Open AccessPrediction of protein–protein interaction sites in heterocomplexes with neural networks
Author(s) -
Fariselli Piero,
Pazos Florencio,
Valencia Alfonso,
Casadio Rita
Publication year - 2002
Publication title -
european journal of biochemistry
Language(s) - English
Resource type - Journals
eISSN - 1432-1033
pISSN - 0014-2956
DOI - 10.1046/j.1432-1033.2002.02767.x
Subject(s) - surface protein , artificial neural network , computer science , representation (politics) , biological system , complement (music) , protein–protein interaction , computational biology , protein structure prediction , chaperone (clinical) , protein structure , proteomics , artificial intelligence , data mining , pattern recognition (psychology) , chemistry , biology , biochemistry , medicine , pathology , virology , complementation , politics , political science , gene , law , phenotype
In this paper we address the problem of extracting features relevant for predicting protein–protein interaction sites from the three‐dimensional structures of protein complexes. Our approach is based on information about evolutionary conservation and surface disposition. We implement a neural network based system, which uses a cross validation procedure and allows the correct detection of 73% of the residues involved in protein interactions in a selected database comprising 226 heterodimers. Our analysis confirms that the chemico‐physical properties of interacting surfaces are difficult to distinguish from those of the whole protein surface. However neural networks trained with a reduced representation of the interacting patch and sequence profile are sufficient to generalize over the different features of the contact patches and to predict whether a residue in the protein surface is or is not in contact. By using a blind test, we report the prediction of the surface interacting sites of three structural components of the Dnak molecular chaperone system, and find close agreement with previously published experimental results. We propose that the predictor can significantly complement results from structural and functional proteomics.