【摘要】 药效团是最常用的虚拟筛选方法之一,主要包括配体药效团模型和受体药效团模型两大类.配体药效团通常具有阳性化合物命中率高的优势,但却具有准确率低的特点,受体药效团通常具有较强的特异性,但却具有漏筛率高的问题.因此,合理地利用两种类型的药效团的优势,有效地规避它们的缺点,是药效团研究与应用的重要方向.本研究拟基于PPARα激动剂探讨融合药效团模型构建的思路与方法.首先,分别构建PPARα激动剂的配体和受体药效团模型.通过比较两种药效团模型的药效特征的差异,确认主要及次要的药效特征.进一步以受体药效团模型为模板,调整其主要和次要药效特征的半径及权重,通过单因素实验及正交试验设计,获得半径及权重的最优值,从而构建最优的融合药效团模型.最优融合药效团被用于进一步的中药化学成分PPARα激动活性预测.随后,通过三个化合物数据库的筛选,评价融合药效团准确性、可靠性和适用性,并以筛选效率为指标,比较融合药效团与其他分子模拟模型及筛选方式的优劣.结果发现,针对PPARα激动剂,融合药效团模型具有比单一药效团模型更好的筛选效率.同时,融合药效团也具有与药效团联合筛选相近的筛选效率,且具有更好的化合物活性评价能力,能合理的规避配体药效团与受体药效团计算预测结果不一致的问题.研究结果显示,融合药效团模型具有独特的优势,可以进一步用于化合物生物活性的计算及药物设计研究.更多还原

【Abstract】 Pharmacophore is a commonly used method for molecular simulation,including ligand-based pharmacophore(LBP) and structure-based pharmacophore(SBP).LBP can be utilized to identify active compounds usual with lower accuracy,and SBP is able to use for distinguishing active compounds from inactive compounds with frequently higher missing rates.Merged pharmacophore(MP) is presented to integrate advantages and avoid shortcomings of LBP and SBP.In this work,LBP and SBP models were constructed for the study of peroxisome proliferator receptor-alpha(PPARa) agonists.According to the comparison of the two types of pharmacophore models,mainly and secondarily pharmacological features were identified.The weight and tolerance values of these pharmacological features were adjusted to construct MP models by single-factor explorations and orthogonal experimental design based on SBP model.Then,the reliability and screening efficiency of the best MP model were validated by three databases.The best MP model was utilized to compute PPARa activity of compounds from traditional Chinese medicine.The screening efficiency of MP model outperformed individual LBP or SBP model for PPARa agonists,and was similar to combinatorial screening of LBP and SBP.However,MP model might have an advantage over the combination of LBP and SBP in evaluating the activity of compounds and avoiding the inconsistent prediction of LBP and SBP,which would be beneficial to guide drug design and optimization.更多还原