Motivation: Clustering protein structures is an important task in structural bioinformatics. De novo structure prediction, for example, often involves a clustering step for finding the best prediction. Other applications include assigning proteins to fold families and analyzing molecular dynamics trajectories. Results: We present Pleiades, a novel approach to clustering protein structures with a rigorous mathematical underpinning. The method approximates clustering based on the root mean square deviation by first mapping structures to Gauss integral vectors-which were introduced by Røgen and co-workers-and subsequently performing K-means clustering. Conclusions: Compared to current methods, Pleiades dramatically improves on the time needed to perform clustering, and can cluster a significantly larger number of structures, while providing state-of-the-art results. The number of low energy structures generated in a typical folding study, which is in the order of 50 000 structures, can be clustered within seconds to minutes. © The Author 2011. Published by Oxford University Press. All rights reserved.
CITATION STYLE
Harder, T., Borg, M., Boomsma, W., Røgen, P., & Hamelryck, T. (2012). Fast large-scale clustering of protein structures using gauss integrals. Bioinformatics, 28(4), 510–515. https://doi.org/10.1093/bioinformatics/btr692
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