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Matching up Phosphosites to Kinases: A Survey of Available Predictive Programs

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Over the past few years, research in phosphoproteomic has assisted a tremendous revolution thanks to instrumental technologies advances in mass spectrometry combined with innovative experimental strategies. This has allowed the identification of thousands of high confidence phosphosites. Presently, almost 60000 non-redundant phosphosites have been identified from ∼10000 non-redundant proteins and about 80% of these phosphosites have been identified from high throughput experiments in the last six years. The vast majority of phosphosites are still functionally uncharacterized and the kinases responsible of their generation are almost unknown.

Several computational approaches have been developed to link kinase families with putative substrates and although these are powerful tools, they are not commonly used.

Here we discuss about the present approaches and tools developed for predicting the functional link between the kinases and their substrates.

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Keywords: Phosphosites; consensus sequence; kinase; predictive programs

Document Type: Research Article

Publication date: June 1, 2010

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  • Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews written by leaders in the field, covering a wide range of the integration of biology with computer and information science.

    The journal focuses on reviews on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.

    Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics.
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