Identification of novel curcumin derivatives against pancreatic cancer: a comprehensive approach integrating 3D-QSAR pharmacophore modeling, virtual screening, and molecular dynamics simulations

Chahbaoui, Narimene; Khamouli, Saida; Alaqarbeh, Marwa; Belaidi, Salah; Sinha, Leena; Chtita, Samir; Bouachrine, Mohammed

doi:10.1080/07391102.2023.2266502

Cited by 2 publications

(1 citation statement)

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“…Consequently, the aforementioned data may be utilized to develop rationally designed new PDEδ inhibitors and encourage us to study these compounds via combined computational approaches. This approach has been reported to design different inhibitors [31][32][33][34]. The molecular structural features of target derivatives, their importance in relation to drug-like characteristics, and the way they interact were investigated using advanced computational methods such as DFT/wB97XD, high-level calculations were performed.…”

Section: Introductionmentioning

confidence: 99%

DFT and molecular simulation validation of the binding activity of PDEδ inhibitors for repression of oncogenic k-Ras

Majrashi,

Sabt,

Almahli

et al. 2024

PLoS ONE

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The development of effective drugs targeting the K-Ras oncogene product is a significant focus in anticancer drug development. Despite the lack of successful Ras signaling inhibitors, recent research has identified PDEδ, a KRAS transporter, as a potential target for inhibiting the oncogenic KRAS signaling pathway. This study aims to investigate the interactions between eight K-Ras inhibitors (deltarazine, deltaflexin 1 and 2, and its analogues) and PDEδ to understand their binding modes. The research will utilize computational techniques such as density functional theory (DFT) and molecular electrostatic surface potential (MESP), molecular docking, binding site analyses, molecular dynamic (MD) simulations, electronic structure computations, and predictions of the binding free energy. Molecular dynamic simulations (MD) will be used to predict the binding conformations and pharmacophoric features in the active site of PDEδ for the examined structures. The binding free energies determined using the MMPB(GB)SA method will be compared with the observed potency values of the tested compounds. This computational approach aims to enhance understanding of the PDEδ selective mechanism, which could contribute to the development of novel selective inhibitors for K-Ras signaling.

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Section: Introductionmentioning

confidence: 99%