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研究生:郭盈秀
研究生(外文):Ying-Hsiu Kuo
論文名稱:蛋白質二級結構預測的信心度有效性評估
論文名稱(外文):Evaluation of the Effectiveness of Confidence Level in Protein Secondary Structure Prediction
指導教授:巫坤品
指導教授(外文):Kun-Pin Wu
學位類別:碩士
校院名稱:國立陽明大學
系所名稱:生物醫學資訊研究所
學門:工程學門
學類:生醫工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:64
中文關鍵詞:蛋白質二級結構預測
外文關鍵詞:Proteinsecondary structureprediction
相關次數:
  • 被引用被引用:0
  • 點閱點閱:109
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  • 下載下載:13
  • 收藏至我的研究室書目清單書目收藏:0
在過去的幾十年裡,許多學者紛紛著手於研究蛋白質與其二級結構的關係。隨著蛋白質二級結構被研究的相對純熟,而其相關的分析工具
、預測軟體也發展的趨於完善,我們亦可以從蛋白質二級結構資訊中找出新的、有趣的應用,尤其是應用在生物資訊這塊領域。
因此,我們利用蛋白質二級結構預測的資訊進而找出可研究的議題。

在本研究中,我們使用了三種蛋白質二級結構預測方法,分別是 Psipred、JNet、以及 Transsec,
綜合這三種預測方法的結果,我們採用預測結果中所提供的信心度分數,把信心度低的部分去掉,
將信心度高的部分裁切出來。之後,裁切出來的序列片段,我們還會做片段延伸及片段結合等動作,
使片段中所包含的生物訊息更加完整。

我們利用 Segment Overlap Score (SOV) 來評估我們所裁切出來片段的好壞。由本研究的結果顯示,我們所裁切的片
段與酵素的活性區做比對,其 SOV 的平均分數達到62,且我們裁切出的片段佔總蛋白質序列的平均包覆率為56\%。
而在相同的 SOV 評估機制與包覆率之下,計算隨機產生的片段與酵素活性區比對的 SOV 分數,其平均分數只有達到30。
就統計學的角度上看來,我們的結果是非常有顯著性的。
In the last several decades there has been a tremendous wave of interest in the
relationship between protein and secondary structure. The continuing improvements
in protein secondary structure prediction have led to many new and fascinating
applications in bioinformatics. With the increasing usage of protein secondary
structure, finding ways to effectively utilize predicted information becomes a
critical issue.

Our research is primarily aimed at extracting effective fragments by predicted
secondary structures. In this investigation we applied three protein secondary
structure prediction methods: PSIPRED, JNET, and TRANSSEC. Following the prediction,
we extracted core fragments whose prediction results are of high confidence by
all the three methods. We demonstrated that with proper extension, the extracted
fragments cover most important parts of proteins.

The average SOV between our extracted core fragments and active sites of enzymes
reaches 62, but the total length of our fragments covers only 56\% of
whole enzymes. The same SOV measure of randomly extracted fragments that also
cover 56\% of proteins is only 30, which is statistically far below our core fragments.
致謝
摘要
Abstract
1 導論
1.1 蛋白質結構
1.2 蛋白質在結構上的分群
1.3 研究動機與目的
2 工具與方法Þ
2.1 研究流程
2.2 蛋白質二級結構預測
2.3 片段裁切
2.3.1 裁切概念
2.3.2 如何定義高信心度數值
2.4 片段延伸與結合
2.4.1 片段延伸
2.4.2 片段結合
2.5 片段結構整合
2.6 產生隨機片段
2.7 應用程式與系統環境
2.7.1 應用程式
2.7.2 系統環境
2.7.3 蛋白質資料庫
2.8 效能分析
2.8.1 Segment OVerlap Score (SOV)
2.8.2 修改SOV
2.9 統計分析
3 結果與討論
3.1 驗證:預測片段對應至真實結構的準確率
3.2 應用:酵素(Enzyme)
3.3 應用:膜蛋白(membrane protein)
3.3.1 Porin蛋白
3.3.2 受器蛋白(receptor protein)
4 結論
參考文獻
附錄
A 蛋白質 3D 圖
A.1 酵素
A.2 Porin
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