{"title":"DFT study and docking of xanthone derivatives indicating their ability to inhibit aromatase, a crucial enzyme for the steroid biosynthesis pathway","authors":"Anamika Singh , Nikita Tiwari , Anil Mishra , Monika Gupta","doi":"10.1016/j.comtox.2023.100289","DOIUrl":null,"url":null,"abstract":"<div><p>Aromatase is a crucial enzyme in the aromatization process, which catalyzes the conversion of androgenic steroids to estrogens. Aromatase dysregulation, as well as elevated estrogen levels, have been linked to a variety of malignancies, including breast cancer. Herein, we present the results of the optimization of Xanthones employing density functional theory (DFT) using the B3LYP/6-311G+(d, p) basis set to determine their frontier molecular orbitals, Mulliken charges, and chemical reactivity descriptors. According to the DFT results, Erythrommone has the smallest HOMO-LUMO gap (3.85 Kcal/mol), as well as the greatest electrophilicity index (5.19) and basicity (4.47). Xanthones and their derivatives were docked into the active site cavity of CYP450 to examine their structure-based inhibitory effect. The docking simulation studies predicted that Erythrommone has the lowest binding energy (-7.43 Kcal/mol), which is consistent with the DFT calculations and may function as a powerful CYP450 inhibitor equivalent to its known inhibitor, Exemestane, which has a binding affinity of −8.13 Kcal/mol. The high binding affinity of Xanthones was linked to the existence of hydrogen bonds as well as various hydrophobic interactions between the ligand and the receptor's essential amino acid residues. The findings demonstrated that Xanthones are more powerful inhibitors of the Aromatase enzyme than the recognized inhibitor Exemestane.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2023-09-28","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/S2468111323000300","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
Aromatase is a crucial enzyme in the aromatization process, which catalyzes the conversion of androgenic steroids to estrogens. Aromatase dysregulation, as well as elevated estrogen levels, have been linked to a variety of malignancies, including breast cancer. Herein, we present the results of the optimization of Xanthones employing density functional theory (DFT) using the B3LYP/6-311G+(d, p) basis set to determine their frontier molecular orbitals, Mulliken charges, and chemical reactivity descriptors. According to the DFT results, Erythrommone has the smallest HOMO-LUMO gap (3.85 Kcal/mol), as well as the greatest electrophilicity index (5.19) and basicity (4.47). Xanthones and their derivatives were docked into the active site cavity of CYP450 to examine their structure-based inhibitory effect. The docking simulation studies predicted that Erythrommone has the lowest binding energy (-7.43 Kcal/mol), which is consistent with the DFT calculations and may function as a powerful CYP450 inhibitor equivalent to its known inhibitor, Exemestane, which has a binding affinity of −8.13 Kcal/mol. The high binding affinity of Xanthones was linked to the existence of hydrogen bonds as well as various hydrophobic interactions between the ligand and the receptor's essential amino acid residues. The findings demonstrated that Xanthones are more powerful inhibitors of the Aromatase enzyme than the recognized inhibitor Exemestane.
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