Ratcheted molecular-dynamics simulations identify efficiently the transition state of protein folding

TL;DR

This paper introduces a ratchet-based biasing algorithm that efficiently identifies the transition state in protein folding, reducing computational costs and enabling detailed characterization of folding mechanisms.

Contribution

The study presents a novel ratchet-and-pawl inspired algorithm for efficiently locating transition states in protein folding simulations, validated on both model and explicit-solvent systems.

Findings

Successfully identified transition states in protein models and real proteins.

Reduced computational effort compared to traditional methods.

Provided detailed conformations of transition states for ACBP and CI2.

Abstract

The atomistic characterization of the transition state is a fundamental step to improve the understanding of the folding mechanism and the function of proteins. From a computational point of view, the identification of the conformations that build out the transition state is particularly cumbersome, mainly because of the large computational cost of generating a statistically-sound set of folding trajectories. Here we show that a biasing algorithm, based on the physics of the ratchet-and-pawl, can be used to identify efficiently the transition state. The basic idea is that the algorithmic ratchet exerts a force on the protein when it is climbing the free-energy barrier, while it is inactive when it is descending. The transition state can be identified as the point of the trajectory where the ratchet changes regime. Besides discussing this strategy in general terms, we test it within a protein model whose transition state can be studied independently by plain molecular dynamics simulations. Finally, we show its power in explicit-solvent simulations, obtaining and characterizing a set of transition--state conformations for ACBP and CI2.