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通过“虚拟配体”筛选发现的幽门螺杆菌蛋白酶 HtrA 抑制剂可抵抗细菌侵袭上皮细胞。

Inhibitors of Helicobacter pylori protease HtrA found by 'virtual ligand' screening combat bacterial invasion of epithelia.

机构信息

Institute of Organic Chemistry and Chemical Biology, Goethe-University, Frankfurt, Germany.

出版信息

PLoS One. 2011 Mar 31;6(3):e17986. doi: 10.1371/journal.pone.0017986.

Abstract

BACKGROUND

The human pathogen Helicobacter pylori (H. pylori) is a main cause for gastric inflammation and cancer. Increasing bacterial resistance against antibiotics demands for innovative strategies for therapeutic intervention.

METHODOLOGY/PRINCIPAL FINDINGS: We present a method for structure-based virtual screening that is based on the comprehensive prediction of ligand binding sites on a protein model and automated construction of a ligand-receptor interaction map. Pharmacophoric features of the map are clustered and transformed in a correlation vector ('virtual ligand') for rapid virtual screening of compound databases. This computer-based technique was validated for 18 different targets of pharmaceutical interest in a retrospective screening experiment. Prospective screening for inhibitory agents was performed for the protease HtrA from the human pathogen H. pylori using a homology model of the target protein. Among 22 tested compounds six block E-cadherin cleavage by HtrA in vitro and result in reduced scattering and wound healing of gastric epithelial cells, thereby preventing bacterial infiltration of the epithelium.

CONCLUSIONS/SIGNIFICANCE: This study demonstrates that receptor-based virtual screening with a permissive ('fuzzy') pharmacophore model can help identify small bioactive agents for combating bacterial infection.

摘要

背景

人类病原体幽门螺旋杆菌(H. pylori)是导致胃炎和胃癌的主要原因。细菌对抗生素的耐药性不断增加,这就需要创新的治疗干预策略。

方法/主要发现:我们提出了一种基于结构的虚拟筛选方法,该方法基于对蛋白质模型上配体结合位点的全面预测和配体-受体相互作用图的自动构建。图谱的药效特征被聚类并转化为相关向量(“虚拟配体”),用于快速虚拟筛选化合物数据库。该计算机技术在回顾性筛选实验中针对 18 种不同的药物靶点进行了验证。使用人病原体 H. pylori 的蛋白酶 HtrA 的同源模型,对其进行了抑制性试剂的前瞻性筛选。在 22 种测试化合物中,有 6 种能够抑制 HtrA 在体外切割 E-钙黏蛋白,并导致胃上皮细胞散射减少和伤口愈合,从而阻止细菌渗透到上皮细胞中。

结论/意义:这项研究表明,使用允许的(“模糊的”)药效特征模型进行基于受体的虚拟筛选,可以帮助识别用于对抗细菌感染的小分子生物活性物质。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b319/3069028/583a6f481eb1/pone.0017986.g001.jpg

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