Discovery of anticancer compound possessing potential to bind γ-secretase catalytic subunit and inhibit notch
Atul Kumar Singh, Kumari Sunita Prajapati, Shashank Kumar1
1Department of Biochemistry, Molecular Signaling & Drug Discovery Laboratory, Central University of Punjab, Guddha, Bathinda, India.
Abstract:
Gamma secretase (GS) is an important therapeutic target in anticancer drug discovery. Increased GS activity activates notch signaling pathway which is associated with cancer stemness and drug resistance in cancer cells. A total of 69,075 natural and their derivative compounds were screened to identify the lead compound on the basis of in silico GS catalytic domain binding potential and in vitro selective anticancer efficacy. STOCK1N-23234 showed higher dock score (-11.82) compared to DAPT (-9.2) in molecular docking experiment and formed hydrogen bond with the key amino acid (Asp385) involve in catalysis process. Molecular dynamics (MD) simulation parameters (RMSD, RMSF, Rg, SASA and hydrogen bond formation) revealed that the STOTCK1N-23234 formed structurally and energetically stable complex with the GS catalytic domain with lower binding energy (-22.79 kcal/mol) compared to DAPT (-16.22 kcal/mol). STOCK1N-23234 showed better toxicity (up to 60%) against colon and breast cancer cells (HCT-116 and MDA-MB-453) at 1-70 µM concentration. Interestingly, STOCK1N-23234 did not showed cytotoxicity against human normal breast cells (MCF-10A). STOCK1N-23234 treatment significantly decreased sphere formation, notch promoter activity, and transcription of notch target genes (Hes-1 and Hey-1) in HCT-116 cells derived colonosphere. Confocal microscopy revealed that STOTCK1N-23234 treatment at test concentration induced apoptosis related morphological changes, reduced mitochondria membrane potential and increased reactive oxygen species production in HCT-116 cells compared to non-treated cells. In conclusion, STOCK1N-23234 is a novel lead natural anticancer compound which requires in depth validation in cancer preclinical models.
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