Exploring Schiff base ligand inhibitor for cancer and neurological cells, viruses and bacteria receptors by homology modeling and molecular docking

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2022-08-01 DOI:10.1016/j.comtox.2022.100231
Hasnia Abdeldjebar, Chafia Ait-Ramdane-Terbouche, Achour Terbouche, Houria Lakhdari
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引用次数: 1

Abstract

Due to their interesting hydrogen-bonding properties, Schiff bases are known for their variety of applications in chemistry and medicinal chemistry. In this work, the interaction between symmetrical Schiff base ligand (L: bis [4-hydroxy-6-methyl-3-{(1E)-N-[2 (ethylamino) ethyl] ethanimidoyl}-2H-pyran-2-one]) and cancer cells, neurological, viruses and bacteria receptors was studied theoretically. Density functional theory (DFT) was used to determine the geometry, reactivity and electronic properties of this ligand. Homology modeling and molecular docking were performed to check their biological and medicinal properties, including anticancer, antiviral, antibacterial and neurological activities. DFT revealed that the mulliken charges, the molecular orbitals (HOMO and LUMO) and MEP results are in a good agreement to the localization of electrophilic and nucleophilic attack sites. The theoretical study showed a high chemical reactivity and a low kinetic stability of the ligand. The docking study results revealed that the ligand exhibits a good biological activity against leukemia, breast cancer, Alzheimer and Covid-19 with binding energy values of −7.36 kcal/mol, −6.35 kcal/mol, −6.19 kcal/mol and −5.58 kcal/mol, respectively. These results are explained by the low values of binding energy and inhibition constant and multiple H-bonds.

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通过同源建模和分子对接探索癌症和神经细胞、病毒和细菌受体的希夫碱配体抑制剂
由于其有趣的氢键性质,希夫碱以其在化学和药物化学中的各种应用而闻名。本研究从理论上研究了对称席夫碱配体(L: bis[4-羟基-6-甲基-3-{(1E)- n-[2(乙胺)乙基]乙胺酰基}- 2h -吡喃-2- 1])与癌细胞、神经系统、病毒和细菌受体的相互作用。用密度泛函理论(DFT)确定了该配体的几何形状、反应性和电子性质。通过同源性建模和分子对接,检测其抗癌、抗病毒、抗菌和神经活性等生物学和药用特性。DFT结果显示,mulliken电荷、分子轨道(HOMO和LUMO)和MEP结果与亲电和亲核攻击位点的定位一致。理论研究表明,该配体具有较高的化学反应活性和较低的动力学稳定性。对接研究结果表明,该配体对白血病、乳腺癌、阿尔茨海默病和Covid-19具有良好的生物活性,结合能分别为−7.36、−6.35、−6.19和−5.58 kcal/mol。这些结果可以用低结合能和抑制常数和多个氢键来解释。
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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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