雌激素受體及過氧化體增生活化受體的配位小分子用於藥物開發之小分子對接及定量結構活性關係之研究

The Study of Estrogen Receptor Ligands and Peroxisome Proliferator Activated Receptor Ligands by Docking and QSAR for Drug Discovery

陳冠宇(Guan-Yu Chen)

指導教授 ： 林盈廷

高雄醫學大學/生命科學院/生物醫學暨環境生物學研究所/碩士(2008年)

https://doi.org/10.6832/KMU.2008.00121

摘要

雌激素受體的拮抗劑對於抑制乳癌細胞MCF-7生長活性的效力因素有三個: (1) 小分子穿過細胞膜的能力，(2) 小分子與受體接合的能力，(3) 小分子抑制受體轉錄活性的能力，其中，小分子進入細胞的速率決定步驟往往與小分子通過細胞膜有關。本實驗我們利用小分子對接計算及定量結構活性關係方法研究雌激素受體的拮抗劑穿過乳癌細胞MCF-7時的效應。我們從文獻上收集並依據小分子的活性實驗方法整理出了二組的小分子群，第一組小分子群是依據試管內小分子競爭性實驗收集而來的小分子，共計有118個小分子；第二組小分子群是根據小分子抑制乳癌細胞MCF-7生長實驗所收集而來的小分子，共計有118個小分子，換言之這二群的小分子最大差異即是有無通過細胞膜。我們使用LigandFit對接計算小分子與蛋白質結合的能力，結果發現計分方法DockScore與小分子的相對結合力對數值（Logarithm of the relative binding affinity, LRBA）具有高度的相關性（r=0.850），然而對於小分子的相對抑制效應對數值（Logarithm of the relative inhibition activity, LRIA）的相關性而言是較低的（r=0.598）。為了解開在這之間造成的相關性差異，我們利用定量結構活性關係方法分析造成這個差異的影響因子，因此從266描述子中篩選出最具有影響力的描述子，而這個描述子將會是造成相關性差異的可能影響因子。實驗結果得知，描述子PSA (Polar surface area) 和PSASA (Polar solvent accessible surface area) 合併DockScore可以獲得較佳的相對抑制力對數值的相關性（r=0.695），文獻指出，PSA及PSASA與小分子穿過細胞膜的效應有關，因此這也說明了PSA或是PSASA是影響小分子穿過乳癌細胞抑制MCF-7活性的影響因子。
PPARα，PPARγ是治療代謝性疾病的重要核受體標靶蛋白。本實驗我們從文獻收集到PPARα小分子114個，PPARγ小分子224個，從收集來的小分子根據結構上的特徵做分群，利用QSAR方法建立PPARα及PPARγ的QSAR藥物模型，得到最佳的QSAR模型分別為: PPARα QSAR模型R2=0.815，q2=0.768，PPARγ的TZD group QSAR模型R2=0.931, q2=0.896,o-analogous-tyrosine group QSAR模型R2=0.768, q2=0.675及non-o-analogous-tyrosine group QSAR模型R2=0.815,q2=0.678，我們也依結構將小分子的支鏈拆解出來並也建立成QSAR模型，希望這些建立的模型能提供過氧化體增生活化受體的藥物開發及虛擬藥物篩選的資訊。

關鍵字

雌激素受體 ； 過氧化體增生活化受體 ； LigandFit ； 定量結構活化關係 ； PSA ； PSASA

並列摘要

The efficacy of estrogen receptor (ER) antagonists in the inhibition of MCF-7 cell lines (breast) in three factors: (1) the transportation through the cell membrane, (2) binding ability to ER, and (3) the inhibition of transcription activity. The process of transportation is that drugs enter the cell from the outside until meet the target. The rate determining step of transportation is generally believed across the membrane boundary. In this study, we explore these three effects of the ER antagonists in MCF-7 cells by QSAR and Docking techniques. Two sets of compounds from the literature are prepared: One includes 118 compounds; all compounds are tested by the cell-free competition binding assay to evaluate antagonists’ binding ability. The other includes 118 compounds is tested by MCF-7 proliferation assay to evaluate the cell inhibition caused. The binding abilities are calculated by using LigandFit. Results show that the binding score, DockScore, is well correlated with the logarithm of the relative binding affinity (LRBA) in the competition assay, r = 0.850. However, the correlation of the binding scores with the logarithm of the relative inhibition effect (LRIA) is somewhat smaller, r = 0.598, in the MCF-7 assay. To estimate the effect quantitatively, we choose 266 descriptors to be utilized in the QSAR exploring. Among the 266 descriptors, PSA (polar surface area) and PSASA (polar solvent accessible surface area) plus with DockScore gains the best correlation with LRIA, r = 0.703 and r = 0.702. This indicates PSA and PSASA is the most significant factor in describing the transportation effect.
PPARα and PPARγ are the important nuclear receptor targets in metabolic disease, and therefore many studies are devoted to discover new compounds. In this study, we prepare PPARα (114 compounds) and PPARγ (224 compounds) agonists from literatures, and then classify those compounds according to drug’s structure to build QSAR models. We get the best TZD group compound’s QSAR model is R2=0.913 and q2=0.896, o-analogous tyrosine group compound’s is R2=0.768 and q2=0.675, and non-o-analogous tyrosine group compound’s is R2=0.815 and q2=0.678. We also take compounds’ side chains as fragments to build QSAR models. These built models may be able to provide useful information to help the screening or design of new candidate drugs for metabolic diseases.

並列關鍵字

Estrogen receptor ； Peroxisome Proliferator Activated Receptor ； LigandFit ； QSAR ； PSA ； PSASA

參考文獻

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