Molecular docking, QSAR and ADMET based mining of natural compounds against prime targets of HIV

Abstract

AIDS is one of the multifaceted diseases and this underlying complexity hampers its complete cure. The toxicity of existing drugs and emergence of multidrug-resistant virus makes the treatment worse. Development of effective, safe and low-cost anti-HIV drugs is among the top global priority. Exploration of natural resources may give ray of hope to develop new anti-HIV leads. Among the various therapeutic targets for HIV treatment, reverse transcriptase, protease, integrase, GP120, and ribonuclease are the prime focus. In the present study, we predicted potential plant-derived natural molecules for HIV treatment using computational approach, i.e. molecular docking, quantitative structure activity relationship (QSAR), and ADMET studies. Receptor-ligand binding studies were performed using three different software for precise prediction – Discovery studio 4.0, Schrodinger and Molegrow virtual docker. Docking scores revealed that Mulberrosides, Anolignans, Curcumin and Chebulic acid are promising candidates that bind with multi targets of HIV, while Neo-andrographolide, Nimbolide and Punigluconin were target-specific candidates. Subsequently, QSAR was performed using biologically proved compounds which predicted the biological activity of compounds. We identified Anolignans, Curcumin, Mulberrosides, Chebulic acid and Neo-andrographolide as potential natural molecules for HIV treatment from results of molecular docking and 3D-QSAR. In silico ADMET studies showed drug-likeness of these lead molecules. Structure similarities of identified lead molecules were compared with identified marketed drugs by superimposing both the molecules. Using in silico studies, we have identified few best fit molecules of natural origin against identified targets which may give new drugs to combat HIV infection after wet lab validation.

Keywords:

• HIV receptors
• molecular docking
• natural ligand
• ADMET
• 3D-QSAR

Acknowledgments

We are thankful to Dr. B. R. Ambedkar Centre for Biomedical Research, University of Delhi, India. In this work, Discovery studio 4.0 client (licence version) software used which provided by that centre during the workshop. We are also thankful to L.M. College of Pharmacy, Ahmedabad for providing Schrodinger software facility. We are gratified to Triveni Pardhi to give the knowledge of basics of docking study. We are also thankful to Dr. John Georgee, Assistant Professor, CHRIST College, Rajkot for providing Molegrow software facility. Authors are also thankful to Dr. Kunal Roy, Department of Pharmaceutical Technology, Jadavpur University, Kolkata and Domenico Gadleta, Department of Environmental Health Sciences, Mario Negri Institute for Pharmacological Research, Italy for guidance of QSAR software tools.