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Naveena Yanamala, Kalyan C. Tirupula and Judith Klein-Seetharaman

Preferential Binding of Allosteric Modulators to Active and Inactive Conformational States of Metabotropic Glutamate Receptors. Naveena Yanamala, Kalyan C. Tirupula and Judith Klein-Seetharaman.

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Naveena Yanamala, Kalyan C. Tirupula and Judith Klein-Seetharaman

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  1. Preferential Binding of Allosteric Modulators to Active and Inactive Conformational States of Metabotropic Glutamate Receptors Naveena Yanamala, Kalyan C. Tirupula and Judith Klein-Seetharaman Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA InCoB 2007

  2. G-Protein Coupled Receptors • 7 transmembrane helices • Bind to diverse ligands • Major classes include • Family A • Rhodopsin like • Family B • Secretin like • Family C • Glutamate receptor like EMBO J. 18: 1723-1729 (1999) GPCR family is pharmacologically important.

  3. Rhodopsin Cytoplasmic side hn Trans-membrane hn Extracellular side a. Palczewski et al, Science 289(5480), 739 (2004) b. Isin et al, Proteins65, 970 (Dec 1, 2006). Only atomic level structure available is for Rhodopsin

  4. Metabotropic Glutamate Receptors (mGluR’s) Potential drug targets for neurological & neurodegenerative diseases • Glutamate is the most important excitatory neurotransmitter in the brain • mGluR function: modulatory • Class C GPCR, very limited homology to rhodopsin • mGluR’s are sub-divided based on sequence similarity • Group I ( mGluR1 and mGluR5 ) • Group II ( mGluR2 and mGluR3 ) • Group III ( mGluR4, mGluR6, mGluR7 and mGluR8 )

  5. mGluR Ligands Allosteric Ligand binding site Glutamate binding site Competitive Ligand binding site • Competitive • Allosteric • Positive modulator enhances response to glutamate • Negative modulator suppresses response to glutamate Modified : http://www.npsp.com/img/img_mGluR_diag.jpg

  6. Open Question Do positive and negative modulators bind differentially to the active and inactive conformations of the receptors?

  7. Approach Dark state rhodopsin crystal structure Light activated rhodopsin model (ANM) • Generated Alignment of TM regions using ClustalX. • Modeler for Homology Modeling • MolProbity, Procheck Homology models for inactive states of mGluR subtypes Homology models for active states of mGluR subtypes • Docking using ArgusDock3.0 • Selection of best model based on energy and buried surface Docked models • Analysis of binding pocket Critical residues within 5Å

  8. Ligands Docked • Ligands for which the nature of their allosteric effects on mGluR’s experimentally known were analyzed: (A) EM-TBPC (B) Ro67-7476 (C) Ro01-6128 (D) Ro67-4853 (E) R214127 (F) triazafluorenone (G) CPCCOEt (H) YM298198 (I) MPEP (J) SIB-1757 (K) SIB-1893 (L) Fenobam (M) MTEP (N) DFB-3,3` (O) PTEB (P) NPS2390 (Q) CPPHA (R) 5MPEP (S) MPEPy (T) PHCCC (U) AMN082

  9. Ligand Binding Site Inactive mGluR5 Model Docked with MPEP Active mGluR5 Model Docked with MPEP Ligands bind at a region between 3,5,6 & 7 TM’s

  10. Binding Energies Positive Modulator Negative Modulator 5 3 Neutral 1 1 1 3 3 3 1 1 1 3 1 1 3 4 1 2 3 3 3 1 • mGluR1 – I • mGluR2 – II • mGluR5 – I • mGluR4 – III • mGluR7 - III Active-Inactive Binding Energy (kcal/mol) Binding energies for the active and inactive models favor positive and negative modulators, respectively.

  11. mGluR’s vs Rhodopsin (5Å) Rhodopsin Inactive Model Rhodopsin Active Model mGluR5 Inactive Model mGluR5 Active Model Ligand binding pocket overlaps with that of rhodopsin

  12. Validation of Docking Results • Example: Positive Modulator for mGluR5: 3,3-DFB • Example: Negative Modulator for mGluR5: MPEP 3,3-Difluorobenzaldazine 2-methyl-6-((3-methoxyphenyl)ethynyl)-pyridine

  13. Model Validation: Comparison with MPEP Experimental Studies *. P. Malherbe et al., Mol Pharmacol64, 823 (Oct, 2003) Residues not predicted Additional Residues predicted Residues predicted Predicted binding site fits well with experimental results

  14. Model Validation: Comparison to 3,3`-DFB Experimental Studies *. A. Muhlemann et al., Eur J Pharmacol529: 95 (2006) Residues not predicted Additional Residues predicted Residues predicted Predicted binding site fits well with experimental results

  15. Summary of Comparison between MPEP and 3,3’DFB Binding Pockets MPEP Ligand docked to active model Ligand docked to Inactive model 3,3`-DFB W784, R647, L743, Y658, and F787 were found to be part of the binding pocket regardless of the type of modulator and conformation of the receptor.

  16. Conclusions High overlap between experimentally determined and predicted binding pockets validate that bovine rhodopsin can be used as template for predicting the distantly related mGluR GPCR family members. Allosteric ligand binding pockets of mGluR’s overlap with retinal binding pocket of rhodopsin. mGluR allosteric modulation occurs via stabilization of different conformations analogous to those identified in rhodopsin. The models predict the residues which might have a critical role in imparting selectivity and high potency, specific to mGluR-ligand interactions.

  17. Future Work Building a queryable database with simple rule based classifier Setting up experimental platforms to further validate our predictions

  18. Acknowledgements Dr. Judith Klein-Seetharaman Assistant Professor Department of Structural Biology University of Pittsburgh Kalyan Tirupula Graduate Student Molecular Biophysics and Structural Biology Graduate Program University of Pittsburgh

  19. Thank You Questions ?

  20. Robust & General Slow, hard to define convergence Not reproducible (Stochastic) Can get caught in a local minima Some ligand/binding site types may cause problems Fast! Reproducible Formally explores all minima Compare GADock & ShapeDock GADock ShapeDock Slide from http://www.planaria-software.com

  21. Parameterization & Validation • Begin with the published XScore parameters.[1] • Begin with Wang’s data set of 100 protein-ligand structures.[2] • Remove incorrect structures to get a final training set of 84 structures: • 39 hydrophilic, 20 hydrophobic, 25 mixed • Modify H-bond parameters & other new parameters to improve correlation of score of x-ray pose and experiment binding free. Slide from http://www.planaria-software.com [1] “Further development and validation of empirical scoring functions for structure-based binding affinity prediction” Wang, R, Lai, L, and Wang, S. J. Comp. Aided Mol. Design 16, 11-26, 2002 [2] “Comparative Evaluation of 11 Scoring Functions for Molecular Docking” Renxiao Wang, Yipin Lu, and Shaomeng Wang. J. Med. Chem.2003, 46, 2287-2303

  22. Parameterization & Validation Dock the training set using the ShapeDock engine. Slide from http://www.planaria-software.com

  23. Neuraminidase DockingsShapeDock 9 of the 10 structures reproduced the experimental binding mode. [1] “The Effect of Small Changes in Protein Structure on Predicted Binding Modes of Known Inhibitors of Influenza Virus Neruaminidase: PMF-Scoring in Dock4” Ingo Muegge, Med. Chem. Res. 9, 1999, 490-500. Slide from http://www.planaria-software.com

  24. AScore an empirical scoring function AScore is based on terms taken from the HPScore piece of XScore [1] DGbind = DGvdw + DGhydrophobic + DGH-bond + DGH-bond (chg) + DGdeformation + DG0 DGvdw= CVDW VDW DGhydrophobic = Chydrophobic HP DGH-bond = CH-bond HB DGH-bond (chg-chg & chg-neutral) = CH-bond(chg) HB DGdeformation = Crotor RT DG0 = Cregression [1] “Further development and validation of empirical scoring functions for structure-based binding affinity prediction” Wang, R, Lai, L, and Wang, S. J. Comp. Aided Mol. Design 16, 11-26, 2002 Slide from http://www.planaria-software.com

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