Protein Complex

Motivation: Detecting known protein complexes and predicting undiscovered protein complexes from protein-protein interaction (PPI) networks helps us understand principles of cellular organization and their functions. However, the discovery of protein complexes based on experiment still needs to be explored. In this regard, computational methods are useful approaches to overcome the experimental limitations. Nevertheless, extraction of protein complexes from PPI network isn’t an easy task. Two major constrains are high noise level and ignoring occurrence time of different interactions in PPI network.

Results: In this paper an efficient algorithm (IMHRC) is developed based on inter-module hub removal in the weighted PPI network which can detect overlapped complexes. by removing some of the inter-module hubs and module hubs, IMHRC eliminates a meaningful percentage of noise in our dataset and indirectly considers difference occurrence time of the PPI in our network. After removing hubs, some proteins turned to seeds. Each seed creates a primary cluster. Then removed module hubs are added to the resulting clusters based on the amount of their interactions with other proteins in the clusters. Clusters are later merged based on their overlaps. Consequently, the performance of the IMHRC is evaluated on several benchmark datasets and the results are compared with other state-of-the-art models. Significantly the protein complexes that discovered by IMHRC method match with the real data and shows much better results in comparison with other methods.

Conclusion: Our algorithm provides an accurate and scalable method to detect and predict protein complexes from PPI networks.

Availability: The IMHRC algorithm and data processing code are available in