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  • 學位論文

人類轉錄因子交互作用之研究與辨識

Investigation and Identification of Transcription Factor Interactions in Human

指導教授 : 李宗夷

摘要


蛋白質交互作用(protein-protein interaction)廣泛參與了生物化學、量子化學、分子動力學、訊息傳遞等代謝或遺傳學。人類的生化功能亦是會依靠蛋白質間的交互作用。蛋白質的生產是由轉錄這個機制所調控的,因而此機制在生化過程中,扮演著重要角色。調控轉錄過程的蛋白質被稱為轉錄調控因子(TF),此蛋白質發揮其功用亦是與其他蛋白質發生交互作用,進而達成其作用目標。目前以生物技術的方法來分析人類轉錄因子的交互作用性,將會是一件費時間與金錢的工作;因此,我們預期能提供生物學家一個有用的計算分析方法,藉由電腦計算達到減輕生物學家人力與時間金錢上的耗費,為人類轉錄因子交互作用研究上能更快速發展。在本篇研究中,藉由參考前人研究與辨識protein-protein interactions 的方法,運用上人類轉錄因子交互作用(human transcription factor interactions )進行分析,預期能達到高效能的辨識。

並列摘要


Protein interactions are extensively involved in biochemistry, quantum chemistry, molecular dynamics, messaging and other metabolic or genetic. Human biochemical function will also depend on protein-protein interactions. Protein production is regulated by the transcription of this mechanism, and therefore this mechanism in biochemical processes, plays an important role. Transcription regulating proteins called transcription factor , this protein functions play its interaction with other proteins, thereby to achieve its objective function. Currently biotechnology methods to analyze the interaction of the human transcription factor, it will be a time-consuming work and money; therefore, we expect to provide biologists a useful computational analysis methods, achieved by computing reduce biologists manpower and time-consuming for the study of the human transcription factor interactions can be more rapid development. In this study, by referring to previous studies and identification of protein-protein interactions of the method, the use of the human transcription factor interactions were analyzed, it is expected to achieve high performance identification.

參考文獻


1. Singh, R., et al., Struct2Net: a web service to predict protein-protein interactions using a structure-based approach. Nucleic Acids Res, 2010. 38(Web Server issue): p. W508-15.
2. Yu, J. and R.L. Finley, Jr., Combining multiple positive training sets to generate confidence scores for protein-protein interactions. Bioinformatics, 2009. 25(1): p. 105-11.
3. van Dijk, A.D., et al., Sequence motifs in MADS transcription factors responsible for specificity and diversification of protein-protein interaction. PLoS Comput Biol, 2010. 6(11): p. e1001017.
4. Roy, S., et al., Exploiting amino acid composition for predicting protein-protein interactions. PLoS One, 2009. 4(11): p. e7813.
5. Shoemaker, B.A., et al., Inferred Biomolecular Interaction Server--a web server to analyze and predict protein interacting partners and binding sites. Nucleic Acids Res, 2010. 38(Database issue): p. D518-24.

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