Deep-learning based repurposing of FDA-approved drugs against Candida albicans dihydrofolate reductase and molecular dynamics study

Abstract

Candida albicans causes the fatal fungal bloodstream infection in humans called Candidiasis. Most of the Candida species are resistant to the antifungals used to treat them. Drug-resistant C. albicans poses very serious public health issues. To overcome this, the development of effective drugs with novel mechanism(s) of action is requisite. Drug repurposing is considered a viable alternative approach to overcome the above issue. In the present study, we have attempted to identify drugs that could target the essential enzyme, dihydrofolate reductase of C. albicans (CaDHFR) to find out potent and selective antifungal antifolates. FDA-approved-drug-library from the Selleck database containing 1930 drugs was screened against CaDHFR using deep-learning, molecular docking, X-score and similarity search methods. The screened compounds showing better binding with CaDHFR were subjected to molecular dynamics simulation (MDS). The results of post-MDS analysis like RMSD, RMSF, RG, SASA, the number of hydrogen bonds and PCA suggest that Paritaprevir, Lumacaftor and Rifampin can make good interaction with CaDHFR. Furthermore, analysis of binding free energy corroborated the stability of interactions as they had binding energy of −114.91 kJ mol⁻¹, −79.22 kJ mol⁻¹ and −78.52 kJ mol⁻¹ for Paritaprevir, Lumacaftor and Rifampin respectively as compared to the reference (−63.10 kJ mol⁻¹). From the results, we conclude that these drugs have great potential to inhibit CaDHFR and would add to the drug discovery against candidiasis, and hence these drugs for repurposing should be explored further.

Graphical Abstract

Communicated by Ramaswamy H. Sarma

Keywords:

• Candida albicans
• deep-learning
• FDA-approved-drugs
• molecular docking & dynamics simulation
• dihydrofolate reductase (CaDHFR)

Acknowledgements

The authors are thankful to the Department of Botany, Kumaun University, S.S.J. Campus, Almora for providing the facility, space and resources for this work. The authors also acknowledge Kumaun University, Nainital for providing high-speed internet facilities. We also extend our acknowledgement to Rashtriya Uchchattar Shiksha Abhiyan (RUSA), Ministry of Human Resource Development, Government of India to provide computational infrastructure for the establishment of Bioinformatics Centre in Kumaun University, S.S.J. Campus, Almora.

Author contributions

Conceptualization: Dr. Subhash Chandra. Methodology: Tanuja Joshi, Hemlata Pundir. Supervision: Dr. Subhash Chandra. Visualization: Hemlata Pundir. Writing – original draft: Tanuja Joshi. Writing, review and editing: Dr. Subhash Chandra, Tanuja Joshi.

Disclosure statement

No potential conflict of interest was reported by the author(s).