Fast large-scale clustering of protein structures using Gauss integrals

Tim Harder, Mikael Borg, Wouter Boomsma, Peter Røgen, Thomas Hamelryck

    Research output: Contribution to journalJournal articleResearchpeer-review


    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.
    Original languageEnglish
    Issue number4
    Pages (from-to)510-515
    Publication statusPublished - 2011


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