Glycosylation site prediction using ensembles of Support Vector Machine classifiers.
Glycosylation site prediction using ensembles of Support Vector Machine classifiers.
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DOI：
10.1186/1471-2105-8-438
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发表时间：
2007-11-09
影响因子：
3
通讯作者：
Honavar V
中科院分区：
文献类型：
--
作者：
Caragea C;Sinapov J;Silvescu A;Dobbs D;Honavar V
糖基化是真核细胞中最复杂的蛋白质翻译后修饰（PTMs）之一。糖基化在从蛋白质折叠和亚细胞定位到配体识别和细胞-细胞相互作用的生物过程中起着重要作用。糖基化位点的实验鉴定是昂贵且费力的。因此，有显着的兴趣，在发展的计算方法，从氨基酸序列的糖基化位点的可靠预测。我们探索了用于训练分类器的机器学习方法，以使用来自目标氨基酸残基及其序列邻居的信息来预测可能被糖基化的氨基酸残基。我们比较了支持向量机分类器和支持向量机分类器集合的性能，这些分类器是在从O-GlycBase版本6.00中提取的实验确定的N-连接，O-连接和C-连接糖基化位点的数据集上训练的，该数据库包含来自几个不同物种的242种蛋白质。我们的实验结果表明，支持向量机分类器的集成优于单一的支持向量机分类器的预测糖基化位点的问题，在一系列的标准措施比较分类器的性能。所得到的方法已在EnsembleGly中实现，EnsembleGly是一个用于糖基化位点预测的Web服务器。支持向量机分类器集成提供了一种准确可靠的方法来自动识别糖蛋白序列中假定的糖基化位点。
Glycosylation is one of the most complex post-translational modifications (PTMs) of proteins in eukaryotic cells. Glycosylation plays an important role in biological processes ranging from protein folding and subcellular localization, to ligand recognition and cell-cell interactions. Experimental identification of glycosylation sites is expensive and laborious. Hence, there is significant interest in the development of computational methods for reliable prediction of glycosylation sites from amino acid sequences. We explore machine learning methods for training classifiers to predict the amino acid residues that are likely to be glycosylated using information derived from the target amino acid residue and its sequence neighbors. We compare the performance of Support Vector Machine classifiers and ensembles of Support Vector Machine classifiers trained on a dataset of experimentally determined N-linked, O-linked, and C-linked glycosylation sites extracted from O-GlycBase version 6.00, a database of 242 proteins from several different species. The results of our experiments show that the ensembles of Support Vector Machine classifiers outperform single Support Vector Machine classifiers on the problem of predicting glycosylation sites in terms of a range of standard measures for comparing the performance of classifiers. The resulting methods have been implemented in EnsembleGly, a web server for glycosylation site prediction. Ensembles of Support Vector Machine classifiers offer an accurate and reliable approach to automated identification of putative glycosylation sites in glycoprotein sequences.
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DOI：
10.1016/s0097-8485(96)80004-0
发表时间：
1996-03-01
期刊：
COMPUTERS & CHEMISTRY
影响因子：
--
作者：
Gribskov, M;Robinson, NL
通讯作者：
Robinson, NL
影响因子：
4.8
作者：
Burges, CJC
通讯作者：
Burges, CJC
影响因子：
2.9
作者：
Mentesana, PE;Konopka, JB
通讯作者：
Konopka, JB
影响因子：
3.1
作者：
Li, Sujun;Liu, Boshu;Li, Yixue
通讯作者：
Li, Yixue
影响因子：
5.6
作者：
Eisenhaber, B;Bork, P;Eisenhaber, F
通讯作者：
Eisenhaber, F