(18.206.12.76) 您好!臺灣時間:2021/04/23 09:38
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:::

我願授權國圖
: 
twitterline
研究生:陳星男
研究生(外文):Hsin-Nan Chen
論文名稱:蛋白質二級結構預測
論文名稱(外文):Protein secondary structure prediction
指導教授:黃鎮剛
指導教授(外文):Jenn-Kang Hwang
學位類別:碩士
校院名稱:國立交通大學
系所名稱:生物科技研究所
學門:生命科學學門
學類:生物科技學類
論文種類:學術論文
論文出版年:2003
畢業學年度:91
語文別:中文
論文頁數:33
中文關鍵詞:蛋白質二級結構預測
外文關鍵詞:Protein secondary structure prediction
相關次數:
  • 被引用被引用:0
  • 點閱點閱:175
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:19
  • 收藏至我的研究室書目清單書目收藏:0
蛋白質二級結構提供關於蛋白質結構預測重要資訊。有許多預測方法使用這樣的編碼來預測蛋白質二級結構:以待測位置為中心,加上前後胺基酸序列形成一個視窗。在這個視窗裡,以㈰種胺基酸在每一個位置出現的頻率為序列特徵表示碼。我們使用了新的序列特徵表示碼:在視窗裡,以Chou and Fasman 所計算出的胺基酸二及結構趨向係數為序列特徵表示碼。另外,為了考慮序列上相鄰較遠的胺基酸之間的作用力對二及結構的影響,我們也設計了七種化學作用力加入編碼裡。結果顯示,我們的預測正確率達到75%, 特別在β摺板的預測有明顯的提高。並且我們的序列特徵表示法比起其他方法精簡了約七倍,所以在時間上也相對快了兩倍以上。

The prediction of secondary structure usually makes use of a sliding sequence window of a specific length (usually 9-15 amino acid residues) of a protein. In this work, we showed that a novel reduced representation of the input sequence vector can give superior results to the existing method based on the usual binary representation of protein sequences. Our approach is based on the multi-class support vector machines that make use of reduced feature vectors consisting of the homology-weighted information of amino acids. Despite the relatively smaller size of our feature vectors, our approach gives prediction accuracy of 75%, which is better than the 73% of the well-known PHD approach.

Contents
中文摘要 2
Abstract 3
1.Introduction 4
2.Method 6
2.1. The support vector machines 6
2.2. Input coding schemes and data sets 7
2.2.1. Secondary structure assignment and data sets 7
2.2.2. Reduced sequence representation (RS) 8
2.2.3. Homology-weighted amino acid compositions 9
2.2.4. Physical-chemical interaction pairs 9
2.3. Training and testing procedures 11
2.3.1. Constructing the classifiers 11
2.3.2. Q3 and MCC 11
2.3.3. Q9 12
2.3.4. SOV 13
3.Result 15
4.Conclusion and Discussion 16
5.References 17
6.Figures 21
7.Tables 27

1. Qian N, Sejnowski TJ. Predicting the secondary structure of globular proteins using neural network models. J Mol Bio 1988; 202:865-884.
2. Rost B, Sander C. Prediction of protein secondary structure at better than 70% accuracy. J. Mol. Biol. 1993; 232:584-599
3. Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 1999; 292:195-202
4. Cuff JA, Barton GJ. Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins 1999; 34:508-519
5. Cuff JA, Barton GJ. Application of multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins 2000; 40:502-511
6. Hua S, Sun Z. A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach J. Mol. Biol. 2001;
7. Cregut D, Civera C, Macias MJ, Wallon G, Serrano L. A tale of two secondary s structure elements: when a beta-hairpin becomes an alpha-helix J. Mol. Biol. 1999; 292:389-401
8. Kabsch W, Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983; 22:2577-2637
9. Sudarsanam S. Structural diversity of sequentially identical subsequences of proteins: Identical octapeptides can have different conformations. Proteins1998; 30: 228-231
10. Minor D.L., Jr. and Kim P.S. Context-dependent secondary structure formation of a designed protein sequence. Nature 1996; 380:730-734.
11. Garnier J., Gibrat J.-f, and Robson B. GOR method for prediction protein secondary structure from amino acid sequence. Methods Enzymol. 1996; 266:540-553.
12. Vapnik V Estimation of dependences based on empirical data. Moscow: Nauka; 1979.
13. Vapnik V The Nature of Statistical Learning Theory. New York: Springer; 1995.
14. Vapnik V Statistical Learning Theory. New York: Wiley; 1998.
15. Ding CHQ, Dubchak I. Multi-class protein fold recognition using support vector machines and neural networks Bioinformatics 2001; 17:349-358
16. Brown MP, Grundy WN, Lin D, Cristianini N, Sugnet CW, Furey TS, Ares MJ, Haussler D. Knowledge-based analysis of microarray gene expression data by using Support Vector Machine Proc Natl Acad Sci U.S.A. 2000;97:262-267
17. Jaakkola T, Diekhans M, Haussler D. Using the Fisher kernel method to detect remote protein homologies ISMB 1999; 149-158
18. Chou PY, Fasman GD. Prediction of protein conformation. Biochemistry 1974; 13:222-245
19. Tusnady GE, Simon I. Principles governing amino acid composition of integral membrane proteins: application to topology prediction. J. Mol. Biol. 1998; 283:489-506
20. Nakai K. Protein sorting signals and prediction of subcellular localization. Adv. Protein Chem. 2000; 54:277-344
21. Hua S, Sun Z. Support vector machine approach for protein subcellular localization prediction. Bioinformatics 2001; 17:721-728
22. Kreil DP, Ouzounis CA. Identification of thermophilic species by the amino acid compositions deduced from their genomes. Nucleic Acids Res. 2001;29:1608-1615
23. Chou KC, Liu WM, Maggiora GM, Zhang CT. Prediction and classification of domain structural classes Proteins 1998; 31:97-103
24. Dubchak I, Holbrook SR, Kim S-H. Prediction of protein folding class from amino acid composition. Proteins 1993; 16:79-91
25. Dubchak I, Muchnik I, Holbrook SR, Kim S-H. Prediction of protein folding class using global description of amino acid sequence Proc Natl Acad Sci U.S.A. 1995; 92:8700-8704
26. Eisenhaber F, Imperiale F, Argos P, Fršmmel C. Prediction of secondary structural content of proteins from their amino acid composition alone. I. New analytic vector decomposition methods. Proteins 1996; 25:157-168
27. Eisenhaber F, Fršmmel C, Argos P. Prediction of secondary structural content of proteins from their amino acid composition alone. II. The paradox with secondary structural class Proteins 1996; 25:169-179
28. Matthews BW. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta. 1975; 405:442-451
29. Zemla A, Venclovas C, Fidelis K, Rost B. A modified definition of Sov, a segment-based measure for protein secondary structure prediction assessment. Proteins 1999; 34:220-223
30. Chang C-C, Lin C-J. LIBSVM: a library for support vector machines. Software available from http://www.csie.ntu.edu.tw/~cjlin/libsvm 2001;
31. Zhang CT, Zhang R. Q9, a content-balancing accuracy index to evaluate algorithms of protein secondary structure prediction. Int J Biochem Cell Biol. 2003 Aug; 35(8):1256-62.
32. Chou P.Y. and Fasman G.D. Prediction of the secondary structure of proteins from their amino acid sequence. Adv. Enzymol. 1978; 47:45-147
33. King, R. D. & Sternberg, M. J. E. Identification and application of the concepts important for accurate and reliable protein secondary structure prediction. Protein Sci. 1996; 5:2298-2310.
34. Salamov, A. A. & Solovyev, V. V.. Prediction of protein secondary structure by combining nearest-neighbor algorithms and multiple sequence alignments. J. Mol. Biol. 1995; 247: 11-15.
35. Frishman, D. & Argos, P. Knowledge-based secondary structure assignment. Proteins. 1995; 23: 566-579

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關論文
 
系統版面圖檔 系統版面圖檔