Markov State Model for the forced unfolding of a small peptide

TL;DR

This paper demonstrates how Markov state modeling can effectively simulate the complex, non-two-state unfolding process of a small peptide under force, capturing detailed conformational transitions beyond simple metrics.

Contribution

It introduces a Markov state modeling approach using collective variables and dimension reduction to accurately represent peptide unfolding dynamics.

Findings

Successfully modeled non-two-state unfolding behavior.

Captured microscopic conformational transitions.

Method applicable to complex biomolecular unfolding processes.

Abstract

In typical single-molecule force spectroscopy experiments the mechanical unfolding of molecular complexes or biomolecules is studied applying a force ramp to one end of the system while the other end is kept fixed in space. The computational counterpart of this type of experiments can routinely be performed using molecular dynamics simulations with atomistic resolution. However, due to the large difference in time scales often coarse graining procedures are applied in the simulations. Most of the applied techniques do not allow to follow the atomistic details of the relevant conformational transitions due to the structural simplifications used to speed up the simulations. Here, we apply an earlier developed dynamic coarse graining technique based on Markov state modeling to a model peptidic system that does not unfold in a simple two-state manner. Using the donor-acceptor distances of the helical hydrogen bonds as collective variables and performing a dimension reduction technique allows us to construct a Markov model of the unfolding process that correctly represents the microscopic behavior of the system. The chosen example shows that the method can be used to mimick the mechanical unfolding process of systems for which the end-to-end distance does not provide a sufficient order parameter and that do not unfold in a simple cooperative manner.