Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

PLoS Comput Biol. 2021 May 13;17(5):e1008936. doi: 10.1371/journal.pcbi.1008936. eCollection 2021 May.

Abstract

The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Ligands
  • Molecular Dynamics Simulation*
  • Protein Binding
  • Protein Conformation
  • Receptors, G-Protein-Coupled / chemistry*
  • Receptors, G-Protein-Coupled / metabolism*

Substances

  • Ligands
  • Receptors, G-Protein-Coupled

Associated data

  • Dryad/10.5061/dryad.98sf7m0j3

Grant support

This project was supported by grants from the Swedish Research Council (2017-04676), the Swedish strategic research program eSSENCE, the Science for Life Laboratory, and the Swedish Brain Foundation (FO2019-0299) to J.C. Computational resources were provided by the Swedish National Infrastructure for Computing (SNIC) to JC. I.R.-E. acknowledges Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya (2015 FI_B00145) for its financial support. J.S. acknowledges support from the Instituto de Salud Carlos III FEDER (PI15/00460 and PI18/00094) and the ERA-NET NEURON & Ministry of Economy, Industry and Competitiveness (AC18/00030). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.