Ryan M Harrison, Jeffrey J Gray

1. Prediction of pKa shifts in proteins using a discrete rotamer search and the Rosetta energy function Ryan M Harrison, Jeffrey J Gray Baltimore Polytechnic Institute Johns Hopkins University, Department of Chemical & Biomolecular Engineering

2. pH has profound effects on proteins • Conformational Change • Catalytic activity • Binding affinity • Stability Influenza Hemagglutinin protein Red: pH-sensitive region of hemagglutinin Harrison RM 2005

3. Rosetta Algorithm Protein Docking Protein Design Protein Folding Harrison RM 2005

4. Objective Improve computational protein structure predictions by describing how proteins react to different pH environments • Developand implement pH-sensitive modeling in Rosetta • PredictpKa shifts in several model proteins • Model pH-sensitive docking and folding • Design a protein with pH-sensitive activity Harrison RM 2005

5. Why model pH in Rosetta? • More accurate predictions… • Enhanced description of protein energy landscape • More physically relevant protein electrostatics, especially __buried charges • Extended Capabilities… • Predict pH-sensitive conformational changes Sidechain, Backbone, Rigid Body (?) • Predict docking and folding pH-optimums • Design novel pH-sensitive motifs and functions Harrison RM 2005

6. Develop the framework Improve computational protein structure predictions by describing how proteins react to different pH environments • Developand implement pH-sensitive modeling in Rosetta • PredictpKa shifts in several model proteins • Model pH-sensitive docking and folding • Design a protein with pH-sensitive activity Harrison RM 2005

7. pKa shifts pH titration (Idealized) pKa shift pKa IpKa pKa: The pH at which an amino acid equally occupies its protononated and deprotonated states Harrison RM 2005

8. Methodology Harrison RM 2005

9. Procedure + + • Allow Rosetta to dynamically select most favorable amino acid protonation state • Introduce an energy function for protonation: • 2. Allow Rosetta to sample alternate protonation states • 3. Modify amino acid parameters for each state Harrison RM 2005

10. Rosetta Score Functions van der Waals (Lennard-Jones 6-12 Potential) Solvation (Implicit Gaussian solvent-exclusion model) : Reference solvation free energy Lazaridis T, Karplus M 1999 Proteins: Struct. Funct. Genet. ε: energy well depth σij : atomic radii sums rij : interatom distance Gray JJ, et al. 2003 J. Mol. Biol. Electrostatics (Coulombic Distance Dependent di-electric) Torsion Energies (Dunbrack rotamer frequencies) Hydrogen Bonding (Orientation Dependent) Dunbrack RL, Cohen FE 1997 Protien Sci. ε: di-electric (ε = rij) q : atomic partial charge Warshel A, Russel ST 1984 Quar. Rev. Bio. Phys. Kortemme T, et al. 2003 J. Mol. Biol. Harrison RM 2005

11. Predict pKa shifts Improve computational protein structure predictions by describing how proteins react to different pH environments • Developand implement pH-sensitive modeling in Rosetta • PredictpKa shifts in several model proteins • Model pH-sensitive docking and folding • Design a protein with pH-sensitive activity (?) Harrison RM 2005

12. Model Systems Turkey Ovomucoid Inhibitor (OMTKY3) Ribonuclease A (RNaseA) Harrison RM 2005

13. Ribonuclease A pKa shift Harrison RM 2005

14. Turkey Ovomucoid Inhibitor Rosetta predicts pKa shifts with 0.77 root mean squared (rms) accuracy Red: Rosetta Prediction, Green: Experimental, Gray: IpKa (Null Value) Harrison RM 2005

15. Turkey Ovomucoid Inhibitor LYS29 ASP27 CPK: Prediction, Green: Experimental Rosetta under shifted pKa’s Harrison RM 2005

16. Ribonuclease A Model rms εprotein IpKa 0.95 Rosetta 0.62 ε=r SCCE 2.69 4 MCCE 0.99 4 MCCE 0.66 8 MCCE 0.44 20 Rosetta predicts pKa shifts with 0.62 rms accuracy Red: Rosetta Prediction, Green: Experimental, Gray: IpKa (Null Value) Harrison RM 2005

17. Ribonuclease A HIS12 CPK: Prediction, Green: Experimental Rosetta predicted pKa precisely Harrison RM 2005

18. Ribonuclease A Low pH High pH Predicted pKa :3.5 Experiment : 3.5 IpKa : 4.0 ASP 83 ASP 121 HIS 119 Harrison RM 2005

19. Conclusions Rosetta can now estimate the local effects of pH (i.e. pKa shifts) in small globular proteins Developed an approach to model pH Accountedfor significant pKa shifts using only side-chain movement Extended the modeling capabilities of Rosetta Increased the overall accuracy of Rosetta(?) Harrison RM 2005

20. Work in Progress • Optimization and calibration on a set of over 200 experimentally determined pKa shifts from 15 proteins • pH-sensitive Docking and Folding • Scientific and performance benchmark on 55 pKa’s from staphylococcal nuclease mutants (in collaboration with Garcia-Moreno lab) Staph. Nuclease at pH 7.2 -helical nano-gel Harrison RM 2005

21. pH-sensitive docking Improve computational protein structure predictions by describing how proteins react to different pH environments • Developand implement pH-sensitive modeling in Rosetta • PredictpKa shifts in several model proteins • Model pH-sensitive docking and folding in several model proteins • Design a protein with pH-sensitive activity (?) Harrison RM 2005

22. Acknowledgements National Institutes of Health National Institute of General Medical Sciences Gray Lab Dr. Jeffrey J. Gray Harden Lab Dr. James L. Harden Baltimore Polytechnic Institute The Ingenuity Project Ms. Charlotte V. Saylor Robert M Harrison Sharon A Harrison Harrison RM 2005

24. Harrison RM 2005

25. What could proteins do for you? Drug Design Imagine targeted treatments for devastating diseases… Blue: antibody, Red: prediction, Green: experimental Antibody binding to ovine prion. Figure from: M Daily, Pymol

26. Rosetta Score Functions: Electrostatics Glutamate Partial Charges Lysine Partial Charges +   + pKa ~ 4.40 pKa ~ 10.40 • Electrostatics require electron density parameters • Predictions were made using both a Generalized Born (GB) and Coulombic electrostatic model. • GB electrostatics are more accurate than Coulombic electrostatics, but also more computationally expensive Harrison RM 2005

27. Rosetta Procedural Detail Rosetta Flowchart Low Resolution_1. Rigid Body Move _ _2. Monte Carlo Minimization High Resolution_1. Sample all side chain positions in ___Dunbrack rotamer set *2. Sample alternate protonation ___state rotamers _ _3. Monte Carlo Minimization Post-Processing *1. External Scripts to determine side ___chain pKa values Start Position Low Resolution Monte Carlo High-Resolution Refinement* 10n Post-Processing* Predictions* * Modified to introduce pH-sensitive side chain modeling or pKa predictions in Rosetta Harrison RM 2005