To improve the efficiency of MOLS algorithm in terms of sampling, scoring and computational time
Designing or repurposing anti-COVID-19 (novel coronavirus disease-19) drugs is the need of the hour. Drug repurposing is helpful in identifying drugs that were developed for treating other diseases to treat a new disease. Here, we propose to identify promising drug repurposing candidates using the iMOLSDOCK docking algorithm. iMOLSDOCK, the induced-fit Protein – Ligand docking algorithm, uses the mutually orthogonal Latin squares (MOLS) technique to systematically search the different conformations and orientations of the ligand as well as for receptor flexibility. The dataset of selected FDA-approved drugs will be docked to the crystal structure of the SARS-CoV-2 main protease (PDB ID: 6LU7). SARS-CoV-2 is the virus that is causing COVID-19. The scoring function in iMOLSDOCK is the sum of intraligand energy, protein-ligand interaction energy, and intra-protein energy. The intraligand energy is calculated using General Amber Force Field. Piecewise Linear Potential is used for finding the steric and hydrogen bond interactions between the protein and the ligand. The intra-protein energy, to assess receptor flexibility, is calculated using the AMBER force field. The number of cycles to be generated has to be specified where each cycle generates an optimal protein-ligand complex. After each cycle the best-ranked protein-ligand complex further undergoes Conjugate Gradient minimization. Top 10 or top 25% ranked hits should be selected for Molecular dynamics simulations to check the stability of the drug in the active site of the receptor protein as per the guidelines. The hits that pass all above criteria shall be proposed as candidates for repurposing. Being a free and open source docking algorithm, iMOLSDOCK may further be optimized by improving the sampling efficiency, scoring function and computation time.