Cysteine focused covalent inhibitors against the main protease of SARS-CoV-2.
Archi Sundar PaulRajib IslamMd Rimon ParvesAbdulla Al MamunImrul ShahriarMd Imran HossainMd Nayeem HossainMd Ackas AliMohammad Abdul HalimPublished in: Journal of biomolecular structure & dynamics (2020)
In viral replication and transcription, the main protease (Mpro) of SARS-CoV-2 plays an important role and appears to be a vital target for drug design. In Mpro, there is a Cys-His catalytic dyad, and ligands that interact with the Cys145 assumed to be an effective approach to inhibit the Mpro. In this study, approximately 1400 cysteine-focused ligands were screened to identify the best candidates that can act as potent inhibitors against Mpro. Our results show that the selected ligands strongly interact with the key Cys145 and His41 residues. Covalent docking was performed for the selected candidates containing the acrylonitrile group, which can form a covalent bond with Cys145. All atoms molecular dynamics (MD) simulation was performed on the selected four inhibitors including L1, L2, L3 and L4 to validate the docking interactions. Our results were also compared with a control ligand, α-ketoamide (11r). Principal component analysis on structural and energy data obtained from the MD trajectories shows that L1, L3, L4 and α-ketoamide (11r) have structural similarity with the apo-form of the Mpro. Quantitative structure-activity relationship method was employed for pattern recognition of the best ligands, which discloses that ligands containing acrylonitrile and amide warheads can show better performance. ADMET analysis displays that our selected candidates appear to be safer inhibitors. Our combined studies suggest that the best cysteine focused ligands can help to design an effective lead drug for COVID-19 treatment. Communicated by Ramaswamy H. Sarma.
Keyphrases
- molecular dynamics
- sars cov
- density functional theory
- respiratory syndrome coronavirus
- structure activity relationship
- depressive symptoms
- fluorescent probe
- living cells
- transcription factor
- big data
- molecular docking
- high resolution
- electronic health record
- artificial intelligence
- adverse drug
- mass spectrometry
- drug induced
- data analysis
- small molecule