Protein folded states are kinetic hubs

Area of Science:

• Molecular biophysics
• Computational biology
• Protein dynamics

Background:

• Protein folding is a complex molecular process.
• Markov state models (MSMs) offer a computational approach to study molecular kinetics.
• MSMs have shown promise in quantitatively matching experimental data for protein structures and folding rates.

Purpose of the Study:

• To utilize existing MSMs for villin headpiece and NTL9 to understand molecular kinetics.
• To develop simplified network models that capture essential protein folding dynamics.
• To explain phenomena like apparent two-state folding observed in experiments.

Main Methods:

• Analysis of existing Markov state models (MSMs) derived from atomistic simulations.
• Construction of simplified network models for easier comprehension of molecular kinetics.
• Comparison of model predictions with experimental observations of protein folding.

Main Results:

• Protein dynamics are characterized by stochastic transitions between numerous metastable states.
• Proteins exhibit heterogeneous unfolded states with varying interconversion rates.
• Native states function as hubs, rapidly accessible from all other states.
• Metastability and nonnative states collectively slow down the overall protein folding rate.

Conclusions:

• MSMs provide valuable insights into the complex kinetics of protein folding.
• Simplified network models can reproduce key experimental observations, such as apparent two-state folding.
• Understanding the network of metastable states is crucial for predicting and controlling protein folding rates.
• These findings have implications for protein design and other areas of molecular biophysics.