跳到主要內容

臺灣博碩士論文加值系統

(44.192.48.196) 您好!臺灣時間:2024/06/26 02:35
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:謝博文
研究生(外文):Po-Wen Hsieh
論文名稱:使用類神經網路預測蛋白質結構
論文名稱(外文):Application of Neural Networks in Predicting Protein Structures
指導教授:蘇木春蘇木春引用關係
指導教授(外文):Mu-Chun Su
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程學系碩士在職專班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2005
畢業學年度:93
語文別:中文
論文頁數:64
中文關鍵詞:蛋白質結構預測倒傳遞
外文關鍵詞:Back PropagationPrediction of Protein Structure
相關次數:
  • 被引用被引用:2
  • 點閱點閱:259
  • 評分評分:
  • 下載下載:29
  • 收藏至我的研究室書目清單書目收藏:0
蛋白質三級結構的定序方式,目前約分兩種─核磁共振(NMR)與X射線(X-ray)。但由於此兩種定序方法成本高且耗時,因此發展出同源模擬法(Homology modeling)、摺疊辨識法(Fold recognition)、重頭起算法(ab initio)來預測蛋白質分子結構。此類研究的技術可以預測出蛋白質結構的模板(template),預測結果可以應用在蛋白質突變(protein mutation)研究,活性位置研究(active site),藥物設計等,並能減少在實驗上、製藥上所須的時間。本文將對蛋白質序列與蛋白質結構,以類神經網路(Neural Networks)之倒傳遞演算法(Back Propagation),來訓練有可靠的NMR及X-ray鑑定的蛋白質,一級序列與三級結構的關係,其結果可預測未知的一級蛋白質序列的三級結構。
Presently, there are two methods for sequence of three-dimensional protein structure, they are NMR (Nuclear Magnetic Resonance) and X-ray. Since these two methods cost high charge and time, the Homology modeling, Fold recognition and ab inition methods were developed to predict the structure of protein molecule. These techniques can predict the template of protein structure and their prediction results can be applied in the research of protein mutation, active site, medicine design, etc. Also, these techniques can reduce the development time for experiment and pharmacy. Therefore, the reliable data of protein 1D sequence obtained by NMR and X-ray are as the training patterns in our prediction model. For our study, we use back propagation algorithm of neural network to do simulation to project the 3D protein template through its 1D sequence.
目 錄
摘要..............................................................I Abstract...........................................................II
誌謝..............................................................III
目錄..............................................................IV
圖目錄............................................................VI
表目錄............................................................IX
第一章 緒論........................................................1
1.1 研究背景與動機..........................................1
1.2 研究目的................................................4
1.3 研究範圍................................................5
1.4 研究架構................................................6
第二章 背景說明....................................................7
2.1 DNA簡介.................................................7
2.2 蛋白質簡介..............................................8
2.3 生物資訊網站簡介.......................................13
2.4 倒傳遞演算法簡介.......................................20
第三章 研究流程...................................................31
3.1 研究方法...............................................32
3.2 建立資料庫.............................................35
3.3訓練資料前置處理........................................43
3.4訓練倒傳遞類神經網路....................................44
3.5測試網路................................................48
3.6綜合評分................................................49
第四章 研究結果...................................................52
4.1預測與實際結果比較......................................52
4.2 統計結果...............................................57
第五章 結論.......................................................60
5.1研究結論................................................60
參考文獻..........................................................62
參考文獻
[1] C. Chothia, “One thousand families for the molecular biologist,” Nature, vol. 357, pp. 543-544, 1992.
[2] C. Y. Cui, D. H. Wang, and J. Y. Shi, “Comparing 3-D protein structures similarity by using fractal features,” IEEE Computational Systems Bioinformatics Conference, 2004.
[3] K. Drlica, Understanding DNA and Gene Cloning: A Guide for the Curious, 3rd ed., John Wiley & Sons, Inc., 1997.
[4] J. H. Kim, G. T. Ahn, and M. J. Lee, “Protein Structure Comparison Basis on Structural Classification Information,” Proceeding of the 7th Korea-Russia International Symposium, 2003.
[5] Z. H. Lin, “Review of new approaches in protein structure prediction,” Immunological Journal, vol. 17, no. 3, June 2001
[6] M. A. Martí-Renom, A. C. Stuart, A. Fiser, R. Sánchez, F. Melo, and A. Šali, “Comparative protein structure modeling of genes and genomes,” Annu. Rev.Biophys. Biomol. Struct., vol. 29, pp. 291-325, 2000.
[7] H. Nakashima et al., “The folding type of a protein in relevant to the aminoacid composition,” Biochem Journal, vol. 99, pp. 157-162, 1986.
[8] L. P. B. Scott, J. Chahine, and J. R. Ruggiero, “Prediction of Protein Structures Using a Hopfield Network,” Proceedings. Sixth Brazilian Symposium on Neural Network, p. 284, Nov. 2000.
[9] H. W. Tang, L. X. Jin, and M. J. Ji, “Optimization Models and Algorithms for Protein Structure Prediction,” Journal of Engineering Mathematic, vol. 19, no. 2, May 2002.
[10] Z. X. Wang, “A re-estimation for the total numbers of protein folds and superfamilies,” Protein Engineering, vol. 11, no. 8, pp. 621-626, 1998.
[11] ChemSOC, The RSC's chemical science network, "Protein Structure with Relation to Carboxypeptidase A," n.d., http://www.chemsoc.org/exemplarchem/entries/2004/durham_mcdowall/index.html [12] Databases and Tools for 3-D Protein Structure Comparison and Alignment, "CE CALCULATE TWO CHAINS," n.d., http://cl.sdsc.edu/ce/ce_align.html
[13] EBI, European Bioinformatics Institute, "Database search for," 2002, http://www.ebi.ac.uk/
[14] EMBL, European Molecular Biology Laboratory, "Research," 2005, http://www.embl.org/
[15] Food, Nutrition & Health, The University of British Columbia, "Amino Acids, Proteins and Enzymes," n.d., http://www.agsci.ubc.ca/courses/fnh/301/protein/protprin.htm
[16] MArkovian TRAnsition of Structure evolution, "Pairwise 3D Alignment," n.d., http://biunit.aist-nara.ac.jp/Matras/matras_pair.html
[17] NCBI, U.S. National Library of Medicine, "National Center for Biotechnology Information," 1988, http://www.ncbi.nlm.nih.gov/
[18] PDB, The State University of New Jersey and the San Diego Supercomputer Center at the University of California, San Diego, "Protein Data Bank," June, 2005, http://www.rcsb.org/pdb/
[19] UMBilogy, University of Miami Department of Biology, "CHEMICAL ARCHITECTURE of CELLS," n.d.,http://fig.cox.miami.edu/~cmallery/150/chemistry/organic.htm
[20] 久我勝利,劉小惠,圖解基因與DNA,品冠出版社,民國九十一年。
[21] 王志新,”蛋白質結構預測的現狀與展望,”生命的化學,vol. 18,no. 6,pp. 19-21,1998.
[22] 王雯靜校閱,張弘志博士 審稿,王政光、李慶孝、洪小芳、張弘志、張芸潔、賴志河 編譯,生物資訊,第一版,九州圖書文物有限公司,民國九十一年。
[23] 波拉克 著,楊玉齡 譯,DNA的語言,第一版,天下文化,民國八十六年。
[24] 靳利霞,蛋白質結構預測方法研究,大連理工大學管理科學與工程學系博士學位論文,2002。
[25] 蘇木春、張孝德 編著,機器學習:類神經網路、模糊系統以及基因演算法則,第二版,全華科技圖書,民國九十一年.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top