Melbourne Veterinary School, Department of Veterinary Biosciences, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, Victoria, 3010, Australia.
Parasit Vectors. 2018 Nov 27;11(1):605. doi: 10.1186/s13071-018-3197-6.
Human schistosomiasis is a neglected tropical disease caused by parasitic worms of the genus Schistosoma that still affects some 200 million people. The mainstay of schistosomiasis control is a single drug, praziquantel. The reliance on this drug carries a risk of resistance emerging to this anthelmintic, such that research towards alternative anti-schistosomal drugs is warranted. In this context, a number of studies have employed computational approaches to prioritise proteins for investigation as drug targets, based on extensive genomic, transcriptomic and small-molecule data now available.
Here, we established a customisable, online application for the prioritisation of drug targets and applied it, for the first time, to the entire inferred proteome of S. haematobium. This application enables selection of weighted and ranked proteins representing potential drug targets, and integrates transcriptional data, orthology and gene essentiality information as well as drug-drug target associations and chemical properties of predicted ligands.
Using this application, we defined 25 potential drug targets in S. haematobium that associated with approved drugs, and 3402 targets that (although they could not be linked to any compounds) are conserved among a range of socioeconomically important flatworm species and might represent targets for new trematocides.
The online application developed here represents an interactive, customisable, expandable and reproducible drug target ranking and prioritisation approach that should be useful for the prediction of drug targets in schistosomes and other species of parasitic worms in the future. We have demonstrated the utility of this online application by predicting potential drug targets in S. haematobium that can now be evaluated using functional genomics tools and/or small molecules, to establish whether they are indeed essential for parasite survival, and to assist in the discovery of novel anti-schistosomal compounds.
人体血吸虫病是一种由血吸虫属寄生虫引起的被忽视的热带病，仍影响着约 2 亿人。血吸虫病控制的主要手段是一种名为吡喹酮的单一药物。对这种驱虫药的依赖存在出现耐药性的风险，因此有必要研究替代抗血吸虫药物。在这种情况下，许多研究已经采用计算方法根据现有的广泛的基因组、转录组和小分子数据，优先选择蛋白质作为药物靶点进行研究。
在这里，我们建立了一个可定制的在线应用程序，用于药物靶点的优先级排序，并首次将其应用于整个 S. haematobium 推断的蛋白质组。该应用程序能够选择代表潜在药物靶点的加权和排名蛋白质，并整合转录数据、同源性和基因必需性信息以及药物-药物靶点关联和预测配体的化学性质。
使用该应用程序，我们在 S. haematobium 中定义了 25 个与已批准药物相关的潜在药物靶点，以及 3402 个与一系列具有社会经济重要性的扁形动物物种保守但不能与任何化合物相关联的靶点，这些靶点可能代表新的驱虫剂的靶点。
这里开发的在线应用程序代表了一种交互式、可定制、可扩展和可重复的药物靶点排序和优先级排序方法，对于预测血吸虫和其他寄生虫物种的药物靶点应该是有用的。我们已经通过预测 S. haematobium 中的潜在药物靶点证明了该在线应用程序的实用性，现在可以使用功能基因组学工具和/或小分子来评估这些靶点，以确定它们是否确实对寄生虫的生存至关重要，并有助于发现新的抗血吸虫化合物。
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