Predicting and Simulating Mutational Effects on Protein Folding Kinetics.

Area of Science:

• Protein dynamics and biophysics
• Computational biology and bioinformatics
• Molecular biophysics

Background:

• Phi-value analysis is crucial for understanding protein folding mechanisms by assessing residue involvement in rate-determining steps.
• Predicting mutation effects on protein folding thermodynamics is established, but kinetics remain challenging due to complex factors.
• Fractional phi-values (~0.3) lack consistent interpretation, hindering precise kinetic analysis.

Purpose of the Study:

• To develop and validate a simple 1D free energy surface model for predicting mutation effects on protein folding kinetics.
• To provide a consistent interpretation for fractional phi-values observed in protein folding studies.
• To simulate and analyze the impact of mutations on folding kinetics using a grounded energy landscape theory approach.

Main Methods:

• Construction, parameterization, and application of a 1D free energy surface model based on energy landscape theory.
• Simulation of 806 mutations across 24 proteins using experimental protein destabilization as input.
• Calculation of relative unfolding activation free energies and correlation with experimental data.

Main Results:

• The 1D model successfully reproduced relative unfolding activation free energies with a high correlation (0.91).
• Simulations covered a large dataset of 806 mutations from 24 diverse proteins.
• Demonstrated that a mean phi-value of 0.3 corresponds to the stabilization energy gained at the folding barrier's transition state.

Conclusions:

• The developed 1D model offers a robust and simplified method for predicting the kinetic effects of mutations in protein folding.
• The study provides a clear biophysical interpretation for the commonly observed fractional phi-values.
• This approach enhances the understanding of protein folding pathways and the role of specific residues in the folding process.