Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/60848
標題: 以蛋白質序列為基礎之結構型B細胞抗原決定位預測
SEP: Sequence-based Strategy for structural B-cell epitope prediction
作者: 梁恆豪
Liang, Heng-Hao
關鍵字: 結構型抗原決定位
structural epitope
支持向量機
關聯式規則
位置特異性分數矩陣
support vector machines
association rule
position-specific scoring matrix
出版社: 基因體暨生物資訊學研究所
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摘要: 免疫反應為人體消滅侵入體內之病原體之重要的防禦機制,而抗原決定位又為抗原抗體結合時,抗原蛋白質上與抗體結合最重要的位置。病原體之抗原決定位多為結構型抗原決定位,但現有之以序列做結構型抗原決定位預測網站,其效能還有很大的進步空間。因此,本研究利用SVM作為學習的工具,從蛋白質的序列進行預測。利用5種特徵編碼: 二進位、蛋白質組成、序列保留性、二級結構與可接觸表面面積及關聯性規則作為建構第一層之模型依據,選出適合個編碼之片段長度。接著將第一層模型輸出之信心分數做為第二層模型之輸入值,利用第二層模型將第一層模型做整合,來提高預測之準確率。最後完成之預測模型對於結構型抗原決定位之預測準確度可達到63%,效能比現有之預測網站較佳,最後再以Paracoccus denitrificans之two-subunit cytochrome c oxidase做測試,準確可達66%。
Immune reaction is the most important defense mechanism for destroying invading-pathogens in our body, and the epitope is the position of antigen-antibody interaction on pathogen proteins. Most of epitopes belong to structural epitope, but the existing sequence-based predicting website still has a lot of ways to improve the predicting performance. Therefore, in this study used SVM as machine learning tool to predict epitope by protein sequences. First, we built five SVM models in the first layer according to five features include binary, composition, position-specific scoring matrix, secondary structure, accessible surface area and association rule, then choosing the patterns which have the best performance in each model. Second, using the confidence score of first layer models as the input value for the SVM model in the second layer that SVM model integrated first layer SVM models for improving the predicting accuracy. The final prediction model can achieve up to 63% of accuracy in epitope predicting result, the predicting performance is batter then existing predicting website. Finally, a case study used two-subunit cytochrome c oxidase of Paracoccus denitrificans as testing, the accuracy can achieve up to 66%.
URI: http://hdl.handle.net/11455/60848
其他識別: U0005-2808201312574800
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2808201312574800
Appears in Collections:基因體暨生物資訊學研究所

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