蛋白質醣基化是一個很重要的轉譯後修飾，這樣的修飾會影響蛋白質的許多功能，例如：結構、活性及細胞間的交互作用。由於利用生物實驗分析較為困難且會有龐大的驗證工作，因此最近幾年有許多研究中提出利用電腦計算方式去分析蛋白質醣基化位置。而在這些研究中所用到的分析醣基化模型主要是利用醣基化位置周圍的氨基酸分佈情形作為分析的特徵。此外，以往的預測工具是只針對特定的醣基化類型作預測。因此，我們根據氨基酸對及與溶劑接觸表面積大小做結合、氨基酸對及氨基酸和氨基酸對在特定區域發生的情形等特徵並使用支持向量機(SVM)建造出可以預測O-linked, N-linked及C-linked的三種醣基化類型其發生位置的方法。最後得到四組準確度數據，分別為在Serine及Threonine上發生的O-linked醣基化其預測準確度為95%及91%；N-linked醣基化發生在Asparagine上其預測準確度為96%；而在Tryptophan 上發生的C-linked醣基化預測準確度則為95%。我們的預測工具：GSI便能提供預測O-linked、N-linked及C-linked三種醣基化類型。 Protein glycosylation is an important post-translational modification (PTM) to affect various molecular functions such as structure, biological activity and protein-protein interaction. Due to the difficulties of biological experiments and the huge amount of identification works, there are several works were proposed in recent years to identify protein glycosylation sites by computational approaches. The features of their identification model were mainly amino acids surrounding the glycosylation sites. All of previous prediction tools are against respective types of glycosylation. Therefore, we develop prediction methods to identify protein glycosylation sites include O-linked, N-linked and C-linked glycosylation using support vector machine (SVM) based on dipeptide combined with accessible surface area, region combined with amino acid, and dipeptide. It shows that the accuracy of O-linked glycosylation on serine and threonine, N-linked on asparagine and C-linked on tryptophan are 95%, 91%, 96% and 95%. We implemented in GSI, a web server to identify O-linked, N-linked and C-linked glycosylation sites.
