Prediction of heterodimeric protein complexes from weighted protein-protein interaction networks using novel features and kernel functions

Since many proteins express their functional activity by interacting with other proteins and forming protein complexes, it is very useful to identify sets of proteins that form complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been deve...

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Bibliographic Details
Published in:PloS one 2013-06, Vol.8 (6), p.e65265-e65265
Main Authors: Ruan, Peiying, Hayashida, Morihiro, Maruyama, Osamu, Akutsu, Tatsuya
Format: Article
Language:English
Subjects:
Acids
Algorithms
Bayesian analysis
Bioinformatics
Biology
Computational Biology - methods
Computer applications
Gene expression
Genomes
Kernel functions
Mathematics
Methods
Ontology
Physics
Predictions
Protein interaction
Protein Interaction Mapping
Protein-protein interactions
Proteins
Proteins - chemistry
Software
Yeast
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Summary:Since many proteins express their functional activity by interacting with other proteins and forming protein complexes, it is very useful to identify sets of proteins that form complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this paper, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on reliability. Furthermore, we make use of prior knowledge on protein domains to develop feature space mappings, domain composition kernel and its combination kernel with our proposed features. We perform ten-fold cross-validation computational experiments. These results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method for predicting heterodimeric protein complexes.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0065265