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研究生: 吳冠緯
論文名稱: 細胞外調節激酶、纖維母細胞生長受體激酶與其抑制劑結合自由能之計算: 熱力學積分分子動態及分子嵌合模擬研究
Computational Study of Binding Energy of Protein-Ligand Complexes for Two Kinases: Thermodynamic Integration Molecular Dynamics and Docking Simulation
指導教授: 孫英傑
學位類別: 碩士
Master
系所名稱: 化學系
Department of Chemistry
論文出版年: 2012
畢業學年度: 100
語文別: 中文
論文頁數: 94
中文關鍵詞: 分子動力學模擬熱力學積分分子嵌合蛋白質激酶
英文關鍵詞: Molecular Dynamics, Thermodynamic Integration, Molecular Docking, Protein kinase
論文種類: 學術論文
相關次數: 點閱:193下載:22
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  • 癌症一直為十大死因之一。因此與癌症有關的標靶藥物研發是目前國際間藥物化學家研究的主要方向之一。由於在實驗上合成藥物需要大量的人力、物力。因此若可以使用電腦來輔助藥物設計、藥物探索,不僅可以縮短研發的時程,更可以大量減少有機合成研發的成本。蛋白質激酶目前被認為與許多癌症有關。在本篇論文中,我們使用電腦輔助的方法來探討化學分子與蛋白質激酶間的作用力型態以及如何提升篩選具有生物活性的小分子。我們使用兩種理論方法,分別是熱力學積分及分子嵌合。

      在熱力學積分中,我們所針對的蛋白質標的為ERK2。我們以原本已知的活性分子開始。預測了一系列新的類似物,這些類似物是以苯環上的極性或非極性官能基做為區別。在我們的結果中,羥基(OH) 取代後發現其與蛋白質間的結合能力約比之前研究中的抑制劑分子好2 kcal/mol,而其他官能基的取代約增進1 kcal/mol。我們的預測結果可為實驗學家提供有機合成、蛋白質與細胞實驗的理論依據。

      除了分子動力學之外,我們也以分子嵌合計算探討FGFR1蛋白質幾個效應對豐富指數(enrichment factor)的影響。豐富指數為評價虛擬篩選優劣的指標之一。在豐富指數的研究中,我們採用DUD資料庫中活性及非活性的分子及設定不同的蛋白質條件。在設定條件時發現擺動514號胺基酸(賴氨酸)能夠大幅度增加正確挑選出活性分子的數量。並且設定絞鍊片段上564號胺基酸(丙胺酸)的氫鍵限制以進一步探究篩選效率。從本論文的研究中,我們尋找到最佳的條件,也希望這些最佳化條件可以進一步成為此類蛋白激酶大量虛擬篩選中的基礎。進而幫助先導化合物的找尋。

    Cancer has been one of the top ten leading causes of death for several decades. The
    target drugs research for cancer therapy is now a popular field among international
    medicinal chemists. Because of the significant amount of money and human resources
    spent in the drug development process, computer-aided drug design method is an
    attractive tool to reduce cost and assist drug discovery. Protein kinases are one of the
    protein families which are drug targets for cancer therapy. Here, we selected two kinases,
    which are ERK2 and FGFR1 kinases, and used computer modeling to investigate binding
    energy of inhibitor-protein complexes for these two kinases.
    In the part of ERK2, we used thermodynamic integration MD method to compute
    relative binding free energy of several ERK2-inhibitor complexes of interest. We carried
    out computations to predict G for new analogs, focusing on placing polar and nonpolar
    functional groups at the meta site of benzene ring, to see if these ligands have better
    binding affinity than the above ligands. The computations resulted that a ligand with polar
    –OH group has better binding affinity than the previous examined ligand by ~2.0
    kcal/mol and two other ligands have better affinity by ~1.0 kcal/mol. The predicted better
    inhibitors of this kind should be of interest to experimentalists for future experimental
    enzyme and/or cell assays.
    In addition to TI-MD simulation, we also worked on interactions of FGFR1
    kinase-inhibitor complexes using docking computation, focusing on how enrichment
    factor (EF) enhances in virtual screening by including side chain movement and applying
    hydrogen bond constraint for this kinase. To this end, active and decoy compounds from
    the Directory of Useful Decoys 1 database was obtained and benchmarked with GOLD
    program. Interestingly, among combinations of side chains which were allowed to move,
    EF is significantly higher with movement of Lys514 compared with others. In addition,
    the effect of adding hydrogen bond constraint at a residue located in the hinge segment,
    Ala564, was also examined. The results were analyzed and discussed. The present results
    should be useful for virtual screening of large databases against this kinase.

    口試委員會審定書 # 誌謝 1 中文摘要 3 ABSTRACT 5 目錄(CONTENTS) 6 圖目錄 (LIST OF FIGURES) 10 表目錄 (LIST OF TABLES) 12 符號(SYMBOLS) 13 第一部分 分子動力學模擬 (Part1: Molecular Dynamics Modeling) 15 Chapter 1 緒論 (Introduction) 17 1.1 前言 (Preface) 17 1.2 細胞外調節激酶二與癌症的關聯(The Relationship Between Extracellular Signal Regulated Kinase Protein Kinase 2 and Cancer) 17 1.3 研究目標 (Research Objective) 20 Chapter 2 模擬方法 (Simulation Methods) 21 2.1 理論 21 2.1.1 分子力學與分子動力學 21 2.1.2 自由能計算 23 2.1.3 熱力學循環與熱力學積分 24 2.2 模型設定 (Modeling Setup) 26 2.2.1 溶液的分子 26 2.2.2 蛋白質中的分子 26 2.2.3 模擬參數 27 Chapter 3 結果與討論 (Results and Discussions) 29 3.1 CF3變換為82A 32 3.2 OHW變換為82A 35 3.3 NH2變換為82A 38 3.4 活化位置環境與氫鍵分析 40 Chapter 4 結論 (Conclusion) 44 第二部分 分子嵌合模擬 (Part2: Molecular Docking Simulation) 47 Chapter 1 緒論 (Introduction) 49 1.1 前言 49 1.2 纖維母細胞生長因子受體一與癌症的關聯(The relationship between Fibroblast Growth Factor Receptor 1 and Cancer) 49 1.3 研究目標 (Research Objective) 52 Chapter 2 模擬方法 (Simulation Methods) 53 2.1 分子嵌合模擬 (Molecular Docking) 53 2.1.1 GOLD與基因演算法 53 2.1.2 評分函數 55 2.1.3 豐富指數與DUD資料庫 57 2.2 實驗設定 (Experimental Setting) 58 Chapter 3 結果與討論 (Results and Discussion) 59 3.1 側鍊擺動 59 3.2 氫鍵位置限制 62 3.3 晶體結構評估 67 3.4 條件收斂性 71 Chapter 4 結論 (Conclusion) 75 第三部分 總結 (Part3: Concluding Remark) 77 第四部份 附錄與參考資料 (Part4: Appendix and Reference) 81 Chapter 1 附錄 (Appendix) 83 1.1 名詞縮寫及翻譯 (Abbreviation and Translation of Nouns) 83 1.2 複合物相TI-2, 所對應之dv/d及自由能差(G) 84 1.2.1 CF3變換為82A 84 1.2.2 OHW變換為82A 85 1.2.3 NH2變換為82A 86 1.3 豐富指數分布(Enrichment Factor Distribution) 87 1.3.1 3JS2 A Chain 87 1.3.2 1AGW 88 1.3.3 1AGW-Complete Sequence 89 Chapter 2 參考文獻 (Reference) 90

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