Ligand unbinding pathway and mechanism analysis assisted by machine learning and graph methods

Abstract

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 intra-ligand hydrogen bond, highlighting the challenges involved in unbinding path reaction coordinate detection.

This preprint is the unformatted version of a manuscript that has been published as article in the Journal of Chemical Information and Modeling and can be downloaded for private use only. Copyright with ACS, the journal and the authors

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Cited by in corpus (3)

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