A Maximum-Caliber Approach to Predicting Perturbed Folding Kinetics Due to Mutations

Area of Science:

• Computational chemistry
• Biophysics
• Statistical mechanics

Background:

• Markov state models (MSMs) are crucial for analyzing molecular dynamics simulations.
• Estimating transition rates in MSMs is challenging, especially with perturbed populations.
• Understanding how mutations affect protein folding dynamics is vital in molecular biology.

Purpose of the Study:

• To develop a robust and simple maximum-caliber method for inferring MSM transition rates with perturbed populations.
• To validate the method's performance using kinetic models and complex protein folding systems.
• To demonstrate the method's utility in predicting mutation-induced changes in protein folding rates.

Main Methods:

• A maximum-caliber approach is employed to infer transition rates of a Markov state model (MSM).
• The method incorporates prior information from an unperturbed MSM to enhance robustness and simplify implementation.
• Performance is evaluated using biased diffusion models and applied to protein folding systems like GB1 hairpin, Fs peptide helix, and WW domain variants.

Main Results:

• The maximum-caliber method accurately predicts changes in folding rates across various protein folding systems.
• The inclusion of prior information leads to a more robust and easily implementable approach compared to previous methods.
• The method successfully accounts for the significant role of non-native interactions in protein folding.

Conclusions:

• Maximum-caliber approaches are highly effective for predicting how mutations perturb protein conformational dynamics.
• This method offers an efficient way to study the impact of genetic variations on protein folding.
• The findings suggest broad applicability of this computational strategy in biophysical and biochemical research.