Post-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa, PB, 58051-900, Brazil.
Bioorganic Chemistry Laboratory, Facultad de Ciencias Básicas y Aplicadas, Universidad Militar Nueva Granada, Cajicá, 250247, Colombia.
Mol Divers. 2021 Nov;25(4):2411-2427. doi: 10.1007/s11030-020-10139-6. Epub 2020 Sep 9.
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. 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.
利什曼病是一种由属于利什曼原虫属的细胞内寄生原生动物引起的疾病。在三种疾病表现类型中，最严重的类型是内脏利什曼病，由利什曼原虫引起，全球每年诊断出超过 20000 例病例。由于目前的疾病治疗方法存在多种局限性，因此仍然需要寻找新的潜在先导药物/药物。在这项研究中，使用了一种组合方法，基于两种不同的虚拟筛选（VS）方法，旨在从 SistematX 中注册的整个倍半萜内酯（SL）数据集中选择有前途的抗利什曼原虫药物，SistematX 是一个用于管理次级代谢物数据库的网络界面，可通过互联网上的多个平台访问。因此，使用了一个包括 3159 和 1569 个结构的 ChEMBL 数据集，这些结构分别在体外对利什曼原虫无鞭毛体和前鞭毛体进行了测试，用于开发两个随机森林模型，在交叉验证和测试集中的准确率均大于 74%。随后，针对 1306 种 SistematX 注册的 SL 进行了基于配体的 VS 检测。同时，还使用了三种利什曼原虫靶蛋白（N-豆蔻酰转移酶、鸟氨酸脱羧酶和丝裂原活化蛋白激酶 3）的晶体结构和蝶呤还原酶 1 的同源模型，使用分子对接对整个 SistematX SL 数据集进行基于结构的 VS。这两种 VS 方法的共识分析导致概率得分归一化，并从 SistematX 中确定了 13 种有前途的、针对酶的抗利什曼原虫 SL，可能对抗利什曼原虫。使用由 1306 种倍半萜内酯组成的内部数据集，使用两种不同的虚拟筛选方法（基于结构和基于配体）执行了一种组合方法，以鉴定潜在的抗利什曼原虫（利什曼原虫）结构。
