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Computer-aided drug design approaches applied to screen natural product’s structural analogs targeting arginase in Leishmania spp [version 2; peer review: 1 approved, 2 approved with reservations]
- Source :
- F1000Research. 12:93
- Publication Year :
- 2023
- Publisher :
- London, UK: F1000 Research Limited, 2023.
-
Abstract
- Introduction: Leishmaniasis is a disease with high mortality rates and approximately 1.5 million new cases each year. Despite the new approaches and advances to fight the disease, there are no effective therapies. Methods: Hence, this study aims to screen for natural products' structural analogs as new drug candidates against leishmaniasis. We applied Computer-aided drug design (CADD) approaches, such as virtual screening, molecular docking, molecular dynamics simulation, molecular mechanics–generalized Born surface area (MM–GBSA) binding free estimation, and free energy perturbation (FEP) aiming to select structural analogs from natural products that have shown anti-leishmanial and anti-arginase activities and that could bind selectively against the Leishmania arginase enzyme. Results: The compounds 2H-1-benzopyran, 3,4-dihydro-2-(2-methylphenyl)-(9CI), echioidinin, and malvidin showed good results against arginase targets from three parasite species and negative results for potential toxicities. The echioidinin and malvidin ligands generated interactions in the active center at pH 2.0 conditions by MM-GBSA and FEP methods. Conclusions: This work suggests the potential anti-leishmanial activity of the compounds and thus can be further in vitro and in vivo experimentally validated.
Details
- ISSN :
- 20461402
- Volume :
- 12
- Database :
- F1000Research
- Journal :
- F1000Research
- Notes :
- Revised Amendments from Version 1 We have enhanced the time for MDS from 10 to 100 ns. While adding the modifications to the text and changing the figures from the resulting new analysis., , [version 2; peer review: 1 approved, 2 approved with reservations]
- Publication Type :
- Academic Journal
- Accession number :
- edsfor.10.12688.f1000research.129943.2
- Document Type :
- research-article
- Full Text :
- https://doi.org/10.12688/f1000research.129943.2