Prediction of the O-glycosylation sites in protein by layered neural networks and support vector machines

Authors:
Ikuko Nishikawa;Hirotaka Sakamoto;Ikue Nouno;Takeshi Iritani;Kazutoshi Sakakibara;Masahiro Ito
Affiliations:
College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan;College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan;College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan;College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan;College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan;College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Year:
2006

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Abstract

O-glycosylation is one of the main types of the mammalian protein glycosylation, which is serine or threonine specific, though any consensus sequence is still unknown. In this paper, a layered neural network and a support vector machine are used for the prediction of O-glycosylation sites. Three types of encoding for a protein sequence within a fixed size window are used as the input to the network, that is, a sparse coding which distinguishes all 20 amino acid residues, 5-letter coding and hydropathy coding. In the neural network, one output unit gives the prediction whether a particular site of serine or threonine is glycosylated, while SVM classifies into the 2 classes. The performance is evaluated by the Matthews correlation coefficient. The preliminary results on the neural network show the better performance of the sparse and 5-letter codings compared with the hydropathy coding, while the improvement according to the window size is shown to be limited to a certain extent by SVM.