university of pennsylvania department of bioengineering multiscale modeling of phosphorylation and...

University of Pennsylvania Department of Bioengineering

Multiscale Modeling of Phosphorylation and Inhibition of the Epidermal Growth Factor Receptor Tyrosine Kinase: Linking Somatic

Mutations to Differential Signaling

Yingting LiuAdvisor: Dr. Ravi Radhakrishnan

Department of BioengineeringUniversity of Pennsylvania


Outline

Backgrounds

Hypothesis and Specific aims

Experimental design and preliminary results


ErbB Family Receptors and the Signaling PathwaysYarden and Sliwkowski, nature reviews, 2001


Tyrosine Phosphorylation and Receptor Inhibition

Zhang and Kuriyan,Cell, 2006


EGFR Kinase Domain Mutations

Choi and Lemmon, Oncogene, 2007 Zhang and Kuriyan,Cell, 2006Carey and Sliwkowski, Cancer Res, 2006


Hypothesis and Methods

We hypothesize that mutants in the EGFR kinase domain will alter the kinase-inhibitor, kinase-substrate interactions, and the catalytic reaction efficiency of the turn-over of different EGFR substrates by affecting the properties of EGFRTK active site, therefore lead to differential characteristics in the downstream signaling in pathways mediated by EGFR.

We propose to employ multiscale computational methods based on molecular docking, molecular dynamics (MD), and quantum mechanics molecular mechanics (QM/MM) simulations to test this hypothesis.

MD simulation for protein kinase

Multiple conformation molecular docking

MD simulation for complex

Structural and energetic analysis

Inhibition

QM/MM calculation on catalysis

MD simulation for

EGFRTK-ATP-MG-Peptide complex

MD simulation for protein kinase

Multiple conformation molecular docking

MD simulation for complex

Structural and energetic analysis

Inhibition

QM/MM calculation on catalysis

MD simulation for

EGFRTK-ATP-MG-Peptide complex


Specific Aims

Aim1. Developing empirical force-field parameters for small molecule inhibitors for use in in-silico docking and molecular dynamics simulations.

Aim2. Exploring the conformational and free energy landscape for wildtype and L834R mutant EGFR kinase complexed with small molecule inhibitors and peptide substrates.

Aim3. Modeling the catalytic mechanism and activity of the EGFR tyrosine kinase.


MD Simulation and CHARMM Potential Energy

V

2 2 2( ) ( - ) ( - ) ( - ) (1 cos( - ))0 0 0

12 6min min2 ( - ) -

0

V K b b K S S K K nb UB

bonds UB angles dihedrals

R R q qij ij i j

K j jimp ij r r erimpropers nonbond ij ij ij

r

Molecular Dynamic (MD) simulations:

CHARMM potential energy:

Essential part is the potential energy function.


Erlotinib Parameterization (1)

Q2AN

CAQ1 N

AQ1

CAQ3

CAQ3

Q2AC

CA

CAQ4

CAQ4

CASO

OS

CT2

T2C

OS

T2C T2C

SO

T3C

T3C

CA

CA

CA

CA

CA

CA

CC3

CC3

HA

H

PH

PH

HP

HP

HP

PH

HA

HA

HA

HA

HA

HA

HA

HA

HA

HA

AH

HPHA

AQ1N

Define new atom types and initiate the parameter set.

Optimize the structure using ab-initio methods and obtain equilibrium constants.

Obtain partial charges of each atom using CHELPG (CHarges from ELectrostatic Potentials using a Grid based method) .

Get Van der Waals constants ( and ) from existing CHARMM parameters.

Guess the force field constants based on those assigned for similar structure in existing CHARMM parameters.

min ijR ij

2 2 2( ) ( - ) ( - ) ( - ) (1 cos( - ))0 0 0

12 6min min2 ( - ) -

0

V K b b K S S K K nb UB

bonds UB angles dihedrals

R R q qij ij i j

K j jimp ij r r erimpropers nonbond ij ij ij

r



N3

C19

N2

C18

C7

C6

C17

C13

C9

C8

N1

N1H

H8

H19

H17

H22

O2

H21

H11

O1

H12

O3

H33

H32

Refine Partial charges manually.

Q2AN

CAQ1 N

AQ1

CAQ3

CAQ3

Q2AC

CA

CAQ4

CAQ4

CASO

OS

CT2

T2C

OS

T2C T2C

SO

T3C

T3C

CA

CA

CA

CA

CA

CA

CC3

CC3

HA

H

PH

PH

HP

HP

HP

PH

HA

HA

HA

HA

HA

HA

HA

HA

HA

HA

AH

HPHA

AQ1N



Refine dihedral parameters to reproduce ab initio dihedral energy surface.

Using genetic algorithm to automatically minimize the merit function:

Q2AN

CAQ1 N

AQ1

CAQ3

CAQ3

Q2AC

CA

CAQ4

CAQ4

CASO

OS

CT2

T2C

OS

T2C T2C

SO

T3C

T3C

CA

CA

CA

CA

CA

CA

CC3

CC3

HA

H

PH

PH

HP

HP

HP

PH

HA

HA

HA

HA

HA

HA

HA

HA

HA

HA

AH

HPHA

AQ1N

2

1

( )NGRID

C Gi i

i

D D

0 100 200 300 4000

0.5

1

1.5

Dihedral (degree)

Ene

rgy

(Kca

l/mol

)

Dihedral potential energy surface

CiD G

iDNGRID is the number of potential values calculated in the surface. and are potential values from CHARMM and GAUSSIAN.



Q2AN

CAQ1 N

AQ1

CAQ3

CAQ3

Q2AC

CA

CAQ4

CAQ4

CASO

OS

CT2

T2C

OS

T2C T2C

SO

T3C

T3C

CA

CA

CA

CA

CA

CA

CC3

CC3

HA

H

PH

PH

HP

HP

HP

PH

HA

HA

HA

HA

HA

HA

HA

HA

HA

HA

AH

HPHA

AQ1N

Refine force constants to reproduce vibrational eigenvalues and eigenvectors.

Using genetic algorithm to automatically minimum the merit function:

3 62

max1

( )

3 6

NC G

i i ji

N

1

max ( )i c Gj i j

Vaiana, Computer Physics Communications, 2005.

, : the ith frequency and eigenvector from CHARMM and GAUSSIAN

Project into { }, and find the index jmax which maxmum .

In the ideal case, and max

; ,

G Gi j

C Ci i

c G c Gi j i j

C G c Gi j i j ij


Erlotinib Parameterization (5) ------

Preliminary resultsWater interactions Interaction Energies

(Kcal/mol) Distance (Å)

GAUSSIAN CHARMM GAUSSIAN CHARMM

N2…HOH -6.69 -6.61 2.13 1.91

N3…HOH_2 -5.33 -5.3 2.32 2.01

N1H…OHH_2 -6.52 -6.52 2.4415 2.63

Dipole moment

(Debye)

GAUSSIAN CHARMM

4.868 5.07

Table 1 Water-mediated interactions and dipole moment for erlotinib. The ab-initio interaction energies are scaled by 1.16, and the distances should offset by –0.1 to –0.2 A. Experimental dipole moments are typically ~10 to 20% larger than HF/6-31G*.


Erlotinib Parameterization (6) ------

Preliminary results

0 50 100 150 200 250 300 3500

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Dihedral (degree)

Ene

rgy

(kca

l/mol

)

Frequency matching Potential surface fitting

Genetic algorithm efficiency


Specific Aims





Methods: MD simulations

Solvated model for MD simulation of EGFRTK.

(Iceblue: sodium; yellow: chlorine; orange: protein; tan: water).

Molecular Dynamic (MD) protocol:

•Prepare protein conformation based on available crystal structure or homologies.

• Solvate the protein and neutralize the systems by placing ions randomly.

• Minimize the solvated models

• Heat the system to 300 K

• Equilibrate at constant temperature and constant pressure (300 K and 1 atm) for 200ps to stable the system.

• Run productive trajectory.


Methods: Multiple-Conformation Molecular Docking

• The idea of molecular docking: to generate a comprehensive set of conformations of the receptor-ligand complex and then to rank them according to their stability.

• Single conformation docking: Ligand is flexibility, while receptors are usually treated as rigid during docking.

• Multiple-conformation docking: An ensemble of 100 snapshots of the protein is collected from the equilibrated trajectory to perform molecular docking. The generated ligand conformations are clustered based on the relative RMSD and analyzed to explore the conformational and free energy landscape of the interaction between protein kinase and the ligands.

The multiple-conformation docking jobs are submitted in parallel so that they will run simultaneously and then cluster the generated conformations upon completion of the docking runs using Fortran 90 program.


Methods: Binding Free Energy Calculation

2 2

12 6 12 6

( / 2 )

( )( )

ij

ij ij ij ij i jvdW hbond elec

ij ij ij ij ijij ij ij ij

r

tor tor sol i j j iij

A B C D q qG G G E t G

r rR R R R

G N G SV S V e

• Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA):

- - ;

- ;

;

.

complex receptor ligand

MM PBSA MM

MM bond angle tors elec vdw

PBSA solvation PB SA

G G G G

G E G TS

E E E E E E

G G G G

Electrostatic solvation energy: Poisson-Boltzmann equation.

Nonpolar term: depend on surface area. Sitkoff and Honig 1993

• AUTODOCK:


Kinase-Inhibitor Interactions ------ Proposed model

• Motivation: Similar binding conformations presented in crystal structures but remarkably increase the binding affinities in L834R mutant systems.

--- erlotinib (Carey and Sliwkowski, 2006), gefitinib and AEE788 (Yun and Eck 2007)

• Specific of Aim: using multiple-conformation molecular docking to obtain six top ranked complex conformations based on the approximate free energy from AUTODOCK and then perform MD based structural and energetic analysis (MMPBSA) for each conformations. Among the six, three conformations will be highlighted for analysis based on the more accurate binding free energy.

• Possible reasons to test: unique interactions between L834R mutant kinase and inhibitors, subtle conformational differences, which is hard to be captured by crystallographic methods, effect of solvation, …


Kinase-Inhibitor Interactions ------ Preliminary results and future work

WT L858RCrystal conf.

Lowest energy conf.

Top ranked Erlotinib conformations in EGFR wildtype and mutant system.

Use MD simulations to refine these structures with explicit solvent and resort the structures using MMPBSA methods.

Perform structural analysis for the refined conformations to explore the effect of mutations on kinase-inhibitor interaction.


Kinase-Substrate interactions ------ Proposed model

• Specific of Aim: perform the multiple-conformation molecular docking protocol followed by the MD based structural analysis and free energy calculation to predict the best binding modes and obtain the corresponding binding affinities, which can be correlated to Km values for each substrate.

• Motivation: to predict the binding modes for different substrates and test the effect of mutation on kinase-substrate interaction.

• Substrates: Four seven-residue sequences derived from the C-terminal tail of EGFRTK (Y1068,Y1173,Y992 and Y1045).


Kinase-Substrate interactions ------ Preliminary results and future work

L858R unphosphorylated EGFR

Binding with 106872GS6


Kinase-Subtrate interactions ------ Preliminary results and future work

Substrates

Approximate Binding Energy(Kcal/mol)

Y1068 Y1173 Y992

Wildtype -5.42 -4.69 -4.7

L834R mutant -5.93 -3.78 -5.91

Liu, Purvis and Radhakrishnan,2007


Kinase-Substrate interactions ------ Preliminary results and future work

Free energy contributions of EGFRTK- peptide (VPEYINQ) binding from MMPBSA calculation. (Kcal/mol)

Internal energy -139.7

Polar solvation 140.5

onpolar solvation -6.4

Total binding free energy -5.6

Future work: Use MD simulations to refine these structures with explicit solvent and recalculate the binding free energy using MMPBSA methods.


Specific Aims





Catalytic Mechanism

• In principle, the reaction mechanism can be either an associative or dissociative pathway.

• pKa and nucleophile coefficient measurements support a dissociative transition state. (Kim and Cole, 1998)

• QM/MM studies of cAMP agree with the dissociate mechanism. (Cheng and McCammon, 2005)


CH2

OP

O-

O

O

P

O

OO-

P

O-

O-

Mg2+

Mg2+

OH H2

C

O

O O-

CH2

ATP

Peptide

Asp813

CH2

OP

O-

O

O

P

O

OO-

P

O-

OMg2+

Mg2+

OH H2

C

O O-

CH2

O-

Asp813

Peptide

ATP

CH2

OP

O-

O

O

P

O

OO-

P

O-

O

Mg2+

Mg2+

OH H2

C

O O-

CH2

O

Asp813

Peptide

ATP

Proposed Catalytic Mechanism for EGFRTK based on cAMP


Prepare the Enzyme-Substrate System

Blue: 2GS6 bisubstrate;

Pink: ATP conformation in 2ITX;

Yellow: proposed peptide conformation in aim 2;


QM/MM CalculationMolecular Mechanics (MM): cannot account for the covalent transformations of chemical bonds.

Quantum Mechanics (QM): limited system size due to computational complexity.

QM/MM: Treat atoms involved in chemical reaction with QM and others MM.

QM region

MM region Link atoms

ATP

PEPTIDEMG


Umbrella Sampling

• Umbrella sampling enables the calculation of the potential of mean force (free energy density) along an a priori chosen set of reaction coordinates (or order parameters), from which free energy changes are calculated by numerical integration.

( ) ( ) ( )u r u r W r

20( ) ( )wW r k r r

( )u r

( )u r


Free Energy Landscape Along Reaction Coordinates

CH2

OP

O-

O

O

P

O

OO-

P

O-

O-

Mg2+

Mg2+

OH H2

C

O

O O-

CH2

ATP

Peptide

Asp813

1r

2r

• Umbrella sampling along two coordinates.

• 25 windows are sampled as a uniform 5×5 grid along r1 and r2 .

• with each window harvesting a QM/MM MD trajectory of 2 ps.

• free energy profile as a function of the coordinate will be calculated using the weighted histogram analysis method (WHAM).

• Explore the effect of mutation on the reaction profile. Gregersen and York 2003


Summary and Significances

Effect of mutation on:

• Kinase-Inhibitor interactions.

• Kinase-Substrate interactions.

• EGFR tyrosine kinase reaction profile.

Significances:

• generate a rich amount of information concerning structural and dynamic properties of the system at atomic level.

• help to further understand the mechanism of protein kinases inhibition and phosphorylation and therefore guide cancer therapy of protein kinase systems.


Thanks.


Mutations increase kinase activities

Yun et al., (Eck) Cancer Cell (2007)

Zhang et al., (Kuriyan) Cell (2006)


Structural Studies of EGFRTK Active Site

αC-helix

peptideC-loop

ATP

GLU738

LYS721

ASP813

ASP831

MET769

G-loop

N-lobe

C-lobe

A-loop

university of pennsylvania department of bioengineering multiscale modeling of phosphorylation and...

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egfr tyrosine kinase

l834r mutant egfr kinase

molecular docking

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molecular dynamics md

small molecule inhibitors

different egfr substrates

kinasesubstrate interactions