Schistosomiasis is an acute and chronic parasitic disease, caused by blood flukes (trematode worms) of the genus Schistosoma. For 2019, the World Health Organization estimated that close to 240 million people required preventive treatment against this disease, mainly poor communities without access to safe drinking water and adequate sanitation. Similarly to others Neglected Tropical Diseases (NTDs), the treatments against this disease are limited, and new chemotherapies are necessary to the control and elimination of the Schistosomiasis. Natural products are an interesting alternative in the development of new treatments against NTDs. Interestingly, several studies have demonstrated the great potential of some sesquiterpene lactones (SLs) as potential therapeutic agents for some NTDs and the relationship between the biological activities with their chemical structure. In this study, using a machine learning model with more than 77% accuracy in both the cross-validation and test sets for Schistosoma mansoni, a ligand-based virtual screening was performed, using 1,300 SLs registered in SistematX database. The results show that close to 42% of the SLs tested reached active probability values above of 0.5, being identifies some common structural features in the best-ranked molecules. Finally, to explore the mechanism of action of these SLs against Schistosoma mansoni, molecular docking calculations were performed for the five best-ranked SLs using the crystal structure of Sm dihydroorotate dehydrogenase (DHODH), with 2-[(4-fluorophenyl)amino]-3-hydroxynaphthalene-1,4-dione as inhibitor. Through these calculations, some critical interactions between the tested SLs and the active site of DHODH were identified.
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In silico Identification of Potential Sesquiterpene Lactones from SistematX Database against Schistosoma mansoni
Published:
20 November 2021
by MDPI
in MOL2NET'21, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 7th ed.
congress CHEMBIO.INFO-07: Cheminfo., Chemom., Comput. Chem. & Bioinfo. Congress München, GR-Cambridge, UK-Ch. Hill, USA, 2021.
Abstract:
Keywords: Schistosoma mansoni; Sesquiterpene lactones; Machine learning; SistematX database; Molecular docking