Accurate protein-folding transition-path statistics from a simple free-energy landscape

TL;DR

This paper introduces a topological configuration model that uses a simple free-energy landscape based on native-like substructures to accurately predict protein-folding transition-path statistics without requiring dynamical data.

Contribution

The study presents a first-principles free-energy landscape model that predicts transition-path dynamics solely from equilibrium data, improving upon traditional one-dimensional landscapes.

Findings

Predicted transition-path transit times match simulation data.

Distribution of displacements on order parameters aligns with simulated paths.

Model accurately reproduces transition-path trajectory statistics.

Abstract

A central goal of protein-folding theory is to predict the stochastic dynamics of transition paths --- the rare trajectories that transit between the folded and unfolded ensembles --- using only thermodynamic information, such as a low-dimensional equilibrium free-energy landscape. However, commonly used one-dimensional landscapes typically fall short of this aim, because an empirical coordinate-dependent diffusion coefficient has to be fit to transition-path trajectory data in order to reproduce the transition-path dynamics. We show that an alternative, first-principles free-energy landscape predicts transition-path statistics that agree well with simulations and single-molecule experiments without requiring dynamical data as an input. This 'topological configuration' model assumes that distinct, native-like substructures assemble on a timescale that is slower than native-contact formation but faster than the folding of the entire protein. Using only equilibrium simulation data to determine the free energies of these coarse-grained intermediate states, we predict a broad distribution of transition-path transit times that agrees well with the transition-path durations observed in simulations. We further show that both the distribution of finite-time displacements on a one-dimensional order parameter and the ensemble of transition-path trajectories generated by the model are consistent with the simulated transition paths. These results indicate that a landscape based on transient folding intermediates, which are often hidden by one-dimensional projections, can form the basis of a predictive model of protein-folding transition-path dynamics.