In Silico Targeting Human Multidrug Transporter ABCG2 in Breast Cancer: Database Screening, Molecular Docking, and Molecular Dynamics Study

ABCG2 is a substantial member of the ABC transporter superfamily that plays a significant role in multidrug resistance in cancer. Until recently, the 3D structure of ABCG2 has not been resolved, which resulted in the limitation of developing potential ABCG2 inhibitors using structure‐based drug disc...

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Published in:Molecular informatics 2022-02, Vol.41 (2), p.e2060039-n/a
Main Authors: Ibrahim, Mahmoud A. A., Badr, Esraa A. A., Abdelrahman, Alaa H. M., Almansour, Nahlah Makki, Mekhemer, Gamal A. H., Shawky, Ahmed M., Moustafa, Mahmoud F., Atia, Mohamed A. M.
Format: Article
Language:English
Subjects:
ABC transporter
ABCG2
Affinity
ATP Binding Cassette Transporter, Subfamily G, Member 2 - chemistry
ATP Binding Cassette Transporter, Subfamily G, Member 2 - metabolism
Binding energy
Breast cancer
Breast Neoplasms
Cancer
Early Detection of Cancer
Female
Humans
Inhibitors
Ligands
Mathematical analysis
Mechanics (physics)
Molecular docking
Molecular Docking Simulation
molecular dynamic
Molecular dynamics
Molecular Dynamics Simulation
Multidrug resistance
Multidrug resistant organisms
Neoplasm Proteins - metabolism
Stability analysis
Thermodynamics
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Summary:ABCG2 is a substantial member of the ABC transporter superfamily that plays a significant role in multidrug resistance in cancer. Until recently, the 3D structure of ABCG2 has not been resolved, which resulted in the limitation of developing potential ABCG2 inhibitors using structure‐based drug discovery. Herein, eMolecules, ChEMBL, and ChEBI databases, containing >25 million compounds, were virtually screened against the ABCG2 transporter in homodimer form. Performance of AutoDock4.2.6 software to predict inhibitor‐ABCG2 binding mode and affinity were validated on the basis of available experimental data. The explored databases were filtered based on docking scores. The most potent hits with binding affinities higher than that of experimental bound ligand (MZ29) were then selected and subjected to molecular mechanics minimization, followed by binding energy calculation using molecular mechanics‐generalized Born surface area (MM‐GBSA) approach. Furthermore, molecular dynamics simulations for 50 ns, followed by MM‐GBSA binding energy calculations, were performed for the promising compounds, unveiling eight potential inhibitors with binding affinities
ISSN:1868-1743
1868-1751
DOI:10.1002/minf.202060039