Detecting temporal protein complexes from dynamic protein-protein interaction networks

BackgroundProteins dynamically interact with each other to perform their biological functions. The dynamic operations of protein interaction networks (PPI) are also reflected in the dynamic formations of protein complexes. Existing protein complex detection algorithms usually overlook the inherent temporal nature of protein interactions within PPI networks. Systematically analyzing the temporal protein complexes can not only improve the accuracy of protein complex detection, but also strengthen our biological knowledge on the dynamic protein assembly processes for cellular organization.ResultsIn this study, we propose a novel computational method to predict temporal protein complexes. Particularly, we first construct a series of dynamic PPI networks by joint analysis of time-course gene expression data and protein interaction data. Then a Time Smooth Overlapping Complex Detection model (TS-OCD) has been proposed to detect temporal protein complexes from these dynamic PPI networks. TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point. Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points.ConclusionsExtensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.

[1]  Haiyuan Yu,et al.  Genome-scale analysis of interaction dynamics reveals organization of biological networks , 2012, Bioinform..

[2]  Pall I. Olason,et al.  A human phenome-interactome network of protein complexes implicated in genetic disorders , 2007, Nature Biotechnology.

[3]  Yuan Zhang,et al.  Evolutionary analysis of functional modules in dynamic PPI networks , 2012, BCB.

[4]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[5]  A. Barabasi,et al.  High-Quality Binary Protein Interaction Map of the Yeast Interactome Network , 2008, Science.

[6]  S. Pu,et al.  Up-to-date catalogues of yeast protein complexes , 2008, Nucleic acids research.

[7]  Adam J. Smith,et al.  The Database of Interacting Proteins: 2004 update , 2004, Nucleic Acids Res..

[8]  Sean R. Collins,et al.  Global landscape of protein complexes in the yeast Saccharomyces cerevisiae , 2006, Nature.

[9]  Ioannis Xenarios,et al.  DIP: The Database of Interacting Proteins: 2001 update , 2001, Nucleic Acids Res..

[10]  Hon Wai Leong,et al.  Temporal dynamics of protein complexes in PPI Networks: a case study using yeast cell cycle dynamics , 2012, BMC Bioinformatics.

[11]  P. Bork,et al.  Proteome survey reveals modularity of the yeast cell machinery , 2006, Nature.

[12]  Lan V. Zhang,et al.  Evidence for dynamically organized modularity in the yeast protein–protein interaction network , 2004, Nature.

[13]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[14]  Adrian E. Raftery,et al.  Integrating external biological knowledge in the construction of regulatory networks from time-series expression data , 2012, BMC Systems Biology.

[15]  Le Song,et al.  KELLER: estimating time-varying interactions between genes , 2009, Bioinform..

[16]  Yudong Chen,et al.  Detecting Overlapping Temporal Community Structure in Time-Evolving Networks , 2013, ArXiv.

[17]  Mark E. J. Newman,et al.  An efficient and principled method for detecting communities in networks , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  Anton J. Enright,et al.  An efficient algorithm for large-scale detection of protein families. , 2002, Nucleic acids research.

[19]  J. Thornton,et al.  Diversity of protein–protein interactions , 2003, The EMBO journal.

[20]  A. Kudlicki,et al.  Logic of the Yeast Metabolic Cycle: Temporal Compartmentalization of Cellular Processes , 2005, Science.

[21]  See-Kiong Ng,et al.  Systematic gene function prediction from gene expression data by using a fuzzy nearest-cluster method , 2006, BMC Bioinformatics.

[22]  K. Schittkowski,et al.  NONLINEAR PROGRAMMING , 2022 .

[23]  Yi Pan,et al.  Construction and application of dynamic protein interaction network based on time course gene expression data , 2013, Proteomics.

[24]  Trey Ideker,et al.  Cytoscape 2.8: new features for data integration and network visualization , 2010, Bioinform..

[25]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[26]  L. Mirny,et al.  Protein complexes and functional modules in molecular networks , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Mona Singh,et al.  How and when should interactome-derived clusters be used to predict functional modules and protein function? , 2009, Bioinform..

[28]  Fang-Xiang Wu,et al.  Identifying protein complexes and functional modules - from static PPI networks to dynamic PPI networks , 2014, Briefings Bioinform..

[29]  N. Perrimon,et al.  Protein Complex–Based Analysis Framework for High-Throughput Data Sets , 2013, Science Signaling.

[30]  Mona Singh,et al.  Toward the dynamic interactome: it's about time , 2010, Briefings Bioinform..

[31]  Kahn Rhrissorrakrai,et al.  MINE: Module Identification in Networks , 2011, BMC Bioinformatics.

[32]  Alain Guénoche,et al.  Multifunctional proteins revealed by overlapping clustering in protein interaction network , 2011, Bioinform..

[33]  Le Ou-Yang,et al.  Protein Complex Detection via Weighted Ensemble Clustering Based on Bayesian Nonnegative Matrix Factorization , 2013, PloS one.

[34]  Mikkel N. Schmidt,et al.  Nonnegative Matrix Factorization with Gaussian Process Priors , 2008, Comput. Intell. Neurosci..

[35]  Alex E. Lash,et al.  Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..

[36]  Amr Ahmed,et al.  Recovering time-varying networks of dependencies in social and biological studies , 2009, Proceedings of the National Academy of Sciences.

[37]  Gary D. Bader,et al.  An automated method for finding molecular complexes in large protein interaction networks , 2003, BMC Bioinformatics.

[38]  Dao-Qing Dai,et al.  Exploring Overlapping Functional Units with Various Structure in Protein Interaction Networks , 2012, PloS one.

[39]  Aidong Zhang,et al.  Semantic integration to identify overlapping functional modules in protein interaction networks , 2007, BMC Bioinformatics.

[40]  Shigehiko Kanaya,et al.  Development and implementation of an algorithm for detection of protein complexes in large interaction networks , 2006, BMC Bioinformatics.

[41]  Chris H. Q. Ding,et al.  On the Equivalence of Nonnegative Matrix Factorization and Spectral Clustering , 2005, SDM.

[42]  Yongjin Park,et al.  How networks change with time , 2012, Bioinform..

[43]  Chen-Ching Lin,et al.  Dynamic protein interaction modules in human hepatocellular carcinoma progression , 2013, BMC Systems Biology.

[44]  P. Bork,et al.  Functional organization of the yeast proteome by systematic analysis of protein complexes , 2002, Nature.

[45]  Xiaoli Li,et al.  Computational approaches for detecting protein complexes from protein interaction networks: a survey , 2010, BMC Genomics.

[46]  Haiyuan Yu,et al.  Detecting overlapping protein complexes in protein-protein interaction networks , 2012, Nature Methods.

[47]  Fang-Xiang Wu,et al.  Detecting protein complexes from active protein interaction networks constructed with dynamic gene expression profiles , 2013, Proteome Science.

[48]  Min Wu,et al.  A core-attachment based method to detect protein complexes in PPI networks , 2009, BMC Bioinformatics.

[49]  Christie S. Chang,et al.  The BioGRID interaction database: 2013 update , 2012, Nucleic Acids Res..

[50]  Seungjin Choi,et al.  Inference of dynamic networks using time-course data , 2014, Briefings Bioinform..

[51]  angesichts der Corona-Pandemie,et al.  UPDATE , 1973, The Lancet.

[52]  Peng Jiang,et al.  SPICi: a fast clustering algorithm for large biological networks , 2010, Bioinform..

[53]  Dmitrij Frishman,et al.  MIPS: analysis and annotation of proteins from whole genomes in 2005 , 2005, Nucleic Acids Res..

[54]  Fang-Xiang Wu,et al.  Dynamic protein interaction network construction and applications , 2014, Proteomics.