Computational study for identifying promising therapeutic agents of hydroxychloroquine analogues against SARS‐CoV‐2

Abstract

Hydroxychloroquine (HCQ) and its derivatives have recently gained tremendous attention as a probable medicinal agent in the COVID-19 outbreak caused by SARS-CoV-2. An efficient agent to act directly in inhibiting the SARS-CoV-2 replication is yet to be achieved. Thus, the goal is to investigate the dynamic nature of HCQ derivatives against SARS-CoV-2 main protease and spike proteins. Molecular docking studies were also performed to understand their binding affinity in silico methods using the vital protein domains and enzymes involved in replicating and multiplying SARS-CoV-2, which were the main protease and spike protein. Molecular Dynamic simulations integrated with MM-PBSA calculations have identified In silico potential inhibitors ZINC05135012 and ZINC59378113 against the main protease with −185.171 ± 16.388, −130.759 ± 15.741 kJ/mol respectively, ZINC16638693 and ZINC59378113 against spike protein −141.425 ± 22.447, −129.149 ± 11.449 kJ/mol. Identified Hit molecules had demonstrated Drug Likeliness features, PASS values and ADMET predictions with no violations.

Communicated by Ramaswamy H. Sarma

GRAPHICAL ABSTRACT

Highlights

• Drug repurposing and drug-related compounds have become an emerging tactic to fight COVID-19.

• Hydroxychloroquine analogues were retrieved from the ZINC database through ZINCPharmer and used for docking with Main protease and Spike proteins of SARS-CoV-2.

• However, the binding energies of the compounds ZINC05135012 and ZINC59378113 were found lower compared to Hydroxychloroquine.

• Both the hit molecules have cleared drug-likeness features and AMDET predictions with no violations

Keywords:

• Hydroxychloroquine analogues
• in silico
• molecular dynamics
• ADMET
• PASS analysis

Acknowledgements

Authors are thankful to K L College of Pharmacy, Koneru Lakshmaiah Education Foundation and MS Ramaiah University of Applied Sciences to provide the necessary support to conduct the research and Siddaganga Institute of Technology, Tumakuru, India, for their encouragement and support in MD simulations. We also thank KBITS, Bangalore, for establishing the computational facility under Biotech Policy-II, BiSEP at the Department of Biotechnology, S.I.T., Tumakuru 572103, Karnataka.

Disclosure statement

No potential conflict of interest was reported by the authors.