Abstract

Leishmaniasis refers to a complex of diseases, caused by the intracellular parasitic protozoans belonging to the genus Leishmania. Among the three types of disease manifestations, the most severe type is visceral leishmaniasis, which is caused by Leishmania donovani, and is diagnosed in more than 20,000 cases annually, worldwide. Because the current therapeutic options for disease treatment are associated with several limitations, the identification of new potential leads/drugs remains necessary. In this study, a combined approach was used, based on two different virtual screening (VS) methods, which were designed to select promising antileishmanial agents from among the entire sesquiterpene lactone (SL) dataset registered in SistematX, a web interface for managing a secondary metabolite database that is accessible by multiple platforms on the Internet. Thus, a ChEMBL dataset, including 3159 and 1569 structures that were previously tested against L. donovani amastigotes and promastigotes in vitro, respectively, was used to develop two random forest models, which performed with greater than 74% accuracy in both the cross-validation and test sets. Subsequently, a ligand-based VS assay was performed against the 1306 SistematX-registered SLs. In parallel, the crystal structures of three L. donovani target proteins, N-myristoyltransferase, ornithine decarboxylase, and mitogen-activated protein kinase 3, and a homology model of pteridine reductase 1 were used to perform a structure-based VS, using molecular docking, of the entire SistematX SL dataset. The consensus analysis of these two VS approaches resulted in the normalization of probability scores and identified 13 promising, enzyme-targeting, antileishmanial SLs from SistematX that may act against L. donovani.

Graphic abstract

A combined approach based on two different virtual screening methods (structure-based and ligand-based) was performed using an in-house dataset composed of 1306 sesquiterpene lactones to identify potential antileishmanial (Leishmania donovani) structures.

中文翻译：

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使用基于配体/结构的组合虚拟筛选方法从 SistematX 数据库中选择抗寄生虫倍半萜内酯。

摘要

利什曼病是指由属于利什曼原虫属的细胞内寄生原生动物引起的一系列疾病。在这三种疾病表现中，最严重的类型是内脏利什曼病，它是由多诺瓦尼利什曼原虫引起的，并且每年在全球范围内被诊断出超过 20,000 例病例。由于当前疾病治疗的治疗选择存在一些局限性，因此仍然有必要确定新的潜在先导/药物。在这项研究中，使用了一种基于两种不同虚拟筛选 (VS) 方法的组合方法，这些方法旨在从在 SistematX 中注册的整个倍半萜内酯 (SL) 数据集中选择有希望的抗寄生虫剂，该数据集是用于管理二级管理的 Web 界面可通过 Internet 上的多个平台访问的代谢物数据库。因此，一个 ChEMBL 数据集，包括之前针对L. donovani进行过测试的 3159 和 1569 个结构体外无鞭毛体和前鞭毛体分别用于开发两个随机森林模型，其在交叉验证和测试集中的准确率均超过 74%。随后，对 1306 个 SistematX 注册的 SL 进行了基于配体的 VS 测定。同时，三种L. donovani靶蛋白的晶体结构N-肉豆蔻酰转移酶、鸟氨酸脱羧酶和丝裂原活化蛋白激酶 3，以及蝶啶还原酶 1 的同源模型，用于使用分子对接对整个 SistematX SL 数据集执行基于结构的 VS。这两种 VS 方法的共识分析导致概率分数的标准化，并从 SistematX 中确定了 13 个有希望的、酶靶向的抗利什曼虫 SL，它们可能对L. donovani起作用。

图形摘要

使用由 1306 个倍半萜内酯组成的内部数据集执行基于两种不同虚拟筛选方法（基于结构和基于配体）的组合方法，以识别潜在的抗利什曼原虫 ( Leishmania donovani ) 结构。