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β-酮酰基-酰基载体蛋白合成酶III(FabH)抑制剂的HQSAR研究

HQSAR study of beta-ketoacyl-acyl carrier protein synthase III (FabH) inhibitors.

作者信息

Ashek Ali, San Juan Amor A, Cho Seung J

机构信息

Korea Institute of Science and Technology, Biochemicals Research Center, Cheongryang, Seoul 130-650, South Korea.

出版信息

J Enzyme Inhib Med Chem. 2007 Feb;22(1):7-14. doi: 10.1080/14756360600920149.

DOI:10.1080/14756360600920149
PMID:17373541
Abstract

The enzyme FabH catalyzes the initial step of fatty acid biosynthesis via a type II fatty acid synthase. The pivotal role of this essential enzyme combined with its unique structural features and ubiquitous occurrence in bacteria has made it an attractive new target for the development of antibacterial and antiparasitic compounds. Predictive hologram quantitative structure activity relationship (HQSAR) model was developed for a series of benzoylamino benzoic acid derivatives acting as FabH inhibitor. The best HQSAR model was generated using atoms and bond types as fragment distinction and 4-7 as fragment size showing cross-validated q2 value of 0.678 and conventional r2 value of 0.920. The predictive ability of the model was validated by an external test set of 6 compounds giving satisfactory predictive r2 value of 0.82. The contribution maps obtained from this model were used to explain the individual atomic contributions to the overall activity. It was confirmed from the contribution map that both ring A and ring C play a vital role for activity. Moreover hydroxyl substitution in the ortho position of ring A is favorable for better inhibitory activity. Therefore the information derived from the contribution map can be used to design potent FabH inhibitors.

摘要

酶FabH通过II型脂肪酸合酶催化脂肪酸生物合成的起始步骤。这种必需酶的关键作用,连同其独特的结构特征以及在细菌中的普遍存在,使其成为开发抗菌和抗寄生虫化合物的一个有吸引力的新靶点。针对一系列作为FabH抑制剂的苯甲酰氨基苯甲酸衍生物建立了预测性全息定量构效关系(HQSAR)模型。使用原子和键类型作为片段区分且片段大小为4 - 7生成了最佳的HQSAR模型,其交叉验证的q2值为0.678,常规r2值为0.920。通过6种化合物的外部测试集验证了该模型的预测能力,得到令人满意的预测r2值0.82。从该模型获得的贡献图用于解释各个原子对整体活性的贡献。从贡献图证实,A环和C环对活性都起着至关重要的作用。此外,A环邻位的羟基取代有利于获得更好的抑制活性。因此,从贡献图获得的信息可用于设计有效的FabH抑制剂。

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