Three-dimensional quantitative structure-activity relationship analysis of a set of plasmodium falciparum dihydrofolate reductase inhibitors using a pharmacophore generation approach
Three-dimensional quantitative structure-activity relationship analysis of a set of plasmodium falciparum dihydrofolate reductase inhibitors using a pharmacophore generation approach
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DOI：
10.1021/jm040769c
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发表时间：
2004-08-12
影响因子：
7.3
通讯作者：
Rastelli, G
中科院分区：
文献类型：
--
作者：
Parenti, MD;Pacchioni, S;Rastelli, G
一个三维药效团模型能够定量预测抑制常数的恶性疟原虫二氢叶酸还原酶（PfDHFR），一个有效的抗疟治疗的目标，一系列的抑制剂。该数据集包括52种抑制剂，其中23种包含训练集，29种包含外部测试集。训练组分子的活性范围（表示为Ki）为0.3至11300 nM。用Catalyst 4.7的HypoGen模块生成的3D药效团由两个氢键供体、一个正离子化特征、一个疏水脂肪族特征和一个疏水芳香族特征组成，并提供了相关系数为0.954的3D-QSAR模型。重要的是，在药效团中编码的化学特征的类型和空间位置与先前通过酶抑制剂复合物的分子建模和晶体学建立的PfDHFR抑制剂的关键结合相互作用完全一致。该模型使用几种技术进行验证，即使用CatScramble的Fisher随机化检验，留一法检验以确保QSAR模型不严格依赖于训练集的一种特定化合物，以及在化合物的外部测试集中的活性预测。此外，药效团能够正确地分类为活性和非活性的二氢叶酸还原酶和醛糖还原酶抑制剂提取的MDDR数据库，分别。进行该试验的目的是用靶向非常不同的结合位点的两类抑制剂挑战药效团的预测能力。最后通过Tanimoto方法估计了数据集的分子多样性。所获得的结果为药效团在化合物库和数据库的虚拟筛选中的效用提供了信心，以发现新的PfDHFR抑制剂。
A 3D pharmacophore model able to quantitatively predict inhibition constants was derived for a series of inhibitors of Plasmodium falciparum dihydrofolate reductase (PfDHFR), a validated target for antimalarial therapy. The data set included 52 inhibitors, with 23 of these comprising the training set and 29 an external test set. The activity range, expressed as K-i, of the training set molecules was from 0.3 to 11300 nM. The 3D pharmacophore, generated with the HypoGen module of Catalyst 4.7, consisted of two hydrogen bond donors, one positive ionizable feature, one hydrophobic aliphatic feature, and one hydrophobic aromatic feature and provided a 3D-QSAR model with a correlation coefficient of 0.954. Importantly, the type and spatial location of the chemical features encoded in the pharmacophore were in full agreement with the key binding interactions of PfDHFR inhibitors as previously established by molecular modeling and crystallography of enzyme-inhibitor complexes. The model was validated using several techniques, namely, Fisher's randomization test using CatScramble, leave-one-out test to ensure that the QSAR model is not strictly dependent on one particular compound of the training set, and activity prediction in an external test set of compounds. In addition, the pharmacophore was able to correctly classify as active and inactive the dihydrofolate reductase and aldose reductase inhibitors extracted from the MDDR database, respectively. This test was performed in order to challenge the predictive ability of the pharmacophore with two classes of inhibitors that target very different binding sites. Molecular diversity of the data sets was finally estimated by means of the Tanimoto approach. The results obtained provide confidence for the utility of the pharmacophore in the virtual screening of libraries and databases of compounds to discover novel PfDHFR inhibitors.