以蛋白質於真核細胞之位置預測蛋白質交互作用

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姓名

陳佑慈(Yu-Tzu Chen) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

以蛋白質於真核細胞之位置預測蛋白質交互作用
(Protein-protein interaction prediction enhancement using subcellular localization)

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★ 利用組成識別和序列及空間特性構成之預測系統來針對蛋白質交互作用上的特殊區段點位進行分析及預測辨識	★ 新聞語意特徵擷取流程設計與股價變化關聯性分析
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摘要(中)

預測蛋白質之間的交互作用是一個重要而且研究相當完整的議題。大多數生物作用產生必須經歷蛋白質交互作用，異常的交互作用可能與某些神經系統症候群有關，因此，指出蛋白質之間是否有關連是必要的。
發生交互作用的蛋白質組，應該落在細胞中相同的位置。目前已存在的方法，大多建立於以蛋白質序列或是特定片段的信號來預測交互作用，很少將蛋白質的位置列入特性。而我們建立一個整合系統，能夠以蛋白質落在真核細胞中的位置為基礎，預測是否產生交互作用。我們取用蛋白質序列的組成、蛋白質的區塊、在細胞中的位置來建構這個系統。我們建立不同的模組來預測交互作用，依照輸入蛋白質組的位置選取其所屬位置的模組。我們的方法提高了蛋白質交互作用的預測效能，若有更完整的蛋白質交互作用以及位置資訊，將得到更高準度的預測。

摘要(英)

Protein–protein interactions are importance for almost every process in living cell. Abnormal interactions may have implications in a number of neurological syndromes. Therefore, it is crucial to recognize the association and dissociation of protein molecules. Current available computational methods of prediction of protein–protein interaction extract information from amino acid sequence or signal peptide. There are few method consider subcellular localization information. The method presented in this paper is based on the assumption that two proteins should appear on same subcellular localization to perform interaction. We develop an integrated system which based on a learning algorithm-support vector machine to predict protein–protein interactions. We construct training models for different subcellular localization. Each test protein pair request one training model to predict according to its localization. This method is take protein sequence composition, protein domains and subcellular localization information as features. The prediction ability of our method is better than other sequence-based protein–protein interaction prediction methods. In addition, a more complete data of protein-protein interactions and subcellular localizations can enhance the prediction ability of the method.

關鍵字(中)

★ 交互作用
★ 蛋白質
★ 預測
★ 位置

關鍵字(英)

★ protein interaction
★ prediction
★ subcellular localization

論文目次

摘要 ……………………………………………………………………………………IV
Abstract ………………………………………………………………………………………V
Table of Contents ……………………………………………………………………………VI
List of Figures ........................................................................................................................ VII
List of Tables ........................................................................................................................ VIII
Chapter 1 Introduction ........................................................................................................... 1
1.1 Background ................................................................................................................. 1
1.2 Motivation ................................................................................................................... 5
1.3 Goal ............................................................................................................................. 5
Chapter 2 Related Works ....................................................................................................... 6
2.1 Related tools ................................................................................................................ 6
2.2 Recent tools of prediction of protein-protein interactions .......................................... 7
2.3 Comparison of the prediction tools ........................................................................... 12
Chapter 3 Materials and methods ....................................................................................... 13
3.1 Data Sources ............................................................................................................. 13
3.2 Methods ..................................................................................................................... 15
3.3 Performance evaluation …………………………………………………………… 20
Chapter 4 Results .................................................................................................................. 21
4.1 Subcellular localization distribution ......................................................................... 21
4.2 Prediction performance of each dataset .................................................................... 23
4.3 Prediction without DDI feature ................................................................................. 26
Chapter 5 Discussion ............................................................................................................ 27
References .............................................................................................................................. 30

參考文獻

1.Young, K.H., Yeast two-hybrid: so many interactions, (in) so little time. Biol Reprod, 1998. 58(2): p. 302-11.
2.Lee, C., Coimmunoprecipitation assay. Methods Mol Biol, 2007. 362: p. 401-6.
3.Han, J.D., et al., Effect of sampling on topology predictions of protein-protein interaction networks. Nat Biotechnol, 2005. 23(7): p. 839-44.
4.Pellegrini, M., et al., Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc Natl Acad Sci U S A, 1999. 96(8): p. 4285-8.
5.Enright, A.J., et al., Protein interaction maps for complete genomes based on gene fusion events. Nature, 1999. 402(6757): p. 86-90.
6.Ogmen, U., et al., PRISM: protein interactions by structural matching. Nucleic Acids Res, 2005. 33(Web Server issue): p. W331-6.
7.Espadaler, J., et al., Prediction of protein-protein interactions using distant conservation of sequence patterns and structure relationships. Bioinformatics, 2005. 21(16): p. 3360-8.
8.Brown, K.R. and I. Jurisica, Online predicted human interaction database. Bioinformatics, 2005. 21(9): p. 2076-82.
9.Huang, C., et al., Predicting protein-protein interactions from protein domains using a set cover approach. IEEE/ACM Trans Comput Biol Bioinform, 2007. 4(1): p. 78-87.
10.Shen, J., et al., Predicting protein-protein interactions based only on sequences information. Proc Natl Acad Sci U S A, 2007. 104(11): p. 4337-41.
11.Pitre, S., et al., PIPE: a protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs. BMC Bioinformatics, 2006. 7: p. 365.
12.Guo, Y., et al., Using support vector machine combined with auto covariance to predict protein-protein interactions from protein sequences. Nucleic Acids Res, 2008. 36(9): p. 3025-30.
13.Zaki, N., et al., Protein-protein interaction based on pairwise similarity. BMC Bioinformatics, 2009. 10: p. 150.
14.Xia, J.F., X.M. Zhao, and D.S. Huang, Predicting protein-protein interactions from protein sequences using meta predictor. Amino Acids, 2010.
15.Soong, T.T., K.O. Wrzeszczynski, and B. Rost, Physical protein-protein interactions predicted from microarrays. Bioinformatics, 2008. 24(22): p. 2608-14.
16.Blum, T., S. Briesemeister, and O. Kohlbacher, MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction. BMC Bioinformatics, 2009. 10: p. 274.
17.Raghavachari, B., et al., DOMINE: a database of protein domain interactions. Nucleic Acids Res, 2008. 36(Database issue): p. D656-61.
18.Yu, C.S., et al., Prediction of protein subcellular localization. Proteins, 2006. 64(3): p. 643-51.
19.Smith, T.F. and M.S. Waterman, Identification of common molecular subsequences. J Mol Biol, 1981. 147(1): p. 195-7.
20.Keshava Prasad, T.S., et al., Human Protein Reference Database--2009 update. Nucleic Acids Res, 2009. 37(Database issue): p. D767-72.
21.Liu, L., et al., Prediction of protein-protein interactions based on PseAA composition and hybrid feature selection. Biochem Biophys Res Commun, 2009. 380(2): p. 318-22.
22.Hunter, S., et al., InterPro: the integrative protein signature database. Nucleic Acids Res, 2009. 37(Database issue): p. D211-5.
23.Finn, R.D., et al., The Pfam protein families database. Nucleic Acids Res, 2010. 38(Database issue): p. D211-22.
24.Salwinski, L., et al., The Database of Interacting Proteins: 2004 update. Nucleic Acids Res, 2004. 32(Database issue): p. D449-51.
25.Mewes, H.W., et al., MIPS: analysis and annotation of proteins from whole genomes. Nucleic Acids Res, 2004. 32(Database issue): p. D41-4.
26.CORTES, C. and V. VAPNIK, Support-Vector Networks. Machine Leaming, 1995. 20(3): p. 273-297.
27.Zaki, N., S. Deris, and H. Alashwal, Protein-Protein Interaction Detection Based on Substring Sensitivity Measure. Inter J of Biomedical Sciences, 2006. 1: p. 148-154.
28.Chen, X.W. and M. Liu, Prediction of protein-protein interactions using random decision forest framework. Bioinformatics, 2005. 21(24): p. 4394-400.
29.Deng, M., et al., Inferring domain-domain interactions from protein-protein interactions. Genome Res, 2002. 12(10): p. 1540-8.
30.Schwikowski, B., P. Uetz, and S. Fields, A network of protein-protein interactions in yeast. Nat Biotechnol, 2000. 18(12): p. 1257-61.
31.Michael, W.M., Nucleocytoplasmic shuttling signals: two for the price of one. Trends Cell Biol, 2000. 10(2): p. 46-50.
32.Shoemaker, B.A. and A.R. Panchenko, Deciphering protein-protein interactions. Part I. Experimental techniques and databases. PLoS Comput Biol, 2007. 3(3): p. e42.
33.Balzer, E. and E.G. Moss, Localization of the developmental timing regulator Lin28 to mRNP complexes, P-bodies and stress granules. RNA Biol, 2007. 4(1): p. 16-25.

指導教授

洪炯宗(Jorng-tzong Horng)

審核日期

2010-7-24

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