博碩士論文 89541010 詳細資訊


姓名 宋文財(Wen-Tsai Sung)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 使用最小能量原理來改進電腦輔助藥物設計中的分子對接技術之研究
(Improving Molecular Docking Technology for Computer Aided Drug Design via Energy Minimum Theorem)
檔案 [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 這篇論文的研究目的是提出一些新的電腦圖學技術與計算方法來解決電腦輔助藥物設計的問題。本論文以幾何、能量與活性等三大方向來探討如何使用最小能量原理來改進電腦輔助藥物設計中的分子對接過程之效能,加速藥物設計時程與降低研發成本。從美國疾病管制局2004年的研究報告中可以說明流行疾病盛行,各種藥物設計必需再加快時程與研發數量。在很多的藥物研究報告中也發現,電腦輔助藥物設計最大的挑戰是分子對接過程,在本論文中我們以最小能量原理為解決問題的主軸。首先從幾何搜尋方面著手,本研究是以受體為基礎,除了比較四種不同的受體特性外,並以研究如何快速模擬蛋白質的摺疊開始,我們提出了改良式遺傳演算法來加速接合位置的幾何搜尋;再來以能量為研究重點,在實驗中使用李亞普諾夫函數中的穩定理論來降低接合位置數以便進一步增進分子對接的效能,並且使用NURBS曲線中的插入頂點與權重調整來加速分子系統達到最小能量狀態。最後我們以各種不同的藥物受體模型來做電腦模擬計算,利用最小能量原理判斷出接近全域能量最小的區域之對接狀態的穩定度,並對其各種分子活性進行評估,研究各種分子對接中的各項活性,從中瞭解分子力場各元素的貢獻度,也成功地驗證出本論文所提出的方法。
本研究導入目前實驗準則X-ray及RMSD的標準,也驗證我們的電腦模擬之結果是否在容許誤差範圍內,並提出Michel,David, Denical,Abraham等人的相關研究來佐證我們的研究之基礎。我們在論文中所提到的模擬環境是以AMBER力場及Ullman’’s演算法為基礎,在模擬的過程中探討每一時刻特徵值λ的物理意義,比較各個不同對接點之表現結果,分析藥物受體模型中的蛋白質的摺疊和各種鍵結力之影響。在經過了蒙地卡羅,退火演算法,遺傳演算法及改良式遺傳演算法等四種最佳化搜尋方法的實驗後可知,我們所提出的改良式遺傳演算法來執行接合位置的搜尋及完成分子對接程序最快,引用Pegg和Camila的兩篇研究報告作比較,得到較接近全體能量最小值及算術過程之收斂時間快1.16小時。本論文亦研究藥物受體互動模型中的親合力係數之測量,除了判斷藥物與受體的關係是增效或對抗,也知悉分子擾動中那一種對接的活性較強。
根據之前我們所出版的研究論文,我們使用創新的WebDeGrator繪圖系統來建立分子對接過程中的電腦模型。使用藥物受體互動模型,以八個配體及HIV 蛋白脢受體為模擬對象做分子對接而得到一些特性結果。最後,在論文中對於最佳解,分子對接及蛋白質摺疊相關議題也將詳細分析與研究。跨越各種不同研究領域,例如結合生物學,資訊科學,系統學及化學等來解決生物資訊的各種新興的議題將是未來最強有力的解決方法。
摘要(英) This investigation presents novel computer graphical and computational schemes for
solving the challenges of computer-aided drug design (CADD). The application of the
energy minimum to enhance the docking performance of CADD is discussed in terms
of three aspects, geometry, energy and activity. American CDC research reports
reveal that an increasing incurrence of disease, resulting in a requirement to
accelerate drug discovery. However, commercialization of a new drug is extremely
complicated. The most significant challenge is the docking procedure in CADD
according to previous literature. This study applies the energy minimum theorem to
solve the objection. A geometry search is performed and compared with four types in
classification of receptors. This work attempts to improve the speed of computer
simulations of protein folding of protein, and proposes an improved genetic algorithm
to accelerate the binding site search; second, we focused on energy theme.
Lyapunov’s stability theorem is adopted to decrease the number of binding sites, thus
enhancing the docking performance in computer simulation examples. The knot
insertion and modifying weights of NURBS curves are utilized to accelerate the
molecular docking system in order to obtain the shortest response route. Finally,
various drug-ligand interaction models are employed to compute docking simulation,
and energy minimum theorem is used to judge the approach global energy minimum
area and docking stability. Various molecular activities are derived at each binding
site, and the contribution of every bond and non-bond’s in the force field is observed.
As a benchmark is reference for testing docking performances, the error tolerance of
computer simulation examples is compared with the X-ray and RMSD experiment
standard, and the values obtained by Michel, David, Denical and Abraham’s researches performance. This investigation develops the AMBER force field and
Ullman’s algorithm to support the computer simulation environments. The
significance of the eigenvalue λ is analyzed at each protein folding, and this study
performance has increased by 25 percents compared with various binding sites.
Additionally, the protein folding and various bond forces in drug-ligand interaction
model are discussed s. Comparing four optimal geometry search methods and referred
to Pegg and Camila the two been published paper in benchmark of drug docking
database, the improved genetic algorithms are specified to undertake the search
binding site and docking, and the global minimum search and the arithmetic
convergence time of 1.16hr is achieved. Analytical results indicate that the improved
genetic algorithm is better than traditions random methods in terms of processing the
geometry graphics operation.
Previous published investigations have employed the WebDeGrator system to
establish molecular computer modeling for the docking process. This study
demonstrates examples in protein folding kinetics and drug docking computations,
and successfully applies the Lyapunov function and molecular dynamics to help
determine the system stability. Optimal solutions, molecular docking and protein
folding kinetics are also discussed herein. This work integrates various research fields
to find advanced and novel solutions to problems in bioinformatics. The combination
of biology, information, system, and chemistry will be a powerful CADD strategy in
the future.
關鍵字(中) ★ 遺傳演算法
★ 最小能量
★ 李亞普諾夫漸進穩定
★ 分子對接
★ 電腦輔助藥物設計
★ 生物資訊
★ 藥物受體互動模型
關鍵字(英) ★ Computer-Aided Drug Design (CADD)
★ WebDeGrator
★ Lyapunov Equation Asymptotically stable
★ Minimum Energy
★ Improved Genetic Algorithms
★ Bioinformatics
★ Docking
論文目次 ACKNOWLEDGMENTS i
TABLE OF CONTENTS ii
LIST OF FIGURES viii
LIST OF TABLES x
CHAPTER 1
INTRODUCTION: 1
1.1. Introduction 1
1.2. Literature Survey 3
1.3. Merits and Contribution 7
1.4. Organization of Dissertation 9
CHAPTER 2
DRUG-RECEPTOR INTERACTION IN GEOMETRY, ENERGY AND ACTIVITY 10
2.1 System Framework 10
2.1.1 Docking benchmark 12
2.2 Problems with CADD 13
2.3 Drug Docking Flowcharts 14
2.3.1 Drug docking 14
2.3.2 Identifying active sites on receptors 16
2.4 Flowchart in Producing the Drug Candidate 19
2.5 Protein Folding is Important to Receptor for Drug Docking 23
2.5.1 Protein folding problem 23
2.5.2 Distributed molecular dynamics computation 24
2.6 Molecular Mechanics and Dynamics (MM and MD) 26
2.6.1 Anatomy of a Molecular Mechanics Force-Field 27
2.6.2 Molecular interaction forces and secondary bonds in Minimum Energy Experiment 3-2 with C2H2X2 Molecular Compound 28
2.7 AMBER Force Field Related to Parameter and Potential Energy Calculation 29
2.8 Drug-Receptor Interaction 34
2.8.1 Drug-receptor interactions and docking free energy calculations 34
2.8.2 Drug-receptor affinity: agonists and antagonists 35
2.8.3 Drug receptor theories excerpt 37
2.8.4 Receptor types 38
2.9 Seven Major Types of Drug-Receptor Interactions 41
2.9.1 Drug-receptor bonding 41
CHAPTER 3
PROTEIN FOLDING SIMULATION FOR RECEPTOR-BASED DRUG DOCKING VIA ENERGY MINIMUM THEOREM 46
3.1 Protein Folding for Finding Active Sites on Receptors 46
3.1.1 Protein folding is important to receptor for drug docking 46
3.1.2 Influence of protein folding in drug design 48
3.1.3 Protein folding and disease 49
3.2 Molecular Folding with Energy Minimum via Simulation Force Field 50
3.2.1 Molecular substructure matching algorithm 51
3.2.2 Force field simulation and scoring function 53
3.2.3 Example 3-1: Force field simulation and scoring function (C2H4OX3) 54
3.3 Global Stability in an Energy Minimum Location 56
3.3.1 Example 3-2: Minimum energy experiments (C2H2X2) 60
3.3.2 Example 3-3: Comparison of results between complete and incomplete molecular folding task 63
3.3.3 Reduced distance matrix order 65
3.3.4 Example 3-4: Reduced Laplace's theorem 65
3.3.5 Langvin equation 67
3.4 Dynamics Docking System Analysis Based on Lyapunov Stability Theorem 68
3.5 Lyapunov First Method (The indirect method) 69
3.5.1 Example 3-5: Stability of infinite small perturbation motion in docking system with n molecular particles 69
3.5.2 Example 3-6: Applying Lyapunov to eliminate some points with local minimum energy 72
3.6 Lyapunov Second Method (The direct method) 74
3.6.1 Example 3-7: Construction of Lyapunov function 74
3.6.2 Example 3-8:H2O (potential energy) 76
3.7 Discriminating Among Lyapunov Stability Types 76
3.8 The Lyapunov Exponent 78
3.8.1 Physical significance of the Lyapunov exponent
參考文獻 [1] Emerging Infectious Diseases, Review of State and Federal Disease Surveillance Efforts.GAO-04-877. Washington, D.C.: September 30, 2004.
[2] Infectious Disease Preparedness, Federal Challenges in Responding to Influenza Outbreaks. GAO-04-1100T. Washington, D.C.: September 28, 2004.
[3] Emerging Infectious Diseases, Asian SARS Outbreak Challenged International and National Responses. GAO-04-564. Washington, D.C.: April 28, 2004.
[4] Public Health Preparedness, Response Capacity Improving, but Much Remains to Be Accomplished. GAO-04-458T. Washington, D.C.:February 12, 2004.
[5] Infectious Diseases, Gaps Remain in Surveillance Capabilities of State and Local Agencies. GAO-03-1176T. Washington, D.C.: September 24, 2003.
[6] Kåre Andersson ,“Encoding Chemical Information into Bit-Strings for the Purpose of Virtual Screening”, Göteborgs Universitet, Sweden - Department of Theoretical Chemistry , Thesis, pp.132-146,Nov.7,2004
[7]Pegg SC, et. al. “A genetic algorithm for structure-based de novo design.” ,Journal Comput Aided Mol Des. 2001;Vol.15,pp.911–933.
[8]Camila S. et. al. “A genetic algorithm for the ligand-protein docking problem”, Genetics and Molecular Biology, Vol.27, No.4, pp.605-610, 2004
[9] N.K.,Shah, P.A.Rejto, and G.M.Verkhivker, “Structural Consensus in Ligand-Protein Docking Identifies Recognition Peptide Motifs that Bind Streptavidin,” Proteins: Structure, Function and Genetics, Vol. 28, pp. 421-433, 1997.
[10] R.S. Merkle, “A Genetic Algorithm Based Method for Docking Flexible Molecules,” Journal of Molecular Structure, Vol.308, pp.191-206, 1994.
[11] B.S. Sinha, “Predicting Protein-Protein Interaction Using Parametric Surfaces,” Paper presented at the 13th Annual Conference of the Molecular Graphics Society: Molecular Graphics at the Frontier, Evanston (IL), U.S.A., July 1994. (An abstract of this paper was published in Chemical Design Automation News 1994, July, 35).
[12] J.S. Singh, “Flexible Docking of Ligands to Receptor Sites using Genetic Algorithms. In Wermuth,” C.G. (Ed.), Trends in QSAR and Molecular Modelling, Vol.92, ESCOM, Leiden, pp. 412-413,1993.
[13] A.,Hellinga, H.,Richards, “Multiple Conformation and Protonation State Representation in 4D-QSAR: The Neurokinin-1 Receptor System.” J. Med. Chem., Vol.43, 2000.
[14] Shih-Ching Ou, Hung-Yuan Chung, Wen-Tsai Sung, Chun-Yen Chung, “Using Directivity Genetic Algorithms to Geometry Search and Conformational Stability for improving molecular simulation techniques,” WSEAS Transactions on Mathematics and Computers in Biology and Biomedicine, Issue 4, Volume 3, pp.291-296, April ,2006
[15] M.,. Alerts, “Quasi-Atomistic Receptor Surrogates for the 5-HT2A Receptor: A 3D-QSAR Study oon Hallucinogenic Substances,” Quant. Struct.-Act. Relat., Vol. 18, pp.548-560,1999.
[16] M.,. Alerts, “Quasi-Atomistic Receptor Modeling: A Bridge Between 3-D QSAR and Receptor Modeling,” Pharm. Acta Helv., Vol.73, pp.11-18, 1998.
[17] D.E..Schnecke, “Genetically Evolved Receptor Models (GERM): A Comparison of Evolved Models with Crystallographically Determined Binding Sites,” Alfred Benzon Symposium, Vol.42, pp.101-114, 1998.
[18] Shih- Ching Ou, Chun- Yen Chung, Wen-Tsai Sung ,Chia - Chih Tsai, Chin - Chih Chien, and Da -Yu Su,” Virtual Screening and Computer-aided Drug Design in Molecular Docking via Lyapunov Function,” WSEAS Transactions on Biology and Biomedicine , Issue 4,Vol. 01, pp.384-390, Oct. 2004.
[19] Shih-Ching Ou, Wen-Tsai Sung, Chun-Yen Chung, “Study on Molecular Docking for Computer-Aided Drug Design via Lyapunov Equation and Minimum Energy,” Information Visualization, Palgrave Macmillan Ltd. Vol. 7, No. 1, pp.210-220, 2005.
[20] Zhou, David, “A Genetic Evolved Algorithm to Predict Bioactivity,” Journal of Chemical Information and Computer Sciences, Vol.38, pp.243-250,1998.
[21] J. Denical, “Receptor Mapping used for Predicting Bioactivity,” J. Chin. Pharm. Sci.,Vol. 6, pp.149-153,1997.
[22] T.R. Abraham, “A New QSAR Research Method Based on Genetic Algorithm,” Chinese Chem. Letters, Vol.8, pp.975-978, 1997.
[23] Shih-Ching Ou, Chun-Yen Chung, Hung-Yuan Chung, Wen-Tsai Sung ,Chien-Chin Cheng, “Molecular Docking for Protein Folding Structure and Drug-likeness Prediction,” WSEAS International Journal on Biology and Biomedicine, Issue 1, Volume 2, pp.57-63, January 20-22 ,2005.
[24]Shih-Ching Ou, Wen-Tsai Sung, Hung-Yuan Chung, Chun-Yen Chung,” Determine Global Energy Minimum Solution via Lyapunov Direct and Indirect Methods” Internet and Multimedia Systems and Applications Conference (IMSA 2005),Noverber,11-15,2005,pp203-209
[25] D.E.Walters, and T.D.Muhammad, “Genetically Evolved Receptor Models (GERM): A Procedure for Construction of Atomic-Level Receptor Site Models in the Absence of a Receptor Crystal Structure,” In: Devillers, J. (Ed.) Genetic Algorithms in Molecular Modelling, Academic Press, pp. 193-210, 1996.
[26] M.D.,Miller, E.M.,Fluder, L.A.,Castonguay, J.C.,Culberson, R.T.,Mosley, K.,Prendergast, S.K.Kearsley, and R.P.Sheridan, MEGA-SQ,”A Method using the SQuEAL Function to Find the Optimal Superposition of Several Quasi-Flexible Molecules,” Med. Chem. Res. ,Vol. 9, pp.513-534,1999.
[27] S.Handschuh, and J.Gasteiger, “Pharmacophores Derived from the 3D Substructure Perception,” IUL Biotechnology Series 2 (Pharmacophore), pp. 431-453, 2000.
[28] I.J.McFadyen, et al. “The Steroid SC17599 is a Selective mu-Opioid Agonist: Implications for the mu-Opioid Pharmacophore,” Mol. Pharmacol. , Vol.58, pp.669-676, 2000.
[29] S.Handschuh, and J.Gasteiger, “The Search for the Spatial and Electronic Requirements of a Drug,” J. Mol. Modell.,Vol. 6, pp.358-378, 2000.
[30] L.,Leherte, N.Meurice, and D.P.Vercauteren, “Critical Point Representations of Electron Density Maps for the Comparison of Benzodiazepine-Type Ligands,” J. Chem. Inf. Comput. Sci. ,Vol.40, pp.816-832, 2000.
[31] G. Jones, and P. Willett, GASP, “Genetic Algorithm Superimposition Program,” IUL Biotechnology Series 2 (Pharmacophore), pp. 85-106, 2000.
[32] Wen-Tsai Sung, Shih-Ching Ou,” Integrating Network, CAD and VR into the Design and Development of Web-Based Computer Graphics Learning Materials,” Journal of Computer Science, Vol.14, No.2, pp.47-63, Dec.2001.
[33] Sung-Jung Hsiao, Wen-Tsai Sung, and Shih-Ching Ou ,” Web-based search system of pattern recognition for the pattern of industrial component by an innovative technology,” Computer in Industry Journal, Elsevier Company, Vol. 53, Issue 2 , pp. 179-192, Feb.2004.
[34] Wen-Tsai Sung, Shih-Ching Ou,” Using Virtual Reality technologies for Manufacturing Applications, ” International Journal of Computer Applications in Technology, Inderscience Enterprises Ltd., Vol.17, No. 4, pp. 213-219, 2003.
[35] Wen-Tsai Sung, Shih-Ching Ou, "Web-Based Learning in CAD Curriculum Sculpture Curves and Surface", International Journal of Computer Assisted Learning, Blackwell Science Publishers, Vol.18, No.1,pp.175-187, June 2002.
[36] Wen-Tsai Sung, Shih-Ching Ou , Sung-Jung Hsiao, "Interactive Web-based Training Tool for CAD in a Virtual Environment ".Journal of Computer Applications in Engineering Education, John Wiley & Sons, Inc. Vol. 10, Issue 4, pp. 182-193. 2002 .
[37] Wen-Tsai Sung, Shih-Ching Ou, "Learning Computer Graphics using Virtual Reality Technologies Based on Constructivism" ,Interactive Learning Environments International Journal, Swets & Zeitlinger Publisher,Vol.10 No.3 , pp.177-197, 2000.
[38] Shih-Ching Ou, Wen-Tsai Sung , Sung-Jung Hsiao, "Study on the Wireless and Virtual Reality Technology ---Based on Web DeGrator System", Journal of Internet Technology, Computer Center ,National Dong Hwa University, Vol. 3, No. 4, pp. 257-266, 2002.
[39] Shih-Ching Ou, Hung-Yuan Chung,Wen-Tsai Sung, “Development of a computer aided geometric design system based on parallel architecture, ” WSEAS Transactions on Computers, Issue 2, Volume 5, pp.278-284, February,2006
[40] Shih-Ching Ou, Hung-Yuan Chung ,Wen-Tsai Sung, ” Enhancing Compression and Encryption of image with FPGA-based”, Multimedia Tools and Applications. Kluwer academic publishers, vol. 28, no. 1, January 2006.
[41] Chun-Yen Chung, Shih-Ching Ou , Chia-Chih Tsai, Wen-Tsai Sung ,“Application of Minimum Energy to Protein Structure Prediction and Drug Design,” Presented at 2004 Chineseand Japan Molecular Design, Nanotechnology and Drug Transmission Conference IRC Biology Medical Center,2004.
[42] Shih- Ching Ou , Jeng-An Liaw , Chun- Yen Chung , Chia -Chih Tsai,Wen-Tsai Sung , Chin - Chih Chien and Da -Yu Su, “ Design and Implementation of Wireless Web-Service Technology Based on Java, ” Presented at 2004 Symposium on Digital Life and Internet Technologies, National Cheng Kung University, 2004.
[43] Chun - Yen Chung, Shih - Ching Ou, Chia - Chih Tsai, Wen-Tsai Sung, Chin – Ch Chien, and Da-Yu Su “ Application of Molecular Docking to Protein Folding Structure Prediction and Drug Discovery ” International Proteomics Conference (IPC’03), In the Grand Hotel on May 14-17, 2004 in Taipei, Taiwan
[44] Chia-Chih Tsai ,Chun-Yen Chung, Shih-Ching Ou, Wen-Tsai Sung “Predict protein structure and similar function via 3D Visualization tools- case study of bamboo shoot,” 2004 IEEE Fourth Symposium on Bioinformatics and Bioengineering (BIBE2004) , IEEE Computer Society Press, May 19-21, 2004, Taichung, Taiwan, ROC.
[45] Chia- Chih Tsai, Shih- Ching Ou , Chun- Yen Chung , Wen-Tsai Sung, Chin Ch Chien, and Da Yu Su “ Predict protein structure and similar function via 3D Visualization tools-case study of bamboo shoot ” International Proteomics Conference(IPC’03), In the Grand Hotel on May 14-17, 2004 in Taipei, Taiwan.
[46] Wen-Tsai Sung, Shih-Ching Ou,” Minimum Energy for Protein Folding Structure via Lyapunov Equation Molecular Dynamics,” Symposium and Workshop of Bioinformatics in Taiwan 2003,Taipei,Tawan.5-7 Sept.2003.
[47] Shih-Ching Ou,Wen-Tsai Sung,” Molecular Docking for Drug Design,” Symposium and Workshop of Bioinformatics in Taiwan 2003,Taipei,Taiwan. 5-7 Sept.2003.
[48] G.,Jones, P.Willett, and R.C.Glen, “A Genetic Algorithm for Flexible Molecular Overlay and Pharmacophore Elucidation,” Journal of Computer-Aided Molecular Design ,Vol. 9, pp.532-549,1995
[49] J.-M. Yang, T.-W. Shen, “A pharmacophore-based evolutionary approach for screening selective estrogen receptor modulators,” Proteins: Structure, Function, and Bioinformatics, vol. 59, pp. 205-220, 2005.
[50] D.J.Wild, and P.Willett, “Field-Based Similarity Searching in Databases of Three-Dimensional Chemical Structures,” Proceedings of the International Chemical Information Conference, Nimes, France, pp.19-22 October, 1997.
[51] Venkatachalam, C.M., Jiang, X., Oldfield, T., and Waldman, M.,"LigandFit: A Novel Method for the Shape-Directed Rapid Docking of Ligands to Protein Active Sites," J. Mol. Graph. Modell., 2003, 21, 289-307.
[52] Ji, H., Zhang, W., Zhang, M., Kudo, M., Aoyama, Y., Yoshida, Y., Sheng, C., Song, Y., Yang, S., Zhou, Y., Lu, J., and Zhu, J., "Structure-Based de Novo Design, Synthesis, and Biological Evaluation of Non-Azole Inhibitors Specific for Lanosterol 14r-Demethylase of Fungi," J. Med. Chem., 2003, 46, pp.474-485.
[53] Jones et. al. “The QSAR Paradigm in the Design of Less Toxic Molecules”. Drug Metab. Rev., 15: 1279-1294. 1985
[54] Randall E. Burton et. al.” The energy landscape of a fast-folding protein mapped by Ala Gly Substitutions”, Nature Structural Biology, Vol. 4, 305 - 310 ,1997
[55] Meng, E.C., Shoichet, B.K., and Kuntz, I.D. “ Automated Docking with Grid-Based Energy Evaluation.” J. Comput. Chem., 13: pp.505-524.1998
[56] Booth, P.J., Templer, R.H., Meijberg, W., Allen, S.J., Curran, A.R., and Lorch, M. “In vitro studies of membrane protein folding.” Crit. Rev. Biochem. Mol. Biol. 36: pp.501-603.2001
[57] M.G.B.,Drew, G.R.H.,Wilden, W.J.,Spillane, R.M.,Walsh, C.A.Ryder, and J.M. Simmie, “Quantitative Structure-Activity Relationship Studies of Sulfamates RNHSO3Na: Distinction Between Sweet, Sweet-Bitter, and Bitter Molecules,” J. Agric. Food Chem., Vol.46, pp.3016-3026,1998.
[58] Brooks, B.R., Bruccoleri, R.E., Olafson, B.D., States, D.J., Swaminathan, S., Karplus, M. “CHARMM: A program for macromolecular energy, minmimization, and dynamics calculations”. J. Comp. Chem. 4, pp.187-217.1983
[59] Damm, W., A. Frontera, J. Tirado-Rives and W. L. Jorgensen "OPLS All-Atom Force Field for Carbohydrates," J. Comp. Chem. 18, pp.1955-1970.2003
[60] Cornell, W. D., Cieplak, P., Bayly, C. I., Gould, I. R., Merz, K. M. Jr., Ferguson, D. M. Spellmeyer, D. C., Fox, T., Caldwell, J. W., and Kollman, P. A. “A second generation force field for the simulation of proteins, nucleic acids and organic molecules, ”J. Am. Chem. Soc. 117, pp.5179-5197.2004
[61] Hansch, C. “Drug Research or the Luck of the Draw”. J. Chem. Ed., 51: 360-365.1974
[62] W.,Linert, P.Margl, and E. Nusterer, “The Use of Enhanced Operator-machine Interfaces in Computer-aided Molecular Design,” Comput. Chem., Vol.15, pp. 1-10, 1991.
[63] T.J.,Hou, J.,Wang, Y.Y.Li, and X.J.Xu, “Application of Genetic Algorithm to the QSAR Research of Pyrrolobenzothiazepinones and Pyrrolobenzoxazepinones: Novel and Specific Non-nucleoside HIV-1 Reverse Transcriptase Inhibitors,” Chin. Chem. Lett., Vol.9, pp.651-654, 1998.
[64]M.G.,Albuquerque, A.J., Hopfinger, E.J.Barriero, and R.B.Alencastro, “Four Dimensional Quantitative Structure-Activity Relationship Analysis of a Series of Interphenylene 7-oxabicycloheptane Oxazole Thromboxane A2 Receptor Antagonists,” Journal of Chemical Information and Computer Sciences, Vol.38, pp.925-938.1998.
[65] Y.Tominaga, “Novel 3-D Descriptors Using Excluded Volume. 2. Application to Drug Classification,” Journal of Chemical Information and Computer Sciences, Vol.38, pp.1157-1160, 1998.
[66] Ullman J.R. (1976) An algorithm for subgraph isomorphism. J. Assoc. Comput. Mach., 23, 31–42.
[67] J. Hespanha, D. Liberzon, E. Sontag. “Nonlinear observability and an invariance principle for switched systems,” In Proc. 41th Conf. on Decision and Control, Dec. 2002.
[68] J. Hespanha, D. Liberzon, A. S. Morse. “Overcoming the limitations of adaptive control by means of logic-based switching,” Syst. & Contr. Lett., Vol.49,No. 1: pp.49-65, Apr. 2003.
[69] S. Boyd, L. El Ghaoui, E. Feron, V. Balakrishnan. “Linear Matrix Inequalities in System and Control Theory.” SIAM, 1994.
[70] D. Liberzon, A. S. Morse, “Basic problems in stability and design of switched systems,” IEEE Control Systems Magazine, Vol. 19, No. 5, pp. 59-70, Oct. 1999.
[71] Ian A. Hiskens et. al.” Lyapunov Function Analysis of Power Systems with Dynamic Loads “Proceedings of the 35th Conference on Daoision end Control Koba, Japan l December 1996. pp.231-236
[72] R.J. Davy and I.A. Hiskens, “Lyapunov functions for multi-machine power systems with dynamic loads”, Technical Report No. EE9472, Department of Electrical and Computer Engineering, The University of Newcastle, IEEE Trans. on Circuits and Systems, 23(4) pp.167-173 ,December 1994
[73] J. Hespanha. “Root-Mean-Square Gains of Switched Linear Systems,” IEEE Trans. on Automat. Contr., Vol.48, No.11,pp.364-376, Nov. 2003.
[74] G. Bhanuprakash Reddy et. al. “Linear free-energy model description of the conformational stability of uracil-DNA glycosylase inhibitor” European Journal of Biochemistry, Volume 261 Issue 3 pp. 610 -620 May (I) 1999
[75] G. Kresse and J. Furthmüller, “Efficiency of Ab-Initio Total Energy Calculations for Metals and Semiconductors Using a Plane-Wave Basis Set,” Computational Material Science 6, 15–50 ,1996.
[76] C. A. Laughton, “A Study of Simulated Annealing Protocols for Use with Molecular Dynamics in Protein Structure Prediction,” Protein Engineering 7, 235–241,1994.
[77] T. P. Lybrand, “Ligand-Protein Docking and Rational Drug Design,” Current Opinion in Structural Biology 5, 224–228 (1995).
[78] J. M. Blaney and J. S. Dixon, “A Good Ligand Is Hard to Find: Automated Docking Method,” Perspectives in Drug Discovery and Design 1, 301–319 (1993).
[79] Berman,H.M., Westbrook,J., Feng,Z., Gilliland,G., Bhat,T.N., Weissig,H., Shindyalov,I.N. and Bourne,P.E. The Protein Data Bank. Nucleic Acids Res., 28, 235–242,2000
[80] Kanehisa, M., Goto, S., Kawashima, S., and Nakaya, A. The KEGG databases at GenomeNet. Nucleic Acids Res. 30, 42-46.2002
[81] Muegge, I., and Martin, Y.C., "A General and Fast Scoring Function for Protein-Ligand Interactions: A Simplified Potential Approach," J. Med. Chem., 1999, 42, pp.791-804.
[82] Diane Joseph-McCarthy et. al.,”Automated generation of MCSS-derived pharmacophoric DOCK site points for searching multiconformation databases”, Proteins: Structure, Function, and Genetics, Volume 51, Issue 2 , pp. 189 - 202
[83] B.T.Luke, “Application of Genetic Methods to Substructure Searches,” Paper presented at the 209th ACS National Meeting, Anaheim (CA), U.S.A., April 1995.
[84] J.S.Taylor, and R.M.Burnett, “DARWIN: A Program for Docking Flexible Molecules,” Proteins, Vol. 41, pp.173-191, 2000.
[85] J.-M.Yang, and C.-Y.Kao, “Flexible ligand docking using a robust evolutionary algorithm,” J. Comput. Chem., Vol. 21, pp.988-998, 2000.
[86]E.J.,Gardiner, P.Willett, and P.J.Artymiuk, “Protein Docking Using a Genetic Algorithm,” Proteins, Vol. 44, pp. 44-56, 2001.
[87] M.,Del Carpio, C.Adriel, and A.Yoshimori, “MIAX: A System for Assessment of Macromolecular Interaction.-Part 3: A Parallel Hybrid GA for Flexible Protein Docking,” Genome Inf. Ser., Vol.11, pp.205-214, 2000.
[88] Pique, J. P.” Molecular dynamics and quantum chaos in small polyatomic molecules (CS2, C2H2) through stimulated-emission pumping experiments and statistical Fourier transform analysis”, Journal of the Optical Society of America B: Optical Physics, Volume 7, Issue 9, September 1990, pp.1816-1828
[89] Preben H. Olesen et.al. ” A New Class of Selective GSK-3 Inhibitors” J. Med. Chem., 2003, Volume 46, Issue 15; pp3333-3341
[90] Huang et. al. “An approach to vanilloid-based adrenergic antagonist. “Chinese Chemical Society 98’ (Poster presentation),1998
[91] Rong Chen, Julian Mintseris, Joe Janin, and Zhiping Weng, “A Protein–Protein Docking Benchmark”, Proteins: Structure, Function, and Genetics, 52:88–91 (2003)
指導教授 歐石鏡、鍾鴻源
(Shih-Ching Ou、Hung-Yuan Chung)
審核日期 2007-1-18

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡