Predicting Protein-Protein Interactions with a Network-based Motif Miner
Date Issued
2009
Date
2009
Author(s)
Yu, Chi-Yuan
Abstract
Protein-Protein interactions (PPIs) are essential to various biological functions in living organisms. Studying PPI not only provides critical clues for understanding how a cell operates but also may lead to development of advanced diagnoses and therapies. In this regard, as it requires huge amounts of time and resources to confirm protein-protein interactions with molecular biology experiments, design of computational approaches to predict possible protein-protein interactions is of scientific significance for advances in systems biology. One existing approach to predict protein-protein interactions is based on the binding motifs extracted by pattern mining algorithms. Motif-based approaches are favored by biologists who want to conduct in-depth analyses on how the concerned proteins interact, instead of just knowing whether these proteins interact with each other or not. With respect to motif-based prediction of protein-protein interactions, there exist two major categories of approaches. One category of approaches simply resorts to analysis of the polypeptide sequences, while another category of approaches further refers to the tertiary structures of proteins. As the availability of the tertiary structures of proteins is still limited to certain groups of proteins, sequence-based approaches are more generally applicable. The conventional motif-based approaches extract binding motifs through identifying evolutionally conserved regions in polypeptide sequences. However, evolutional conservation is just a necessary condition and is not a sufficient condition for presence of interaction sites. Certain regions in a protein chain may be conserved in order to maintain a conformation. Therefore, in recent years, researchers have proposed a novel approach to identify protein-protein interaction motifs through analysis of interaction networks. Nevertheless, latest studies did not report a comprehensive analysis on the quality of the interaction motifs identified, let alone the effects with alternative pattern mining algorithms. The study reported in this thesis has followed the recent development and has employed a state-of-the-art pattern mining algorithm to deliver superior performance in identifying protein-protein interaction motifs. The most distinctive feature of the pattern mining algorithm employed in this study is its capability in identifying patterns composed of several short gapped segments. Experimental results reveal that the predictor designed in this study really outperforms the predictors that incorporate other pattern mining algorithms.
Subjects
protein-protein interaction
pattern mining
binding motif
protein sequence
all-versus-all interaction network.
Type
thesis
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