Protein complexes are the basic units of macro-molecular organizations and help us to understand the cell's mechanism.The development of the yeast two-hybrid,tandem affinity purification,and mass spectrometry high...Protein complexes are the basic units of macro-molecular organizations and help us to understand the cell's mechanism.The development of the yeast two-hybrid,tandem affinity purification,and mass spectrometry high-throughput proteomic techniques supplies a large amount of protein-protein interaction data,which make it possible to predict overlapping complexes through computational methods.Research shows that overlapping complexes can contribute to identifying essential proteins,which are necessary for the organism to survive and reproduce,and for life's activities.Scholars pay more attention to the evaluation of protein complexes.However,few of them focus on predicted overlaps.In this paper,an evaluation criterion called overlap maximum matching ratio(OMMR) is proposed to analyze the similarity between the identified overlaps and the benchmark overlap modules.Comparison of essential proteins and gene ontology(GO) analysis are also used to assess the quality of overlaps.We perform a comprehensive comparison of serveral overlapping complexes prediction approaches,using three yeast protein-protein interaction(PPI) networks.We focus on the analysis of overlaps identified by these algorithms.Experimental results indicate the important of overlaps and reveal the relationship between overlaps and identification of essential proteins.展开更多
Protein complexes play important roles in integrating individual gene products to perform useful cellular functions.The increasing mount of protein–protein interaction(PPI)data has enabled us to predict protein compl...Protein complexes play important roles in integrating individual gene products to perform useful cellular functions.The increasing mount of protein–protein interaction(PPI)data has enabled us to predict protein complexes.In spite of the advances in these computational approaches and experimental techniques,it is impossible to construct an absolutely reliable PPI network.Taking into account the reliability of interactions in the PPI network,we have constructed a weighted protein–protein interaction(WPPI)network,in which the reliability of each interaction is represented as a weight using the topology of the PPI network.As overlaps are likely to have biological importance,we proposed a novel method named WN-PC(weighted network-based method for predicting protein complexes)to predict overlapping protein complexes on the WPPI network.The proposed algorithm predicts neighborhood graphs with an aggregation coefficient over a threshold as candidate complexes,and binds attachment proteins to candidate complexes.Finally,we have filtered redundant complexes which overlap other complexes to a very high extent in comparison to their density and size.A comprehensive comparison between competitive algorithms and our WN-PC method has been made in terms of the F-measure,coverage rate,and P-value.We have applied WN-PC to two different yeast PPI data sets,one of which is a huge PPI network consisting of over 6000 proteins and 200000 interactions.Experimental results show that WN-PC outperforms the state-of-the-art methods.We think that our research may be helpful for other applications in PPI networks.展开更多
基金Project supported by the National Scientific Research Foundation of Hunan Province, China （Nos. 14C0096, 10C0408, and 10B010）, the Natural Science Foundation of Hunan Province, China （Nos. 13JJ4106 and 14J J3138）, and the Science and Technology Plan Project of Hunan Province, China （No. 2010FJ3044）
文摘Protein complexes are the basic units of macro-molecular organizations and help us to understand the cell's mechanism.The development of the yeast two-hybrid,tandem affinity purification,and mass spectrometry high-throughput proteomic techniques supplies a large amount of protein-protein interaction data,which make it possible to predict overlapping complexes through computational methods.Research shows that overlapping complexes can contribute to identifying essential proteins,which are necessary for the organism to survive and reproduce,and for life's activities.Scholars pay more attention to the evaluation of protein complexes.However,few of them focus on predicted overlaps.In this paper,an evaluation criterion called overlap maximum matching ratio(OMMR) is proposed to analyze the similarity between the identified overlaps and the benchmark overlap modules.Comparison of essential proteins and gene ontology(GO) analysis are also used to assess the quality of overlaps.We perform a comprehensive comparison of serveral overlapping complexes prediction approaches,using three yeast protein-protein interaction(PPI) networks.We focus on the analysis of overlaps identified by these algorithms.Experimental results indicate the important of overlaps and reveal the relationship between overlaps and identification of essential proteins.
基金Project supported by the Scientific Research Foundation of HunanProvince(No.11C0125)the Scientific Planning Project of Hunan Province(No.XJK011CXJ002)the Science and Technology Foundation of Changsha City(Nos.K1205049-11 and K1205048-11),China
文摘Protein complexes play important roles in integrating individual gene products to perform useful cellular functions.The increasing mount of protein–protein interaction(PPI)data has enabled us to predict protein complexes.In spite of the advances in these computational approaches and experimental techniques,it is impossible to construct an absolutely reliable PPI network.Taking into account the reliability of interactions in the PPI network,we have constructed a weighted protein–protein interaction(WPPI)network,in which the reliability of each interaction is represented as a weight using the topology of the PPI network.As overlaps are likely to have biological importance,we proposed a novel method named WN-PC(weighted network-based method for predicting protein complexes)to predict overlapping protein complexes on the WPPI network.The proposed algorithm predicts neighborhood graphs with an aggregation coefficient over a threshold as candidate complexes,and binds attachment proteins to candidate complexes.Finally,we have filtered redundant complexes which overlap other complexes to a very high extent in comparison to their density and size.A comprehensive comparison between competitive algorithms and our WN-PC method has been made in terms of the F-measure,coverage rate,and P-value.We have applied WN-PC to two different yeast PPI data sets,one of which is a huge PPI network consisting of over 6000 proteins and 200000 interactions.Experimental results show that WN-PC outperforms the state-of-the-art methods.We think that our research may be helpful for other applications in PPI networks.
