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• W4291792719 abstract "Abstract Background The pivotal role of mutant P53 protein in Ovarian cancer and the efficacy of natural compounds in cancer treatment necessitated the current study to identify novel mutant P53 modulators from medicinal plants. Homology modelling was deployed to assemble the 3-D structure of the mutant P53 protein from its amino acid sequences, while Findsitecom2.0 was used to predict the active binding site of the mutant P53 protein model. The bioactive constituents obtained from seven plants were used as ligands and docked against the binding pocket of mutant P53 protein. Autodock tools, PyRx and Discovery Studio, were used to prepare the protein, dock the ligands and visualize the complexes, respectively. Thiotepa and Germcitabine were used as reference drugs. The hit compounds were selected based on their highest binding affinity and further analyzed to identify their pharmacokinetic properties and acute Rat Toxicity using SWISSADME and Gusar, with their electronic properties calculated using the density functional theory(DFT) method. Results Screening results of 50 bioactive phytochemicals confirmed that 15 leads showed superior binding energies to mutant P53 as compared to the standard FDA-approved drugs (Thiotepa and Germcitabine with binding scores of -3.5 and − 5.4, respectively). After considering their drug-like, pharmacokinetic properties and acute toxicity prediction, four major hits (Morusin, Irinotecan, Rubitecan, and 10-hydroxycamptothecin) were identified to have minimal toxicities and are safe to be used. The DFT calculations showed regions of the molecules prone to electrophilic and nucleophilic attacks. Conclusions The current study revealed drug-like compounds that can serve as potential modulators of mutant P53 in Ovarian cancer treatment." @default.
• W4291792719 created "2022-08-16" @default.
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• W4291792719 date "2022-08-15" @default.
• W4291792719 modified "2023-09-28" @default.
• W4291792719 title "Therapeutic capability of selected medicinal plants' bioactive constituents against the mutant ovarian TP53 gene; A computational approach." @default.
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• W4291792719 doi "https://doi.org/10.21203/rs.3.rs-1943447/v1" @default.
• W4291792719 hasPublicationYear "2022" @default.
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