Role of molecular dynamics in optimising ligand discovery: Case study with novel inhibitor search for peptidyl t-RNA hydrolase

Abstract

High-throughput virtual screening methods are known to play key roles in drug discovery. However, the computational docking approaches are often blamed for delivering false-positives in the predicted molecules. Here we demonstrate that incorporation of receptor/ligand dynamics in the computational drug discovery approaches can be instrumental in removing false-positives. In particular, we consider a protein with no well-known inhibitor, Peptidyl-tRNA hydrolase (PTH) and show that a combined protocol of ensemble docking and Molecular Dynamics simulation can efficiently screen out a large set of approximately 1600 candidate molecules. Finally, the addition of metadynamics enhanced sampling technique can assist in probing the resilience of ligands against unbinding and in turn provide a re-ranked selection which can be used for synthetic priority. Overall, the investigation recommends the usage of dynamics in a computational drug discovery program for judging their efficacy.

Keywords

• MD simulation
• Docking
• Metadynamics simulation
• Protein peptidyl-tRNA hydrolase
• Computational drug discovery