Transcript Document
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 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 Rosetta Algorithm Protein Folding Protein Docking Protein Design Harrison RM 2005 Objective Improve computational protein structure predictions by describing how proteins react to different pH environments Develop and implement pH-sensitive modeling in Rosetta Predict pKa shifts in several model proteins Model pH-sensitive docking and folding Design a protein with pH-sensitive activity Harrison RM 2005 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 Develop the framework Improve computational protein structure predictions by describing how proteins react to different pH environments Develop and implement pH-sensitive modeling in Rosetta Predict pKa shifts in several model proteins Model pH-sensitive docking and folding Design a protein with pH-sensitive activity Harrison RM 2005 pKa shifts pH titration (Idealized) pKa IpKa pKa shift pKa: The pH at which an amino acid equally occupies its protononated and deprotonated states pKa pH if :[HA] [ A] Harrison RM 2005 Methodology Glocal Harrison RM 2005 Glocal Procedure Allow Rosetta to dynamically select most favorable amino acid protonation state 1. Introduce an energy function for protonation: G solution protonation zRT (IpKa pH )ln10 2. Allow Rosetta to sample alternate protonation + states IpKa 10.4 + 3. Modify amino acid parameters for each state Harrison RM 2005 Rosetta Score Functions van der Waals (Lennard-Jones 6-12 Potential) G vdw ij ij12 ij6 ij 12 2 6 r r ij ij ε : energy well depth Solvation (Implicit Gaussian solvent-exclusion model) G slv i G ref i fi (rij)Vj j i Giref : Reference solvation free energy Lazaridis T, Karplus M 1999 Proteins: Struct. Funct. Genet. σij : atomic radii sums rij : interatom distance Torsion Energies Gray JJ, et al. 2003 J. Mol. Biol. (Dunbrack rotamer frequencies) Electrostatics (Coulombic Distance Dependent di-electric) G elec ij ε : di-electric (ε = rij) 332qi q j rij q : atomic partial charge Warshel A, Russel ST 1984 Quar. Rev. Bio. Phys. Harrison RM 2005 Gdun log Pi (rot | i i) i Dunbrack RL, Cohen FE 1997 Protien Sci. Hydrogen Bonding (Orientation Dependent) G hbond kT ln(hbprobi ) i 1 Kortemme T, et al. 2003 J. Mol. Biol. Predict pKa shifts Improve computational protein structure predictions by describing how proteins react to different pH environments Develop and implement pH-sensitive modeling in Rosetta Predict pKa shifts in several model proteins Model pH-sensitive docking and folding Design a protein with pH-sensitive activity (?) Harrison RM 2005 Model Systems Turkey Ovomucoid Inhibitor (OMTKY3) Ribonuclease A (RNaseA) Harrison RM 2005 Ribonuclease A pKa shift pK a pH if :[ HA] [ A1 ] Harrison RM 2005 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 Turkey Ovomucoid Inhibitor LYS29 ASP27 CPK: Prediction, Green: Experimental Rosetta under shifted pKa’s Harrison RM 2005 Ribonuclease A Model rms 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 εprotein Ribonuclease A HIS12 CPK: Prediction, Green: Experimental Rosetta predicted pKa precisely Harrison RM 2005 Ribonuclease A Low pH High pH Predicted pKa : 3.5 ASP 83 ASP 121 HIS 119 Harrison RM 2005 Experiment : 3.5 IpKa : 4.0 Conclusions Rosetta can now estimate the local effects of pH (i.e. pKa shifts) in small globular proteins Developed an approach to model pH Accounted for significant pKa shifts using only side-chain movement Extended the modeling capabilities of Rosetta Increased the overall accuracy of Rosetta(?) Harrison RM 2005 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) -helical nano-gel Harrison RM 2005 Staph. Nuclease at pH 7.2 pH-sensitive docking Improve computational protein structure predictions by describing how proteins react to different pH environments Develop and implement pH-sensitive modeling in Rosetta Predict pKa 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 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 Harrison RM 2005 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 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 Rosetta Procedural Detail Rosetta Flowchart Start Position Low Resolution Monte Carlo High-Resolution Refinement* 10n Post-Processing* Predictions* 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 * Modified to introduce pH-sensitive side chain modeling or pKa predictions in Rosetta Harrison RM 2005