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通过虚拟筛选方法识别针对耐氯喹恶性疟原虫的β-血红素形成抑制剂。

Identifying inhibitors of β-haematin formation with activity against chloroquine-resistant Plasmodium falciparum malaria parasites via virtual screening approaches.

机构信息

Department of Chemistry, University of Cape Town, Rondebosch, 7701, South Africa.

Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, Rondebosch, 7701, South Africa.

出版信息

Sci Rep. 2023 Feb 14;13(1):2648. doi: 10.1038/s41598-023-29273-w.

Abstract

The biomineral haemozoin, or its synthetic analogue β-haematin (βH), has been the focus of several target-based screens for activity against Plasmodium falciparum parasites. Together with the known βH crystal structure, the availability of this screening data makes the target amenable to both structure-based and ligand-based virtual screening. In this study, molecular docking and machine learning techniques, including Bayesian and support vector machine classifiers, were used in sequence to screen the in silico ChemDiv 300k Representative Compounds library for inhibitors of βH with retained activity against P. falciparum. We commercially obtained and tested a prioritised set of inhibitors and identified the coumarin and iminodipyridinopyrimidine chemotypes as potent in vitro inhibitors of βH and whole cell parasite growth.

摘要

生物矿化血红素,或其合成类似物β-血晶素(βH),一直是针对恶性疟原虫寄生虫的基于靶标的几种筛选的焦点。结合已知的βH 晶体结构,该筛选数据使该靶标易于进行基于结构和基于配体的虚拟筛选。在这项研究中,分子对接和机器学习技术,包括贝叶斯和支持向量机分类器,被连续用于筛选虚拟化学药物库 300k 代表化合物库中对 βH 具有保留活性的抑制剂,以对抗恶性疟原虫。我们商业获得并测试了一组优先抑制剂,并确定香豆素和亚氨基二吡啶嘧啶类作为βH 和整个细胞寄生虫生长的有效体外抑制剂。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c5f/9929333/c5f9e16e9417/41598_2023_29273_Fig1_HTML.jpg

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