title: Fast large-scale clustering of protein structures using Gauss integrals.
abstract: BACKGROUND
  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.
authors.affiliation: The Bioinformatics Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
authors.forename: Tim
authors.initials: T
authors.lastname: Harder
authors.forename: Mikael
authors.initials: M
authors.lastname: Borg
authors.forename: Wouter
authors.initials: W
authors.lastname: Boomsma
authors.forename: Peter
authors.initials: P
authors.lastname: Røgen
authors.forename: Thomas
authors.initials: T
authors.lastname: Hamelryck
mesh_terms: D000263:Adenylate Kinase / Q000737:chemistry; D002175:Candida / Q000737:chemistry; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D004926:Escherichia coli / Q000201:enzymology; D005656:Fungal Proteins / Q000737:chemistry; D056004:Molecular Dynamics Simulation; D011506:Proteins / Q000737:chemistry*
chemical_list: D005656:Fungal Proteins; D011506:Proteins; D000263:Adenylate Kinase