Clustering of molecular dynamics trajectories via peak-picking in multidimensional PCA-derived distributions
AS Baltzis, PI Koukos, NM Glykos - arXiv preprint arXiv:1512.04024, 2015 - arxiv.org
We describe a robust, fast, and memory-efficient procedure that can cluster millions of
structures derived from molecular dynamics simulations. The essence of the method is
based on a peak-picking algorithm applied to three-and five-dimensional distributions of the
principal components derived from the trajectory and automatically supports both Cartesian
and dihedral PCA-based clustering. The density threshold required for identifying isolated
peaks (which correspond to discrete clusters) is determined through the application of a …
structures derived from molecular dynamics simulations. The essence of the method is
based on a peak-picking algorithm applied to three-and five-dimensional distributions of the
principal components derived from the trajectory and automatically supports both Cartesian
and dihedral PCA-based clustering. The density threshold required for identifying isolated
peaks (which correspond to discrete clusters) is determined through the application of a …
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