Ryan M Harrison, Jeffrey J Gray

Prediction of pKa shifts in proteins using a discrete rotamer search and the Rosetta

energy functionRyan M Harrison, Jeffrey J Gray

Baltimore Polytechnic Institute

Johns Hopkins University, Department of Chemical & Biomolecular Engineering

Influenza Hemagglutinin protein

Conformational Change

Catalytic activity

Binding affinity

Stability

Red: pH-sensitive region of hemagglutinin

pH has profound effects on proteins

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 DevelopDevelop and implement pH-sensitive modeling in Rosetta

PredictPredict pKa shifts in several model proteins

ModelModel pH-sensitive docking and folding

DesignDesign a protein with pH-sensitive activity Harrison RM 2005

Why model pH in Rosetta?

More accurate predictions…More accurate predictions… Enhanced description of protein energy landscape

More physically relevant protein electrostatics, especially __buried charges

Extended Capabilities…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

Harrison RM 2005

Improve computational protein structure predictions by describing how proteins react to different pH environments DevelopDevelop and implement pH-sensitive modeling in Rosetta

PredictPredict pKa shifts in several model proteins

ModelModel pH-sensitive docking and folding

DesignDesign a protein with pH-sensitive activity

pKa: The pH at which an amino acid equally occupies its protononated and deprotonated states

pKa shifts

Harrison RM 2005

pH titration (Idealized)

IpKapKa pKa shift

:[ ] [ ]apK pH if HA A

Methodology

localG localG

Harrison RM 2005

Procedure

Allow Rosetta to dynamically select most favorable amino acid protonation state

1. 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

( ) ln10solutionprotonation aG zRT IpK pH

+

+

10.4aIpK

Rosetta Score Functions

Harrison RM 2005

ε : energy well depth σij : atomic radii sums rij : interatom distanceGray JJ, et al. 2003 J. Mol. Biol.

12 6

12 62ij ijvdw

ij ijij ij

Gr r

van der Waals (Lennard-Jones 6-12 Potential)

ε : di-electric (ε = rij) q : atomic partial chargeWarshel A, Russel ST 1984 Quar. Rev. Bio. Phys.

332 i jelecij

ij

q qG

r

Electrostatics (Coulombic Distance Dependent di-

electric) Dunbrack RL, Cohen FE 1997 Protien Sci.

log ( | )duni i i

i

G P rot

Torsion Energies (Dunbrack rotamer frequencies)

Kortemme T, et al. 2003 J. Mol. Biol.1

ln( )hbondi

i

G kT hbprob

Hydrogen Bonding

(Orientation Dependent)

: Reference solvation free energy Lazaridis T, Karplus M 1999 Proteins: Struct. Funct. Genet.

j

ij

ijirefi

slvi VrfGG

)(

refiG

Solvation (Implicit Gaussian solvent-exclusion

model)

Predict pKa shifts

Harrison RM 2005

Improve computational protein structure predictions by describing how proteins react to different pH environments DevelopDevelop and implement pH-sensitive modeling in Rosetta

PredictPredict pKa shifts in several model proteins

ModelModel pH-sensitive docking and folding

DesignDesign a protein with pH-sensitive activity (?)

Model Systems

Turkey Ovomucoid Inhibitor (OMTKY3)

Ribonuclease A (RNaseA)

Harrison RM 2005

Ribonuclease A

1:[ ] [ ]apK pH if HA A

pKa shift

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Turkey Ovomucoid Inhibitor

Rosetta predicts pKa shifts with 0.77 root mean squared (rms) accuracy

Red: Rosetta Prediction, Green: Experimental, Gray: IpKa (Null Value)

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Turkey Ovomucoid Inhibitor

CPK: Prediction, Green: Experimental

Rosetta under shifted pKa’s

ASP27

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LYS29

Ribonuclease A

Rosetta predicts pKa shifts with 0.62 rms accuracy

Red: Rosetta Prediction, Green: Experimental, Gray: IpKa (Null Value)

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Model rms εprotein

IpKa 0.95

Rosetta 0.62ε=r

SCCE 2.69 4

MCCE 0.99 4

MCCE 0.66 8

MCCE 0.44 20

Ribonuclease A

HIS12

CPK: Prediction, Green: Experimental

Rosetta predicted pKa preciselyHarrison RM 2005

Ribonuclease A

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ASP 121

ASP 83

Predicted pKa : 3.5

Experiment : 3.5

IpKa : 4.0

Low pH High pH

HIS 119

Conclusions

Rosetta can now estimate the local effects of pH (i.e. pKa shifts) in small globular proteins

Harrison RM 2005

DevelopedDeveloped an approach to model pH

AccountedAccounted for significant pKa shifts using only side-chain movement

ExtendedExtended the modeling capabilities of Rosetta

IncreasedIncreased the overall accuracy of Rosetta(?)

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)

Harrison RM 2005

Staph. Nuclease at pH 7.2

-helical nano-gel

pH-sensitive docking

Improve computational protein structure predictions by describing how proteins react to different pH environments DevelopDevelop and implement pH-sensitive modeling in Rosetta

PredictPredict pKa shifts in several model proteins

ModelModel pH-sensitive docking and folding in several model proteins

DesignDesign a protein with pH-sensitive activity (?)

Harrison RM 2005

Acknowledgements

Harrison RM 2005

National Institutes of HealthNational Institute of General Medical Sciences

Gray LabDr. Jeffrey J. Gray

Harden LabDr. James L. Harden

Baltimore Polytechnic Institute

The Ingenuity ProjectMs. Charlotte V. Saylor

Robert M HarrisonSharon A Harrison

Harrison RM 2005

Figure from: M Daily, Pymol

What could proteins do for you?

Drug DesignImagine targeted treatments for devastating diseases…

Blue: antibody, Red: prediction, Green: experimental

Antibody binding to ovine prion.

Rosetta Score Functions: Electrostatics

+

pKa ~ 4.40

Glutamate Partial Charges Lysine Partial Charges

+

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

Low Resolution Monte Carlo

Start Position

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

Rosetta Rosetta FlowchartFlowchart

** Modified to introduce pH-sensitive side chain modeling or pKa predictions in RosettaHarrison RM 2005