Subject Area物理化学
A Novel model for Mucin-type O-glycosylation sites Prediction
Zhou K(周坤); Ai CZ(艾纯芝); Yang L(杨凌)
Conference Name9th International Meeting of the International-Society-for-the-Study-of-Xenobiotics
Conference Date2010-9-04
Conference Place土耳其
Funding Organization国际药物代谢学会
AbstractGlycosylation, one of the most common protein post-translational modifications, is involved in a variety of important biological processes, including protein stability, solubility, secretion of signal, extracellular recognition, regulation of interactions, etc. The identification of glycosylation sites in a query protein will help to understand its biological function, while till now only a little work has done for the recognition sequence for the O-glycosylation in experiment due to the difficulties in isolation and analysis, therefore, it is necessary to develop computational method for identifying the glycosylation sites in protein to bridge the gap between the large number of known protein sequences and the small munber of proteins experimentally identified glycosylation sites. To accurately predict O-glycosylation sites, a new model has been developed with genetic algorithm to select crucial features among the large amount of amino acid properties encoded feature vectors[1]. 328 nonapeptides of O-glycosylation and 2855 of non-O-glycosylation sites, were retrieved from Swiss-Prot database. The peptides represented by 526 amino acid properties are transformed into a numeric vector with 4737 features. Based on the selected top 50 features with genetic algorithm, a predictive model was established with neural network method. The model exhibited a good predictability with a cross-validation accuracy of 80.5% for the whole set, and the accuracies were 81.7% and 68.0% for non-glycosylation and glycosylation sites respectively, which is comparative to the NetOGlyc3.1,one of useful O-glycosylation site predictors at present[2]. According to the features selected, we can conclude that the propeties of the 4th and the 8th amino acid of the nonapeptides contribute more to the glycosylation, and the accessibility of the 8th amino acid impact most greatly. This model can not only be used as fast and accurate screener to identify the glycosylation sites of large protein darasets, but can determine the crucial amino acid properties contributing more to glycosylation.
WOS IDWOS:000281147700083
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Document Type会议论文
Corresponding AuthorYang L(杨凌)
Recommended Citation
GB/T 7714
Zhou K,Ai CZ,Yang L. A Novel model for Mucin-type O-glycosylation sites Prediction[C],2010:46/2.
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