{"title":"Exploring Schiff base ligand inhibitor for cancer and neurological cells, viruses and bacteria receptors by homology modeling and molecular docking","authors":"Hasnia Abdeldjebar, Chafia Ait-Ramdane-Terbouche, Achour Terbouche, Houria Lakhdari","doi":"10.1016/j.comtox.2022.100231","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Toxicology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468111322000196","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TOXICOLOGY","Score":null,"Total":0}
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.
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