PSI Structural Biology Knowledgebase

PSI | Structural Biology Knowledgebase
Header Icons

Related Articles
Signaling: A Platform for Opposing Functions
May 2015
Protein Folding and Misfolding: It's the Journey, Not the Destination
March 2015
Molecular Portraits of the Cell
February 2015
Nuclear Pore Complex: A Flexible Transporter
February 2015
Nuclear Pore Complex: Higher Resolution of Macromolecules
February 2015
Nuclear Pore Complex: Integrative Approach to Probe Nup133
February 2015
Piecing Together the Nuclear Pore Complex
February 2015
Updating ModBase
January 2015
Transmembrane Spans
December 2014
Mining Protein Dynamics
May 2014
Novel Proteins and Networks: Assigning Function
May 2014
Cancer Networks: Predicting Catalytic Residues from 3D Protein Structures
November 2013
The Immune System: A Brotherhood of Immunoglobulins
June 2013
The Immune System: Super Cytokines
June 2013
Infectious Diseases: Targeting Meningitis
May 2013
PDZ Domains
April 2013
Protein Interaction Networks: Adding Structure to Protein Networks
April 2013
Design and Discovery: Flexible Backbone Protein Redesign
February 2013
Pocket changes
July 2012
Predictive protein origami
July 2012
Refining protein structure prediction
March 2012
Metal mates
February 2012
Devil is in the details
January 2012
Playing while you work
November 2011
Docking and rolling
October 2011
Fit to serve
October 2011
Rosetta hone
July 2011
Structure from sequence
July 2011
An easier solution for symmetry
June 2011
Solutions in the solution
June 2011
Regulating nitrogen assimilation
January 2011
Guard cells pick up the SLAC
December 2010
Alpha/Beta Barrels
October 2010
Modeling RNA structures
May 2010
Deducing function from small structural clues
February 2010
Spot the pore
January 2010
Network coverage
November 2009
GPCR modeling: any good?
August 2009
Protein modeling made easy
July 2009
Model proteins in your lunch break
April 2009
Click for cancer-protein interactions
December 2008
Modeling with SAXS
October 2008
Designing activity
September 2008

Technology Topics Modeling

GPCR modeling: any good?

PSI-SGKB [doi:10.1038/th_psisgkb.2009.35]
Technical Highlight - August 2009
Short description: Results from a competition that invited groups to predict the structure of a GPCR show some promise, but accurate modeling of these receptors remains a challenge.Nature Rev. Drug Discov. 8, 455-463 (2009)

Stefano Constanzi submitted the best overall model of the human adenosine A2A receptor.

In October 2008 a blind prediction assessment was announced. The crystallographic structure of human adenosine A2A receptor bound to the ligand ZM241385 had just been solved by Ray Stevens' group at the Scripps Research Institute. Before the coordinates were released and the structure published, groups were invited to submit their predicted models of the receptor and the ligand-binding site.

The competition ran along similar lines to CASP (Critical Assessment of methods of Protein Structure) and CAPRI (Critical Assessment of PRediction of Interactions) and was called GPCR Dock 2008. It was designed to test how good GPCR structure prediction and ligand-docking programs are by using a blind prediction assessment.

Twenty-nine groups submitted 206 structural models. They were assessed partly by measuring the ligand root mean squared deviation (RMSD) between the real structure and the predicted structure and partly by scoring the number of correct receptor–ligand contacts. These two measurements were combined to produce a z-score to rank the models.

The quality of the predictions for the receptor alone was quite good, with an RMSD of 4.2 Å (standard deviation 0.9 Å) for the alpha carbons. However, the submitted structures varied widely in their accuracy in predicting the ligand binding mode, with an average ligand RMSD of 9.5 Å (standard deviation 3.8 Å) and four (with a standard deviation of seven) ligand–receptor contacts correctly modeled. Thus, the majority of models did not predict the ligand position and the binding interactions very well.

There were good performances by several groups, and the best model overall was submitted by Stefano Costanzi. His model had a ligand RMSD of 2.8 Å from the actual structure and 34 out of 75 contacts were correctly predicted. This model accurately predicts some of the key receptor–ligand interactions.

Some of the models approximated well the real structure and captured the essence of the ligand–receptor interactions, but improvements in GPCR modeling are still needed before models can consistently be relied upon.

Related articles

A pocket guide to GPCRs

Tips for crystallizing membrane proteins

Evolving a better-expressing GPCR

Maria Hodges


  1. M. Michino et al. Community-wide assessment of GPCR structure modelling and ligand docking: GPCR Dock 2008.
    Nature Rev. Drug Discov. 8, 455-463 (2009), doi: 10.1038/nrd2877.

Structural Biology Knowledgebase ISSN: 1758-1338
Funded by a grant from the National Institute of General Medical Sciences of the National Institutes of Health