Exploring the Free Energy Landscape: From Dynamics to Networks and Back

TL;DR

This paper introduces a novel framework that combines molecular dynamics and network analysis to characterize the free energy landscape of biomolecules, revealing conformers, transition paths, and kinetics.

Contribution

The work presents a new method using Conformational Markov Networks to analyze free energy landscapes from molecular dynamics data, enabling detailed insights into conformers and transition dynamics.

Findings

Successfully applied to a toy funnel model

Efficiently computed conformers of dialanine peptide

Revealed hierarchical relationships among conformers

Abstract

The knowledge of the Free Energy Landscape topology is the essential key to understand many biochemical processes. The determination of the conformers of a protein and their basins of attraction takes a central role for studying molecular isomerization reactions. In this work, we present a novel framework to unveil the features of a Free Energy Landscape answering questions such as how many meta-stable conformers are, how the hierarchical relationship among them is, or what the structure and kinetics of the transition paths are. Exploring the landscape by molecular dynamics simulations, the microscopic data of the trajectory are encoded into a Conformational Markov Network. The structure of this graph reveals the regions of the conformational space corresponding to the basins of attraction. In addition, handling the Conformational Markov Network, relevant kinetic magnitudes as dwell times or rate constants, and the hierarchical relationship among basins, complete the global picture of the landscape. We show the power of the analysis studying a toy model of a funnel-like potential and computing efficiently the conformers of a short peptide, the dialanine, paving the way to a systematic study of the Free Energy Landscape in large peptides.