PyPLIF HIPPOS-Assisted Prediction of Molecular Determinants of Ligand Binding to Receptors

Molecules. 2021 Apr 22;26(9):2452. doi: 10.3390/molecules26092452.

Abstract

Identification of molecular determinants of receptor-ligand binding could significantly increase the quality of structure-based virtual screening protocols. In turn, drug design process, especially the fragment-based approaches, could benefit from the knowledge. Retrospective virtual screening campaigns by employing AutoDock Vina followed by protein-ligand interaction fingerprinting (PLIF) identification by using recently published PyPLIF HIPPOS were the main techniques used here. The ligands and decoys datasets from the enhanced version of the database of useful decoys (DUDE) targeting human G protein-coupled receptors (GPCRs) were employed in this research since the mutation data are available and could be used to retrospectively verify the prediction. The results show that the method presented in this article could pinpoint some retrospectively verified molecular determinants. The method is therefore suggested to be employed as a routine in drug design and discovery.

Keywords: AutoDock Vina; G protein-coupled receptor; PyPLIF HIPPOS; drug discovery; fragment-based; molecular determinant.

MeSH terms

  • Binding Sites
  • Decision Trees
  • Drug Discovery / methods
  • Humans
  • Ligands*
  • Models, Molecular*
  • Molecular Docking Simulation
  • Molecular Dynamics Simulation
  • Protein Binding
  • Protein Conformation
  • Receptors, G-Protein-Coupled / chemistry*

Substances

  • Ligands
  • Receptors, G-Protein-Coupled