Repurposing natural compounds as potent inhibitors of β-catenin: an integrative in-silico approaches

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Abstract

Non-small cell lung cancer is one of the hostile diseases among lung cancer diseases and it is mainly due to the presence of cancer stem cells (CSCs). Since CSCs play a crucial role in homeostasis and embryonic development. β-catenin is one of the promising targets in CSC pathways for the development of novel therapeutic agents against NSCLC. Keeping this in mind, an effort was made to unearth novel β-catenin inhibitors from the plant-derived NPACT database which exhibits 1574 compounds. Initially, an e-pharmacophore hypothesis, receptor-cavity-based hypothesis, and shape-based screening were performed based on four β-catenin inhibitors employing the Schrödinger suite. Subsequently, a virtual screening protocol was used and the screened hit molecules were scrutinized for ADME using the QikProp module of Schrödinger, respectively. Furthermore, the OSIRIS and PASS algorithm predicted the toxicity and anti-neoplastic activity for all the six hit molecules. Moreover, a mutational study was performed to find the efficacy of the hits namely: NPACT01437, NPACT01438, NPACT00803, NPACT01571, NPACT01415, and NPACT00922. Intriguingly, the hits NPACT00803 (Nectandrin B) and NPACT00922 (Rohitukine) exhibited better XP GScore of − 4.758 and − 4.739, respectively, for the native whereas the mutant structures exhibited a XP GScore of − 5.551 (Nectandrin B) and − 4.448 (Rohitukine), respectively. Moreover, density functional and molecular dynamics studies were carried out to validate the chemical reactivity and stability of these hit compounds. Notably, scaffold analysis revealed, our hits molecules possessed methoxy phenol and piperidine scaffolds which exhibits anti-cancerous activity against lung cancer cell lines. Overall, we speculate that the results could be a promising candidate to combat NSCLC in the upcoming future.

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Saranyadevi, S. (2021). Repurposing natural compounds as potent inhibitors of β-catenin: an integrative in-silico approaches. Network Modeling Analysis in Health Informatics and Bioinformatics, 10(1). https://doi.org/10.1007/s13721-021-00320-8

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