人類乙醯膽鹼酯脢 (huAChE) 和Arthrobacter globiformis 組織胺氧化酵素 (AGHO) 之 QSAR 模型研究

Abstract

在新藥開發的過程中，電腦輔助藥物設計技術的應用，可以大幅度減少新藥開發過程中所耗費的時間和與金錢，而分子嵌合(Molecular docking)和QSAR模型是電腦輔助藥物設計的關鍵技術。在本研究中我們利用分子嵌合工具— GEMDOCK產生蛋白質-配體原子交互作用描述表(profiles)，並將交互作用描述表作為建立QSAR模型的敘述子（descriptors），輔以演化式方法為基之QSAR建模工具— GEMPLS與GEMkNN篩選並建立人類乙醯膽鹼酯脢 (huAChE) 和Arthrobacter globiformis 組織胺氧化酵素 (AGHO)之QSAR模型。我們的QSAR建模方法主要是以蛋白質-配體的原子交互作用情況來描述蛋白質活性區域與分子間的作用特徵。而後我們將會藉由GEMPLS與GEMkNN建立的初步模型中，篩選一致性的敘述子群（consensus feature set）以及化合物結構差異部分(specific skeleton)產生敘述子群，建立並增進最終的QSAR模型預測能力。目前我們的方法已驗證於huAChE之QASR模型建立，其leave-one-out交互驗證之q2 達到0.818、實驗值與預測值之相關係數r2 亦達0.781。除此之外，本方法也實際應用在AGHO之QSAR模型建立。此為目前首次應用在AGHO之QSAR模型，其實驗值與預測值之相關係數r2高達0.983。藉由本研究發展的AGHO之QSAR模型，我們探討了AGHO與一系列的受質及其衍生物之結合親合力和疏水特性的關係，其中包括取代基的長度和環的大小對其催化能力之影響。此外我們由AGHO之QSAR模型預測新的受質結構— benzylamine，並藉酵素催化實驗證實預測結果之正確性。藉由成功的發展具有高度預測準度的QSAR模型與新受質發現，說明了我們的QSAR模型之應用性，並證明本研究發展之QSAR 建模方法是有用且有效的工具。

Molecular docking and quantitative structure activity relationships (QSAR) are the core technologies in computer-aided drug design. These technologies would help to save much time and cost to find out potential leads for the target protein in drug discovery. In this study, we introduced molecular docking tool, GEMDOCK to generate the atom-based protein-ligand interaction profile. We utilized the interaction profile to be descriptor and integrate with GEMPLS and GEMkNN for QSAR model of human acetylcholinesterase (huAChE) and Arthrobacter globiformis histamine oxidase (AGHO). Our method has adopted the atom-based interaction profile of protein-ligand complex to represent the molecular descriptor. The atom-based interaction profile would be used in GEMPLS and GEMkNN to construct the preliminary QSAR models. By collecting the selected feature of preliminary models, we generated the consensus feature set. Finally, the consensus feature set and ligand specific skeleton set were used to generate the final QSAR model and improve the prediction accuracy of model. We have verified our method for QSAR model of human acetylcholinesterase (huAChE). The model shows the leave-one-out cross validation of q2 is 0.818 and the correlation of r2 is 0.781 between the predicted and experimental values. After verifying the utility of our method on huAChE, we applied it to develop a novel QSAR model for Arthrobacter globiformis histamine oxidase (AGHO). This model is the first QSAR model for AGHO, and it shows a correlation of r2 is 0.983 between the predicted values and experimental values. This model has also been employed to a series of substrates and derivatives to probe the relationship between affinities of AGHO and hydrophobicities of ligands (including the length of substitution group and ring size). From QSAR models of AGHO, we discovered a novel substrate, which was called benzylamine and was evaluated by experiments. Experiments show that our QSAR model was capable of predicting with reasonable accuracy even that the activity of novel compounds not included in the original dataset. The successful development of highly predictive QSAR models implies that our method is a robust and useful tool for QSAR models.