Bioinformatics Advance Access originally published online on July 26, 2006
Bioinformatics 2006 22(19):2333-2339; doi:10.1093/bioinformatics/btl403
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A novel structure-based encoding for machine-learning applied to the inference of SH3 domain specificity
Centre of Molecular Bioinformatics, Department of Biology, University of Tor Vergata Rome, Italy
*To whom correspondence should be addressed.
Motivation: Unravelling the rules underlying protein–protein and protein–ligand interactions is a crucial step in understanding cell machinery. Peptide recognition modules (PRMs) are globular protein domains which focus their binding targets on short protein sequences and play a key role in the frame of protein–protein interactions. High-throughput techniques permit the whole proteome scanning of each domain, but they are characterized by a high incidence of false positives. In this context, there is a pressing need for the development of in silico experiments to validate experimental results and of computational tools for the inference of domain–peptide interactions.
Results: We focused on the SH3 domain family and developed a machine-learning approach for inferring interaction specificity. SH3 domains are well-studied PRMs which typically bind proline-rich short sequences characterized by the PxxP consensus. The binding information is known to be held in the conformation of the domain surface and in the short sequence of the peptide. Our method relies on interaction data from high-throughput techniques and benefits from the integration of sequence and structure data of the interacting partners. Here, we propose a novel encoding technique aimed at representing binding information on the basis of the domain–peptide contact residues in complexes of known structure. Remarkably, the new encoding requires few variables to represent an interaction, thus avoiding the ‘curse of dimension’. Our results display an accuracy >90% in detecting new binders of known SH3 domains, thus outperforming neural models on standard binary encodings, profile methods and recent statistical predictors. The method, moreover, shows a generalization capability, inferring specificity of unknown SH3 domains displaying some degree of similarity with the known data.
Contacts: enrico{at}cbm.bio.uniroma2.it
Supplementary information: Supplementary data are available at Bioinformatics online.
Received on March 30, 2006; revised on July 13, 2006; accepted on July 19, 2006
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