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研究生:邱世浩
研究生(外文):Shih-Hau Chiu
論文名稱:由蛋白質的功能區域組成預測酵素類別並探討其類源意義之研究
論文名稱(外文):A study of enzyme class prediction - from functional domain composition and phylogenetic relationships of protein sequence
指導教授:林志侯
指導教授(外文):Thy-Hou Lin
學位類別:博士
校院名稱:國立清華大學
系所名稱:分子醫學研究所
學門:醫藥衛生學門
學類:醫學學類
論文種類:學術論文
論文出版年:2007
畢業學年度:96
語文別:英文
論文頁數:85
中文關鍵詞:關聯演算法支持向量機演算法功能區域組成物理化學特性類源分類器類源組別
外文關鍵詞:association algorithmApriorisupport vector machinesfunctional domain compositionenzyme classInterPro entriesphysicochemical featuresphylogenetics
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  • 被引用被引用:0
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  • 下載下載:6
  • 收藏至我的研究室書目清單書目收藏:1
由序列資料直接預測蛋白質的生理功能,目前仍是計算分子生物的一大挑戰。傳統上,由序列相似度預測蛋白質的生理功能一直是被學者廣為採行的方法,只是單靠比對相似度,預測的準確度常會落入相似度臨界值如何選定的迷思中,尤其當比對相似度不高或是比對不到已知蛋白質時,更會面臨無法準確註解的窘境。發展非序列比對式的方法來輔助序列比對的缺失,一直是近年來學者所努力的方向。在預測蛋白質生理功能的工作中,最能彰顯酵素類蛋白質功能的註解,就屬酵素類別的預測。一旦瞭解基因體中酵素所屬的酵素類別,就可能瞭解該生物體的生理代謝路徑。只是現存的預測系統中,酵素類別的預測只能準確預測到前三階甚至是只到前兩階,四階式的預測系統目前仍未完善。在本論文的第一部份,我們試圖從已知的蛋白質資料庫中,挖掘酵素類別和蛋白質功能區域組成的關聯,希望從關聯中建立預測的規則,我們所用的方法是關聯演算法。在挖掘出關聯規則後,我們也用已知的資料當測試組來評估關聯規則的準確度。由所得的結果發現,該系統的預測準確度最高可達88%。而在本論文的第二部份,主要是以支持向量機演算法,利用蛋白質序列上的氨基酸的物理化學特性,建構簡易的類源分類器,此部分的研究主要是配合第一部份研究中依類源組別所建立的關聯規則。希望此一簡易類源分類器的建立,可以更強化所挖掘出的關聯規則利用率。而第二部份的所得結果,和我們原先的假設是一致的,亦即蛋白質序列上的氨基酸的物理化學特性,確實和物種類源有一定的關係。由所得結果證實,單靠氨基酸的物理化學特性確實可以建立簡易的類源分類器。
Identifying the function of protein sequence is still a challenging task in computational and informational biology. Traditionally, functional prediction mostly relies on detecting similarity between a functionally annotated protein and the query protein, then transferring the annotations across. However, sequence composition bias influence the results of similarity searches, and they do not yield the exact share between biological function and domain composition based on the similarity threshold used. Moreover, for the enzyme class prediction, the predictive capacity of previous studies are just to the top level or sublevel of EC classification system, which no research findings are yet available concerning the exact (four-digit) EC numbers prediction. In this work, we attempt to construct a work flow for automatic mapping protein sequence to their corresponding enzyme class based on functional domain composition of protein. The association algorithm, Apriori, is utilized to mine the relationship between the enzyme class and significant InterPro entries. The candidate rules are evaluated for their classificatory capacity. A correct enzyme classification rate of 70% was obtained for the prokaryote datasets and a similar rate of about 80% was obtained for the eukaryote datasets. Furthermore, we found that the rules were different among five taxonomic datasets studied. Consequently, to use these rules, one has to know the phylogeny of protein sequences beforehand. Here, we provide a straightforward method to predict the phylogeny of protein sequences by using a support vector machines classifier based on the biochemical features of amino acid sequences of the genomes. The classification accuracies of the trained SVM classifiers by the Enzymatic and All proteins are 84 and 79%, respectively. Results show that some compositions or biochemical features of amino acid sequences of the genomes can be used to cluster proteins of different taxonomic natures. The sequence compositions of proteins analyzed are originated from some special characteristics corresponding to the taxonomic clades. We prove that the phylogenetic class of protein sequence can be predicted just by amino acid physicochemical properties alone.
Chapter 1 Introduction ----------------------------------------------------------------------- 1
1.1 General Introduction --------------------------------------------------------------- 2
1.2 Enzyme class prediction ------------------------------------------------------------5
1.3 Phylogenomic studies -------------------------------------------------------------- 7
1.4 Research Questions ----------------------------------------------------------------- 9
1.5 Figures andTables ------------------------------------------------------------------ 10
Chapter 2 Materials and Methods ---------------------------------------------------------- 11
2.1 Data preparation for association experiments----------------------------------- 12
2.2 Applying association rules determine potential enzyme composition ------- 12
2.3 Evaluation of the associative candidate rules------------------------------------ 15
2.4 Feature vector extraction for SVM classifier------------------------------------ 16
2.5 Support vector machine------------------------------------------------------------ 18
2.6 Feature ranking and SVM parameter determination--------------------------- 20
2.7 Evaluating SVM classifier--------------------------------------------------------- 21
2.8 HGTs prediction--------------------------------------------------------------------- 21
2.9 Phylogeny reconstruction---------------------------------------------------------- 22
Chapter 3 Association algorithm to mine the rules that govern enzyme definition and to classify protein sequences---------------------------------------------------------- 24
3.1 Introduction ------------------------------------------------------------------------- 25
3.2 Results and Discussion------------------------------------------------------------- 26
3.2.1 Data preparation ------------------------------------------------------------------ 26
3.2.2 Association algorithm used to mine enzyme composition ------------------ 27
3.2.3 Evaluation of candidate rules --------------------------------------------------- 29
3.3 Conclusions -------------------------------------------------------------------------- 33
3.4 Tables and Figures ------------------------------------------------------------------ 34
Chapter 4 Phylogeny classification by a Support Vector Machine-based method ----51
4.1 Introduction -------------------------------------------------------------------------- 52
4.2 Results -------------------------------------------------------------------------------- 55
4.2.1 Feature selection and SVM model evaluation -------------------------------- 55
4.2.2 Comparing with other machine learning algorithms ------------------------- 58
4.2.3 Dependence of classification performance on sequence length ------------ 59
4.2.4 Composition of 255 selected features ----------------------------------------- 59
4.2.5 Horizontal gene transfer --------------------------------------------------------- 60
4.3 Discussion --------------------------------------------------------------------------- 62
4.4 Tables and Figures ------------------------------------------------------------------ 67
Chapter 5 Conclusion ------------------------------------------------------------------------- 83
5.1 Figures and Tables ------------------------------------------------------------------ 85
References -------------------------------------------------------------------------------------- 86
Appendix 1 Publication list ------------------------------------------------------------------ 92
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