Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA

Preferential Binding of Allosteric Preferential Binding of Allosteric Modulators to Active and Inactive Modulators to Active and Inactive

Conformational States of Conformational States of Metabotropic Glutamate ReceptorsMetabotropic Glutamate Receptors

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

InCoB 2007

G-Protein Coupled ReceptorsG-Protein Coupled Receptors

EMBO J. 18: 1723-1729 (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 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 Metabotropic Glutamate Receptors (mGluR’s)(mGluR’s)

Glutamate is the most important excitatory neurotransmitter in the brain

mGluR function: modulatory Class C GPCR, very limited homology to rhodopsinmGluR’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 LigandsmGluR Ligands

Modified : http://www.npsp.com/img/img_mGluR_diag.jpg

Competitive

Allosteric Positive modulator enhances

response to glutamate Negative modulator suppresses

response to glutamate

Glutamate binding siteAllosteric Ligand

binding siteCompetitive Ligand binding site

Open QuestionOpen Question

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

ApproachApproach1. 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 DockedLigands DockedLigands 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

Ligands bind at a region between 3,5,6 & 7 TM’s

Ligand Binding Site Ligand Binding Site Inactive mGluR5 Model

Docked with MPEPActive mGluR5 Model

Docked with MPEP

Binding EnergiesBinding EnergiesPositive Modulator

Negative Modulator

Neutral

1. mGluR1 – I2. mGluR2 – II3. mGluR5 – I4. mGluR4 – III5. mGluR7 - III

Binding energies for the active and inactive models favor positive and negative

modulators, respectively.

23

5

1 1 1

11

1

1

11 1

33 3

3

33 3 3 4

Act

ive-

Inac

tive

Bin

din

g E

ner

gy

(kca

l/mo

l)

Ligand binding pocket overlaps with that of rhodopsin

mGluR’s vs Rhodopsin (5Å)mGluR’s vs Rhodopsin (5Å)Rhodopsin Inactive Model

Rhodopsin Active Model

mGluR5 Inactive Model

mGluR5 Active Model

Example: Positive Modulator for mGluR5: 3,3-DFBExample: Negative Modulator for mGluR5: MPEP

3,3-Difluorobenzaldazine 2-methyl-6-((3-methoxyphenyl)ethynyl)-pyridine

Validation of Docking ResultsValidation of Docking Results

Predicted binding site fits well with experimental results

Model Validation: Comparison Model Validation: Comparison with MPEP Experimental Studieswith MPEP Experimental Studies

*. P. Malherbe et al., Mol Pharmacol 64, 823 (Oct, 2003) Residues not predicted Additional Residues predicted Residues predicted

  MPEP Data *mGluR5/MPEPActive Model

mGluR5/MPEP Inactive Model

TM3Arg-647, Pro-654, Tyr-658

Arg-647, Ile-650 ,Tyr-658

Arg-647, Ile-650, Pro-654, Tyr-658

EC2 Asn-733

Arg-726, Glu-727,Ile-731, Cys-732,Asn-733, Asn-736

Ile-731, Cys-732, Asn-733

TM5 Leu-743Leu-737, Leu-743, Pro-742 Pro-742, Leu-743

TM6

Thr-780, Trp-784, Phe-787, Val-788, Tyr-791

Trp-784, Phe-787,Val-788 Trp-784, Phe-787

TM7 Met-801, Ala-809Met-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 Model Validation: Comparison to 3,3`-DFB Experimental Studies 3,3`-DFB Experimental Studies

*. A. Muhlemann et al., Eur J Pharmacol 529: 95 (2006) Residues not predicted Additional Residues predicted Residues predicted

  3,3’-DFB Data *mGluR5/3,3’-DFB

Active ModelmGluR5/3,3’-DFB Inactive

Model

TM3Arg-647, Pro-654, Ser-657, Tyr-658 Arg-647, Pro-654, Tyr-658

Arg-647, Pro-654, Ser-657, Tyr-658

EC2 Asn-733Arg-726, Ile-731, Cys-732, Asn-733

Cys-732, Asn-733, Thr-734, Asn-736

TM5 Leu-743Leu-737, Gly-738, Leu-743, Gly-744, Pro-742 Leu-743

TM6

Thr-780, Trp-784, Phe-787, Val-788, Tyr-791 Trp-784, Phe-787, Val-788

Thr-780, Trp-784, Phe-787, Cys-781, Leu-785, Val-788, Tyr-791

TM7 Met-801Thr-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 Summary of Comparison between MPEP and 3,3’DFB Binding PocketsMPEP and 3,3’DFB Binding Pockets

MPEP

3,3`-DFB

Ligand docked to active model

Ligand docked to Inactive model

ConclusionsConclusionsHigh 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 WorkFuture Work

Building a queryable database with simple rule based classifier

Setting up experimental platforms to further validate our predictions

AcknowledgementsAcknowledgements

Kalyan TirupulaGraduate Student

Molecular Biophysics and Structural Biology

Graduate Program

University of Pittsburgh

Dr. Judith Klein-SeetharamanAssistant Professor

Department of Structural Biology

University of Pittsburgh

Thank You Thank You

Questions ?

Compare GADock & ShapeDockCompare GADock & ShapeDock

Robust & GeneralSlow, hard to define

convergenceNot reproducible

(Stochastic)Can get caught in a local

minima

Some ligand/binding site types may cause problems

Fast!ReproducibleFormally explores all

minima

GADock ShapeDock

Slide from http://www.planaria-software.com

•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, 2287-2303

Parameterization & ValidationParameterization & Validation

Slide from http://www.planaria-software.com

Dock the training set using the ShapeDock engine.

Parameterization & Validation

Slide from http://www.planaria-software.com

Neuraminidase DockingsNeuraminidase DockingsShapeDockShapeDock

9 of the 10 structures reproduced the experimental binding mode.

Correlation of predicted and measured binding affinities

R2 = 0.70Ave. RMSD = 1.55 Angstroms

-12

-11

-10

-9

-10 -9 -8 -7 -6 -5 -4 -3 -2

log IC50

AS

co

re S

co

re (kcal

/mo

l)

[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

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

Gbind = Gvdw + Ghydrophobic + GH-bond + GH-bond (chg) + Gdeformation + G0

Gvdw = CVDW VDW

Ghydrophobic = Chydrophobic HP

GH-bond = CH-bond HB

GH-bond (chg-chg & chg-neutral) = CH-bond(chg) HB

Gdeformation = Crotor RT

G0 = Cregression

Slide from http://www.planaria-software.com