1. 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 Liu Advisor: Dr. Ravi Radhakrishnan Department of Bioengineering University of Pennsylvania

2. Department of Bioengineering Outline Backgrounds Hypothesis and Specific aims Experimental design and preliminary results

3. University of Pennsylvania Department of Bioengineering ErbB Family Receptors and the Signaling Pathways Yarden and Sliwkowski, nature reviews, 2001

4. University of Pennsylvania Department of Bioengineering Tyrosine Phosphorylation and Receptor Inhibition Zhang and Kuriyan,Cell, 2006

5. University of Pennsylvania Department of Bioengineering EGFR Kinase Domain Mutations Choi and Lemmon, Oncogene, 2007 Zhang and Kuriyan,Cell, 2006 Carey and Sliwkowski, Cancer Res, 2006

6. 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.

7. 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.

8. 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.

9. University of Pennsylvania Department of Bioengineering MD Simulation and CHARMM Potential Energy Molecular Dynamic (MD) simulations: CHARMM potential energy: Essential part is the potential energy function.

10. University of Pennsylvania Department of Bioengineering Erlotinib Parameterization (1) 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.

11. University of Pennsylvania Department of Bioengineering Erlotinib Parameterization (2) Refine Partial charges manually.

12. 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: NGRID is the number of potential values calculated in the surface. and are potential values from CHARMM and GAUSSIAN.

13. University of Pennsylvania Department of Bioengineering Erlotinib Parameterization (4) Refine force constants to reproduce vibrational eigenvalues and eigenvectors. Using genetic algorithm to automatically minimum the merit function: Vaiana, Computer Physics Communications, 2005.

14. University of Pennsylvania Department of Bioengineering Erlotinib Parameterization (5) ------ Preliminary results Water interactionsInteraction Energies (Kcal/mol) Distance (Å) GAUSSIANCHARMMGAUSSIANCHARMM N2…HOH-6.69-6.612.131.91 N3…HOH_2-5.33-5.32.322.01 N1H…OHH_2-6.52 2.44152.63 Dipole moment (Debye) GAUSSIANCHARMM 4.8685.07 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*.

15. University of Pennsylvania Department of Bioengineering Erlotinib Parameterization (6) ------ Preliminary results Frequency matching Potential surface fitting Genetic algorithm efficiency

16. 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.

17. 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.

18. 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.

19. University of Pennsylvania Department of Bioengineering Methods: Binding Free Energy Calculation Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA): Electrostatic solvation energy: Poisson- Boltzmann equation. Nonpolar term: depend on surface area. Sitkoff and Honig 1993 AUTODOCK:

20. 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, …

21. University of Pennsylvania Department of Bioengineering Kinase-Inhibitor Interactions ------ Preliminary results and future work WT L858R Crystal 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.

22. 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).

23. University of Pennsylvania Department of Bioengineering Kinase-Substrate interactions ------ Preliminary results and future work L858R unphosphorylated EGFR Binding with 10687 2GS6

24. University of Pennsylvania Department of Bioengineering Kinase-Subtrate interactions ------ Preliminary results and future work Substrate s Approximate Binding Energy(Kcal/mol) Y1068Y1173Y992 Wildtype-5.42-4.69-4.7 L834R mutant -5.93-3.78-5.91 Liu, Purvis and Radhakrishnan,2007

25. 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 solvation140.5 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.

26. 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.

27. 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)

28. University of Pennsylvania Department of Bioengineering Proposed Catalytic Mechanism for EGFRTK based on cAMP

29. 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;

30. University of Pennsylvania Department of Bioengineering QM/MM Calculation Molecular 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 PEPTIDE MG

31. 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.

32. University of Pennsylvania Department of Bioengineering Free Energy Landscape Along Reaction Coordinates Umbrella sampling along two coordinates. 25 windows are sampled as a uniform 5×5 grid along r 1 and r 2. 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

33. 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.

34. University of Pennsylvania Department of Bioengineering Thanks.

35. University of Pennsylvania Department of Bioengineering Mutations increase kinase activities Yun et al., (Eck) Cancer Cell (2007) Zhang et al., (Kuriyan) Cell (2006)

36. 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