Structure-based virtual screening, molecular docking, ADMET and molecular simulations to develop benzoxaborole analogs as potential inhibitor against Leishmania donovani trypanothione reductase

Abstract

Visceral leishmaniasis (VL) is the most fatal form of leishmaniasis and it affects 70 countries worldwide. Increasing drug resistant for antileishmanial drugs such as miltefosine, sodium stibogluconate and pentamidine has been reported in the VL endemic region. Amphotericin B has shown potential antileishmanial activity in different formulations but its cost of treatment and associated nephrotoxicity have limited its use by affected people living in the endemic zone. To control the VL infection in the affected countries, it is necessary to develop new antileishmanial compounds with high efficacy and negligible toxicity. Computer aided programs such as binding free energy estimation; ADMET prediction and molecular dynamics simulation can be used to investigate novel antileishmanial molecules in shorter duration. To develop antileishmanial lead molecule, we performed standard precision (SP) docking for 1160 benzoxaborole analogs along with reference inhibitors against trypanothione reductase of Leishmania parasite. Furthermore, extra precision (XP) docking, ADMET prediction, prime MM-GBSA was conducted over 115 ligands, showing better docking score than reference inhibitors to get potential antileishmanial compounds. Simultaneously, area under the curve (AUC) was estimated using ROC plot to validate the SP and XP docking protocol. Later on, two benzoxaborole analogs with best MM-GBSA ΔG-bind were subjected to molecular simulation and docking confirmation to ensure the ligand interaction with TR. The presented drug discovery based on computational study confirms that BOB27 can be used as a potential drug candidate and warrants further experimental investigation to fight against VL in endemic areas.

Keywords:

• Benzoxaborole
• molecular docking
• molecular dynamics
• trypanothione reductase
• visceral leishmaniasis

Disclosure statement

The authors have declared no competing interest.

Funding information

RKP is thankful to Department of Science and Technology for providing INSPIRE fellowship. VKP is thankful to UGC (30-66/2014-BSR) and SERB (YSS/2015/000716) for providing research grant. BVK is thankful to Indian Institute of Technology Bombay for Post-Doctoral fellowship. AK is thankful to Department of Biotechnology for Innovative Young Biotechnologist Award (Grant Number BT/06/IYBA/2012), New Delhi, India. We are also thankful to Central University of Rajasthan for providing Schrodinger, SimulationPlus and Prof. Dulal Panda at Indian Institute of Technology, Bombay, for providing access to AMBER12.