Algorithmic graph theory for post-processing molecular dynamics trajectories

This paper reviews some of the graph theory-based methods that were recently developed in our group for post-processing molecular dynamics trajectories. We show that the use of algorithmic graph theory not only provides a direct and fast methodology to identify conformers that are sampled over time, but it also allows to follow in time the interconversions between the conformers through graphs of transitions, also provides statistical characterizations, that would otherwise be hard to obtain. Examples for a gas phase peptide and for the more complex inhomogeneous charged air-liquid water interface are presented in order to demonstrate the power of topological 2D-graphs and their versatility and transferability.