3D Protein Structures from Experiment and Computational Molecular Biophysics
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1 3D Protein Structures from Experiment and Computational Molecular Biophysics July 29 th, 2014 Michael J. Schnieders Biochemistry & Biomedical Engineering
2 Stephen Jacob Julia LuCore Litman Park Ava Lynn Ian Nessler Will Tollefsen Shibo Gao College of Medicine College of Engineering 7/30/2014 3D Protein Refinement 2
3 Overview Introduction to Protein Force Fields Application to Experimental Biomolecular X-ray Refinement Application to the End-Game of 3D Protein Homology Modeling 7/30/2014 3D Protein Refinement 3
4 A Range of Resolutions for 3D Protein Modeling Atomic Resolution Coarse Grain (Elcock) 7/30/2014 3D Protein Refinement 4
5 A Range of Resolutions for 3D Protein Modeling Electronic Resolution Coarse Grain (Elcock) 7/30/2014 3D Protein Refinement 5
6 Protein Modeling that Should Benefit from Advanced Polarizable Force Fields 1. Structure Determination (X-ray, NMR) 2. Protein Structure Prediction and Design Approach: New algorithms using the polarizable AMOEBA force field 7/30/2014 3D Protein Refinement 6
7 Introduction to Force Fields for 3D Structure Determination A Force Field is the Potential Energy U(X) as a Function of Atomic Coordinates X U bonds = å k b ( b - b 0 ) 2 bonds ( ) 2 å angles U angles = k q q -q 0 θ Φ b Acetamide å torsions ( ) +1 U torsions = k f éë cos nf +d ù û 7/30/2014 3D Protein Refinement 7
8 The Non-Bonded Terms of Previous Generation Force Fields U vdw = A å ij 12 r - B ij 6 pairs ij r ij?? 60 o U elec = å pairs q i q j r ij r ij Hydrogen-Bond U vdw Acetamide Dimer Lone Pair Electrons 7/30/2014 3D Protein Refinement 8
9 Aspherical Electron Density can be Modeled Using Atomic Multipoles M t = é ëq,d x,d y, d z,q xx,q xy,q xz,q yy,q yz,q zz é ù 1 x j y j z j T ij = ê ê ê ê ê ê ê ê ê ê ê ê ê ê ë x i x i x j x i y j x i z j y i y i x j y i y j y i z j z i z i x j z i y j z i z j å U perm elec = pairs M i t T ij M j ú ú ú ú ú ú æ ú úç è ú ú ú ú ú ú û ù û 1 ö ø r ij 7/30/2014 3D Protein Refinement 9
10 The Electron Cloud is Polarized by the Electric Field of the Environment Vapor Dipole Moment: 3.9 Debye Crystalline Dipole Moment: 5.8 Debye (50% increase) - + 7/30/2014 3D Protein Refinement 10
11 - + The Polarization Response Can Be Modeled Using Induced Dipoles α: Atomic Polarizability u: Induced Dipole E: Electric Field u i = a i E i æ u i = a å ( 1 i T ) 2 ç ij M j + T ik è j¹i å k¹i ( ) u k ö ø α H α N α C α O pol U elec = å u i E i atoms 7/30/2014 3D Protein Refinement 11
12 Limitations of Fixed Atomic Charges Addressed by Polarizable Multipoles U elec = å pairs q i q j r ij U elec = U perm pol elec +U elec = åm t i T ij M j - 1 pairs 2 Permanent Multipoles Induced Dipoles å u i E i atoms Fixed Charges (Rosetta) Spherically Symmetric No Polarization Response Polarizable Atomic Multipole Optimized Energetics for Biomolecular Applications (AMOEBA) Ponder, J. W.; Wu, C.; Ren, P.; Pande, V. S.; Chodera, J. D.; Schnieders, M. J.; Haque, I.; Mobley, D. L.; Lambrecht, D. S.; DiStasio, R. A.; Head-Gordon, M.; Clark, G. N. I.; Johnson, M. E.; Head-Gordon, T. The Journal of Physical Chemistry B 2010, 114, /30/2014 3D Protein Refinement 12
13 Q. How Much Does the Dipole Moment of a Protein Change Between Vacuum & Solvent? a) 0% (a fixed charge force field) b) 10% c) 25% d) 50% (a water molecule) 7/30/2014 3D Protein Refinement 13
14 Continuum Electrostatics via the Linearized Poisson-Boltzmann Equation ε protein = 1 Warwicker, J.; Watson, H. C., Calculation of the electric potential in the active site cleft due to alpha-helix dipoles. J. Mol. Biol. 1982, 157 (4), Im, W.; Beglov, D.; Roux, B., Continuum solvation model: Computation of electrostatic forces from numerical solutions to the Poisson-Boltzmann equation. Comput. Phys. Commun. 1998, 111 (1-3), Smooth Boundary Between Protein and Solvent ε solvent = 78.3 Baker, N. A.; Sept, D.; Joseph, S.; Holst, M. J.; McCammon, J. A., Electrostatics of nanosystems: application to microtubules and the ribosome. Proc. Natl. Acad. Sci. U.S.A. 2001, 98 (18), /30/2014 3D Protein Refinement 14
15 The Dipole Moment of Proteins in Vacuum and Solvent (in Debye) Crambin (CRN) Engrailed Homeodomain (ENH) Full Sequence Design (FSV) Immunoglobulinbinding Domain (PGB) Villin Headpiece (VII) Schnieders, M. J.; Baker, N. A.; Ren, P. Y.; Ponder, J. W., Polarizable atomic multipole solutes in a Poisson-Boltzmann continuum. J. Chem. Phys. 2007, 126 (12). 7/30/2014 3D Protein Refinement 15
16 Polarizable Multipole Algorithms in Force Field X (GPLv3) 7/30/2014 3D Protein Refinement 16
17 The Computation of Aspirin Solid- Vapor Phase Transition Free Energy 7/30/2014 3D Protein Refinement 17
18 7/30/2014 3D Protein Refinement 18
19 Selected Five Compounds with Expt. Sublimation Free Energies Acetanilide Paracetamol B Methylparaben Ethylparaben Phenacetin Perlovich, G. L.; Volkova, T. V.; Bauer-Brandl, A., Polymorphism of paracetamol. J. Therm. Anal. Calorim. 2007, 89 (3), /30/2014 3D Protein Refinement 19
20 Lattice Potential Energy, End-State (ES) Approximation and GAUCHE Free Energy Absolute Organic Crystal Thermodynamics: Growth of the Asymmetric Unit into a Crystal via Alchemy, Jooyeon Park, Ian Nessler, Brian McClain, Dainius Macikenas, Jonas Baltrusaitis and Michael J. Schnieders, /30/2014 3D Protein Refinement 20
21 Polarizable Force Field Example: Biomolecular X-ray Refinement 7/30/2014 3D Protein Refinement 21
22 The Resolution of Biomolecular Diffraction Data Ranges from ~1-4 Å All heavy atoms are visible As data degrades, protein electrostatics becomes critical Courtesy of the Protein Databank at 7/30/2014 3D Protein Refinement 22
23 Symmetry and Parallelization Permit AMOEBA Computations on Any Organic Crystal Symmetry Mate Asymmetric Unit Symmetry Mate Symmetry Mate Explicit support for crystal symmetry Parallelization across CPU cores and a GPU 1 CPU core vs. 8 CPU cores + GPU 24x Average Speed-Up Schnieders, M. J.; Fenn, T. D.; Pande, V. S., Polarizable atomic multipole X-ray refinement: Particle mesh Ewald electrostatics for macromolecular crystals. J. Chem. Theory Comput. 2011, 7 (4), /30/2014 3D Protein Refinement 23
24 S-Adenosyl-Homocysteine Bound to DNA-Methyltransferase 1 (3PTA, 3.6 Å) Syeda, F.; Fagan, R. L.; Wean, M.; Avvakumov, G. V.; Walker, J. R.; Xue, S.; Dhe-Paganon, S.; Brenner, C., The Replication Focus Targeting Sequence (RFTS) Domain Is a DNA-competitive Inhibitor of Dnmt1. J. Biol. Chem. 2011, 286 (17), /30/2014 3D Protein Refinement 24
25 MolProbity Helps to Validate X-ray Crystallography Models Against Chemistry Chen, V. B.; Arendall, W. B.; Headd, J. J.; Keedy, D. A.; Immormino, R. M.; Kapral, G. J.; Murray, L. W.; Richardson, J. S.; Richardson, D. C. Acta Crystallographica Section D 2009, 66, 12. 7/30/2014 3D Protein Refinement 25
26 The MolProbity Report for DNA-Methyltransferase 1 (Dnmt1) The MolProbity score is calibrated to reflect the resolution of the data Do structures refined with AMOEBA score higher than their actual resolution? 7/30/2014 3D Protein Refinement 26
27 Electrostatics Drive Dnmt1 X-ray Refinement in Force Field X 7/30/2014 3D Protein Refinement 27
28 Dnmt1 Now Shows 9 H-Bonds to S-Adenosyl-Homocysteine Model Clashes per 1000 Atoms Backbone Outliers Backbone Favored MolProbity Score Original 59.9 (29 th ) 3.1% 85.2% 3.65 (57 th ) Force Field X 3.0 (100 th ) 1.0% 90.6% 2.41 (99 th ) Shi, Y.; Schnieders, M. J.; Piquemal, J. P.; Ren, P., Polarizable Force Fields for Biomolecular Modeling. In Reviews in Computational Chemistry, 28 ed.; Lipkowitz, K. B., Ed. Springer: /30/2014 3D Protein Refinement 28
29 Averages for 17 Biomolecular Refinements (2.7 Å Mean Resolution) Method R (%) R free (%) MolProbity Score MolProbity Percentile PDB Refmac Phenix CNS Force Field X Improving MolProbity Score Fenn, T. D.; Schnieders, M. J., Polarizable atomic multipole X-ray refinement: Weighting schemes for macromolecular diffraction. Acta Crystallogr. D 2011, 67 (11), /30/2014 3D Protein Refinement 29
30 Improvement of 5 MAP Kinase Structures Example: JNK2 with inhibitor (BIRB-796) Original Force Field X Source R/R free MolProbity Protein Databank (2.5 Å) 23.0 / (51 st ) Force Field X 20.3 / (97 th ) Schnieders, M. J.; Kaoud, T. S.; Yan, C.; Dalby, K. N.; Ren, P., Computational insights for the discovery of non-atp competitive inhibitors of MAP kinases. Curr. Pharm. Des. 2012, 18 (9), /30/2014 3D Protein Refinement 30
31 PCNA: Involved in DNA replication, repair & recombination J-Loop Dimer Interface DNA goes in here Back Face Front Face Dieckman, L. M.; Boehm, E. M.; Hingorani, M. M.; Washington, M. T., Distinct Structural Alterations in Proliferating Cell Nuclear Antigen Block DNA Mismatch Repair. Biochemistry 2013, 52 (33), /30/2014 3D Protein Refinement 31
32 Mutations can destabilize the interface J-Loop βh 1 βi 1 Wild Type E113G βd 1 G178S 7/30/2014 3D Protein Refinement 32
33 Averages Over 6 PCNA Data Sets ( Å) PDB PDB_Redo Force Field X Local Opt. ClashScore Clash Percentile MolProbity Score MolProbity Percentile Poor Rotamers Ramachandran Outliers Ramachandran Favored R free Ouch! 7/30/2014 3D Protein Refinement 33
34 Dead-End Elimination can Globally Optimize Protein Side-Chains & Sequence, but 1) Non-Polarizable 2) No Continuum Solvent Desmet, J., et al. (1992). "The dead-end elimination theorem and its use in protein side-chain positioning." Nature 356(6369): /30/2014 3D Protein Refinement 34
35 Existing Dead-End Elimination Rotamer Elimination (DEE) Criteria If rotamer α of residue i cannot produce a lower energy that rotamer β, then rotamer r i α is eliminated Rotamer Pair Elimination Desmet, J., et al. (1992). "The dead-end elimination theorem and its use in protein side-chain positioning." Nature 356(6369): /30/2014 3D Protein Refinement 35
36 Fig. C J-Loop Optimization in FFX Lengthens Beta- Strands, Separating the Interface E113G (FFX) βh1 E113G (3GPM) βi1 βd1 Protein Electrostatic Network Optimization via Dead End Elimination: Improved PCNA Structures Yield Functional Insights, LuCore, Litman, Tollefson, Lynn, Fenn, Powers, Washington, Schnieders (in prep.) 7/30/2014 3D Protein Refinement 36
37 Figure D J-Loop Wild Type J-Loop E113G Protein Electrostatic Network Optimization via Dead End Elimination: Improved PCNA Structures Yield Functional Insights, LuCore, Litman, Tollefson, Lynn, Fenn, Powers, Washington, Schnieders (in prep.) 7/30/2014 3D Protein Refinement 37
38 Averages Over 6 PCNA Data Sets ( Å) PDB PDB_Redo Force Field X Local Opt. DEE + Local ClashScore Clash Percentile MolProbity Score MolProbity Percentile Poor Rotamers Ramachandran Outliers Ramachandran Favored R free Protein Electrostatic Network Optimization via Dead End Elimination: Improved PCNA Structures Yield Functional Insights, LuCore, Litman, Tollefson, Lynn, Fenn, Powers, Washington, Schnieders (in prep.) 7/30/2014 3D Protein Refinement 38
39 Deafness Variation Database Wild Type Protein Structures Protein and model residue range Original Homology Models MolProbity Score MolProbity Percentile Force Field X Refined MolProbity Score MolProbity Percentile DFNB DFNB DFNB OTOF GPIC USH1C USH1C Mean /30/2014 3D Protein Refinement 39
40 Conclusions of 3D Protein Refinement with AMOEBA Dead-end elimination extended to many-body potentials (force fields, QM & continuum solvents) PCNA optimization improved MolProbity (to 98 th percentile) and lowered R free by 4-5% Refinement Approach in Force Field X: Polarizable AMOEBA Force Field Parallelized Long-Range Electrostatics (PME) Optimization Methods (L-BFGS, SA, DEE) Protein Electrostatic Network Optimization via Dead End Elimination: Improved PCNA Structures Yield Functional Insights, LuCore, Litman, Tollefson, Lynn, Fenn, Powers, Washington, Schnieders (in prep.) 7/30/2014 3D Protein Refinement 40
41 Acknowledgments Many-Body Dead-End Elimination Lab members Stephen LuCore, Jacob Litman, Will Tollefson and Ava Lynn Prof. M. Todd Washington & Kyle Powers (U of Iowa) Dr. Timothy Fenn (Merck) Thanks also to: Jeff Blaney (Genentech) Tom Darden (Open Eye) JP Piquemal (Sorbonne U.) Crystal Thermodynamics Lab members Julia Park & Ian Nessler Brian McClain (Vertex) Polarizable AMOEBA Force Field Prof. Jay Ponder (WUSTL) Prof. Pengyu Ren (UT Austin) Charlie Brenner (U of Iowa) Richard Smith (U of Iowa) Wei Yang (Florida State) CHE R01DC T32GM T32GM /30/2014 3D Protein Refinement 41
Dr. Michael J. Schnieders
Dr. Departments of Biomedical Engineering and Biochemistry The University of Iowa 51 Newton Rd., 4-403 Bowen Science Building Iowa City, IA 52358 (650) 995-3526 Michael-schnieders@uiowa.edu http://ffx.kenai.com
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