臺灣博碩士論文加值系統

熱穩定酵素在工業以及分子生物實驗中都扮演了極為重要的角色，其在高溫環境下作用的機制，更是研究者急欲解開的奧秘。傳統的研究侷限於以單一點突變來觀察蛋白質的熱穩定性，而對結構的研究以及電腦模擬軟體的開發則更進一步輔助實驗的進行；近年來更有研究學者以直接演化(direct evolution)的方式來加快研究的速度。然而，這些方法還是需要大量的時間以及人力來進行。
隨著基因體計畫的發展，包括嗜高溫微生物在內的許多生物基因已經被完整的定序，這也使得以統計為基礎的研究有足夠數量的資料來源，希望能歸納出影響酵素熱穩定性的關鍵機制及通則。這類研究大致可分為三類，一是整合了序列及結構的資料，透過理論及統計相關的分析來獲得熱穩定性與結構關係的資訊；部分研究是藉由比較同源的高低溫蛋白質作胺基酸偏好的統計；第三類的研究則是對高低溫物種的整個基因體作統計或是預測。
本論文共蒐集了4428個蛋白質序列，並依據蛋白質的功能分類，以支持向量機(support vector machine, SVM)為預測工具，以各種不同的轉譯法 — n-peptide composition測試，結果對蛋白質熱穩定性預測率可達95%以上，甚至99.6% (預測DNA polymerase)。我們也計算了各種n-peptide composition在高低溫酵素間的平均差，以t-test來檢測顯著性。與單一胺基酸組成的分部圖作比較，我們可以更為清楚的觀察到不同種類酵素之間的差異。
本研究提供了一個利用支持向量機快速準確預測蛋白質熱穩定性的方法；也從不同性質的胺基酸組成統計發現高低溫酵素之間小片段胺基酸的差異。相信這些結果能為蛋白質熱穩定性的研究及預測帶來有價值的參考資訊。

The secret of thermophiles living under (extreme) high temperature is an interesting topic for scientists to uncover. Natural or artificial thermostable proteins are quite important in molecular biological and commercial application. However, it is still a challenge to increase the thermostability of proteins by traditional molecular biological engineering. Thus, computational methods may be helpful to analyze and identify the key parts of protein sequences that related to thermostability. We tried to classify the thermostability of proteins using the Support Vector Machine (SVM) with sequence information. In this study, our results show that SVM coupled with suitable global sequence-code schemes can assist the precise prediction of enzymes. Moreover, we have obtained encouraging results from our statistics that may reveal factors that influence thermostability from sequence information. Our approach should be useful in the study and prediction of protein thermostability.

Table of Contents
Chinese Abstract i
English Abstract ii
Acknowledgement iii
Introduction 1
Materials and Methods 4
Data sets 4
Sequence Alignment 4
Secondary Structure Prediction 4
Input Coding Schemes 5
SVM Training and Testing Procedure 6
Measuring Performance 6
Statistic Analysis of Sequence Composition 7
Results 8
Sequence Alignment 8
Analysis of Amino Acid Composition 8
Prediction of thermostability of proteins 9
Statistic significance of SVM codes from protein sequences 13
Discussion 17
References 21
Tables 27
Figures 47

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