自從鴉片類藥物(opioid drug)被發現以來，其止痛的效用一直被廣泛應用在醫學領域。當促進劑(agonist)鴉片類藥物進入體內，會和體內固有的鴉片類受體(opioid receptor)結合以產生止痛效用。若是抑制劑(antagonist)與受體結合，則會阻止鴉片類受體被活化或降低其被活化的程度。現有的鴉片類藥物大多會引發許多令人不適的副作用及成癮性，許多人因而投入新型鴉片類藥物的研發，期望新藥物能降低副作用及成癮性。然而，一個開發上的困難點是鴉片類促進劑和抑制劑在受體上結合的位置高度重疊，化學結構也相似，如欲設計促進劑，除了需透過配體結合實驗( ligand binding assay)確認與受體有好的親和力之外，最終要到了動物實驗才知道所開發藥物是促進劑還是抑制劑。本篇論文用小分子對接及電腦模擬建立能預測鴉片類藥物為促進劑或抑制劑的結構動態模型，以期未來可在此前提下設計更有效的促進劑。本篇論文測試九個來自蛋白質資料庫中的鴉片類複合物之配體，利用分子對接軟體、分子動力學模擬、MM/PBSA & MM/GBSA結合能之計算等三個主要的步驟進行鴉片類藥物的預測，判斷藥物為促進劑或抑制劑的正確率可達100%。目前除了對此九個有結晶結構之配體進行預測，亦測試了兩個沒有已知與受體結合之晶體結構的配體，分別為: leucine-enkephalin和KK103(英屬哥倫比亞大學藥物科學系黎世達教授實驗室所設計之測試藥物)，且以本論文方法預測之結果與該實驗室所提供的實驗結果相符，皆為促進劑，並且何者結合效率較強也正確地預測。我們期望此論文開發之平台能幫助測試並設計更多的鴉片類藥物，並在動物模型上驗證這些電腦輔助設計的新型促進劑。

Since the discovery of opioid drugs, their pain-relieving effects have been widely used in the medical field. When opioid agonists enter the body, they will bind to the opioid receptor inherent in the body to produce analgesic effects. If the opioid antagonist binds to the receptor, it will prevent the opioid receptor from being activated or reduce its degree of activation. Most of the existing opioids will cause many uncomfortable side effects and addiction. Many people have therefore invested in the research and development of new opioids, hoping that new drugs can reduce the side effects and addiction. However, one difficulty in development is that the opioid agonists and antagonists bind to the receptor at a high degree of overlap, and their chemical structures are also similar. If you want to design opioid agonists, you need to confirm through ligand binding assays. In addition to having a good affinity with the receptor, it is not until the end of animal experiments to know whether the drug being developed is an agonist or an antagonist. This paper uses small molecule docking and computer simulation to establish a structural dynamic model that can predict opioids as agonists or antagonists, in the hope that more effective agonists can be designed on this premise in the future. This paper tests nine opioid complex ligands from the protein database, using the three main steps which are molecular docking software, molecular dynamics simulation, and MM/PBSA & MM/GBSA binding energy calculation for prediction of opioid drugs. The accuracy of drug prediction can reach 100%. At present, in addition to the prediction of these nine ligands, the other two ligands that do not have a crystal structure that bind to the receptor have also been tested, namely: leucine-enkephalin and KK103 (designed by Professor Shi-Dar Li’s group in Faculty of Pharmaceutical Sciences, the University of British Columbia), and the results predicted by the method of this paper are consistent with the experimental results, all are agonists. We hope the herein presented computational platform can add value to correct classification of opioids as well as prediction for relative affinity of newly developed OR agonists.

目錄
1 緒論 7
1.1 鴉片類藥物(opioid) 7
1.2 鴉片類受體(opioid receptor) 7
1.3 在細胞實驗與動物實驗中，如何分辨鴉片類促進劑、抑制劑 8
1.4 研究的重要性與困難點 10
1.5 電腦輔助開發GPCR之配體 11
1.6 活化態和非活化態的鴉片類受體之結構比較 11
1.7 鴉片類促進劑和抑制劑之結構比較 14
1.8 受體與配體的在結合位置的交互作用 16
1.9 分子訊號傳遞機制 16
1.10 受體的中間活化狀態(Intermediate state of opioid receptor) 16
1.11以電腦方法預測促進劑與抑制劑的例子 17
2方法 19
2.1利用主成分分析(PCA)方法了解活化及非活化狀態的受體之結合位殘基(binding site residues)之位置是否有差異 19
2.1.1 九個鴉片類受體之結晶結構的分析 19
2.1.2 無配體DOR受體之分子動態模擬 21
2.1.3 主成分分析(PCA) 23
2.2 使用AutoDock Vina進行分子對接(Molecular docking) 24
2.2.1鴉片類受體與配體之來源結構 24
2.2.2受體結構 25
2.2.3配體結構 25
2.2.4分子對接(Molecular docking)之參數設定 26
2.3使用PatchDock web server進行分子對接(molecular docking) 26
2.3.1鴉片類受體與配體之來源結構 26
2.3.2受體結構 26
2.3.3配體結構 27
2.3.4分子對接(Molecular docking)之參數設定 27
2.4 接觸分析 27
2.5 分子對接分析後之分子動態模擬(Molecule dynamics simulation) 31
2.5.1 鴉片類受體結構 31
2.5.2 鴉片類受體之配體結構 33
2.5.3 鴉片類受體與配體結合之結構 33
2.5.4 模擬設定及方法 34
2.6 使用Poisson–Boltzmann & Generalized Born and surface area continuum solvation(MM/PBSA & MM/GBSA)方法計算配體和受體的結合能(Binding free energy) 36
3 結果與討論 39
3.1 結晶結構之主成分分析結果 39
3.2 分子對接(Molecular docking)之結果分析 41
3.3 使用分子動力學模擬及MM/PBSA & MM/GBSA來進行方法計算結合能(binding free energy) 計算之結果 45
3.4 分子動力學模擬之主成分分析結果 49
4 結論 51
5 參考文獻 52

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