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研究生:陳建任
研究生(外文):Jian-Ren Chen
論文名稱:利用改良式蜜蜂群演算法於蛋白質結構模擬
論文名稱(外文):Protein Folding Simulation Using a Modified Artificial Bee Colony Algorithm
指導教授:王德譽王德譽引用關係林正堅
指導教授(外文):De-Yu WangCheng-Jian Lin
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:資訊工程系碩士班
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:2009
畢業學年度:97
語文別:英文
論文頁數:53
中文關鍵詞:親疏水性晶格模型蛋白質結構預測進化策略群體智慧蜜蜂群演算法
外文關鍵詞:Swarm intelligenceEvolution strategies.Artificial Bee Colony AlgorithmHP lattice modelProtein structure prediction
相關次數:
  • 被引用被引用:0
  • 點閱點閱:300
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  • 下載下載:13
  • 收藏至我的研究室書目清單書目收藏:1
從胺基酸序列預測蛋白質結構在生物計算領域是一個著名的問題,解決蛋白質結構的問題是研究蛋白質最重要的工作,親疏水性模型是一個高度簡化的模型,用來表示真實世界蛋白質的特性,在親疏水性模型的蛋白質摺疊問題是要找出最低自由能的蛋白質結構。為了增進預測蛋白質結構的能力,本篇論文提出改良式蜜蜂群演算法從序列來決定蛋白質結構,我們證明我們的演算法可以成功應用在蛋白質摺疊問題在親疏水性晶格模型上,模擬結果也顯示出我們所提出的方法比起其他的演算法效能要來的好。
In the computational biology, the prediction of proteins conformation from its amino acid sequence is one of the most prominent problems, solving the problem of protein structure is the most important works for study proteins, Hydrophobic-hydrophilic (HP) model is highly simplified model; it can represent behavioral properties of real-world proteins. Protein folding problem in the HP lattice model is the problem of finding the lowest free energy conformation. In order to enhance the performance of predicting protein structure, we propose a modified artificial bee colony (MABC) algorithm to determine the protein structure from sequence. We demonstrate that our algorithm can be applied successfully to the protein folding problem based on the hydrophobic-hydrophilic lattice model. Simulation results indicate that our approach performs better than those of existing evolutionary algorithms.
摘要 I
Abstract II
Acknowledgment IV
List of Figures VII
List of Tables IX
Chapter 1 1
1.1 Motivation 1
1.2 Literature Survey 2
1.3 Thesis Organization 3
Chapter 2 5
2.1 Properties of Proteins 5
2.2 The HP Protein Model 8
2.3 Calculating the Free Energy 11
Chapter 3 13
3.1 Review the Artificial Bee Colony Algorithm 13
3.1.1 Behavior of Real Bees 14
3.1.2 Artificial Bee Colony Algorithm 18
3.2 Modified Artificial Bee Colony Algorithm 21
3.3 Numerical Simulation Results 23
Chapter 4 32
4.1 2D Protein Structure Simulation 32
4.2 3D Protein Structure Simulation 39
Chapter 5 47
Bibliography 48

List of Figures
Figure 1: A sequence of all the protein structures from primary to quaternary 7 Figure 2: An optimal conformation for the sequence “(HP)2PH(HP)2(PH)2HP(PH)2”; (a) the 2D HP lattice model; (b)the 3D HP lattice model 10
Figure 3: Behavior of honeybee foraging for nectar 17
Figure 4: The flowchart of the modified artificial bee colony algorithm 23
Figure 5: Evolution of mean best values for function 1 27
Figure 6: Evolution of mean best values for function 2 28
Figure 7: Evolution of mean best values for function 3 29
Figure 8: Evolution of mean best values for function 4 30
Figure 9: Evolution of mean best values for function 5 31
Figure 10: The schemes were represented by internal coordinates (the black cube represents the current location). 33
Figure 11: Opposite motion: the second to fifth residues motioned opposite position. 34
Figure 12: Rotation motion. The second to fifth residues will move to 35
clockwise position and form the structure as the right 35
Figure 13: Results of the structure of 8 protein sequences. 38
Figure 14: The schemes were represented by internal coordinates (the black cube VIII represents the current location). 40
Figure 15: All the residues move in the opposite direction 41
Figure 16: (a) The clockwise rotation motion; (b) the counterclockwise rotation motion 42
Figure 17: Results of the structure of 7 protein sequence 45
List of Tables
Table 1: The symbolic expression of twenty amino acids and their hydrophobic 6
Table 2: Numerical benchmark functions 24
Table 3: Results from function 1 27
Table 4: Results from function 2 28
Table 5: Results from function 3 29
Table 6: Results from function 4 30
Table 7: Results from function 5 31
Table 8: In 2D case, the residue folds direction with local search 35
Table 9: The 2D HP benchmarks 36
Table 10: Comparison of our approach with the genetic algorithm (GA), ant
colony optimization (ACO), Monte Carlo (MC), and tabu search with genetic
algorithm (GTS) 39
Table 11: In 3D case, the residue folds direction with local search 43
Table 12: The 3D HP benchmarks 43
Table 13: The simulation results obtained from the proposed algorithms
compared with the methods given in the literature 46
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