Multiple e-Pharmacophore modeling to identify a single molecule that could target both streptomycin and paromomycin binding sites for 30S ribosomal subunit inhibition

Abstract

The bacterial ribosome is an established target for anti-bacterial therapy since decades. Several inhibitors have already been developed targeting both defined subunits (50S and 30S) of the ribosome. Aminoglycosides and tetracyclines are two classes of antibiotics that bind to the 30S ribosomal subunit. These inhibitors can target multiple active sites on ribosome that have a complex structure. To screen putative inhibitors against 30S subunit of the ribosome, the crystal structures in complex with various known inhibitors were analyzed using pharmacophore modeling approach. Multiple active sites were considered for building energy-based three-dimensional (3D) pharmacophore models. The generated models were validated using enrichment factor on decoy data-set. Virtual screening was performed using the developed 3D pharmacophore models and molecular interaction towards the 30S ribosomal unit was analyzed using the hits obtained for each pharmacophore model. The hits that were common to both streptomycin and paromomycin binding sites were identified. Further, to predict the activity of these hits a robust 2D-QSAR model with good predictive ability was developed using 16 streptomycin analogs. Hence, the developed models were able to identify novel inhibitors that are capable of binding to multiple active sites present on 30S ribosomal subunit.

Keywords:

• ribosome
• bacteria
• pharmacophore
• QSAR
• virtual screening
• docking

Abbreviations:

• 2D-QSAR – two-dimensional quantitative structure activity relationship
• e-Pharmacophore – energy-based Pharmacophore
• PPF – paromomycin pharmacophore
• SPF – streptomycin pharmacophore
• VS – virtual screening
• EF – enrichment factor
• ADMET – absorption distribution metabolism excretion toxicity
• Tc – tanimotto coefficient
• MACCS – molecular access system

Acknowledgements

The authors are grateful to the Centre for Nanosciences and Molecular Medicine, Amrita University, Kochi for computational infrastructural support. The author ACP acknowledges Kerala State Council for Science, Technology & Environment (KSCSTE) for Junior Research Fellowship (012-35/FSHP/2012/KSCSTE), India. CGM is grateful to ICMR-New Delhi for Senior Biomedical International Fellowship (INDO/FRC/452(S-37)/2016-17-IHD) to visit Professor Jerzy Leszczynski, Bioinformatics and Computational biology laboratory at Jackson State University, Jackson, USA for joint research collaborations.