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

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 8; no. 5; pp. 1344 - 1357
• Main Authors: Boyen, P., Van Dyck, D., Neven, F., van Ham, R. C. H. J., van Dijk, A. D. J.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-09-2011; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Amino Acid Motifs; Amino acids; Bioinformatics; biology and genetics; Chi-Square Distribution; Computational Biology - methods; Coordinate measuring machines; Databases, Protein; Fungal Proteins - chemistry; Graphs and networks; Humans; Optimization; pairs; Polynomials; Protein Interaction Mapping - methods; Protein Interaction Maps; Proteins; Proteins - chemistry; scale; Sequence Analysis, Protein; sequences; Studies
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2011.17

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.