In silico identification and validation of phenolic lipids as potential inhibitor against bacterial and viral strains

Abstract

The recurrence of coronavirus disease and bacterial resistant strains has drawn attention to naturally occurring bioactive molecules that can demonstrate broad-spectrum efficacy against bacteria as well as viral strains. The drug-like abilities of naturally available “anacardic acids” (AA) and their derivatives against different bacterial and viral protein targets through in-silico tools were explored. Three viral protein targets [P DB: 6Y2E (SARS-CoV-2), 1AT3 (Herpes) and 2VSM (Nipah)] and four bacterial protein targets [P DB: 2VF5 (Escherichia coli), 2VEG (Streptococcus pneumoniae), 1JIJ (Staphylococcus aureus) and 1KZN (E. coli)] were selected to evaluate the activity of bioactive AA molecules. The potential ability to inhibit the progression of microbes has been discussed based on the structure, functionality and interaction ability of these molecules on the selected protein targets for multi-disease remediation. The number of interactions, full-fitness value and energy of the ligand-target system were determined from the docked structure in SwissDock and Autodock Vina. In order to compare the efficacy of these active derivatives to that of commonly used drugs against bacteria and viruses, a few of the selected molecules were subjected to 100 ns long MD simulations. It was found that the phenolic groups and alkyl chains of AA derivatives are more likely to bind with microbial targets, that could be responsible for the improved activity against these targets. The results suggest that the proposed AA derivatives have demonstrated potential to become active drug ingredients against microbial protein targets. Further, experimental investigations are essential for clinical verification of the drug-like abilities of AA derivatives.

Communicated by Ramaswamy H. Sarma

Keywords:

• Herpes
• Nipah
• 2019-CoV
• E. coli
• S. aureus
• antibacterial
• antiviral
• molecular docking
• molecular dynamics simulation

Acknowledgments

F Zafar acknowledges Department of Science and Technology, New Delhi, India, for the Women Scientist Scheme (WOS) for Research in Basic/Applied Sciences, Rf# SR/WOSA/CS-97/2016. Authors also thank Mohd Ahsan, Post-Doctoral Fellow, Palermo Lab, University of California for performing MD simulation analysis. All the authors are thankful to the respective departments and universities for providing facilities.

Disclosure statement

The authors declare no conflict of interest.

Additional information

Funding

This work was supported by the Department of Science and Technology, Ministry of Science and Technology, India.