分子對接為通過計算配體原子與受體原子之間的非共價鍵結及幾何互補性來找出兩剛體分子(rigid body)最有可能的結合位向(pose)並可以此進行藥物的設計與篩選。影響對接篩選藥物結果的因素主要有兩個: 一為對接結果的給分方式; 另外為對接所使用的受體結構狀態。其中，對接使用的固態晶體結構，常已有co-crystallizer結合在主要的功能位(功能位的殘基被誘導適合於結合此co-crystallizer)，所以在用此蛋白篩選其他藥物的時候其結合位甚至無法好好對接上實驗已證明相當有效的藥。蛋白被當為剛體無法被考慮其固有的動態和沒有配體時的弛豫結構是一個長久以來的問題。本篇研究使用分子模擬的技術，挑選出模擬結果中標的蛋白(在此為LC3)的兩個不同別構進行系綜對接(ensemble docking)，最後以細胞存活率實驗證明，由別構挑選出的藥物(本來x-ray結構無法挑出)可以有效抑制癌細胞的生長。挑選的藥物中由別構二篩選出來的藥物如Moxidectin於shATG4B/HCT116與shCtrl/HCT116兩株細胞分別都將細胞存活率抑制至1%、由別構一篩選出來的藥物如Aclacinomycin A HCl於shATG4B/HCT116與shCtrl/HCT116兩株細胞可分別使細胞存活率抑制至38%與28%。而所測試之19種藥物中，2個藥物如Moxidectin、Aclacinomycin A HCl，在四株癌細胞shATG4B/HCT116、shCtrl/HCT116、HCT116、AsPC-1都有至少抑制50%的癌細胞生長效果(細胞存活率依序為Moxidectin: 1%、1%、2%、8% ; Aclacinomycin A HCl: 38%、28%、28%、48%)。Netupitant為對別構二親和力最好的藥物，只排名於別構二的前20名(但不在其他兩個結構之前20名)，在二株癌細胞HCT116、AsPC-1也都分別有97%與57%的抑制效果(也就是細胞存活率分別為3%與43%)。Ponatinib只能由原始晶體結構所篩選出(但不在其他兩個結構之前20名)，對癌細胞也有至少50%的抑制效果。因系綜對接被證明有效，我們欲繼續將此法擴展於所有FDA藥物的標的蛋白，我們首先模擬了30個標的蛋白，每個有1 μs以上的軌跡。未來我們將使用此方法再搭配既有的FDA藥物資料庫進行藥物篩選，挑選出已經通過認證之藥物治療相關疾病，期望可以在新藥物開發上省下許多資源和時間，並及早提供治療給有需要的病患。

Molecular docking is to find out the most likely binding pose of two rigid bodies by calculating the non-covalent bond and geometric complementarity between the ligand atom and the acceptor atom, which can be used for drug design and screening. There are two main factors that affect the results of docking: one is the way of scoring the docking results; the other is the structure of the receptor used in the docking. Among them, the crystal structure used for docking often has a co-crystallizer bound to the main functional site (residues in the functional site are induced to bind to this co-crystallizer) so the drugs that have been proven to be effective in experiments can't properly dock this protein to screen other drugs. It has been a long-standing problem that proteins are regarded as rigid bodies and cannot be considered for their dynamics and relaxation structure without ligands. This study uses molecular simulation technology to select two different alternative structures of the target protein (in this case LC3) in the simulation results for ensemble docking. And then using the cell viability assay to prove that the drugs (which cannot select by the original x-ray structure) selected by the alternative structures can effectively inhibit the growth of cancer cells. Among the selected drugs, the drugs selected by alternative conformation 2, such as Moxidectin can inhibit the cell viability to 1% in shATG4B/HCT116 and shCtrl/HCT116 cancer cells, respectively. The drugs selected by alternative conformation 1, such as Aclacinomycin A HCl can inhibit cell viability to 38% and 28% in shATG4B /HCT116 and shCtrl/HCT116 cancer cells, respectively. Among the 19 drugs tested, 2 drugs such as Moxidectin and Aclacinomycin A HCl have at least 50% inhibition of cancer cell growth in the four cancer cell lines shATG4B/HCT116, shCtrl/HCT116, HCT116, and AsPC-1. (The cell viability was Moxidectin: 1%, 1%, 2%, 8%; Aclacinomycin A HCl: 38%, 28%, 28%, 48%). Netupitant is the drug with the best affinity of alternative conformation 2 which ranking only in the top 20 of alternative conformation 2 (but not the top 20 in the other two conformations) also has 97% and 57% inhibitory effect in HCT116 and AsPC-1 cancer cells (i.e. cell viability is 3% and 43%, respectively). Ponatinib can only be selected by the original crystal structure (but not in the top 20 of the other two conformations) has at least 50% inhibitory effect on cancer cells. Since the ensemble docking has proven effective, we want to continue to extend this method to the target proteins that all FDA drugs target. We first simulated 30 target proteins, each with a trajectory of more than 1 μs. In the future, we will use this method together with the existing FDA drug database for drug screening and select drugs that have been certified to treat related diseases. We hope to save lots of resources and time in the development of new drugs and provide early treatment to the patients in need.

目錄
1 緒論 1
1.1 老藥新用 (Drug Repurposing) 1
1.2 分子對接 (Molecular Docking)與系綜對接 (Ensemble Docking) 2
1.3 自噬作用 (Autophagy pathway) 3
1.4 Cysteine protease ATG4B (Autophagy-related 4B) 5
1.5 Microtubule-associated proteins 1A/1B light chain 3B (LC3) 5
2 方法與材料 7
2.1 分子模擬 (Molecular Dynamics Simulations) 7
2.1.1 LC3 蛋白結構 7
2.1.2 模擬參數設定與流程 7
2.1.2.1 能量最小化 8
2.1.2.2 NVT 升溫 8
2.1.2.3 NPT 平衡 8
2.1.2.4 NPT 產出 9
2.2 收集其他LC3蛋白質結構 9
2.3 主成分分析 (Principal Component Analysis, PCA) 10
2.3.1 模擬結構疊加分析 10
2.3.2 結構RMSD階層式分群 (Hierarchical clustering) 11
2.4 分子對接 (Molecular Docking) 11
2.4.1 蛋白與藥物分子的對接前準備 11
2.4.1.1 LC3蛋白 11
2.4.1.2 藥物分子 12
2.4.2 對接軟體與參數設定 12
2.4.3 分子對接結果分析 13
2.4.3.1 各個物理量的分數總合排名 (Normal Rank) 13
2.4.3.2 對數勝算比排名 (Log-Odds Ratio, LOD) 16
2.4.4 對接結果藥物篩選標準 19
2.5 細胞存活率實驗 20
2.5.1 材料與試劑 20
2.5.2 細胞株與細胞培養 20
2.5.3 細胞存活率試驗 (Cell viability Assay) 21
2.5.3.1 實驗流程 21
2.5.3.2 細胞存活率試劑原理 21
2.6 FDA藥物標的蛋白資料收集 22
2.6.1 藥物標的蛋白資料數據來源及內容 22
2.6.2 各列數據收集方法 22
2.6.2.1 原文獻資料列 22
2.6.2.2 新增資料列 23
2.6.3 長分子動力學模擬參數設定與流程 24
2.6.3.1 能量最小化 25
2.6.3.2 NVT 升溫 25
2.6.3.3 NPT 平衡 25
2.6.3.4 NPT 產出 25
3 結果與討論 26
3.1 模擬結果PCA分析與分群結構挑選 26
3.2 對接結果排名 28
3.3 細胞實驗結果 32
3.3.1 shATG4B/HCT116(S株)、shCtrl/HCT116(C株)大腸癌細胞 32
3.3.2 HCT116大腸癌細胞 34
3.3.3 AsPC-1胰臟癌細胞 35
3.4 開始建立藥物標的的長分子動力學模擬資料庫(Druggable DynOmics) 37
3.4.1 各列數據說明及結果 37
3.4.1.1 原文獻資料列 37
3.4.1.2 新增資料列 39
4 結論 43
5 參考文獻 44

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