De novo designing, assessment of target affinity and binding interactions against aromatase: Discovery of novel leads as anti-breast cancer agents
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Date
2020-11-13T00:00:00
Authors
Verma, Sant Kumar
Ratre, Pooja
Jain, Akhlesh Kumar
Liang, Chengyuan
Gupta, Ghanshyam Das
Thareja, Suresh
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Abstract
Aromatase inhibitors (AIs) have been emerged as promising anti-cancer agents for the treatment of hormone dependent breast cancer (HDBC) in women because of their excellent ability of inhibiting oestrogen synthesis. Here, we have implicated structure-based comprehensive approaches to discover novel drug/lead-like AIs. The molecular modelling and energy optimization were performed using Chem Office package. The e-LEA3D web server was used to design novel drug/lead-like AIs as well as generation of ADME/drug-likeness parameters. Target binding affinities and mode of binding interactions were mapped using Molegro Virtual Docker (MVD) to re-optimize the best de novo generated molecules. We have successfully designed novel AIs (compounds 1�7) using de novo technique performed on e-LEA3D. All the designed molecules were found optimum drug-like candidates based on various in silico screening parameters including �rule of five�. The energy optimized conformers of generated molecules (1�7) were docked in the active site, corresponding to co-crystallized androstenedione (ASD), of aromatase to predict ligand-target binding affinity and their binding interactions. The molecules (1�7) showed comparable to higher binding affinity towards aromatase with MolDock Score ranges from ? 134.881 to ? 152.453�Kcal/mol as compared with natural substrate ASD (? 128.639�Kcal/mol) and standard letrozole (? 136.784�Kcal/mol). The de novo designed molecules (1�7) can be developed as novel AIs, and their binding properties can be used for the further designing of newer AIs by medicinal chemists. � 2020, Springer Science+Business Media, LLC, part of Springer Nature.
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Keywords
Anti-cancer agents, Aromatase inhibitors, Breast cancer, De novo designing, e-LEA3D, Molecular docking