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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/5940

Title: Automatic generation of 3D motifs for classification of protein binding sites
Authors: Nebel, J
Herzyk, P
Gilbert, D
Keywords: Protein 3D structures
Adenine based ligands
AMP
ADP
ATP
Publication Date: 2007
Publisher: BioMed Central Ltd
Citation: BMC Bioinformatics, 8: 321, 2007
Abstract: Background: Since many of the new protein structures delivered by high-throughput processes do not have any known function, there is a need for structure-based prediction of protein function. Protein 3D structures can be clustered according to their fold or secondary structures to produce classes of some functional significance. A recent alternative has been to detect specific 3D motifs which are often associated to active sites. Unfortunately, there are very few known 3D motifs, which are usually the result of a manual process, 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. Conclusion: 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.
Description: Coyright @ 2007 Nebel et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
URI: http://www.biomedcentral.com/1471-2105/8/321
http://bura.brunel.ac.uk/handle/2438/5940
DOI: http://dx.doi.org/10.1186/1471-2105-8-321
ISSN: 1471-2105
Appears in Collections:Information Systems and Computing
School of Information Systems, Computing and Mathematics Research Papers
Publications

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