ABSTRACT
Protein kinases are essential for signal transduction and control of most cellular processes, including metabolism, membrane transport, motility, and cell cycle. Despite the critical role of kinases in cells and their strong association with diseases, good coverage of their interactions is available for only a fraction of the 535 human kinases. Here, we present a comprehensive mass-spectrometry-based analysis of a human kinase interaction network covering more than 300 kinases. The interaction dataset is a high-quality resource with more than 5,000 previously unreported interactions. We extensively characterized the obtained network and were able to identify previously described, as well as predict new, kinase functional associations, including those of the less well-studied kinases PIM3 and protein O-mannose kinase (POMK). Importantly, the presented interaction map is a valuable resource for assisting biomedical studies. We uncover dozens of kinase-disease associations spanning from genetic disorders to complex diseases, including cancer.
Subject(s)
Gene Regulatory Networks , Genetic Diseases, Inborn/genetics , Neoplasms/genetics , Protein Kinases/genetics , Protein Serine-Threonine Kinases/genetics , Proto-Oncogene Proteins/genetics , Computational Biology/methods , Datasets as Topic , Gene Expression Regulation , Gene Ontology , Genetic Diseases, Inborn/enzymology , Genetic Diseases, Inborn/pathology , Humans , Metabolic Networks and Pathways/genetics , Molecular Sequence Annotation , Muscular Dystrophies/enzymology , Muscular Dystrophies/genetics , Muscular Dystrophies/pathology , Neoplasms/enzymology , Neoplasms/pathology , Neurodegenerative Diseases/enzymology , Neurodegenerative Diseases/genetics , Neurodegenerative Diseases/pathology , Protein Interaction Mapping/methods , Protein Kinases/chemistry , Protein Kinases/classification , Protein Kinases/metabolism , Protein Serine-Threonine Kinases/chemistry , Protein Serine-Threonine Kinases/metabolism , Proto-Oncogene Proteins/chemistry , Proto-Oncogene Proteins/metabolism , Signal TransductionABSTRACT
Quality control is increasingly recognized as a crucial aspect of mass spectrometry based proteomics. Several recent papers discuss relevant parameters for quality control and present applications to extract these from the instrumental raw data. What has been missing, however, is a standard data exchange format for reporting these performance metrics. We therefore developed the qcML format, an XML-based standard that follows the design principles of the related mzML, mzIdentML, mzQuantML, and TraML standards from the HUPO-PSI (Proteomics Standards Initiative). In addition to the XML format, we also provide tools for the calculation of a wide range of quality metrics as well as a database format and interconversion tools, so that existing LIMS systems can easily add relational storage of the quality control data to their existing schema. We here describe the qcML specification, along with possible use cases and an illustrative example of the subsequent analysis possibilities. All information about qcML is available at http://code.google.com/p/qcml.
