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研究生:盧培育
研究生(外文):LU PEI-YU
論文名稱:蛋白質穩定性之預測
論文名稱(外文):Thermal Stability Prediction of Proteins
指導教授:呂平江
指導教授(外文):Lyu Ping Chiang
學位類別:碩士
校院名稱:國立清華大學
系所名稱:生命科學系
學門:生命科學學門
學類:生物學類
論文種類:學術論文
論文出版年:2003
畢業學年度:91
語文別:英文
論文頁數:86
中文關鍵詞:蛋白質穩定雙胜肽預測
外文關鍵詞:Thermal stabilitydipeptideprediction
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不論對嗜溫性蛋白質或是厭溫性蛋白質而言,所有與穩定性相關的訊息全部都內嵌於組成蛋白質的基本20種氨基酸的排列與組合。因此對於分子生物及生物資訊的研究領域而言,目前最大的挑戰即來自於希望能藉由蛋白質的一級結構:序列的組成及排列之變化,來預測其穩定度的影響。隨著近年來基因體計畫的陸續完成,目前已知的蛋白質序列資訊,其增加的速度已經遠遠超過於已解出的蛋白質結構。在本研究當中,我們採用了傳統的統計方法,來檢測蛋白質變性臨界值與其雙胜肽組成的關係。
經過統計運算之後,我們建立了一個用以預測蛋白質變性臨界值的參考矩陣,並透過程式撰寫,建構了一個網站以方便使用者參考及運用。我們所建立的研究模型,可以準確地為所採用的樣本作分類,精確度可達97.5%。為了驗證所建立模型的可用性,我們也針對了48個超高溫及一般性質的細菌之基因體內含的蛋白質作檢測,並確立我們的模型的可用性,也間接地證實蛋白質的雙胜肽組成與其穩定度具有相當的影響力。

For all the information needed to create thermal tolerance is encoded in the protein sequence as proteins of both thermalphiles and mesophiles are composed of the same 20 amino acids. Predicting sequence-depend changes in protein stability is one of the greatest challenges in molecular biology and bioinformatics. The number of protein sequences is exploding as a result of genome and other sequencing projects, and the increasing number of protein is much greater than the increasing number of known protein structures. In this work, we use classical statistical method to determine the correlation between Tm value of protein and its dipeptide.
After statistical calculation, we form out a reference table to predict Tm value of protein, and built a web-based interface program on our web site. The accuracy of prediction of our method is up to more than 97.5% in all groups including material data set. For another evidences to confirm the accuracy of our Tm table, we also apply our Tm table to predict about 48 hyperthermal and common genomes, and got a clear boundary of predict results.

Tables 3
Figures 4
Abstract 5
中文摘要 6
謝誌 7
Chapter 1 : INTRODUCTION 8
1.1 Protein stability overview 8
1.2 Statistics and computation in biology 12
Chapter 2 : SYSTEMS AND METHODS 15
2.1 Database and Materials 15
2.2 Probability distribution overview 19
2.2.1 Features of the Poisson distribution. 19
2.2.2 Comparisons to other distributions. 23
2.3 Hypothesis Test 26
2.4 Chi-square Test 28
2.5 Methods in process 29
Chapter 3 : RESULTS 32
3.1 Amino acid composition 32
3.2 Di-peptide propensities 33
3.3 Result of materials prediction 34
3.4 Result of complete genomes prediction 35
Chapter 4 : DISCUSSION 46
Chapter 5 : EXPERIMENTAL DATAS 49
5.1 Proteins’ sequence of High Tm group: 49
5.2 Proteins’ sequence of Low Tm group: 51
5.3 Experimental data matrices: 54
Chapter 6 : SOURCE CODES 67
6.1 Program architecture 67
6.2 Web site interface and location 67
6.2 Web site interface and location 68
6.3 Comp-dipeptide-1st 68
6.4 Comp-amino 72
6.5 Sum-matrix 74
6.6 Chi-Exp 76
6.7 Potential 79
REFERENCES 84

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