Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/60845
標題: PERT: 一個設計耐高溫蛋白質的綜合資訊平台
PERT: an integrated informatics platform for thermostable protein design
作者: 洪聖柔
Hung, Sheng-Jou
關鍵字: 蛋白質工程
protein engineering
蛋白質熱穩定性
支持向量機 (SVM)
特徵選取
國立中興大學
protein thermostability
support vector machine (SVM)
feature selection
National Chung Hsing University
出版社: 基因體暨生物資訊學研究所
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摘要: 如何設計具有高熱穩定性的蛋白質是蛋白質工程探討的重點之一。選擇適當的突變位置通常需要結構資訊,但在大部分情況下,結構資訊仍是未知。為此,本研究提出一個以蛋白質序列為基礎的資訊綜合網路平台PERT (Protein Engineering Research Tool)。有助於研究人員了解目標蛋白質的結構特性,PERT整合了目前常用的二級結構預測、序列保留性計算、胺基酸暴露程度預測、蛋白質表面亂度計算、蛋白質無結構區域預測、 domains 以及 domain linkers預測、帶電量計算預測、疏水性指數計算等生物資訊。另外,基於目前已發表增加蛋白質穩定度的突變策略,PERT會建議研究人員合適的突變位置,並同時計算出可能的穩定度變化。為了更精準預測蛋白質在突變後的穩定度,我們並開發以支持向量機為基礎的分類器模型PERTsvm。來自序列資訊、結構預測資訊和AAindex數據庫中胺基酸屬性的資料做為特徵集。為了提升支持向量機分類的性能,利用特徵選取工具feature selection toolbox (FST) 中序列前向搜索法sequential forward selection (SFS)挑出對分類有助益的特徵集。目前結果顯示我們預測的準確度優於目前現有的預測軟體I-Mutant和MUpro。這項研究將有助於研究人員了解目標蛋白質的結構特性,並且能減少實驗的次數和成本。
One of the focus on protein engineering is how to design a high thermostable protein. However, in many cases, it requires experts to have a detailed structural information of the protein being modified, which is not always available. In this research we present a service platform PERT (Protein Engineering Research Tool). To provide a comprehensive structural information for researchers, PERT gathers many sequence-based prediction results, including secondary structure, sequence conservation, solvent accessibility, disorder region, domains and domain linkers. Based on some current strategies to increase protein stability, PERT will suggest the mutation site candidates, and predicting the stability change after mutation. In order to achieve a better stability prediction, we also develop a new classification model based on the support vector machine (SVM) technique. The feature set is derived from sequence information, the predicted structural information, and amino acid properties from AAindex database. These features were selected by sequential forward selection (SFS) using feature selection toolbox (FST). Current results showed the new predictor PERTsvm would be more accurate compared to I-Mutant and MU-pro. This study would be very helpful for researchers to understand the structural information of the target protein, and to reduce the cost of trials and errors.
URI: http://hdl.handle.net/11455/60845
其他識別: U0005-2607201215433600
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2607201215433600
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