Open Access Research

Statistical discovery of site inter-dependencies in sub-molecular hierarchical protein structuring

Kirk K Durston1*, David KY Chiu1, Andrew KC Wong2 and Gary CL Li2

Author Affiliations

1 School of Computer Science, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1, Canada

2 Department of System Design Engineering, University of Waterloo, 200 University Ave. W, Waterloo, ON, N2L 3G1, Canada

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EURASIP Journal on Bioinformatics and Systems Biology 2012, 2012:8  doi:10.1186/1687-4153-2012-8

Published: 13 July 2012

Abstract

Background

Much progress has been made in understanding the 3D structure of proteins using methods such as NMR and X-ray crystallography. The resulting 3D structures are extremely informative, but do not always reveal which sites and residues within the structure are of special importance. Recently, there are indications that multiple-residue, sub-domain structural relationships within the larger 3D consensus structure of a protein can be inferred from the analysis of the multiple sequence alignment data of a protein family. These intra-dependent clusters of associated sites are used to indicate hierarchical inter-residue relationships within the 3D structure. To reveal the patterns of associations among individual amino acids or sub-domain components within the structure, we apply a k-modes attribute (aligned site) clustering algorithm to the ubiquitin and transthyretin families in order to discover associations among groups of sites within the multiple sequence alignment. We then observe what these associations imply within the 3D structure of these two protein families.

Results

The k-modes site clustering algorithm we developed maximizes the intra-group interdependencies based on a normalized mutual information measure. The clusters formed correspond to sub-structural components or binding and interface locations. Applying this data-directed method to the ubiquitin and transthyretin protein family multiple sequence alignments as a test bed, we located numerous interesting associations of interdependent sites. These clusters were then arranged into cluster tree diagrams which revealed four structural sub-domains within the single domain structure of ubiquitin and a single large sub-domain within transthyretin associated with the interface among transthyretin monomers. In addition, several clusters of mutually interdependent sites were discovered for each protein family, each of which appear to play an important role in the molecular structure and/or function.

Conclusions

Our results demonstrate that the method we present here using a k-modes site clustering algorithm based on interdependency evaluation among sites obtained from a sequence alignment of homologous proteins can provide significant insights into the complex, hierarchical inter-residue structural relationships within the 3D structure of a protein family.

Keywords:
k-modes algorithm; Site cluster; Associations; Ubiquitin; Transthyretin; Pattern discovery; Cluster tree; Attribute clustering; Protein structural sub-domains