An In-silico Screening Strategy to the Prediction of New Inhibitors of COVID-19 Mpro Protein

Document Type : Research article


1 Department of Medicinal Chemistry, Faculty of Pharmacy, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.

2 Department of Medicinal Chemistry, Faculty of Pharmacy, Isfahan University of Medical Sciences, Isfahan, Iran.



The coronavirus disease-2019 (COVID-19) was first recognized in Wuhan, China, and quickly spread worldwide. Between all proposed research guidelines, inhibition of the main protease (Mpro) protein of the virus will be one of the main strategies for COVID-19 treatment. The present work was aimed to perform a computational study on FDA-approved drugs, similar to piperine scaffold, to find possible Mpro inhibitors. Firstly, virtual screening studies were performed on a library of FDA-approved drugs (43 medicinal compounds, similar to piperine scaffold). Among imported 43 drugs to virtual screening, 34 compounds were extracted. Four top-ranked drugs in terms of the highest interactions and the lowest binding energy were selected for the IFD study. Among these selections, lasofoxifene showed the lowest IFD score (-691.743 kcal mol-1). The stability of lasofoxifene in the COVID-19 Mpro protein active site was confirmed with 100 ns MD simulation. Lasofoxifene binding free energy was obtained -107.09 and -173.97 kcal mol-1, using Prime MM-GBSA and g_mmpbsa methods, respectively. The identified lasofoxifene by the presented computational approaches could be a suitable lead for inhibiting Mpro protein and COVID-19 treatment.

Graphical Abstract

An In-silico  Screening Strategy to the Prediction of New Inhibitors of COVID-19 Mpro Protein


(1)        Rismanbaf A. Potential treatments for COVID-19; a narrative literature review. Arch. Aca. Emerg. Med. (2020) 8: 1-4.
(2)        Hosseini FS and Amanlou M. Simeprevir, potential candidate to repurpose for coronavirus infection: virtual screening and molecular docking study. Preprints (2020). Available from: URL:
(3)        Fan K, Ma L, Han X, Liang H, Wei P, Liu Y and Lai L. The substrate specificity of SARS coronavirus 3C-like proteinase. Biochem. Biophys. Res. Commun. (2005) 329: 934-40.
(4)        Xu C, Ke Z, Liu C, Wang Z, Liu D, Zhang L, Wang J, He W, Xu Z and Li Y. Systemic in silico screening in drug discovery for coronavirus disease (COVID-19) with an online interactive web server. J. Chem. Inf. Model. (2020) 60: 5735–45.
(5)        Elmezayen AD, Al-Obaidi A, Şahin AT, and Yelekçi K. Drug repurposing for coronavirus (COVID-19): in-silico screening of known drugs against coronavirus 3CL hydrolase and protease enzymes. J. Biomol. Struct. Dyn. (2020) 1-12.
(6)        Jin Z, Du X, Xu Y, Deng Y, Liu M, Zhao Y, Zhang B, Li X, Zhang L, and Duan Y. Structure of Mpro from COVID-19 virus and discovery of its inhibitors. Nature. (2020) 582: 289-93.
(7)        Khan MF, Khan MA, Khan ZA, Ahamad T and Ansari WA. Identification of Dietary Molecules as Therapeutic Agents to Combat COVID-19 Using Molecular Docking Studies. Preprints (2020). Available from: URL:
(8)        Saakre M, Mathew D and Ravisankar V. Perspectives on plant flavonoid quercetin-based drugs for novel SARS-CoV-2. Beni-Suef Univ. J. Basic Appl. Sci. (2021) 10: 1-13.
(9)        Kumar S, Sharma PP, Shankar U, Kumar D, Joshi SK, Pena L, Durvasula R, Kumar A, Kempaiah P and Poonam. Discovery of new hydroxyethylamine analogs against 3CLpro protein target of SARS-CoV-2: Molecular docking, molecular dynamics simulation, and structure–activity relationship studies. J. Chem. Inf. Model. (2020) 60: 5754–70.
(10)      Khaerunnisa S, Kurniawan H, Awaluddin R, Suhartati S and Soetjipto S. Potential inhibitor of COVID-19 main protease (Mpro) from several medicinal plant compounds by molecular docking study. Preprints (2020). Available from: URL:
(11)      Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS and Olson AJ. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem. (2009) 30: 2785-91.
(12)      Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK and Olson AJ. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem. (1998) 19: 1639-62.
(13)      Makarewicz T and Kaźmierkiewicz R. Molecular dynamics simulation by GROMACS using GUI plugin for PyMOL. J. Chem. Inf. Model. (2013) 53: 1229–34.
(14)      Release S. Schrödinger Suite 2015-2 Protein Preparation Wizard; Epik version 3.2, Schrödinger, LLC, New York (2015). Available from: URL:
(15)      Release S. Epik, version 3.4. Schrödinger, LLC, New York (2015). Available from: URL:
 (16)     Jorgensen WL, Maxwell DS and Tirado-Rives J. Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc. (1996) 118: 11225-36.
(17)      Release S. LigPrep, version 3.3, Schrödinger. New York (2015). Available from: URL:
 (18)     Schrödinger L. Schrödinger Suite 2015-2, Protein Preparation Wizard, Epik version 3.2. New York (2015). Available from: URL:
(19)      Zhong H, Tran LM and Stang JL. Induced-fit docking studies of the active and inactive states of protein tyrosine kinases. J. Mol. Graph. Model. (2009) 28: 336-46.
(20)      Schrödinger L. Induced fit docking. New York (2015). Available from: URL:
(21)      Huang N, Kalyanaraman C, Irwin JJ and Jacobson MP. Physics-based scoring of protein− ligand complexes: Enrichment of known inhibitors in large-scale virtual screening. J. Chem. Inf. Model. (2006) 46: 243-53.
(22)      Lyne PD, Lamb ML and Saeh JC. Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring. J. Med. Chem. (2006) 49: 4805-8.
(23)      Massova I and Kollman PA. Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to predict ligand binding. Perspect. Drug Discov. Des. (2000) 18: 113-35.
(24)      Abraham M, van der Spoel D, Lindahl E and Hess B. The GROMACS development team GROMACS User Manual. (2019). Available from: URL:
(25)      Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B and Lindahl E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. (2015) 1: 19-25.
(26)      Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM, Ferguson DM, Spellmeyer DC, Fox T, Caldwell JW and Kollman PA. A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J. Am. Chem. Soc. (1995) 117: 5179-97.
(27)      Da Silva AWS and Vranken WF. ACPYPE-Antechamber python parser interface. BMC Res. Notes (2012) 5: 367.
(28)      Søndergaard CR, Olsson MH, Rostkowski M and Jensen JH. Improved treatment of ligands and coupling effects in empirical calculation and rationalization of p K a values. J. Chem. Theory Comput. (2011) 7: 2284-95.
(29)      Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW and Klein ML. Comparison of Simple Potential Functions for Simulating Liquid Water. J. Chem. Phys. (1983) 79: 926.
(30)      Humphrey W, Dalke A, and Schulten K. VMD: visual molecular dynamics. J. Mol. Graph. (1996) 14: 33-8.
(31)      Cosconati S, Forli S, Perryman AL, Harris R, Goodsell DS and Olson AJ. Virtual screening with AutoDock: theory and practice. Expert Opin. Drug Discov. (2010) 5: 597-607.