BACKGROUND: Identification of protein complexes in large interaction networks is 
crucial to understand principles of cellular organization and predict protein 
functions, which is one of the most important issues in the post-genomic era. 
Each protein might be subordinate multiple protein complexes in the real 
protein-protein interaction networks. Identifying overlapping protein complexes 
from protein-protein interaction networks is a considerable research topic.
RESULT: As an effective algorithm in identifying overlapping module structures, 
clique percolation method (CPM) has a wide range of application in social 
networks and biological networks. However, the recognition accuracy of algorithm 
CPM is lowly. Furthermore, algorithm CPM is unfit to identifying protein 
complexes with meso-scale when it applied in protein-protein interaction 
networks. In this paper, we propose a new topological model by extending the 
definition of k-clique community of algorithm CPM and introduced distance 
restriction, and develop a novel algorithm called CP-DR based on the new 
topological model for identifying protein complexes. In this new algorithm, the 
protein complex size is restricted by distance constraint to conquer the 
shortcomings of algorithm CPM. The algorithm CP-DR is applied to the protein 
interaction network of Sacchromyces cerevisiae and identifies many well known 
complexes.
CONCLUSION: The proposed algorithm CP-DR based on clique percolation and 
distance restriction makes it possible to identify dense subgraphs in protein 
interaction networks, a large number of which correspond to known protein 
complexes. Compared to algorithm CPM, algorithm CP-DR has more outstanding 
performance.