本論文主要為使用分子模擬方法進行電腦輔助藥物設計相關研究，本論文包含蛋白質藥物與小分子藥物設計設計兩大部分。本研究結果冀望能幫助藥物開發學者改良藥物分子，本研究分成兩大部分敘述：
蛋白質藥物：
丙型肝炎病毒核單蛋白與單株抗體19D9D6平均力勢研究：在免疫學研究中，為了了解抗體抗原之間結合活性與機制，研究抗體抗原作用力具有關鍵性的角色。由於傳統使用表面薄膜共振技術與原子力顯微鏡等實驗儀器，實驗方法較為困難且無法測得抗體作用區域上胺基酸構形變化情形；故本研究採用分子模擬方法分離丙型肝炎病毒核單蛋白與單株抗體19D9D6複合體，並觀察分離過程中抗體抗原平均力勢變化。本研究結果顯示在抗體上七個胺基酸(Gly70, Gly72, Gly134, Gly158, Glu219, Gln221 and Tyr314)與14個抗原抗體之間的氫鍵結對於此抗原抗體複合體結合過程中具有重要性，特別是這三個胺基酸(Gly134, Gly158, and Tyr314)在整理計算中有非常顯著的變化，此結果也許可成為日後實驗學家修正抗體結構之分子模擬參考資料。
金色毛髮敘利亞倉鼠普恩蛋白抗原決定部位蛋白與單株抗體3f4 平均力勢研究：在免疫學抗原抗體之間結合活性與機制是相當重要的研究。本研究採用分子模擬方法分離金色毛髮敘利亞倉鼠普恩抗原決定部位蛋白與單株抗體3f4複合體，並觀察分離過程中抗體抗原平均力勢變化。本研究結果顯示在抗體上四個胺基酸與兩個抗原抗體之間的氫鍵結對於此抗原抗體複合體結合過程中具有重要性。
人類水解酵素與單株抗體vhh hl6 平均力勢研究：計算抗原抗體之間結合活性是了解抗原抗體作用機制相當重要的議題。為了深入研究抗原抗體結合機制本研究採用分子模擬方法分離人類水解酵素與單株抗體vhh hl6複合體，並觀察分離過程中抗體抗原平均力勢變化。本研究結果顯示在抗體上六個胺基酸對於此抗原抗體複合體結合過程中具有重要性。

小分子藥物：
藉由SIE方法預測多巴胺D2受體拮抗劑活性：多巴胺D2受體已經被驗證為治療精神分裂症重要標把蛋白質，四氫化萘二醇衍生物具有高度抑制D2受體活性功效；由於缺乏實驗相關D2受體三維結構資訊，故無法由實驗資料中求得四氫化萘二醇衍生物與D2受體結合機制與模型。本研究採用分子模擬方法與蛋白質同源結構模擬方法，建立D2受體與四氫化萘二醇衍生物三維結合結構模型，並由模擬結果中預測出D2受體上五個胺基酸(Asp72, Val73, Cys76, Leu183, and Phe187)為四氫化萘二醇衍生物重要結合點。本研究亦採用溶解作用能(SIE)計算方法求出四氫化萘二醇衍生物與D2受體結合能，並比對文獻中的實驗數據後得到，本結合能計算有統計系數(R2)為0.815之正相關。
結合同元模擬、分子接合與分子動力學方法預測克流感、瑞樂沙與傳統中草藥分子與H1N1流感病毒神經胺酸水解酵素之結合模型：神經胺酸水解酶為治療病毒型流行感冒中重要標把蛋白，克流感與瑞樂沙亦為治療病毒型流行感冒重要藥物。在實驗上亦驗證克流感與瑞樂沙對於治療H1N1新流感病毒有強效的作用性，由於H1N1病毒神經胺酸水解酵素實驗三維結構資訊缺乏，故本研究採用分子模擬方法建立克流感、瑞樂沙與一系列中草藥分子藥物模型，並由結果得到位於神經胺酸水解酵素上，編號為第278號之穀胺酸具有藥物篩選性。

This dissertation, studies two specific topics related to the research of computer-aided drug design(CADD) by employing the molecular simulations approach, that of protein drugs and that of small agents. These results can help drug designers to improve their products for treating special diseases. This work is divided into two parts:
Protein drugs:
Potential of mean force of the hepatitis C virus core protein–monoclonal 19D9D6 antibody interaction: Antigen-antibody interactions are critical for understanding antigen-antibody associations in immunology. To shed further light on this question, we studied a dissociation of the 19D9D6-HCV core protein antibody complex structure. However, forced separations in single molecule experiments are difficult, and therefore molecular simulation techniques were applied in our study. The stretching, that is, the distance between the centre of mass of the HCV core protein and the 19D9D6 antibody, has been studied using the potential of mean force calculations based on molecular dynamics and the explicit water model. Our simulations indicate that the 7 residues Gly70, Gly72, Gly134, Gly158, Glu219, Gln221 and Tyr314, the interaction region (antibody), and the 14 interprotein molecular hydrogen bonds might play important roles in the antigen-antibody interaction, and this finding may be useful for protein engineering of this antigen-antibody structure. In addition, the 3 residues Gly134, Gly158 and Tyr314 might be more important in the development of bioactive antibody analogues.
Potential of mean force for syrian hamster prion epitope protein - monoclonal fab 3f4 antibody interaction studies: Simulating antigen-antibody interactions is crucial for understanding antigen-antibody associations in immunology. To shed further light into this question, we study a dissociation of syrian hamster prion epitope protein-fab3f4 antibody complex structure. The stretching (the distance between the center of mass of the prion epitope protein and the fab3f4 antibody) have been studied using potential of mean force (PMF) calculations based on molecular dynamics (MD) and implicit water model. For the complex structure, there are four important intermediates and two inter protein molecular hydrogen bonds in the stretching process. Inclusion of our simulations may help to understand the binding mechanics of the complex structure and will be an important consideration in design of antibodies against the prion disease.
Potential of mean force for human lysozyme - camelid vhh hl6 antibody interaction studies: Calculating antigen-antibody interaction energies is crucial for understanding antigen-antibody associations in immunology. To shed further light into this equation, we study a separation of human lysozyme-camelid vhh hl6 antibody (cAb-HuL6) complex. The c-terminal end-to-end stretching of the lysozyme-antibody complex structures have been studied using potential of mean force (PMF) calculations based on molecular dynamics (MD) and explicit water model. For the lysozyme-antibody complex, there are six important intermediates in the c-terminal extensions process. Inclusion of our simulations may help to understand the binding mechanics of lysozym- cAb-HuL6 antibody complex.

Small agents:
Predictions of binding for dopamine D2 receptor antagonists by the SIE method: The control of tetralindiol derivative antagonists released through the inhibition of dopamine D2 receptors has been identified as a potential target for the treatment of schizophrenia. We employed molecular dynamics simulation techniques to identify the predicted D2 receptor structure. Homology models of the protein were developed on the basis of crystal structures of four receptor crystals. Compound docking revealed the possible binding mode. In addition, the docking analyses results indicate that five residues (Asp72, Val73, Cys76, Leu183, and Phe187) were responsible for the selectivity of the tetralindiol derivatives. Our molecular dynamics simulations were applied in combination with the solvated interaction energies (SIE) technique to predict the compounds' docking modes in the binding pocket of the D2 receptor. The simulations revealed satisfactory correlations between the calculated and experimental binding affinities of all seven tetralindiol derivative antagonists, as indicated by the obtained R2 value of 0.815.
Combining homology modeling, docking, and molecular dynamics to predict the binding modes of oseltamivir, zanamivir, and Chinese natural herb products with the neuramindase of the H1N1 influenza A virus: The neuraminidase of the influenza virus is the target of the anti-flu drugs oseltamivir and zanamivir. Clinical practices show that zanamivir and oseltamivir are effective to treat the 2009 H1N1 influenza virus. Herein, we report the findings of molecular simulations for zanamivir, oseltamivir, and Chinese natural herb products with the neuramindase of the 2009 H1N1 influenza. Our approach theoretically suggests that the Glu278 residue is responsible for the neuramindase of the 2009 influenza drug selectivity.

Contents ix
List of Figures xii
List of Symbols xv
List of Abbreviations xviii
Chapter 1 Introduction 1
1-1 Introduction to Protein Drugs 1
1-1-1 Review of Protein Drugs 1
1-1-2 Review of Human Camelid Vhh Hl6, Monoclonal Fab 3f4 and Monoclonal 19D9D6 Antibodies 2
1-1-3 Review of Protein Drugs Experimental Design and Computational Modeling 3
1-1-4 Overview of Our Dynamic Binding Hot Spots (DBHS) 4
1-2 Introduction to Small Agents 5
1-2-1 Review of Small Agent Drugs Experimental Design and Computational Modeling 5
1-2-2 Review of Human Dopamine D2 Receptor 6
1-2-3 Review of 2009 H1N1 Influenza A Virus 7
1-3 Outline of the Dissertation 8
Chapter 2 Molecular Simulation Techniques 10
2-1 Introduction 10
2-2 Equations of Motion In Molecular Dynamics Simulations 11
2-3 Empirical Force Field Models 11
2-3-1 Outline of Force Field for Bio-molecule or small agent systems 11
2-3-2 Related CHARMm Potential Energy Functions 12
2-3-3 Related Amber Potential Energy Functions 13
2-4 Statistical Ensembles 14
2-5 Constant temperature dynamics 14
2-5-1 Rescaling of Velocity 14
2-5-2 Heat Bath: weak coupling method 15
2-5-3 Heat Bath: Stochastic collision method 16
2-5-4 Heat Bath: extended system coupling method 16
2-6 Constant Pressure dynamics 17
2-7 Periodic boundary conditions 18
2-8 Treatment of non-bonded interactions 20
2-9 Non-bonded neighbor lists 22
2-10 Solvated interaction energies 22
2-11 Weighted histogram analysis method 23
Chapter 3 Simulations method 25
3-1 Protein drugs simulations 25
3-1-1 Potential of mean force of the hepatitis C virus core protein–monoclonal 19D9D6 antibody interaction 25
3-1-2 Potential of mean force for syrian hamster prion epitope protein - monoclonal fab 3f4 antibody interaction studies 26
3-1-3 Potential of mean force for human lysozyme-camelid vhh hl6 antibody interaction studies 26
3-2 Small agents simulations 30
3-1-1 Predictions of binding for dopamine D2 receptor antagonists by the SIE method 30
3-1-2 Combining homology modeling, docking, and molecular dynamics to predict the binding modes of oseltamivir, zanamivir, and Chinese natural herb products with the neuramindase of the H1N1 influenza A virus 32
Chapter 4 Results and Discussions 40
4-1 Protein drugs simulations 40
4-1-1 Potential of mean force of the hepatitis C virus core protein–monoclonal 19D9D6 antibody interaction 40
4-1-2 Potential of mean force for syrian hamster prion epitope protein - monoclonal fab 3f4 antibody interaction studies 41
4-1-3 Potential of mean force for human lysozyme-camelid vhh hl6 antibody interaction studies 42
4-2 Small agents simulations 61
4-2-1 Predictions of binding for dopamine D2 receptor antagonists by the SIE method 61
4-2-2 Combining homology modeling, docking, and molecular dynamics to predict the binding modes of oseltamivir, zanamivir, and Chinese natural herb products with the neuramindase of the H1N1 influenza A virus 62
Chapter 5 Conclusions 72
5-1 Protein drugs simulations 72
5-1-1 Potential of mean force of the hepatitis C virus core protein–monoclonal 19D9D6 antibody interaction 72
5-1-2 Potential of mean force for syrian hamster prion epitope protein - monoclonal fab 3f4 antibody interaction studies 72
5-1-3 Potential of mean force for human lysozyme-camelid vhh hl6 antibody interaction studies 73
5-2 Small agents simulations 74
5-2-1 Predictions of binding for dopamine D2 receptor antagonists by the SIE method 74
5-2-2 Combining homology modeling, docking, and molecular dynamics to predict the binding modes of oseltamivir, zanamivir, and Chinese natural herb products with the neuramindase of the H1N1 influenza A virus 74
References 75
Publications List 84

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