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  • 學位論文

蛋白質功能域相互作用的預測

Large Scale Prediction of Domain-Domain Interactions

指導教授 : 吳家樂
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摘要


蛋白質之間的作用乃是蛋白質功能的表現。蛋白質與蛋白質之交互作用可被視為是由於蛋白質功能域與其他功能域的交互作用,因此,透過了解蛋白質功能域的交互作用,對於蛋白質與蛋白質交互作用網路能夠有一個整體性的瞭解。 在本研究中應用了Han等人[Han et. al. 2004]蛋白質功能域結合對(domain combination pair)的做法,分析蛋白質蛋白質交互作用資料庫DIP的資料,推斷蛋白質功能域與功能域間的交互作用。DIP資料庫中每個交互作用的蛋白質則透過蛋白質功能域資料庫Pfam取得相關的功能域註釋。本研究延伸Han等人的結果至七個物種(C.elegan, D. melanogaster, E. coli, H. pylori, H. sapiens, M. musculus and S. cerevisiae),並且還將七個物種結合在一起推論。為了使蛋白質功能域交互作用的預測更加精確,因此針對每個物種都建立了非交互作用對照組,藉此改善本研究預測的正確性。研究最後針對所獲得的domain-domain pairs交互作用及PIP結果建立一個查詢網頁,供使用者查詢本研究的成果,網頁位址如下:http://210.70.80.163/kzbio2/。

並列摘要


The interaction between proteins is an important feature of protein functions. Behind protein-protein interactions there are protein domains interacting with each others to perform the necessary functions. Therefore, understanding proteins interactions at the domain level gives a global view of the protein-protein interaction network. In this study, we employ the domain combination pair approach, introduced by Han et. al [Han et. al. 2004], 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) and a combination of all seven species. For each case, a negative learning set (non-interacting protein sequences) is constructed in order to improve the accuracy of domain-domain interactions prediction. The predicted domain-domain interaction pairs and PIP results are available at http://210.70.80.163/kzbio2/.

參考文獻


Bock JR, Gough DA. 2001. Predicting protein-protein interactions from primary structure. Bioinformatics. 17(5):455-60.
Deane CM, Salwinski L, Xenarios I, Eisenberg D. 2002. Protein interactions: two methods for assessment of the reliability of high throughput observations Mol Cell Proteomics. 1(5):349-56.
Deng M, Mehta S, Sun F, Chen T. 2002. Inferring domain-domain interactions from protein-protein interactions. Genome Res. 2002 Oct;12(10):1540-8.
Ding C. and Dubchak I., 2001. Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics. 2001 Apr;17(4):349-58.
Han DS, Kim HS, Jang WH, Lee SD, Suh JK. 2004. PreSPI: a domain combination based prediction system for protein-protein interaction. Nucleic Acids Res. 2004 Dec 1;32(21):6312-20.

被引用紀錄


洪蜚鴻(2007)。建立一個基於機器學習方法的蛋白質交互作用預測系統〔碩士論文,臺北醫學大學〕。華藝線上圖書館。https://doi.org/10.6831/TMU.2007.00106
劉學銓(2006)。從蛋白質功能域相互作用重建蛋白質相互作用網路〔碩士論文,亞洲大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0118-0807200916271807
陳怡仲(2008)。利用微陣列擾動數據及蛋白質功能資料 建置MAPK生物路徑預測系統〔碩士論文,亞洲大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0118-0807200916271936

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