In silico approach towards polyphenols as targeting glucosamine-6-phosphate synthase for Candida albicans

Abstract

Candida albicans is one of the most common species of fungus with life-threatening systemic infections and a high mortality rate. The outer cell wall layer of C. albicans is packed with mannoproteins and glycosylated polysaccharide moieties that play an essential role in the interaction with host cells and tissues. The glucosamine-6-phosphate synthase enzyme produces N-acetylglucosamine, which is a crucial chemical component of the cell wall of Candida albicans. Collectively, these components are essential to maintain the cell shape and for infection. So, its disruption can have serious effects on cell growth and morphology, resulting in cell death. Hence, it is considered a good antifungal target. In this study, we have performed an in silico approach to analyze the inhibitory potential of some polyphenols obtained from plants. Those can be considered important in targeting against the enzyme glucosamine-6-phosphate synthase (PDB-2VF5). The results of the study revealed that the binding affinity of complexes theaflavin and 3-o-malonylglucoside have significant docking scores and binding free energy followed by significant ADMET parameters that predict the drug-likeness property and toxicity of polyphenols as potential ligands. A molecular dynamic simulation was used to test the validity of the docking scores, and it showed that the complex remained stable during the period of the simulation, which ranged from 0 to 100 ns. Theaflavins and 3-o-malonylglucoside may be effective against Candida albicans using a computer-aided drug design methodology that will further enable researchers for future in vitro and in vivo studies, according to our in silico study.

Communicated by Ramaswamy H. Sarma

Keywords:

• Antifungal
• Candida albicans
• glucosamine-6-phosphate synthase
• polyphenols
• molecular docking
• ADMET
• MD simulation

Acknowledgments

The authors would like to thank the Schrodinger team for providing an evaluation license.

Authors’ contribution

Sachin Dhawale: Conceptualization, Software, Methodology and interpretation of data. Madhuri Pandit: Writing-original draft. Kanchan Thete: Writing-review and editing, Formatting. Dnyaneshwari Ighe: Resources and literature survey. Sachin Gawale: MD simulation data analysis and review manuscript. Pallavi Bhosale: Supervision, Analysis of data. Deepak Lokwani: MD simulation interpretation and revised manuscript.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

Ethical statement

This work does not involve the use of humans or animals.

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sector.