More Sophisticated Is Not Always Better: A Comparison of Similarity Measures for Unsupervised Learning of Pathways in Biomolecular Simulations
Miriam Jäger, Steffen Wolf
TL;DR
This paper compares different similarity measures for analyzing molecular simulation data and finds that simpler methods can be as effective as more complex ones.
Contribution
The study evaluates and compares the effectiveness of four similarity measures in clustering biomolecular simulation trajectories.
Findings
Wasserstein distances provided the best clustering performance in a streptavidin–biotin system.
Euclidean distances were sufficient for meaningful clustering in a more complex A2a receptor-inhibitor system.
More sophisticated similarity measures did not consistently outperform simpler ones.
Abstract
Finding process pathways in molecular simulations such as the unbinding paths of small molecule ligands from their binding sites at protein targets in a set of trajectories via unsupervised learning approaches requires the definition of a suitable similarity measure between trajectories. Here, we evaluate the performance of four such measures with varying degree of sophistication, i.e., Euclidean and Wasserstein distances, Procrustes analysis, and dynamic time warping, when analyzing trajectory data from two different biased simulation driving protocols in the form of constant velocity constraint targeted MD and steered MD. In a streptavidin–biotin benchmark system with known ground truth clusters, Wasserstein distances yielded the best clustering performance, closely followed by Euclidean distances, both being the most computationally efficient similarity measures. In a more complex…
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Taxonomy
TopicsProtein Structure and Dynamics · Microbial Metabolic Engineering and Bioproduction · Bioinformatics and Genomic Networks