Virtual screening, molecular dynamics and structure–activity relationship studies to identify potent approved drugs for Covid-19 treatment

Rahman, Md. Mahbubur; Saha, Titon; Islam, Kazi Jahidul; Suman, Rasel Hosen; Biswas, Sourav; Rahat, Emon Uddin; Hossen, Md. Rubel; Islam, Rajib; Hossain, Md Nayeem; Mamun, Abdulla Al; Khan, Maksud; Ali, Md Ackas; Halim, Mohammad A.

Computer-aided drug screening by molecular docking, molecular dynamics (MD) and structural–activity relationship (SAR) can offer an efficient approach to identify promising drug repurposing candidates for COVID-19 treatment. In this study, computational screening is performed by molecular docking of 1615 Food and Drug Administration (FDA) approved drugs against the main protease (Mpro) of SARS-CoV-2. Several promising approved drugs, including Simeprevir, Ergotamine, Bromocriptine and Tadalafil, stand out as the best candidates based on their binding energy, fitting score and noncovalent interactions at the binding sites of the receptor. All selected drugs interact with the key active site residues, including His41 and Cys145. Various noncovalent interactions including hydrogen bonding, hydrophobic interactions, pi–sulfur and pi–pi interactions appear to be dominant in drug–Mpro complexes. MD simulations are applied for the most promising drugs. Structural stability and compactness are observed for the drug–Mpro complexes. The protein shows low flexibility in both apo and holo form during MD simulations. The MM/PBSA binding free energies are also measured for the selected drugs. For pattern recognition, structural similarity and binding energy prediction, multiple linear regression (MLR) models are used for the quantitative structural–activity relationship. The binding energy predicted by MLR model shows an 82% accuracy with the binding energy determined by molecular docking. Our details results can facilitate rational drug design targeting the SARS-CoV-2 main protease. Communicated by Ramaswamy H. Sarma