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

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

Rhodopsin Only atomic level structure available is for Rhodopsin h Cytoplasmic side Extracellular side b. Isin et al, Proteins 65, 970 (Dec 1, 2006). h Trans-membrane a. Palczewski et al, Science 289(5480), 739 (2004)

Metabotropic Glutamate Receptors (mGluR’s) 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 ) Potential drug targets for neurological & neurodegenerative diseases

mGluR Ligands Modified : Competitive Allosteric  Positive modulator enhances response to glutamate  Negative modulator suppresses response to glutamate Glutamate binding siteAllosteric Ligand binding site Competitive Ligand binding site

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

Approach 1. Dark state rhodopsin crystal structure 2. Light activated rhodopsin model (ANM) Docked models Critical residues within 5Å 1. Homology models for inactive states of mGluR subtypes 2. Homology models for active states of mGluR subtypes 1.Generated Alignment of TM regions using ClustalX. 2.Modeler for Homology Modeling 3.MolProbity, Procheck 4.Docking using ArgusDock3.0 5.Selection of best model based on energy and buried surface 6.Analysis of binding pocket

Ligands Docked Ligands for which the nature of their allosteric effects on mGluR’s experimentally known were analyzed: (A) EM-TBPC (B) Ro (C) Ro (D) Ro (E) R (F) triazafluorenone (G) CPCCOEt (H) YM (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

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

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

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

Example: Positive Modulator for mGluR5: 3,3-DFB Example: Negative Modulator for mGluR5: MPEP 3,3-Difluorobenzaldazine2-methyl-6-((3-methoxyphenyl)ethynyl)-pyridine Validation of Docking Results

Predicted binding site fits well with experimental results Model Validation: Comparison with MPEP Experimental Studies *. P. Malherbe et al., Mol Pharmacol 64, 823 (Oct, 2003) Residues not predictedAdditional Residues predictedResidues predicted MPEP Data * mGluR5/MPEP Active Model mGluR5/MPEP Inactive Model TM3 Arg-647, Pro-654, Tyr-658 Arg-647, Ile-650, Tyr-658 Arg-647, Ile-650, Pro-654, Tyr-658 EC2Asn-733 Arg-726, Glu-727, Ile-731, Cys-732, Asn-733, Asn-736 Ile-731, Cys-732, Asn-733 TM5Leu-743 Leu-737, Leu-743, Pro-742Pro-742, Leu-743 TM6 Thr-780, Trp-784, Phe-787, Val-788, Tyr-791 Trp-784, Phe-787, Val-788Trp-784, Phe-787 TM7Met-801, Ala-809 Met-801, Cys-802, Ser-804, Val-805 Thr-800, Met-801, Cys-802, Ser-804, Val-805

Predicted binding site fits well with experimental results Model Validation: Comparison to 3,3`-DFB Experimental Studies *. A. Muhlemann et al., Eur J Pharmacol 529: 95 (2006) Residues not predictedAdditional Residues predictedResidues predicted 3,3’-DFB Data * mGluR5/3,3’-DFB Active Model mGluR5/3,3’-DFB Inactive Model TM3 Arg-647, Pro-654, Ser-657, Tyr-658Arg-647, Pro-654, Tyr-658 Arg-647, Pro-654, Ser-657, Tyr-658 EC2Asn-733 Arg-726, Ile-731, Cys-732, Asn-733 Cys-732, Asn-733, Thr-734, Asn-736 TM5Leu-743 Leu-737, Gly-738, Leu-743, Gly-744, Pro-742Leu-743 TM6 Thr-780, Trp-784, Phe-787, Val-788, Tyr-791Trp-784, Phe-787, Val-788 Thr-780, Trp-784, Phe-787, Cys-781, Leu-785, Val-788, Tyr-791 TM7Met-801 Thr-800, Met-801, Cys-802, Ser-804 Met-801, Ser-804

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. Summary of Comparison between MPEP and 3,3’DFB Binding Pockets MPEP 3,3`-DFB Ligand docked to active model Ligand docked to Inactive model

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.

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

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

Thank You Questions ?

Compare GADock & ShapeDock 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 GADockShapeDock Slide from

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. [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, Parameterization & Validation Slide from

Dock the training set using the ShapeDock engine. Parameterization & Validation Slide from

Neuraminidase Dockings ShapeDock 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, Slide from

AScore an empirical scoring function AScore is based on terms taken from the HPScore piece of XScore [1] [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  G bind =  G vdw +  G hydrophobic +  G H-bond +  G H-bond (chg) +  G deformation +  G 0  G vdw = C VDW VDW  G hydrophobic = C hydrophobic HP  G H-bond = C H-bond HB  G H-bond ( chg-chg & chg-neutral ) = C H-bond(chg) HB  G deformation = C rotor RT  G 0 = C regression Slide from