Ligand Unbinding Pathway and Mechanism Analysis Assisted by Machine Learning and Graph Methods

We present two methods
to reveal protein–ligand
unbinding
mechanisms in biased unbinding simulations by clustering trajectories
into ensembles representing unbinding paths. The first approach is
based on a contact principal component analysis for reducing the dimensionality
of the input data, followed by identification of unbinding paths and
training a machine learning model for trajectory clustering. The second
approach clusters trajectories according to their pairwise mean Euclidean
distance employing the neighbor-net algorithm, which takes into account
input data bias in the distances set and is superior to dendrogram
construction. Finally, we describe a more complex case where the reaction
coordinate relevant for path identification is a single intraligand
hydrogen bond, highlighting the challenges involved in unbinding path
reaction coordinate detection.