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High-Resolution Free-Energy Landscape Analysis of α-Helical Protein Folding: HP35 and Its Double Mutant.

Banushkina PV, Krivov SV - J Chem Theory Comput (2013)

Bottom Line: Natl.Four different estimations of the pre-exponential factor for both proteins give k 0 (-1) values of approximately a few tens of nanoseconds.Our analysis gives detailed information about folding of the proteins and can serve as a rigorous common language for extensive comparison between experiment and simulation.

View Article: PubMed Central - PubMed

Affiliation: Astbury Center for Structural Molecular Biology, Faculty of Biological Sciences, University of Leeds , Leeds LS2 9JT, United Kingdom.

ABSTRACT
The free-energy landscape can provide a quantitative description of folding dynamics, if determined as a function of an optimally chosen reaction coordinate. Here, we construct the optimal coordinate and the associated free-energy profile for all-helical proteins HP35 and its norleucine (Nle/Nle) double mutant, based on realistic equilibrium folding simulations [Piana et al. Proc. Natl. Acad. Sci. U.S.A. 2012, 109, 17845]. From the obtained profiles, we directly determine such basic properties of folding dynamics as the configurations of the minima and transition states (TS), the formation of secondary structure and hydrophobic core during the folding process, the value of the pre-exponential factor and its relation to the transition path times, the relation between the autocorrelation times in TS and minima. We also present an investigation of the accuracy of the pre-exponential factor estimation based on the transition-path times. Four different estimations of the pre-exponential factor for both proteins give k 0 (-1) values of approximately a few tens of nanoseconds. Our analysis gives detailed information about folding of the proteins and can serve as a rigorous common language for extensive comparison between experiment and simulation.

No MeSH data available.


Related in: MedlinePlus

Model potential U1(x) = 2.5 cos[(4xπ/50) –2π] (bluecrosses) and the free-energy profile along the reaction coordinatedetermined from the trajectory, generated by simulating MC on thepotential (red line). The model potential U1(x) is shifted along the axes.
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fig10: Model potential U1(x) = 2.5 cos[(4xπ/50) –2π] (bluecrosses) and the free-energy profile along the reaction coordinatedetermined from the trajectory, generated by simulating MC on thepotential (red line). The model potential U1(x) is shifted along the axes.

Mentions: Consider a 1D model system with potential energy profile U1(x) = 2.5 cos[(4xπ/50) – 2π] for x ∈ [0,50](see Figure A1). The trajectory was generatedby simulating Metropolis Monte Carlo (MC) dynamics with Gaussian stepscorresponding to D(x) = 1 for 1.5× 106 steps. The time step is considered to be 1 nsfor straightforward comparison with the MD simulations. Figure A1 shows the free-energy profile along the reactioncoordinate (rescaled such that D(x) = 1) determined from the generated trajectory. The FEP is verysimilar to the model potential U1(x). The small difference is due to a slight underestimationof the diffusion coefficient, which leads to expansion of the reactioncoordinate during rescaling.


High-Resolution Free-Energy Landscape Analysis of α-Helical Protein Folding: HP35 and Its Double Mutant.

Banushkina PV, Krivov SV - J Chem Theory Comput (2013)

Model potential U1(x) = 2.5 cos[(4xπ/50) –2π] (bluecrosses) and the free-energy profile along the reaction coordinatedetermined from the trajectory, generated by simulating MC on thepotential (red line). The model potential U1(x) is shifted along the axes.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3860327&req=5

fig10: Model potential U1(x) = 2.5 cos[(4xπ/50) –2π] (bluecrosses) and the free-energy profile along the reaction coordinatedetermined from the trajectory, generated by simulating MC on thepotential (red line). The model potential U1(x) is shifted along the axes.
Mentions: Consider a 1D model system with potential energy profile U1(x) = 2.5 cos[(4xπ/50) – 2π] for x ∈ [0,50](see Figure A1). The trajectory was generatedby simulating Metropolis Monte Carlo (MC) dynamics with Gaussian stepscorresponding to D(x) = 1 for 1.5× 106 steps. The time step is considered to be 1 nsfor straightforward comparison with the MD simulations. Figure A1 shows the free-energy profile along the reactioncoordinate (rescaled such that D(x) = 1) determined from the generated trajectory. The FEP is verysimilar to the model potential U1(x). The small difference is due to a slight underestimationof the diffusion coefficient, which leads to expansion of the reactioncoordinate during rescaling.

Bottom Line: Natl.Four different estimations of the pre-exponential factor for both proteins give k 0 (-1) values of approximately a few tens of nanoseconds.Our analysis gives detailed information about folding of the proteins and can serve as a rigorous common language for extensive comparison between experiment and simulation.

View Article: PubMed Central - PubMed

Affiliation: Astbury Center for Structural Molecular Biology, Faculty of Biological Sciences, University of Leeds , Leeds LS2 9JT, United Kingdom.

ABSTRACT
The free-energy landscape can provide a quantitative description of folding dynamics, if determined as a function of an optimally chosen reaction coordinate. Here, we construct the optimal coordinate and the associated free-energy profile for all-helical proteins HP35 and its norleucine (Nle/Nle) double mutant, based on realistic equilibrium folding simulations [Piana et al. Proc. Natl. Acad. Sci. U.S.A. 2012, 109, 17845]. From the obtained profiles, we directly determine such basic properties of folding dynamics as the configurations of the minima and transition states (TS), the formation of secondary structure and hydrophobic core during the folding process, the value of the pre-exponential factor and its relation to the transition path times, the relation between the autocorrelation times in TS and minima. We also present an investigation of the accuracy of the pre-exponential factor estimation based on the transition-path times. Four different estimations of the pre-exponential factor for both proteins give k 0 (-1) values of approximately a few tens of nanoseconds. Our analysis gives detailed information about folding of the proteins and can serve as a rigorous common language for extensive comparison between experiment and simulation.

No MeSH data available.


Related in: MedlinePlus