Fast large-scale clustering of protein structures using Gauss integrals

Bioinformatics. 2012 Feb 15;28(4):510-5. doi: 10.1093/bioinformatics/btr692. Epub 2011 Dec 22.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adenylate Kinase / chemistry
  • Candida / chemistry
  • Cluster Analysis*
  • Computational Biology / methods*
  • Escherichia coli / enzymology
  • Fungal Proteins / chemistry
  • Molecular Dynamics Simulation
  • Proteins / chemistry*

Substances

  • Fungal Proteins
  • Proteins
  • Adenylate Kinase