Search for β2 adrenergic receptor ligands by virtual screening via grid computing and investigation of binding modes by docking and molecular dynamics simulations

PLoS One. 2014 Sep 17;9(9):e107837. doi: 10.1371/journal.pone.0107837. eCollection 2014.

Abstract

We designed a program called MolGridCal that can be used to screen small molecule database in grid computing on basis of JPPF grid environment. Based on MolGridCal program, we proposed an integrated strategy for virtual screening and binding mode investigation by combining molecular docking, molecular dynamics (MD) simulations and free energy calculations. To test the effectiveness of MolGridCal, we screened potential ligands for β2 adrenergic receptor (β2AR) from a database containing 50,000 small molecules. MolGridCal can not only send tasks to the grid server automatically, but also can distribute tasks using the screensaver function. As for the results of virtual screening, the known agonist BI-167107 of β2AR is ranked among the top 2% of the screened candidates, indicating MolGridCal program can give reasonable results. To further study the binding mode and refine the results of MolGridCal, more accurate docking and scoring methods are used to estimate the binding affinity for the top three molecules (agonist BI-167107, neutral antagonist alprenolol and inverse agonist ICI 118,551). The results indicate agonist BI-167107 has the best binding affinity. MD simulation and free energy calculation are employed to investigate the dynamic interaction mechanism between the ligands and β2AR. The results show that the agonist BI-167107 also has the lowest binding free energy. This study can provide a new way to perform virtual screening effectively through integrating molecular docking based on grid computing, MD simulations and free energy calculations. The source codes of MolGridCal are freely available at http://molgridcal.codeplex.com.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Amino Acid Sequence
  • Drug Evaluation, Preclinical / methods*
  • Ligands
  • Molecular Docking Simulation*
  • Molecular Dynamics Simulation*
  • Molecular Sequence Data
  • Protein Binding
  • Protein Conformation
  • Receptors, Adrenergic, beta-2 / chemistry
  • Receptors, Adrenergic, beta-2 / metabolism*
  • Small Molecule Libraries / metabolism
  • Thermodynamics
  • User-Computer Interface

Substances

  • Ligands
  • Receptors, Adrenergic, beta-2
  • Small Molecule Libraries

Grant support

This work was supported by the National Natural Science Foundation of China (Grant No. 21175063) and the Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT: IRT1137). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.