The transcription factor family includes the cluster protein signal transducer and activator of transcription 3 (STAT3). Colorectal cancer (CRC) is predisposed to develop and spread when STAT 3 is abnormally activated because it speeds up cell division, prevents differentiation, and suppresses apoptosis. Only a small number of phytochemicals such as curcumin have been solidly demonstrated in clinical trials to lower the risk of CRC based on dose as well as confounding variables. Enthused by literature citations, we aimed in the present work to design novel antagonists against STAT3 using phytocompounds of two medicinal plants (Catharanthus roseus and Moringa oleifera) by integration of in-vivo and in-silico strategy. In-silico analysis was intended with receptor STAT3 (PDB ID: 6QHD), ligands (97 tentatively identified GC-MS compounds), and standard drugs (regorafenib and irinotecan) by docking and simulation analysis via tools such as Maestro v.11.9, LigPrep, Epik v.4.7, QikProp v.5.1, Glide v.8.2 of Schrodinger LLC-2019.1, and Desmond v.3.8. Docking studies exemplifies that STAT3 depicted better binding affinity with pyrrole (3,4-C) pyrrole-1-carboxylic acid, octadecanoic acid, and 2,3-dihydroxypropyl ester, (−7.266 kcal/mol, and −7.124 kcal/mol) as compared with standard drugs (regorafenib, and irinotecan). Further, the molecular dynamics analysis of these two lead complexes for 100 ns validates the stability, superiority of the vital interactions and also retained all over the trajectories besides fulfilling the ADMET criteria. Thus, the proposed leads act as valuable starting points and ideal candidates to be developed as drug candidates for CRC patients if synthesized and tested in-vitro, in-vivo, and in animal models after further detailed investigations. 

Identification of a natural antagonist for signal transducer and activator of transcription 3 (STAT3) by computational approach

Capsaicin-Entangled Multi-Walled Carbon Nanotubes Against Breast Cancer: A Theoretical and Experimental Approach

Background: Alzheimer's disease (AD) is a neurodegenerative disorder in elderly persons aged above 65 years. The cleavage of amyloid precursor protein (APP) by -secretase generates peptide fragment A42, the main cause of memory and cognitive defects in AD. Therefore, elevated level of -secretase results in accumulation of insoluble form of A peptides (senile plaques) representing the protein as an attractive drug target of AD. Methods: Five recently reported -secretase antagonist (thiazolidinediones, rosiglitazone, pioglitazone, SC7 and tartaric acid) structural analogs were searched from ligand info database and prepared using LigPrep. The crystal structure of -secretase was optimized using Maestro v9.2 protein preparation wizard applying optimized potential for liquid simulations (OPLS)-2005 force field. Structure based virtual screening was performed for -secretase from prepared ligands using virtual screening workflow of Maestro v9.2 and was ranked based on XP Gscore. Results: Eight lead molecules were identified to have better binding affinity (lower XP Gscore) compared to five existing -secretase antagonists and appear to have good pharmacological properties. Binding orientations of the lead molecules were in well agreement with existing inhibitors. Lead1 showed lowest XP Gscore (-10.72 Kcal/mol). Molecular dynamics (MD) simulations of -secretase-lead1 complex was stable in all trajectories. Conclusion: Lead1 is proposed as potent inhibitor of -secretase and thus could be considered for rational drug design

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Identification of potent inhibitors for β-secretase through structure based virtual screening and molecular dynamics simulations

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Identification of potent inhibitors for β-secretase through structure based virtual screening and molecular dynamics simulations