臺灣博碩士論文加值系統

蛋白質之間的交互作用在細胞內的結構上以及功能組織上扮演相當重要的角色，而對它們完整的描述是了解整個細胞所不可或缺的。然而蛋白質功能域與蛋白質功能域之間的交互作用對於研究、預測以及註解蛋白質與蛋白質間交互作用是很有幫助的。
本論文中應用了Han等人所提出的蛋白質功能域結合對(domain combination pair)的做法[1,2]，從分析蛋白質與蛋白質交互作用資料庫DIP的資訊所推斷的蛋白質功能域與功能域間的交互作用，並延伸結果至七個物種(C.elegan, D. melanogaster, E. coli, H. pylori, H. sapiens, M. musculus and S. cerevisiae)。根據前面的基礎，本研究在此提出一個分析蛋白質調控的方法，試圖預測出蛋白質與蛋白質之間彼此的調控順序關係。我們的方法利用順序指標「ordering index」簡稱OI[7,8]，結合統計上的顯著性檢定來分析預測蛋白質之間調控的資訊。使用順序指標「ordering index」透過蛋白質功能域相互作用的關聯，利用機率理論中條件機率的特性使得我們建立的模型具有預測表現蛋白質與蛋白質之間調控的能力。研究結果網頁位址如下: http://210.70.80.163/kzbio2/index.php。

Protein-protein interactions (PPIs) play pivotal roles in various aspects of the structural and functional organization of the cell, and their complete description is indispensable to thorough understanding of the cell. Currently, domain-domain interactions can be useful for validating, annotating, and predicting protein-protein interactions.
In this study, we employ the domain combination pair approach, introduced by Han et. al [1,2] to derive putative protein domain-domain interactions from the protein-protein interaction database DIP. Domain annotation of each protein-protein interaction record in DIP is obtained from the protein domain database, Pfam.The results of putative domain-domain interaction by Han et al. was extended to seven species (C.elegan, D.melanogaster, E.coli, H.pylori, H.sapiens, M.musculus and S.cerevisiae). According to former basis, we offer an ordering method protein-portein interactions to predict the ordering relationship. We use an ordering index(OI) combined to analyze data[7,8], and we use the ordering index to predict the association of proteins through the dynamics of protein expression. All results are available at http://210.70.80.163/kzbio2/index.php

摘　　要 iii
Abstract iv
表　目　錄 vii
圖　目　錄 viii
附　錄 ix
第一章 　緒論 - 1 -
1.1　研究背景 - 1 -
1.2　研究目的 - 2 -
1.3　系統流程架構 - 3 -
第二章　文獻探討 - 4 -
2.1　相關研究背景資料簡介 - 4 -
2.2　蛋白質功能域結合對探討 - 9 -
2.3　蛋白質交互作用預測方法探討(一) - 10 -
2.4　蛋白質交互作用預測方法探討(二) - 11 -
第三章　研究方法及進行步驟 - 12 -
3.1　研究方法 - 12 -
3.1.1　資料來源 - 12 -
3.1.2　APi數據值之可用性評估 - 12 -
3.1.3　圖形界面之應用 - 13 -
3.1.4　順序指標「ordering index」(OI)模型演算法 - 14 -
3.2　採用的開發平台 - 15 -
3.3　進行步驟 - 15 -
3.3.1　取得來源資料 - 15 -
3.3.2　比對APi預測數據的準確度 - 16 -
3.3.3　比對生物路徑預測數據準確度 - 16 -
3.3.4　建立圖形軟體Cytoscape所輸入格式的轉譯程式 - 17 -
3.3.5　建立順序指標OI模型並檢測其預測結果準確性 - 17 -
第四章　結果 - 18 -
4.1　 APi數據值與Interdom比較準確度的結果 - 18 -
4.2　 生物路徑預測數據之比對結果與評分 - 19 -
4.3　 OI模型預測蛋白質間調控的結果 - 21 -
4.3.1　 調控方向-E.coli chemotaxis pathway - 21 -
4.3.2　 調控方向-coagulation pathway - 22 -
4.3.3　 調控方向-the yeast cell cycle DNA repair pathway - 23 -
4.4　 圖型化呈現結果 - 26 -
第五章　結論與未來研究方向 - 28 -
5.1　結論 - 28 -
5.2　未來研究方向 - 29 -
參考文獻 - 30 -
附錄 - 33 -

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[19] Database of Interacting Proteins
http://dip.doe-mbi.ucla.edu/
[20] KEGG PATHWAY
http://www.genome.ad.jp/kegg/pathway.html
[21] Protein families database of alignments and HMMs
http://www.sanger.ac.uk/Software/Pfam/
[22] ExPASy Molecular Biology Server
http://us.expasy.og/sprot/