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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
University of Pennsylvania Department of Bioengineering
Outline
Backgrounds
Hypothesis and Specific aims
Experimental design and preliminary results
University of Pennsylvania Department of Bioengineering
ErbB Family Receptors and the Signaling PathwaysYarden and Sliwkowski, nature reviews, 2001
University of Pennsylvania Department of Bioengineering
Tyrosine Phosphorylation and Receptor Inhibition
Zhang and Kuriyan,Cell, 2006
University of Pennsylvania Department of Bioengineering
EGFR Kinase Domain Mutations
Choi and Lemmon, Oncogene, 2007 Zhang and Kuriyan,Cell, 2006Carey and Sliwkowski, Cancer Res, 2006
University of Pennsylvania Department of Bioengineering
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
University of Pennsylvania Department of Bioengineering
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.
University of Pennsylvania Department of Bioengineering
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.
University of Pennsylvania Department of Bioengineering
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.
University of Pennsylvania Department of Bioengineering
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
University of Pennsylvania Department of Bioengineering
Erlotinib Parameterization (2)
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
University of Pennsylvania Department of Bioengineering
Erlotinib Parameterization (3)
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.
University of Pennsylvania Department of Bioengineering
Erlotinib Parameterization (4)
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
University of Pennsylvania Department of Bioengineering
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*.
University of Pennsylvania Department of Bioengineering
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
University of Pennsylvania Department of Bioengineering
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.
University of Pennsylvania Department of Bioengineering
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.
University of Pennsylvania Department of Bioengineering
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.
University of Pennsylvania Department of Bioengineering
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:
University of Pennsylvania Department of Bioengineering
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, …
University of Pennsylvania Department of Bioengineering
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.
University of Pennsylvania Department of Bioengineering
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).
University of Pennsylvania Department of Bioengineering
Kinase-Substrate interactions ------ Preliminary results and future work
L858R unphosphorylated EGFR
Binding with 106872GS6
University of Pennsylvania Department of Bioengineering
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
University of Pennsylvania Department of Bioengineering
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.
University of Pennsylvania Department of Bioengineering
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.
University of Pennsylvania Department of Bioengineering
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)
University of Pennsylvania Department of Bioengineering
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
University of Pennsylvania Department of Bioengineering
Prepare the Enzyme-Substrate System
Blue: 2GS6 bisubstrate;
Pink: ATP conformation in 2ITX;
Yellow: proposed peptide conformation in aim 2;
University of Pennsylvania Department of Bioengineering
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
University of Pennsylvania Department of Bioengineering
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
University of Pennsylvania Department of Bioengineering
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
University of Pennsylvania Department of Bioengineering
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.
University of Pennsylvania Department of Bioengineering
Thanks.
University of Pennsylvania Department of Bioengineering
Mutations increase kinase activities
Yun et al., (Eck) Cancer Cell (2007)
Zhang et al., (Kuriyan) Cell (2006)
University of Pennsylvania Department of Bioengineering
Structural Studies of EGFRTK Active Site
αC-helix
peptideC-loop
ATP
GLU738
LYS721
ASP813
ASP831
MET769
G-loop
N-lobe
C-lobe
A-loop