MyProteinNet: build up-to-date protein interaction networks for organisms, tissues and user-defined contexts

The identification of the molecular pathways active in specific contexts, such as disease states or drug responses, often requires an extensive view of the potential interactions between a subset of proteins. This view is not easily obtained: it requires the integration of context-specific protein list or expression data with up-to-date data of protein interactions that are typically spread across multiple databases. The MyProteinNet web server allows users to easily create such context-sensitive protein interaction networks. Users can automatically gather and consolidate data from up to 11 different databases to create a generic protein interaction network (interactome). They can score the interactions based on reliability and filter them by user-defined contexts including molecular expression and protein annotation. The output of MyProteinNet includes the generic and filtered interactome files, together with a summary of their network attributes. MyProteinNet is particularly geared toward building human tissue interactomes, by maintaining tissue expression profiles from multiple resources. The ability of MyProteinNet to facilitate the construction of up-to-date, context-specific interactomes and its applicability to 11 different organisms and to tens of human tissues, make it a powerful tool in meaningful analysis of protein networks. MyProteinNet is available at http://netbio.bgu.ac.il/myproteinnet.

[1]  Damian Szklarczyk,et al.  The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored , 2010, Nucleic Acids Res..

[2]  Chunlei Wu,et al.  BioGPS and MyGene.info: organizing online, gene-centric information , 2012, Nucleic Acids Res..

[3]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

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

[5]  M. Selbach,et al.  Global quantification of mammalian gene expression control , 2011, Nature.

[6]  Jonathan D. G. Jones,et al.  Evidence for Network Evolution in an Arabidopsis Interactome Map , 2011, Science.

[7]  B. Kuster,et al.  Mass-spectrometry-based draft of the human proteome , 2014, Nature.

[8]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[9]  H. Lehrach,et al.  A Human Protein-Protein Interaction Network: A Resource for Annotating the Proteome , 2005, Cell.

[10]  Zhaohui S. Qin,et al.  A Global Protein Kinase and Phosphatase Interaction Network in Yeast , 2010, Science.

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

[12]  Ralf Herwig,et al.  IntScore: a web tool for confidence scoring of biological interactions , 2012, Nucleic Acids Res..

[13]  S. L. Wong,et al.  Towards a proteome-scale map of the human protein–protein interaction network , 2005, Nature.

[14]  G. von Heijne,et al.  Tissue-based map of the human proteome , 2015, Science.

[15]  A. Barabasi,et al.  Interactome Networks and Human Disease , 2011, Cell.

[16]  Ben Lehner,et al.  Tissue specificity and the human protein interaction network , 2009, Molecular systems biology.

[17]  Matthew A. Hibbs,et al.  Discovery of biological networks from diverse functional genomic data , 2005, Genome Biology.

[18]  Esti Yeger Lotem,et al.  ResponseNet2.0: revealing signaling and regulatory pathways connecting your proteins and genes—now with human data , 2013, Nucleic Acids Res..

[19]  Alessandro Vullo,et al.  Ensembl 2015 , 2014, Nucleic Acids Res..

[20]  Kara Dolinski,et al.  The BioGRID Interaction Database: 2008 update , 2008, Nucleic Acids Res..

[21]  Ellen T. Gelfand,et al.  The Genotype-Tissue Expression (GTEx) project , 2013, Nature Genetics.

[22]  Ron Shamir,et al.  SPIKE: a database of highly curated human signaling pathways , 2010, Nucleic Acids Res..

[23]  A. Barabasi,et al.  Network medicine : a network-based approach to human disease , 2010 .

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

[25]  James R. Knight,et al.  A Protein Interaction Map of Drosophila melanogaster , 2003, Science.

[26]  E. Lundberg,et al.  A Genecentric Human Protein Atlas for Expression Profiles Based on Antibodies* , 2008, Molecular & Cellular Proteomics.

[27]  Uwe Schlattner,et al.  Yeast Two-Hybrid, a Powerful Tool for Systems Biology , 2009, International journal of molecular sciences.

[28]  Pablo Villoslada,et al.  Modules, networks and systems medicine for understanding disease and aiding diagnosis , 2014, Genome Medicine.

[29]  M. Gerstein,et al.  Genomic analysis of regulatory network dynamics reveals large topological changes , 2004, Nature.

[30]  Gary D Bader,et al.  A draft map of the human proteome , 2014, Nature.

[31]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[32]  Roded Sharan,et al.  Enhancing the Prioritization of Disease-Causing Genes through Tissue Specific Protein Interaction Networks , 2012, PLoS Comput. Biol..

[33]  D. Karger,et al.  Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity , 2009, Nature Genetics.

[34]  K. Gunsalus,et al.  Network modeling links breast cancer susceptibility and centrosome dysfunction. , 2007, Nature genetics.

[35]  P. Uetz,et al.  From protein networks to biological systems , 2005, FEBS letters.

[36]  Bridget E. Begg,et al.  A Proteome-Scale Map of the Human Interactome Network , 2014, Cell.

[37]  Martin H. Schaefer,et al.  HIPPIE: Integrating Protein Interaction Networks with Experiment Based Quality Scores , 2012, PloS one.

[38]  Eric T. Wang,et al.  An Abundance of Ubiquitously Expressed Genes Revealed by Tissue Transcriptome Sequence Data , 2009, PLoS Comput. Biol..

[39]  S. Batalov,et al.  A gene atlas of the mouse and human protein-encoding transcriptomes. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

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

[41]  Carlos Prieto,et al.  APID: Agile Protein Interaction DataAnalyzer , 2006, Nucleic Acids Res..

[42]  Yuanfang Guan,et al.  Tissue-Specific Functional Networks for Prioritizing Phenotype and Disease Genes , 2012, PLoS Comput. Biol..

[43]  P. Bork,et al.  Dynamic Complex Formation During the Yeast Cell Cycle , 2005, Science.

[44]  Gary D Bader,et al.  PSICQUIC and PSISCORE: accessing and scoring molecular interactions , 2011, Nature Methods.

[45]  Teruyoshi Hishiki,et al.  BodyMap: a human and mouse gene expression database , 2000, Nucleic Acids Res..

[46]  Ilan Y. Smoly,et al.  Comparative Analysis of Human Tissue Interactomes Reveals Factors Leading to Tissue-Specific Manifestation of Hereditary Diseases , 2014, PLoS Comput. Biol..

[47]  Bonnie Berger,et al.  A Quantitative Chaperone Interaction Network Reveals the Architecture of Cellular Protein Homeostasis Pathways , 2014, Cell.

[48]  Javier De Las Rivas,et al.  Protein–Protein Interactions Essentials: Key Concepts to Building and Analyzing Interactome Networks , 2010, PLoS Comput. Biol..

[49]  G. Hong,et al.  Nucleic Acids Research , 2015, Nucleic Acids Research.

[50]  Ilan Y. Smoly,et al.  The TissueNet database of human tissue protein–protein interactions , 2012, Nucleic Acids Res..