Identification of potent inhibitors against transmembrane serine protease 2 for developing therapeutics against SARS-CoV-2

Authors

Abdulla Al Mamun, Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Farjana Akter, Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Maksud Khan, Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Sayeda Samina Ahmed, Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Md Giash Uddin, Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Nabila Tabassum Tasfia, Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Faiyaz Md Efaz, Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Md Ackas Ali, Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Mossammad Umme Sultana, Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Mohammad A. Halim, Kennesaw State University

Department

Chemistry and Biochemistry

Document Type

Article

Publication Date

9-30-2021

Abstract

In viral binding and entry, the Spike(S) protein of SARS-CoV-2 uses transmembrane serine protease 2 (TMPRSS2) for priming to cleavage themselves. In this study, we have screened 'drug-like' 7476 ligands and found that over thirty ligands can effectively inhibit the TMPRSS-2 better than the control ligand. Finally, the three best drug agents L1, L2, and L6 were selected according to their average binding affinities and fitting score. These ligands interact with Asp435, Cys437, Ser436, Trp461, and Cys465 amino acid residues. The three best candidates and a reported drug Nafamostat mesylate (NAM) were selected to run 250 ns molecular dynamics (MD) simulations. Various properties of ligand-protein interactions obtained from MD simulation such as bonds, angle, dihedral, planarity, coulomb, and van der Waals (VdW) were used for principal component analysis (PCA) calculation. PCA discloses the evidence of the structural similarities to the corresponding complexes of L1, L2, and L6 with the complex of TMPRSS2(TM) and Nafamostat mesylate (TM-NAM). Moreover, Quantitative structure-activity relationship (QSAR) pattern recognition was generated using PCA for the investigation of structural similarities among the selected ligands. Multiple Linear Regression (MLR) model was built to predict the binding energy compared to the binding energy obtained from molecular docking. The MLR regression model reveals an accuracy of 80% for the prediction of the binding energy of ligands. ADMET analysis demonstrates that these drug agents are appeared to be safer inhibitors. These three ligands can be used as potential inhibitors against the TMPRSS2.Communicated by Ramaswamy H. Sarma.

Journal Title

Journal of biomolecular structure & dynamics

First Page

1

Last Page

13

Digital Object Identifier (DOI)

10.1080/07391102.2021.1980109