Protein Folding Kinetics and Structure Prediction

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1 Protein Folding Kinetics + Structure Prediction Department of Biostatistics Johns Hopkins Bloomberg School of Public Health June 16, 29 Proteins

2 Proteins Space Resolution limit of a light microscope Glucose Ribosome Red blood cell C!C bond Hemoglobin Bacterium nm 1µm Distance [ A ]

3 Energy C!C bond Noncovalent bond Green light Glucose Thermal ATP Energy [ kcal/mol ] Protein folding and structure prediction Protein structure prediction What is the structure the folded protein adopts in the native state? Homology modeling Fold recognition Ab initio structure prediction Rosetta@home Protein folding How does the protein get from the unfolded to the folded state? Folding@home

4 Energy Profile Transition State Denatured State Native State Deviations from random coil behavior Are there site-specific deviations from random coil dimensions? Förster Resonance Energy Transfer enables us to measure the distance between two dye molecules within a certain range. This can be used to study site-specific deviations from random coil dimensions in denatured peptides. Schuler et al (22). Nature, 419 (698):

5 Deviations from random coil behavior number of photons time 3 number of photons time Deviations from random coil behavior 2 We have two underlying distributions for the green and red photons: number of green green One stemming from a peptide only having a donor dye. One stemming from a peptide being properly tagged with a donor and an acceptor dye number of red photons

6 Deviations from random coil behavior..2.4 nred nred + ngreen Deviations from random coil behavior 8 number of red photons total number of photons

7 Deviations from random coil behavior 8 number of red photons total number of photons Reproducibility Estimates of the speed of light, with confidence intervals Speed of light [million mph] year Youden W (1972). Technometrics 14: 1-11.

8 Reproducibility jl33b_11 jl33b_12 se325b_4 se325b_5 se325b_6 jl39b_ jl39b_1 jl39b_2 oc31b_6 oc31b_7 ju33b_7 ju33b_8 se326b_1 se326b_2 se326b_3 Reproducibility jl33b_11 jl33b_12 se325b_4 se325b_5 se325b_6 jl39b_ jl39b_1 jl39b_2 oc31b_6 oc31b_7 ju33b_7 ju33b_8 se326b_1 se326b_2 se326b_3

9 Photon lifetime Single Exponential Stretch Exponential 1 1 Photoelectrons 5 1 Photoelectrons Residuals Residuals time [ns] time [ns] Photon lifetime 1..8 profile likelihood h

10 Energy profile Energy profile

11 Energy profile T variant T wildtype D variant D wildtype N variant N wildtype Energy profile T variant!!g T T wildtype D variant D wildtype N variant!!g N N wildtype

12 Energy profile T variant!!g T T wildtype D variant D wildtype N variant!!g N N wildtype The Φ-value is defined as the ratio G T / G N. Energy profile T variant!!g T =!!G N T wildtype T variant T wildtype!!g T = D variant D variant D wildtype N variant D wildtype N variant!!g N!!G N N wildtype N wildtype If the part of the protein that contains the variant amino acid is fully structured in the transition state, we have G T G N, and therefore Φ 1. If the part of the protein that contains the variant amino acid is equal in denatured and the transition state, we have G T, and therefore Φ. At least this is the idea...

13 Chevron curves ln(k obs ) 2 1!1! [GuHCl] (M) h log(k obs ) = log exp log(k f )+ m f [GuHCl] RT i h + exp log(k u )+ m u [GuHCl] RT i Φ-value estimation 6 Wildtype Variant 4 log(k obs ) 2! Denaturant concentration ( GuHCl [M] ) h log(k obs ) = log exp log(k f )+ m f [GuHCl] RT i h + exp log(k u )+ m u [GuHCl] RT i h G T = RT h G N = RT i log(k wildtype ) log(k f variant ) f log(k wildtype f ) log(k wildtype ) log(k variant ) + log(k variant u f u ) i

14 Standard error se bφ = Φ s σt G T «2 2ρ G σt G T «σn G N ««2 σn + G N Web server

15 Web server Reproducibility !2 Wild type I28A I28L I28V V55A V55M V55T V55G ! ! !

16 Reproducibility "! values ".2.!.2 Wild type!v55g V55M!V55G Wild type!i28a Wild type!v55t Wild type!v55a I28A!I28V I28A!I28L V55M!V55T V55A!V55M V55A!V55G V55T!V55G Contact order C N C N

17 Contact order 5 4 log ( folding rate ) contact order Structure prediction

18 Acknowledgments UC Santa Barbara Department of Chemistry Biochemistry Evan McCarney, Miguel de los Rios, Kevin Plaxco. University of Chicago Department of Chemistry Tobin Sosnick. Rice University Department of Biochemistry and Cell Biology BK Muralidhara, Pernilla Wittung-Stafshede. UC Berkeley Department of Molecular and Cell Biology David Wildes, Susan Marqusee. University of Washington Department of Biochemistry David Baker & Lab. New York University Department of Biology and Department of Computer Science Richard Chili Bonneau.