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研究生：	周慧萍 Chou, Hui-Ping
論文名稱：	應用蛋白質同源關係預測人類細胞循環調節之蛋白質交互作用網路 Computational Detection for Human Protein Interaction Network in Cell Cycle Regulation
指導教授：	蔣榮先 Chiang, Jung-Hsien
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 資訊工程學系 Department of Computer Science and Information Engineering
論文出版年：	2003
畢業學年度：	91
語文別：	英文
論文頁數：	62
中文關鍵詞：	蛋白質交互作用，同源關係
外文關鍵詞：	homoloy, protein-protein interaction
相關次數：	點閱：58 下載：1
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蛋白質之間的交互作用在細胞內的結構上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瞭解整個細胞所不可或缺的。蛋白質之間的二元關係，即兩個蛋白質之間的交互作用，可視為複雜的生理機制或生理途徑的最小構成單位。由兩兩蛋白質對所組成的交互作用網路圖，可以一窺蛋白質之間的全面關係，同時提供複雜生理過程的路徑。近來，針對酵母菌，作大規模分子間交互作用的實驗，產生大量有關蛋白質交互作用的資料。本論文嘗試透過已知在酵母菌的蛋白質交互作用，藉由不同物種蛋白質間的在演化上的同源關係，來推測在人類中可能存在的蛋白質交互作用。在本文中，選擇與細胞循環有關的蛋白質作為實驗的對象。本文希望由推測所得的蛋白質交互作用，去進一步瞭解在人類身上的致癌機轉。此外，我們評估預測得到的蛋白質交互作用的蛋白質，在Swiss-Prot (蛋白質資料庫) 中含有相同的關鍵字的比例，以及在MEDLNIE (醫學文件資料庫) 中，蛋白質名稱一起出現在文章中的次數。最後將預測的人類蛋白質交互作用與DIP (蛋白質交互作用資料庫) 中的人類蛋白質交互作用來驗證結果。實驗結果證明，此種以蛋白序列的同源關係去預測其它物種的蛋白質交互作用的方法是可行。

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. The binary relationship between two proteins, protein-protein interaction, is the simplest unit of the complex biological process or pathway. Interaction network constructed by binary relationship of protein pairs depicts the global view of relationships among proteins and provide the insight of the complex biological process. Recently, large-scale two-hybrid screens have generated a wealth of information of PPIs for Saccharomyces cerevisiae (yeast). We propose an approach to deduce the PPIs in human from yeast by the homologous relationship. We choose the cell cycle related yeast proteins as the target of our approach. This work tempts to speculate potential interactions that may help in understanding the molecular mechanisms involved in human tumorigenesis. In addition to provide more evidences from different information resources, we evaluate the shared keywords from Swiss-Prot and co-occurrence of protein names from MEDLINE for the predicted PPIs. Finally, we verify the predicted PPIs in human with those in DIP (Database of Interacting Proteins). The result shows the sequence-based approach can be used globally to identify potential PPIs across species.

CONTENTS					 i
LIST OF FIGURES			        iii
LIST OF TABLES			         iv

Introduction				 1
1  Overview  			          1
2  Motivation				 4
3  Methods				 5

Literature Review                           7
1  Evidence for Protein Interactions         8
1.1  Direct evidence                    8
1.2  Indirect evidence		 9
2  Correlation between Sequence Homology and Literature 
Information												14
3  Protein Interaction Network								18
3.1  Why do we need visualization?						18
3.2  Protein interaction maps versus metabolic pathways	19
3.3  The display of protein interaction networks			20

Computation Detection for Human Protein Interaction 
Network in Cell Cycle Regulation						22
1  Computational Detection									22
2  Proposed Methods										24
2.1  Flowchart of the Proposed Method					24
2.2  Homologous Relation Among Species				25
2.3  Biomedical Literature Search						27
2.4  Annotation In Swiss-Prot							29

Implementation											32
1  Data													32
2  Experiment												33
2.1  Acquire Yeast Cell Cycle Regulatory Interacting 
Proteins											34
2.2  Searching for Homologous Sequences				35
2.2.1  Standalone BLAST						35
2.2.2  The BLAST algorithm						35
2.2.3  Protein sequences in “FASTA” format		36
2.2.4  Identification	of potential homologies		37
2.3  Identifying Biological Relationship in MEDLNIE		38
2.4  Comparing Biological Relationship by Swiss-Prot
Annotation										39
2.5  Displaying Hypothetic Human Cell Cycle Regulatory
Network											40
3  Result													40

Discussion and Conclusion								43
1  Discussion												43
2  Conclusion												44
3  Future Work												45

REFERENCES													46
APPENDIX													51
                                    

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校內：立即公開
校外：2003-08-04公開

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