Biophys Chem 267: 106478
1,2,4 triazolo[1,5-a] pyrimidin-7-ones as novel SARS-CoV-2 Main protease inhibitors: In silico screening and molecular dynamics simulation of potential COVID-19 drug candidates
#Cytochrome P450 Inhibitors include inhibitors of CYP1A2, CYP2C19, CYP2C9, CYP2D6 and CYP3A4; all the molecules showed a bioavailability score of 0.55; Pan assay interference compounds alert; 105 fragments identified by Brenk database; Synthetic accessibility score on a scale of 1–10 (1 easy to 10 difficult to synthesize).
#Cytochrome P450 Inhibitors include inhibitors of CYP1A2, CYP2C19, CYP2C9, CYP2D6 and CYP3A4; all the molecules showed a bioavailability score of 0.55; Pan assay interference compounds alert; 105 fragments identified by Brenk database; Synthetic accessibility score on a scale of 1–10 (1 easy to 10 difficult to synthesize).A) Root-Mean-Square Deviation (RMSD) of the unliganded SARS-CoV-2-M (Black), SARS-CoV-2-M-Lead1 complex (Red) and SARS-CoV-2-M-Lead2 complex (Magenta), SARS-CoV-2-M-Lead3 complex (Blue), SARS-CoV-2-M-Lopinavir complex (Green) and SARS-CoV-2-M-Nelfinavir complex (Cyan) in nm plotted against time (ps). B) Radius of gyration (Rg) of unliganded SARS-CoV-2-M (Black), SARS-CoV-2-M-Lead1 complex (Red) and SARS-CoV-2-M-Lead2 complex (Magenta), SARS-CoV-2-M-Lead3 complex (Blue), SARS-CoV-2-M-Lopinavir complex (Green) and SARS-CoV-2-M-Nelfinavir complex (Cyan) in nm plotted against time (ps).Root-Mean-Square Fluctuation (RMSF) of unliganded SARS-CoV-2-M (Black), SARS-CoV-2-M-Lead1 complex (Red) and SARS-CoV-2-M-Lead2 complex (Magenta), SARS-CoV-2-M-Lead3 complex (Blue), SARS-CoV-2-M-Lopinavir complex (Green) and SARS-CoV-2-M-Nelfinavir complex (Cyan) in nm plotted against the number of amino acid residues.Number of hydrogen bond interactions during simulation between protein and ligand complexes of SARS-CoV-2-M-Lead1 complex (Red) and SARS-CoV-2-M-Lead2 complex (Magenta), SARS-CoV-2-M-Lead3 complex (Blue), SARS-CoV-2-M-Lopinavir complex (Green) and SARS-CoV-2-M-Nelfinavir complex (Cyan) plotted against time (ps).Thermodynamic parameters for complexes of lead compounds and positive controls with SARS-CoV-2-M.Energy contribution by the binding of ligands during simulation between protein and ligand complexes of SARS-CoV-2-M-Lead1 complex (Red), SARS-CoV-2-M-Lead2 complex (Magenta) and SARS-CoV-2-M-Lead3 complex (Blue) SARS-CoV-2-M-Lopinavir complex (Green) and SARS-CoV-2-M-Nelfinavir complex (Cyan) plotted against amino acid residues. Negative values indicate a stabilization effect for SARS-CoV-2-M-ligand interactions, whereas positive values indicate a destabilization effect for SARS-CoV-2-M-ligand interactions.Declaration of Competing Interest
The authors declare that they have no conflicts of interest.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.bpc.2020.106478.
References
- 1. UN-DESA Global economy could shrink by almost 1% in 2020 due to COVID-19 pandemic: United Nations. Econ. Time. 2020 Accessed on 15/04/2020. [PubMed]
- 2. Gupta M.K., Vemula S., Donde R., Gouda G., Behera L., Vadde RIn-silico approaches to detect inhibitors of the human severe acute respiratory syndrome coronavirus envelope protein ion channel. J. Biomol. Struct. Dyn. 2020:1–11. doi: 10.1080/07391102.2020.1751300.] [[Google Scholar]
- 3. Elfiky ASARS-CoV-2 RNA dependent RNA polymerase (RdRp) targeting: an in silico perspective. J. Biomol. Struct. Dyn. 2020:1–9. doi: 10.1080/07391102.2020.1761882.] [[Google Scholar]
- 4. Wahedi H.M., Ahmad S., Abbasi S.WStilbene-based natural compounds as promising drug candidates against COVID-19. J. Biomol. Struct. Dyn. 2020:1–10. doi: 10.1080/07391102.2020.1762743.] [[PubMed][Google Scholar]
- 5. Gautret P., Lagier J.-C., Parola P., Hoang V.T., Meddeb L., Mailhe M., Doudier B., Courjon J., Giordanengo V., Vieira V.E., Dupont H.T., Honoré S., Colson P., Chabrière E., La Scola B., Rolain J.-M., Brouqui P., Raoult DHydroxychloroquine and azithromycin as a treatment of COVID-19: results of an open-label non-randomized clinical trial. Int. J. Antimicrob. Agents. 2020:105949. doi: 10.1016/j.ijantimicag.2020.105949.] [[Google Scholar]
- 6. Molina J.M., Delaugerre C., Le Goff J., Mela-Lima B., Ponscarme D., Goldwirt L., de Castro NNo Evidence of Rapid Antiviral Clearance or Clinical Benefit with the Combination of Hydroxychloroquine and Azithromycin in Patients with Severe COVID-19 Infection. Médecine Mal. Infect. 2020 doi: 10.1016/J.MEDMAL.2020.03.006.] [[Google Scholar]
- 7. Manli W., Cao R., Zhang L., Yang X., Liu J., Xu M., Shi Z., Hu Z., Zhong W., Xiao GRemdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro. Cell Res. 2020;30:269–271. doi: 10.1038/s41422-020-0282-0.] [[Google Scholar]
- 8. Arabi Y.M., Alothman A., Balkhy H.H., Al-Dawood A., AlJohani S., Al Harbi S., Kojan S., Al Jeraisy M., Deeb A.M., Assiri A.M., Al-Hameed F., AlSaedi A., Mandourah Y., Almekhlafi G.A., Sherbeeni N.M., Elzein F.E., Memon J., Taha Y., Almotairi A., Maghrabi K.A., Qushmaq I., Al Bshabshe A., Kharaba A., Shalhoub S., Jose J., Fowler R.A., Hayden F.G., Hussein M.A., A. the M. trial group Treatment of Middle East Respiratory Syndrome with a combination of lopinavir-ritonavir and interferon-β1b (MIRACLE trial): study protocol for a randomized controlled trial. Trials. 2018;19:81. doi: 10.1186/s13063-017-2427-0.] [
- 9. Qiang W., Zhao Y., Abcde an X. Chen, Abcde H., Wang B.Q. 2020. Virtual screening of approved clinic drugs with main protease (3CL pro) reveals potential inhibitory effects on SARS-CoV-2.
- 10. Yamamoto N., Yang R., Yoshinaka Y., Amari S., Nakano T., Cinatl J., Rabenau H., Doerr H.W., Hunsmann G., Otaka A., Tamamura H., Fujii N., Yamamoto NHIV protease inhibitor nelfinavir inhibits replication of SARS-associated coronavirus. Biochem. Biophys. Res. Commun. 2004;318:719–725. doi: 10.1016/J.BBRC.2004.04.083.] [[Google Scholar]
- 11. Zhijian X., Yao H., Shen J., Wu N., Xu X., Yechun LuNelfinavir Is Active Against SARS-CoV-2 in Vero E6 Cells. ChemRxiv. Prepr. 2020 doi: 10.26434/chemrxiv.12039888.v1.[PubMed][Google Scholar]
- 12. Hasan A., Paray B.A., Hussain A., Qadir F.A., Attar F., Aziz F.M., Sharifi M., Derakhshankhah H., Rasti B., Mehrabi M., Shahpasand K., Saboury A.A., Falahati MA review on the cleavage priming of the spike protein on coronavirus by angiotensin-converting enzyme-2 and furin. J. Biomol. Struct. Dyn. 2020:1–9. doi: 10.1080/07391102.2020.1754293.] [[Google Scholar]
- 13. Boopathi S., Poma A.B., Kolandaivel PNovel 2019 coronavirus structure, mechanism of action, antiviral drug promises and rule out against its treatment. J. Biomol. Struct. Dyn. 2020:1–10. doi: 10.1080/07391102.2020.1758788.] [[Google Scholar]
- 14. Xue X., Yu H., Yang H., Xue F., Wu Z., Shen W., Li J., Zhou Z., Ding Y., Zhao Q., Zhang X.C., Liao M., Bartlam M., Rao ZStructures of two coronavirus Main proteases: implications for substrate binding and antiviral drug design. J. Virol. 2008;82:2515–2527. doi: 10.1128/jvi.02114-07.] [[Google Scholar]
- 15. Muralidharan N., Sakthivel R., Velmurugan D., Gromiha M.MComputational studies of drug repurposing and synergism of lopinavir, oseltamivir and ritonavir binding with SARS-CoV-2 protease against COVID-19. J. Biomol. Struct. Dyn. 2020:1–6. doi: 10.1080/07391102.2020.1752802.] [[PubMed][Google Scholar]
- 16. Zhang L., Lin D., Sun X., Curth U., Drosten C., Sauerhering L., Becker S., Rox K., Hilgenfeld RCrystal structure of SARS-CoV-2 main protease provides a basis for design of improved α-ketoamide inhibitors. Science. 2020;3405:1–9. doi: 10.1126/science.abb3405.] [[Google Scholar]
- 17. Ullrich S., Nitsche CThe SARS-CoV-2 main protease as drug target. Bioorg. Med. Chem. Lett. 2020;30:127377. doi: 10.1016/j.bmcl.2020.127377.] [[Google Scholar]
- 18. Chang G.-GQuaternary structure of the SARS coronavirus main protease. Mol. Biol. SARS-Coronavirus. 2009:115–128. doi: 10.1007/978-3-642-03683-5_8.[PubMed][Google Scholar]
- 19. Alamri M.A., Qamar M. Tahir ul, Mirza M.U., Bhadane R., Alqahtani S.M., Muneer I., Froeyen M., Salo-Ahen O.M.H. Pharmacoinformatics and molecular dynamics simulation studies reveal potential covalent and FDA-approved inhibitors of SARS-CoV-2 main protease 3CLpro. J. Biomol. Struct. Dyn. 2020:1–13. doi: 10.1080/07391102.2020.1782768.] [
- 20. Aljoundi A., Bjij I., El Rashedy A., Soliman M.E.SCovalent versus non-covalent enzyme inhibition: which route should we take? A justification of the good and bad from molecular modelling perspective. Protein J. 2020;39:97–105. doi: 10.1007/s10930-020-09884-2.] [[PubMed][Google Scholar]
- 21. Berman H.M., Westbrook J., Feng Z., Gilliland G., Bhat T.N., Weissig H., Shindyalov I.N., Bourne P.EThe Protein Data Bank. Nucleic Acids Res. 2000;28:235–242. doi: 10.1093/nar/28.1.235.] [[Google Scholar]
- 22. Prlic A., Bliven S., Rose P.W., Bluhm W.F., Bizon C., Godzik A., Bourne P.EPre-calculated protein structure alignments at the RCSB PDB website. Bioinformatics. 2010;26:2983–2985. doi: 10.1093/bioinformatics/btq572.] [[Google Scholar]
- 23. Liu X., Zhang B., Jin Z., Yang H., Rao ZThe crystal structure of COVID-19 main protease in complex with an inhibitor N3. PDB Release. 2020:17–20. doi: 10.2210/PDB6LU7/PDB.[PubMed][Google Scholar]
- 24. Ton A.-T., Gentile F., Hsing M., Ban F., Cherkasov ARapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3 Billion Compounds. Mol. Inform. 2020;2000028:1–8. doi: 10.1002/minf.202000028.] [[Google Scholar]
- 25. Sterling T., Irwin J.JZINC 15 – ligand discovery for everyone. J. Chem. Inf. Model. 2015;55:2324–2337. doi: 10.1021/acs.jcim.5b00559.] [[Google Scholar]
- 26. Madeira F., Park Y.M., Lee J., Buso N., Gur T., Madhusoodanan N., Basutkar P., Tivey A.R.N., Potter S.C., Finn R.D., Lopez RThe EMBL-EBI search and sequence analysis tools APIs in 2019. Nucleic Acids Res. 2019;47:W636–W641. doi: 10.1093/nar/gkz268.] [[Google Scholar]
- 27. Saitou N.N.M., Nei M., Saitou N., Nei MThe neighbor-joining method-a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 1987;4:406–425.[PubMed][Google Scholar]
- 28. Kumar S., Stecher G., Li M., Knyaz C., Tamura KMEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 2018;35:1547–1549. doi: 10.1093/molbev/msy096.] [[Google Scholar]
- 29. Felsenstein JConfidence limits on phylogenies: An approach using the bootstrap. Evolution (N. Y) 1985;39:783. doi: 10.2307/2408678.] [[PubMed][Google Scholar]
- 30. Zuckerkandl E., Pauling LEvolutionary divergence and convergence in proteins. Evol. Genes Proteins. 1965:97–166. doi: 10.1016/B978-1-4832-2734-4.50017-6.[PubMed][Google Scholar]
- 31. Robert X., Gouet PDeciphering key features in protein structures with the new ENDscript server. Nucleic Acids Res. 2014;42:W320–W324. doi: 10.1093/nar/gku316.] [[Google Scholar]
- 32. Pettersen E.F., Goddard T.D., Huang C.C., Couch G.S., Greenblatt D.M., Meng E.C., Ferrin T.EUCSF Chimera--a visualization system for exploratory research and analysis. J. Comput. Chem. 2004;25:1605–1612. doi: 10.1002/jcc.20084.] [[PubMed][Google Scholar]
- 33. Guex N., Peitsch M.CSWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis. 1997;18:2714–2723. doi: 10.1002/elps.1150181505.] [[PubMed][Google Scholar]
- 34. Tian W., Chen C., Lei X., Zhao J., Liang JCASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Res. 2018;46:W363–W367. doi: 10.1093/nar/gky473.] [[Google Scholar]
- 35. ChemAxon, MarvinView 20.9.0. 2020. [PubMed]
- 36. O’Boyle N.M., Banck M., James C.A., Morley C., Vandermeersch T., Hutchison G.ROpen babel: an open chemical toolbox. J. Cheminform. 2011;3:33. doi: 10.1186/1758-2946-3-33.] [[Google Scholar]
- 37. Trott O., Olson A.JAutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 2010;31:455–461. doi: 10.1002/jcc.21334.] [[Google Scholar]
- 38. Dallakyan S., Olson A.JSmall-molecule library screening by docking with PyRx. Methods Mol. Biol. 2015;1263:243–250. doi: 10.1007/978-1-4939-2269-7_19.] [[PubMed][Google Scholar]
- 39. Yang Z., Lasker K., Schneidman-Duhovny D., Webb B., Huang C.C., Pettersen E.F., Goddard T.D., Meng E.C., Sali A., Ferrin T.EUCSF Chimera, MODELLER, and IMP: an integrated modeling system. J. Struct. Biol. 2012;179:269–278. doi: 10.1016/j.jsb.2011.09.006.] [[Google Scholar]
- 40. Wang Z., Chen X., Lu Y., Chen F., Zhang WClinical characteristics and therapeutic procedure for four cases with 2019 novel coronavirus pneumonia receiving combined Chinese and Western medicine treatment. Biosci. Trends. 2020;14:64–68. doi: 10.5582/bst.2020.01030.] [[PubMed][Google Scholar]
- 41. Tahir M., Alqahtani S.M., Alamri M.A., Chen L.-LStructural basis of SARS-CoV-2 3CL pro and anti-COVID-19 drug discovery from medicinal plants. J. Pharm. Anal. 2020:1–26. doi: 10.20944/preprints202002.0193.v1.] [[Google Scholar]
- 42. Daina A., Michielin O., Zoete VSwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 2017;7:42717. doi: 10.1038/srep42717.] [[Google Scholar]
- 43. Cheng F., Li W., Zhou Y., Shen J., Wu Z., Liu G., Lee P.W., Tang YadmetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem. Inf. Model. 2012;52:3099–3105. doi: 10.1021/ci300367a.] [[PubMed][Google Scholar]
- 44. Abraham A., Philip S., Jacob M.K., Narayanan S.P., Jacob C.K., Kochupurackal J. Phenazine-1-carboxylic acid mediated anti-oomycete activity of the endophytic Alcaligenes sp. EIL-2 against Phytophthora meadii. Microbiol. Res. 2015;170:229–234. doi: 10.1016/j.micres.2014.06.002.] [[PubMed]
- 45. Schüttelkopf A.W., van Aalten D.M.FPRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr. D. Biol. Crystallogr. 2004;60:1355–1363. doi: 10.1107/S0907444904011679.] [[PubMed][Google Scholar]
- 46. Hess BP-LINCS: a parallel Linear constraint solver for molecular simulation. J. Chem. Theory Comput. 2008;4:116–122. doi: 10.1021/ct700200b.] [[PubMed][Google Scholar]
- 47. Humphrey W., Dalke A., Schulten KVMD: visual molecular dynamics. J. Mol. Graph. 1996;14 doi: 10.1016/0263-7855(96)00018-5. 27–28,33–38. [] [[PubMed][Google Scholar]
- 48. Salentin S., Schreiber S., Haupt V.J., Adasme M.F., Schroeder MPLIP: fully automated protein–ligand interaction profiler. Nucleic Acids Res. 2015;43:W443–W447. doi: 10.1093/nar/gkv315.] [[Google Scholar]
- 49. Baker N.A., Sept D., Joseph S., Holst M.J., McCammon J.AElectrostatics of nanosystems: application to microtubules and the ribosome. Proc. Natl. Acad. Sci. 2001;98:10037–10041. doi: 10.1073/PNAS.181342398.] [[Google Scholar]
- 50. Kumari R., Kumar R., Lynn Ag_mmpbsa—A GROMACS Tool for High-Throughput MM-PBSA Calculations. J. Chem. Inf. Model. 2014;54:1951–1962. doi: 10.1021/ci500020m.] [[PubMed][Google Scholar]
- 51. Khan S.A., Zia K., Ashraf S., Uddin R., Ul-Haq ZIdentification of chymotrypsin-like protease inhibitors of SARS-CoV-2 via integrated computational approach. J. Biomol. Struct. Dyn. 2020:1–10. doi: 10.1080/07391102.2020.1751298.] [[PubMed][Google Scholar]
- 52. Islam R., Parves M.R., Paul A.S., Uddin N., Rahman M.S., Al Mamun A., Hossain M.N., Ali M.A., Halim M.AA molecular modeling approach to identify effective antiviral phytochemicals against the main protease of SARS-CoV-2. J. Biomol. Struct. Dyn. 2020:1–12. doi: 10.1080/07391102.2020.1761883.] [[Google Scholar]
- 53. Sarma P., Shekhar N., Prajapat M., Avti P., Kaur H., Kumar S., Singh S., Kumar H., Prakash A., Dhibar D.P., Medhi BIn-silico homology assisted identification of inhibitor of RNA binding against 2019-nCoV N-protein (N terminal domain) J. Biomol. Struct. Dyn. 2020:1–9. doi: 10.1080/07391102.2020.1753580.] [[Google Scholar]
- 54. Lipinski C.A., Lombardo F., Dominy B.W., Feeney P.JExperimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 1997;23:3–25. doi: 10.1016/S0169-409X(96)00423-1.] [[PubMed][Google Scholar]
- 55. 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;1:55–68. doi: 10.1021/cc9800071.] [[PubMed]
- 56. Veber D.F., Johnson S.R., Cheng H.-Y., Smith B.R., Ward K.W., Kopple K.DMolecular properties that influence the Oral bioavailability of drug candidates. J. Med. Chem. 2002;45:2615–2623. doi: 10.1021/jm020017n.] [[PubMed][Google Scholar]
- 57. Egan W.J., Merz K.M.J., Baldwin J.JPrediction of drug absorption using multivariate statistics. J. Med. Chem. 2000;43:3867–3877. doi: 10.1021/jm000292e.] [[PubMed][Google Scholar]
- 58. Muegge I., Heald S.L., Brittelli DSimple selection criteria for drug-like chemical matter. J. Med. Chem. 2001;44:1841–1846. doi: 10.1021/jm015507e.] [[PubMed][Google Scholar]
- 59. Baell J.B., Holloway G.ANew substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J. Med. Chem. 2010;53:2719–2740. doi: 10.1021/jm901137j.] [[PubMed][Google Scholar]
- 60. Brenk R., Schipani A., James D., Krasowski A., Gilbert I.H., Frearson J., Wyatt P.GLessons learnt from assembling screening libraries for drug discovery for neglected diseases. ChemMedChem. 2008;3:435–444. doi: 10.1002/cmdc.200700139.] [[Google Scholar]
- 61. Elmezayen A.D., Al-Obaidi A., Şahin A.T., Yelekçi KDrug repurposing for coronavirus (COVID-19): in silico screening of known drugs against coronavirus 3CL hydrolase and protease enzymes. J. Biomol. Struct. Dyn. 2020:1–13. doi: 10.1080/07391102.2020.1758791.] [[Google Scholar]