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利用遗传算法、Pareto 评分和结构生物学开发炭疽毒素致死因子 (LF) 抑制剂的综合、验证药理学假说。

Development of a comprehensive, validated pharmacophore hypothesis for anthrax toxin lethal factor (LF) inhibitors using genetic algorithms, Pareto scoring, and structural biology.

机构信息

Department of Medicinal Chemistry and Minnesota Supercomputing Institute for Advanced Computational Research, University of Minnesota, 717 Delaware St. SE, Minneapolis, Minnesota 55414-2959, USA.

出版信息

J Chem Inf Model. 2012 Jul 23;52(7):1886-97. doi: 10.1021/ci300121p. Epub 2012 Jun 25.

Abstract

Anthrax is an acute infectious disease caused by the spore-forming bacterium Bacillus anthracis. The anthrax toxin lethal factor (LF), an 89-kDa zinc hydrolase secreted by the bacilli, is the toxin component chiefly responsible for pathogenesis and has been a popular target for rational and structure-based drug design. Although hundreds of small-molecule compounds have been designed to target the LF active site, relatively few reported inhibitors have exhibited activity in cell-based assays, and no LF inhibitor is currently available to treat or prevent anthrax. This study presents a new pharmacophore map assembly, validated by experiment, designed to rapidly identify and prioritize promising LF inhibitor scaffolds from virtual compound libraries. The new hypothesis incorporates structural information from all five available LF enzyme-inhibitor complexes deposited in the Protein Data Bank (PDB) and is the first LF pharmacophore map reported to date that includes features representing interactions involving all three key subsites of the LF catalytic binding region. In a wide-ranging validation study on all 546 compounds for which published LF biological activity data exist, this model displayed strong selectivity toward nanomolar-level LF inhibitors, successfully identifying 72.1% of existing nanomolar-level compounds in an unbiased test set, while rejecting 100% of weakly active (>100 μM) compounds. In addition to its capabilities as a database searching tool, this comprehensive model points to a number of key design principles and previously unidentified ligand-receptor interactions that are likely to influence compound potency.

摘要

炭疽是一种由形成孢子的细菌炭疽芽孢杆菌引起的急性传染病。炭疽毒素致死因子 (LF) 是芽孢杆菌分泌的一种 89kDa 的锌水解酶,是主要负责发病机制的毒素成分,已成为合理和基于结构的药物设计的热门目标。尽管已经设计了数百种针对 LF 活性位点的小分子化合物,但在基于细胞的测定中,报道的具有活性的抑制剂相对较少,目前尚无 LF 抑制剂可用于治疗或预防炭疽。本研究提出了一种新的药效团图谱组装,通过实验验证,旨在从虚拟化合物库中快速识别和优先考虑有前途的 LF 抑制剂支架。新假设结合了来自所有五个可用 LF 酶抑制剂复合物的结构信息,这些复合物都已存入蛋白质数据库 (PDB),是迄今为止报告的第一个包含代表 LF 催化结合区域所有三个关键亚基位点相互作用特征的 LF 药效团图谱。在对所有 546 种具有已发表 LF 生物活性数据的化合物进行的广泛验证研究中,该模型对纳摩尔级 LF 抑制剂具有很强的选择性,在无偏测试集中成功识别了 72.1%的现有纳摩尔级化合物,而拒绝了 100%的弱活性 (>100 μM) 化合物。除了作为数据库搜索工具的功能外,这个综合模型还指出了一些关键的设计原则和以前未识别的配体 - 受体相互作用,这些原则和相互作用可能会影响化合物的效力。

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