Journal article
Fast large-scale clustering of protein structures using Gauss integrals
Motivation: Clustering protein structures is an important task in structural bioinformatics. De novo structure prediction, for example, often involves a clustering step for nding 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 rst 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 signicantly larger number of structures, while providing state-ofthe- 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.
Language: | English |
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Publisher: | Oxford University Press |
Year: | 2011 |
Pages: | 510-515 |
ISSN: | 13674811 , 13674803 , 14602059 and 02667061 |
Types: | Journal article |
DOI: | 10.1093/bioinformatics/btr692 |
ORCIDs: | Røgen, Peter and 0000-0003-2917-3602 |