Abstract
BackgroundFor many predictive applications a large number of models is generated and later clustered in subsets based on structure similarity. In most clustering algorithms an all-vs-all root mean square deviation (RMSD) comparison is performed. Most of the time is typically spent on comparison of non-similar structures. For sets with more than, say, 10,000 models this procedure is very time-consuming and alternative faster algorithms, restricting comparisons only to most similar structures would be useful.ResultsWe exploit the inverse triangle inequality on the RMSD between two structures given the RMSDs with a third structure. The lower bound on RMSD may be used, when restricting the search of similarity to a reasonably low RMSD threshold value, to speed up similarity searches significantly. Tests are performed on large sets of decoys which are widely used as test cases for predictive methods, with a speed-up of up to 100 times with respect to all-vs-all comparison depending on the set and parameters used. Sample applications are shown.ConclusionsThe algorithm presented here allows fast comparison of large data sets of structures with limited memory requirements. As an example of application we present clustering of more than 100000 fragments of length 5 from the top500H dataset into few hundred representative fragments. A more realistic scenario is provided by the search of similarity within the very large decoy sets used for the tests. Other applications regard filtering nearly-indentical conformation in selected CASP9 datasets and clustering molecular dynamics snapshots.AvailabilityA linux executable and a Perl script with examples are given in the supplementary material (Additional file 1). The source code is available upon request from the authors.
Highlights
For many predictive applications a large number of models is generated and later clustered in subsets based on structure similarity
The structure and dynamics of biomolecules and biocomplexes are of utmost importance in determining their function, whose knowledge and elucidation is the ultimate goal of structural biology
Between structures i and j, after optimal superposition and with no optimal superposition, respectively. It has been shown by Edwards et al [24] and by Steipe and Kaindl [25,26] that RMSDopt is a metric on the space of the classes of equivalent structures (i. e. structures that can be superimposed exactly by a rototranslation)
Summary
For many predictive applications a large number of models is generated and later clustered in subsets based on structure similarity. Going down from ecological systems, to organisms, organs and cells and subcellular components, the lowest level description of biological systems is in terms of single molecules and atoms [1]. At this level, the structure and dynamics of biomolecules and biocomplexes are of utmost importance in determining their function, whose knowledge and elucidation is the ultimate goal of structural biology. Experimental methods for structural characterization of biomolecules are often too slow or have limitations in targets and resolution that cannot be overcome For these reasons one often resorts to computational predictions or simulations. A common feature of computational methods is that they generate a large number, typically in the range of thousands, of molecular models which are meant as samples of the large conformational space of a molecule or of a complex
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