IOPTICS-GSO for identifying protein complexes from dynamic PPI networks

Background: Identifying protein complexes plays an important role for understanding cellular organization and functional mechanisms. As plenty of evidences have indicated that dense sub-networks in dynamic protein-protein interaction network (DPIN) usually correspond to protein complexes, identifying protein complexes is formulated as density-based clustering. Methods: In this paper, a new approach named iOPTICS-GSO is developed, which is the improved Ordering Points to Identify the Clustering Structure (OPTICS) algorithm with Glowworm swarm optimization algorithm (GSO) to optimize the parameters in OPTICS when finding dense sub-networks. In our iOPTICS-GSO, the concept of core node is redefined and the Euclidean distance in OPTICS is replaced with the improved similarity between the nodes in the PPI network according to their interaction strength, and dense sub-networks are considered as protein complexes. Results: The experiment results have shown that our iOPTICS-GSO outperforms of algorithms such as DBSCAN, CFinder, MCODE, CMC, COACH, ClusterOne MCL and OPTICS-PSO in terms of f-measure and p-value on four DPINs, which are from the DIP, Krogan, MIPS and Gavin datasets. In addition, our predicted protein complexes have a small p-value and thus are highly likely to be true protein complexes. Conclusion: The proposed iOPTICS-GSO gains optimal clustering results by adopting GSO algorithm to optimize the parameters in OPTICS, and the result on four datasets shows superior performance. What's more, the results provided clues for biologists to verify and find new protein complexes.