Research On The Methods Of Identifying Complexes Based On Dynamic Protein Interaction Networks

With the deepening of systems biology and proteomics research,studying the structure and function of protein and Protein-Protein Interaction(PPI)network to comprehensively understand protein and systematically analyze life activities are become one of the hottest bioinformatics researches.In particular,the identification of protein complexes and functional modules plays a key role in understanding major cellular processes and biological functions from protein-protein interaction(PPI)networks.Various computational algorithms have also been proposed based on static Protein-Protein Interaction(PPI)network accumulated in different conditions and time points leading to the dynamics inherent to these networks has been overlooked and computational algorithms cannot effectively reflect the real dynamic changes of protein interaction network in cell.Therefore,the performance of the algorithm has negative influence to identify protein complex.Focused on this problem,we construct a series of active and significance networks(AS-PINs)based on static PPI network and protein's active and significance judging principle which is used to identify the active and significance time points for each protein.To evaluate the performances of the network,MCL algorithm is applied to predict protein complex and compare their performances in AS-PIN,A-PIN,TC-PIN and SPIN.The experimental results show that the active and significance network can reflect the dynamic changes of protein network.Furthermore,identifying protein complexes from the network exhibit a very good experimental results.In addition,the network can effectively improve the performance of the clustering algorithms.DCPIA,a novel protein complex clustering algorithm based on just-in-time mechanism and core-attachment structure,is proposed.The DPCIA algorithm consists of three steps.Firstly,we construct a series of active and significance protein network(AS-PIN)according to protein's active and significance judging principle.Secondly,all of the maximal cliques are selected as seeds from each AS-PIN to build the protein complex cores.Finally,during the growth process,proteins with a higher interaction coefficient(IC)with the cores than the given threshold and the protein in these cores are then added to a new attachment set.After we obtain an attachment protein set,the neighbor of continuous adjacent TSNs is recursively checked similarly to determine whether or not these protein are part of the protein complex.The paper compare DPCIA with several efficient,existing clustering algorithms and reveal that the accuracy values of DPCIA clusters are much higher than those of other algorithms.Active and significance networks proposed in this paper start off from dynamic characteristics of protein interaction network and solve some problems effectively based on static protein interaction network.The proposed clustering algorithms,DPCIA not only have good clustering performances,and have high efficient and high reliability.