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G-protein coupled receptor modeling
Movement of helix VI relative to helix III was first predicted computationally: Luo, X., D. Zhang, and H. Weinstein, Ligand-induced domain motion in the activation mechanism of a G-protein-coupled receptor. Protein Eng, 1994. 7(12): p. 1441-8
Rapidly accumulating information about the structures and functions of transmembrane proteins in the class of G-protein-coupled receptors is facilitating the exploration of molecular details in the processes of cellular signal transduction. We have described recently a 3-D molecular model of the transmembrane portion of the 5-HT2A type of receptor of the neurotransmitter serotonin (5-hydroxytryptamine; 5-HT), constructed from such convergent empirical and theoretical considerations, and have used it for a computational simulation of the mechanisms of ligand-induced receptor activation and signal transduction. The molecular dynamics (MD) simulation of the interaction between the receptor model and ligands of different pharmacological efficacies pointed to a set of specific conformational changes propagated from the ligand binding site to a distal region of the receptor that is essential for signal transduction. The ligand-induced changes were found to correlate well with the known pharmacological properties, but it remained unclear how the binding of the small 5-HT2A receptor agonist molecules in the distal binding pocket could give rise to the specific conformational changes in a distant part of the receptor. As the MD simulations showed the secondary structure of the helical transmembrane domains of the receptor to be well maintained, and the conformational changes to involve mainly translations and rotations of the helices in the bundle relative to one another, an algorithm was developed to treat the ligand-induced conformational changes as rigid domain movements of transmembrane helices.
Gilda Loew library of 3D models of GPCR:
Filizola, M., H.O. Villar, and G.H. Loew, Differentiation of delta, mu, and kappa opioid receptor agonists based on pharmacophore development and computed physicochemical properties. J Comput Aided Mol Des, 2001. 15(4): p. 297-307
other references in visiers et al. 2002
Old models (before the crystal structure came out) - based on bacteriorhodopsin:
http://fulcrum.physbio.mssm.edu/~hwlab/online/dan_paper.html
http://fulcrum.physbio.mssm.edu/~hwlab/online/joan_paper.html
http://www.gpcr.org/7tm/articles/model.html
rhodopsin family model based on ~500 sequences
table gives % homology for those 500 sequences
- uses similar approach to Baldwin, (in fact uses their alignment) but generates atomistic model rather than backbone only
- based on electron density map and sequence alignment
- identifies hydrophobic cluster at CP ends of TM V adn VI and H-bonding network in central region of TM VI and VII
- mutants causing constitutive activity release either H bond network or disturb hydrophobic cluster
- models of delta, mu and kappa opioid receptors using distance geometry algorithm and H-bonding constraints
- network of H-bonds
- ligand binding crevice that is covered by a beta-hairpin formed by E-II
- the only prediction I know of that is not TM only and that got some of the features of the interface between EC and TM domains in the crystal structure correct
- 18 aa that are conserved core in all three subtypes
- 19 aa that are variable and account for specificity of the subtypes
- modeled various agonists and antagonists into the binding crevice and results qualitatively compare to ligand affinities, crosslinking studies and mutagenesis data
Prediction of protein protein interfaces of proteins in the GPCR signaling cascade:
- prediction of rhodopsin-G protein interaction sites based on evolutionary trace ET analysis in which surfaces are selected that do not vary within functional subgroups and that form spatial clusters
- emphasis is on the G protein side, so in order to validate model, one needs to know the literature on mutagenesis of G protein
Lichtarge, O., H.R. Bourne, and F.E. Cohen, An evolutionary trace method defines binding surfaces common to protein families. J Mol Biol, 1996. 257(2): p. 342-58
Lichtarge, O. and M.E. Sowa, Evolutionary predictions of binding surfaces and interactions. Curr Opin Struct Biol, 2002. 12(1): p. 21-7.
Madabushi, S., H. Yao, M. Marsh, D.M. Kristensen, A. Philippi, M.E. Sowa, and O. Lichtarge, Structural clusters of evolutionary trace residues are statistically significant and common in proteins. J Mol Biol, 2002. 316(1): p. 139-54.
Lichtarge, O., M.E. Sowa, and A. Philippi, Evolutionary traces of functional surfaces along G protein signaling pathway. Methods Enzymol, 2002. 344: p. 536-56.
Sowa, M.E., W. He, K.C. Slep, M.A. Kercher, O. Lichtarge, and T.G. Wensel, Prediction and confirmation of a site critical for effector regulation of RGS domain activity. Nat Struct Biol, 2001. 8(3): p. 234-7.
Sowa, M.E., W. He, T.G. Wensel, and O. Lichtarge, A regulator of G protein signaling interaction surface linked to effector specificity. Proc Natl Acad Sci U S A, 2000. 97(4): p. 1483-8.
- same as above for G protein, this paper predicts the binding sites of RGS (regulator of G protein signaling) family of proteins
- ET identifies residues important for all members of a family, therefore the residues are likely to form a general site for regulating of signaling cascades