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Identification of steroid-like natural products as antiplasmodial agents by 2D and 3D similarity-based virtual screening.

ABSTRACT: The emergence of drug resistance in Plasmodium falciparum to available antimalarial drugs has challenged current antimalarial treatments. New antimalarials, particularly those with novel mechanisms of action and no cross resistance to current drugs, are therefore urgently needed. To identify new growth inhibitors of Plasmodium falciparum, 2D and 3D similarity-based virtual screening methods were employed in parallel with an in-house database of steroid-type natural products using fusidic acid as a search query. The resulting hit compounds were further filtered based on the predicted partition coefficient, log?P. The virtual screening strategy resulted in the identification of nine new compounds that inhibited parasite growth with IC₅₀ values of <20 ?M. Four compounds exhibited IC₅₀ values in the range of 1.39-3.45 ?M and three of which showed a promising selectivity index. Further, the predicted ADME properties of the four most active compounds were found to be comparable to fusidic acid. These compounds can be further explored using structural modifications in the identification and development of more potent parasite growth inhibitors with improved selectivity.

SUBMITTER: Pavadai E

PROVIDER: S-EPMC6071788 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Identification of steroid-like natural products as antiplasmodial agents by 2D and 3D similarity-based virtual screening.

Pavadai Elumalai E Kaur Gurminder G Wittlin Sergio S Chibale Kelly K

MedChemComm 20170322 6

The emergence of drug resistance in <i>Plasmodium falciparum</i> to available antimalarial drugs has challenged current antimalarial treatments. New antimalarials, particularly those with novel mechanisms of action and no cross resistance to current drugs, are therefore urgently needed. To identify new growth inhibitors of <i>Plasmodium falciparum</i>, 2D and 3D similarity-based virtual screening methods were employed in parallel with an in-house database of steroid-type natural products using f ...[more]

PMID: 30108825

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Project description:BackgroundDespite continued efforts using chemical similarity methods in virtual screening, currently developed approaches suffer from time-consuming multistep procedures and low success rates. We recently developed a machine learning-based chemical binding similarity model considering common structural features from molecules binding to the same, or evolutionarily related targets. The chemical binding similarity measures the resemblance of chemical compounds in terms of binding site similarity to better describe functional similarities that arise from target binding. In this study, we have shown how the chemical binding similarity could be used in virtual screening together with the conventional structure-based methods.ResultsThe chemical binding similarity, receptor-based pharmacophore, chemical structure similarity, and molecular docking methods were evaluated to identify an effective virtual screening procedure for desired target proteins. When we tested the chemical binding similarity method with test sets of 51 kinases, it outperformed the traditional structural similarity-based methods as well as structure-based methods, such as molecular docking and receptor-based pharmacophore modeling, in terms of finding active compounds. We further validated the results by performing virtual screening (using the chemical binding similarity and receptor-based pharmacophore methods) against a completely blind dataset for mitogen-activated protein kinase kinase 1 (MEK1), ephrin type-B receptor 4 (EPHB4) and wee1-like protein kinase (WEE1). The in vitro kinase binding assay confirmed that 6 out of 13 (46.2%) for MEK1 and 2 out of 12 (16.7%) for EPHB4 were newly identified only by the chemical binding similarity model.ConclusionsWe report that the virtual screening results could further be improved by combining the chemical binding similarity model with 3D-QSAR pharmacophore and molecular docking models. Not only the new inhibitors are identified in this study, but also many of the identified molecules have low structural similarity scores against already reported inhibitors and that show the revelation of novel scaffolds.

Dataset Information

Identification of steroid-like natural products as antiplasmodial agents by 2D and 3D similarity-based virtual screening.

Publications

Identification of steroid-like natural products as antiplasmodial agents by 2D and 3D similarity-based virtual screening.

Similar Datasets

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