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Identification of potent inhibitors against transmembrane serine protease 2 for developing therapeutics against SARS-CoV-2.
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.
  • Mamun AA; Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
  • Akter F; Key Laboratory of Soft Chemistry and Functional Materials of MOE, School of Chemical Engineering, Nanjing University of Science and Technology, Nanjing, P. R. China.
  • Khan M; Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
  • Ahmed SS; Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
  • Uddin MG; Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
  • Tasfia NT; Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
  • Efaz FM; Department of Pharmacy, University of Chittagong, Chittagong, Bangladesh.
  • Ali MA; Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
  • Sultana MUC; Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
  • Halim MA; Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
J Biomol Struct Dyn ; 40(23): 13049-13061, 2022.
Article en En | MEDLINE | ID: mdl-34590967
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.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: SARS-CoV-2 / COVID-19 Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Año: 2022 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: SARS-CoV-2 / COVID-19 Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Año: 2022 Tipo del documento: Article