SLIDER: A Generic Metaheuristic for the Discovery of Correlated Motifs in Protein-Protein Interaction Networks

Authors:
Peter Boyen;Dries Van Dyck;Frank Neven;Roeland C. H. J. van Ham;Aalt D. J. van Dijk
Affiliations:
Hasselt University, Diepenbeek and Transnational University of Limburg;Hasselt University, Diepenbeek and Transnational University of Limburg;Hasselt University, Diepenbeek and Transnational University of Limburg;Applied Bioinformatics - Plant Research International, Wageningen;Applied Bioinformatics - Plant Research International, Wageningen
Venue:
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Year:
2011

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Mining Minimal Motif Pair Sets Maximally Covering Interactions in a Protein-Protein Interaction Network

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

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Abstract

Correlated motif mining (cmm) is the problem of finding overrepresented pairs of patterns, called motifs, in sequences of interacting proteins. Algorithmic solutions for cmm thereby provide a computational method for predicting binding sites for protein interaction. In this paper, we adopt a motif-driven approach where the support of candidate motif pairs is evaluated in the network. We experimentally establish the superiority of the Chi-square-based support measure over other support measures. Furthermore, we obtain that cmm is an np-hard problem for a large class of support measures (including Chi-square) and reformulate the search for correlated motifs as a combinatorial optimization problem. We then present the generic metaheuristic slider which uses steepest ascent with a neighborhood function based on sliding motifs and employs the Chi-square-based support measure. We show that slider outperforms existing motif-driven cmm methods and scales to large protein-protein interaction networks. The slider-implementation and the data used in the experiments are available on http://bioinformatics.uhasselt.be.