NDLI: Prediction of the O-glycosylation by Support Vector Machines and Characteristics of the Crowded and Isolated O-glycosylation Sites

Content Provider	IEEE Xplore Digital Library
Author	Nakajima, Y. Sakakibara, K. Ito, M. Nishikawa, I.
Copyright Year	2009
Abstract	O-glycosylation of the mammalian protein is studied. It is serine or threonine specific, though any consensus sequence is still unknown. We have been applied support vector machines (SVM) for the prediction of O-glycosylation sites from various kinds of protein information, aiming to investigate a glycosylation condition and elucidate the mechanisms. In the present study, we focus on the distribution of the glycosylation sites. Many O-glycosylated sites are observed in clusters of closely spaced glycosylated sites, whereas the other sites are found sparsely or isolated. These two types of crowded and isolated sites may have different glycosylation mechanisms. We divide the whole O-glycosylation sites into the crowded and the isolated groups. For each group, SVM is trained to predict the O-glycosylation sites separately. The prediction results of two groups have different input information dependency. The results indicate that some motifs are expected for the isolated group, while the interaction between the glycosylated sites and the relative proportion of the surrounding amino acids affect the glycosylation for the crowded group. Then, we also compare the statistics of amino acid sequences around the glycosylation sites of both groups. As the results, some amino acids (proline, valine, alanine etc.) have high existence probabilities at each specific positions relative to a glycosylation site, especially for the isolated glycosylation. Moreover, independent component analysi (ICA) for the amino acid sequences elucidates position specific existences of the above amino acids, including well known proline at -1 and +3, which are found as different independent components.
Starting Page	1209
Ending Page	1212
File Size	283496
Page Count	4
File Format	PDF
ISBN	9781424447176
DOI	10.1109/IIH-MSP.2009.154
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-12
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Protein engineering Sequences Independent Component Analysis Independent component analysis Probability Amino acids Educational institutions glycosylation Support Vector Machine Statistics Support vector machines Information science protein prediction Alzheimer's disease
Content Type	Text
Resource Type	Article

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