Abstract
BackgroundIdentification of protein-protein interactions (PPIs) is essential for a better understanding of biological processes, pathways and functions. However, experimental identification of the complete set of PPIs in a cell/organism (“an interactome”) is still a difficult task. To circumvent limitations of current high-throughput experimental techniques, it is necessary to develop high-performance computational methods for predicting PPIs.ResultsIn this article, we propose a new computational method to predict interaction between a given pair of protein sequences using features derived from known homologous PPIs. The proposed method is capable of predicting interaction between two proteins (of unknown structure) using Averaged One-Dependence Estimators (AODE) and three features calculated for the protein pair: (a) sequence similarities to a known interacting protein pair (FSeq), (b) statistical propensities of domain pairs observed in interacting proteins (FDom) and (c) a sum of edge weights along the shortest path between homologous proteins in a PPI network (FNet). Feature vectors were defined to lie in a half-space of the symmetrical high-dimensional feature space to make them independent of the protein order. The predictability of the method was assessed by a 10-fold cross validation on a recently created human PPI dataset with randomly sampled negative data, and the best model achieved an Area Under the Curve of 0.79 (pAUC0.5% = 0.16). In addition, the AODE trained on all three features (named PSOPIA) showed better prediction performance on a separate independent data set than a recently reported homology-based method.ConclusionsOur results suggest that FNet, a feature representing proximity in a known PPI network between two proteins that are homologous to a target protein pair, contributes to the prediction of whether the target proteins interact or not. PSOPIA will help identify novel PPIs and estimate complete PPI networks. The method proposed in this article is freely available on the web at http://mizuguchilab.org/PSOPIA.
Highlights
Identification of protein-protein interactions (PPIs) is essential for a better understanding of biological processes, pathways and functions
The Averaged One-Dependence Estimators (AODE) is trained using three features: (a) sequence similarities to known interacting proteins (FSeq), (b) statistical propensities of domain pairs observed in interacting proteins (FDom) and (c) a sum of edge weights along the shortest path between homologous proteins in a PPI network (FNet)
MCC is Results we first assess critically the AODE models based on three homology-based features encoded in a single feature vector
Summary
Identification of protein-protein interactions (PPIs) is essential for a better understanding of biological processes, pathways and functions. Efforts have been made to develop methods based only on information about amino acid sequences, for example, by using the number of amino acid triplets in each sequence [6,10,13], a product of signatures defined as a set of subsequences [7], auto-correlation values of seven different physicochemical scales [11,15] and normalized counts of single or pairs of consecutive amino acid residues [12]. It has been shown to be an informative feature for predicting PPIs [14], methods utilizing domain information alone are not applicable to proteins without domain assignments
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