Literature DB >> 15231530

Prediction of phosphorylation sites using SVMs.

Jong Hun Kim1, Juyoung Lee, Bermseok Oh, Kuchan Kimm, Insong Koh.   

Abstract

MOTIVATION: Phosphorylation is involved in diverse signal transduction pathways. By predicting phosphorylation sites and their kinases from primary protein sequences, we can obtain much valuable information that can form the basis for further research. Using support vector machines, we attempted to predict phosphorylation sites and the type of kinase that acts at each site.
RESULTS: Our prediction system was limited to phosphorylation sites catalyzed by four protein kinase families and four protein kinase groups. The accuracy of the predictions ranged from 83 to 95% at the kinase family level, and 76-91% at the kinase group level. The prediction system used-PredPhospho-can be applied to the functional study of proteins, and can help predict the changes in phosphorylation sites caused by amino acid variations at intra- and interspecies levels.

Mesh:

Substances:

Year:  2004        PMID: 15231530     DOI: 10.1093/bioinformatics/bth382

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  79 in total

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7.  Identifying human kinase-specific protein phosphorylation sites by integrating heterogeneous information from various sources.

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8.  Computational identification of protein methylation sites through bi-profile Bayes feature extraction.

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9.  Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling.

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10.  AtSIG6, a plastid sigma factor from Arabidopsis, reveals functional impact of cpCK2 phosphorylation.

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