Identification of potent inhibitors against transmembrane serine protease 2 for developing therapeutics against SARS-CoV-2. Issue 23 (19th December 2022)

• Record Type: Journal Article
• Authors: Mamun, Abdulla Al; Akter, Farjana; Khan, Maksud; Ahmed, Sayeda Samina; Uddin, Md. Giash; Tasfia, Nabila Tabassum; Efaz, Faiyaz Md.; Ali, Md Ackas; Sultana, Mossammad Umme Chand; Halim, Mohammad A.
• Is Part Of: Journal of biomolecular structure & dynamics. Volume 40:Issue 23(2022)
• Page Start: 13049
• Page End: 13061
• Publication Date: 2022-12-19
• Subjects: SARS-CoV-2 -- TMPRSS2 -- virtual screening -- molecular dynamics -- QSAR; Biomolecules -- Periodicals; Molecular structure -- Periodicals; Molecular Biology -- Periodicals; Biomechanics -- Periodicals; 572
• DOI: 10.1080/07391102.2021.1980109
• Languages: English
• ISSNs: 0739-1102

Abstract

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