Antimicrobial potential of phytocompounds of Acorus calamus: in silico approach

Abstract

Medicinal plants are used from prehistoric time to cure various life-threatening bacterial diseases. Acorus calamus is an important medicinal plant widely used to cure gastrointestinal, respiratory, kidney and liver disorders. The objective of the current research was to investigate the interaction of major phytoconstituents of Acorus calamus with bacterial (6VJE) and fungal (1EA1) protein targets. Protein-ligand interactions were estimated using the AutoDock software, drug likeness was predicted by using the molinspiration server and toxicity was predicted with the swissADME and protox II servers. MD simulation of phytocompounds with the best profiles was done on the GROMACS software for 100 ns. Molecular docking results showed among all the selected major phytoconstituents, that β-cadinene showed best binding interaction in complex with bacterial (6VJE) and fungal (1EA1) protein targets with binding energy −7.66 ± 0.1 and −7.73 ± 0.15 kcal mol⁻¹, respectively. Drug likeness and toxicity predictions showed that β-cadinene follows all rules of drug likeness and toxicity. MD simulation study revealed that β-cadinene fit in binding pocket of bacterial and fungal targets and found to be stable throughout the duration of the simulation. Based on the observations from this in-silico study it is being proposed that β-cadinene, a major phytocompound of Acorus calamus, can be considered for the treatment of bacterial and fungal infections since the study shows that it might be one of the compounds that contributes majorly to the plant’s biological activity. This study needs in vitro and in vivo validation.

Communicated by Ramaswamy H. Sarma

Keywords:

• Multidrug resistance
• medicinal plant
• molecular docking
• toxicity and MD simulation

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was funded by the National Research Foundation (NRF) of Korea, funded by the Korean government (2021R1A6A1A03038785, 2021R1F1A1055694, 2021R1C1C1013875), and by Kwangwoon University in 2022.