Abstract
How to identify protein complex is an important and challenging task in proteomics. It would make great contribution to our knowledge of molecular mechanism in cell life activities. However, the inherent organization and dynamic characteristic of cell system have rarely been incorporated into the existing algorithms for detecting protein complexes because of the limitation of protein-protein interaction (PPI) data produced by high throughput techniques. The availability of time course gene expression profile enables us to uncover the dynamics of molecular networks and improve the detection of protein complexes. In order to achieve this goal, this paper proposes a novel algorithm DCA (Dynamic Core-Attachment). It detects protein-complex core comprising of continually expressed and highly connected proteins in dynamic PPI network, and then the protein complex is formed by including the attachments with high adhesion into the core. The integration of core-attachment feature into the dynamic PPI network is responsible for the superiority of our algorithm. DCA has been applied on two different yeast dynamic PPI networks and the experimental results show that it performs significantly better than the state-of-the-art techniques in terms of prediction accuracy, hF-measure and statistical significance in biology. In addition, the identified complexes with strong biological significance provide potential candidate complexes for biologists to validate.
Highlights
Cellular functions are completed by protein complex formed by multiple proteins aggregating together, rather than by individual protein
To capture the dynamics of protein complex, time course gene expression data are integrated into the original static protein-protein interaction (PPI) network and generate the dynamic PPI network with three sigma method[26]
Our DCA method can achieve the highest F-measure by providing the highest specificity and comparable sensitivity, which shows that our method can predict protein complexes very accurately
Summary
Cellular functions are completed by protein complex formed by multiple proteins aggregating together, rather than by individual protein. Identifying protein complex has significant implications in revealing the important principle of protein organization within cell [1, 2]. Protein complexes can help us to predict the functions of protein [3]. Accumulated evidences suggest that protein complexes are involved in many disease mechanisms [4]. Tracking the protein complexes could reveal important insights into modular mechanisms and improve our understanding on the disease pathways [5].
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