Multiple protein folding nuclei and the transition state ensemble in two-state proteins

Area of Science:

• Computational Biology
• Biophysics
• Protein Folding Dynamics

Background:

• Protein folding is crucial for biological function.
• Understanding the folding pathways of two-state folders is a key challenge.
• Lattice models provide a simplified yet powerful framework for studying protein folding.

Purpose of the Study:

• To elucidate the folding mechanism of optimized two-state folders.
• To investigate the role of non-native interactions in protein folding kinetics and stability.
• To characterize the transition states involved in the nucleation-collapse mechanism.

Main Methods:

• Exhaustive simulations of lattice models with side-chains.
• Analysis of folding kinetics and identification of critical nuclei.
• Cluster analysis to determine transition states without assuming a reaction coordinate.

Main Results:

• Optimized two-state folders fold via a nucleation-collapse mechanism with multiple folding nuclei (MFN).
• High-probability contacts in nuclei are determined by the native state structure.
• Non-native interactions in folding nuclei decrease cooperativity and stability, and increase folding times.

Conclusions:

• Non-native interactions significantly impact protein folding, compromising stability and altering kinetics.
• Go models are insufficient for accurately describing the transition state characteristics of two-state folders.
• The developed method for computing transition state ensembles is robust and equivalent to the stochastic separatrix technique.