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Automatic generation of 3D motifs
for classification of protein binding sites

J.-C. Nebel, P. Herzyk and D. R. Gilbert

BMC Bioinformatics
8:32, 2007
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Abstract
Background: Structural genomics projects aim at high-throughput delivery of protein structures regardless of the state of their functional annotation. Moreover, roughly half of the gene-products delivered by completed genomes of various organisms do not show sequence homology to existing proteins of known function. Therefore, structure-based prediction of protein molecular function is essential. Protein 3D structures can be clustered according to their fold or secondary structures in classes which usually have some functional significance. More recently, researchers have investigated the detection of 3D patterns associated to active sites. Unfortunately, many patterns do not correspond to any specific function. Currently, known 3D motifs are mainly the result of manual experiments, and therefore, their number is rather small compared to the number of sequential motifs already known. In this paper, we report a method to automatically generate 3D motifs of protein structure binding sites based on consensus atom positions and evaluate it on a set of adenine based ligands.
Results: Our new approach was validated by generating automatically 3D patterns for the main adenine based ligands, i.e. AMP, ADP and ATP. Out of the 18 detected patterns, only one, the ADP4 pattern, is not associated with well defined structural patterns. Moreover, most of the patterns could be classified as binding site 3D motifs. Literature research revealed that the ADP4 pattern actually corresponds to structural features which show complex evolutionary links between ligases and transferases. Therefore, all of the generated patterns prove to be meaningful. Each pattern was used to query all PDB proteins which bind either purine based or guanine based ligands, in order to evaluate the classification and annotation properties of the pattern. Overall, our 3D patterns matched 31 of proteins with adenine based ligands and 95.5 of them were classified correctly.
Conclusions: A new metric has been introduced allowing the classification of proteins according to the similarity of atomic environment of binding sites, and a methodology has been developed to automatically produce 3D patterns from that classification. A study of proteins binding adenine based ligands showed that these 3D patterns are not only biochemically meaningful, but can be used for protein classification and annotation.

Cited by ( Google Scholar: 28, ISI Web of Knowledge: 20 & SCOPUS: 21 ): 29