AutoMotif Server for prediction of phosphorylation sites in proteins using support vector machine: 2007 update

作者：Plewczynski Dariusz^*; Tkacz Adrian; Wyrwicz Lucjan S; Rychlewski Leszek; Ginalski Krzysztof

来源：Journal of Molecular Modeling, 2008, 14(1): 69-76.

DOI：10.1007/s00894-007-0250-3

• machine learning
• protein kinases
• phosphorylation
• phosphoserine
• phosphothreonine
• phosphotyrosine
• Swiss-Prot database
• post-translational modifications
• support vector machine

We present here the recent update of AutoMotif Server (AMS 2.0) that predicts post-translational modification sites in protein sequences. The support vector machine (SVM) algorithm was trained on data gathered in 2007 from various sets of proteins containing experimentally verified chemical modifications of proteins. Short sequence segments around a modification site were dissected from a parent protein, and represented in the training set as binary or profile vectors. The updated efficiency of the SVM classification for each type of modification and the predictive power of both representations were estimated using leave-one-out tests for model of general phosphorylation and for modifications catalyzed by several specific protein kinases. The accuracy of the method was improved in comparison to the previous version of the service (Plewczynski et al., "AutoMotif server: prediction of single residue post-translational modifications in proteins", Bioinformatics 21: 2525-7, 2005). The precision of the updated version reached over 90% for selected types of phosphorylation and was optimized in trade of lower recall value of the classification model. The AutoMotif Server version 2007 is freely available at http://ams2.bioinfo.pl/. Additionally, the reference dataset for optimization of prediction of phosphorylation sites, collected from the UniProtKB was also provided and can be accessed at http://ams2.bioinfo.pl/data/.

• 出版日期2008-1