Bioinformatics Advance Access published online on July 26, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl403
1 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 greater than 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.
Received March 30, 2006
Revised July 13, 2006
Accepted July 19, 2006
Article
A novel structure-based encoding for machine-learning applied to the inference of SH3 domain specificity
E. Ferraro 1 *, A. Via 1, G. Ausiello 1, and M. Helmer-Citterich 1
E. Ferraro, E-mail: enrico{at}cbm.bio.uniroma2.it
Abstract
Associate Editor: Anna Tramontano
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  Z. Wunderlich and L. A. Mirny Using genome-wide measurements for computational prediction of SH2-peptide interactions Nucleic Acids Res., August 1, 2009; 37(14): 4629 - 4641. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. Schillinger, P. Boisguerin, and G. Krause Domain Interaction Footprint: a multi-classification approach to predict domain-peptide interactions Bioinformatics, July 1, 2009; 25(13): 1632 - 1639. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 T. Hou, Z. Xu, W. Zhang, W. A. McLaughlin, D. A. Case, Y. Xu, and W. Wang Characterization of Domain-Peptide Interaction Interface: A Generic Structure-based Model to Decipher the Binding Specificity of SH3 Domains Mol. Cell. Proteomics, April 1, 2009; 8(4): 639 - 649. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. Ferraro, D. Peluso, A. Via, G. Ausiello, and M. Helmer-Citterich SH3-Hunter: discovery of SH3 domain interaction sites in proteins Nucleic Acids Res., July 13, 2007; 35(suppl_2): W451 - W454. [Abstract] [Full Text] [PDF]
|
|