, Shitu, Mostafa, Drug repurposing of nitazoxanide: can it be an effective therapy for COVID-19?, J Genet Eng Biotechnol, doi:10.1186/s43141-020-00055-5
Mckee, Sternberg, Stange, Laufer, Naujokat, Since
Mudatsir, Yufika, Nainu, Frediansyah, Megawati, Antiviral activity of ivermectin against SARS-CoV-2: An old-fashioned dog with a new trick-A literature review, Sci Pharm
Muthusamy, Gopal, Manivarma, Pradhan, Prabhu, Virtual Screening Reveals Potential Anti-Parasitic Drugs Inhibiting the Receptor Binding Domain of SARS-CoV-2 Spike protein, J Virol Antivir Res
Nguyen, Nguyen, Pham, Huy, Bay, Autodock Vina Adopts More Accurate Binding Poses but Autodock4 Forms Better Binding Affinity, J Chem Inf Model, doi:10.1021/acs.jcim.9b00778.
Othman, Bouslama, Brandenburg, Da Rocha, Hamdi, Interaction of the spike protein RBD from SARS-CoV-2 with ACE2: Similarity with SARS-CoV, hot-spot analysis and effect of the receptor polymorphism, Biochem Biophys Res Commun, doi:10.1016/j.bbrc.2020.05.028
Pandey, Rane, Chatterjee, Kumar, Khan, Targeting SARS-CoV-2 spike protein of COVID-19 with naturally occurring phytochemicals: an in silico study for drug development, J Biomol Struct Dyn, doi:10.1080/07391102.2020.1796811
Peña-Silva, Duffull, Steer, Jaramillo-Rincon, Gwee, Pharmacokinetic considerations on the repurposing of ivermectin for treatment of COVID-19, Br J Clin Pharmacol, doi:10.1111/bcp.14476
Pink, Hudson, Mouriès, Bendig, Opportunities and challenges in antiparasitic drug discovery, Nat Re Drug Discov, doi:10.1038/nrd1824.
Roy, Kucukural, Zhang, I-TASSER: A unified platform for automated protein structure and function prediction, Nat Protoc, doi:10.1038/nprot.2010.5.
Sethi, Joshi, Sasikala, Alvala, Molecular Docking in Modern Drug Discovery: Principles and Recent Applications, Drug Discov Dev New Adv, doi:10.5772/intechopen.85991.
Tahir Ul Qamar, Alqahtani, Alamri, Chen, Structural basis of SARS-CoV-2 3CLpro and anti-COVID-19 drug discovery from medicinal plants, J Pharm Anal, doi:10.1016/j.jpha.2020.03.009.
Ton, Gentile, Hsing, Ban, Cherkasov, Rapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3 Billion Compounds, Mol Inform, doi:10.1002/minf.202000028
Trott, Olson, AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading, J Comput Chem, doi:10.1002/jcc.21334.
Unni, Aouti, Balasundaram, Identification of a Potent Inhibitor Targeting the Spike Protein of Pandemic Human Coronavirus, SARS-CoV-2 by Computational Methods, doi:10.26434/chemrxiv.12197934
Vieira, Sousa, Comparing AutoDock and Vina in ligand/decoy discrimination for virtual screening, Appl Sci, doi:10.3390/app9214538
Wishart, Feunang, Guo, Lo, Marcu, J DrugBank 5.0: A major update to the DrugBank database for 2018, Nucleic Acids Res, doi:10.1093/nar/gkx1037.
Xia, Liu, Wang, Xu, Lan, Inhibition of SARS-CoV-2 (previously 2019-nCoV) infection by a highly potent pan-coronavirus fusion inhibitor targeting its spike protein that harbors a high capacity to mediate membrane fusion, Cell Res, doi:10.1038/s41422-020-0305-x.
Yang, Sun, Li, Liu, Tang, Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and, Structural Alerts Front Chem, doi:10.3389/fchem.2018.00030.
Yang, Yan, Roy, Xu, Poisson, The I-TASSER suite: Protein structure and function prediction, Nat Methods, doi:10.1038/nmeth.3213.
Yang, Zhang, I-TASSER server: New development for protein structure and function predictions, Nucleic Acids Res, doi:10.1093/nar/gkv342.
Yuan, Chan, Hu, Using PyMOL as a platform for computational drug design, Wiley Interdiscip. Rev Comput Mol Sci, doi:10.1002/wcms.1298.
Zhang, Skolnick, Scoring function for automated assessment of protein structure template quality, Proteins Struct Funct Genet, doi:10.1002/prot.20264
Zhang, Zheng, Huang, Bell, Zhou, Protein Structure and Sequence Reanalysis of 2019-nCoV Genome Refutes Snakes as Its Intermediate Host and the Unique Similarity between Its Spike Protein Insertions and HIV-1, J Proteome Res, doi:10.1021/acs.jproteome.0c00129
Zheng, Li, Zhang, Pearce, Mortuza, Deep-learning contact-map guided protein structure prediction in CASP13, Proteins Struct Funct Bioinforma, doi:10.1002/prot.25792