{"title":"Phytochemical modulation of DNMT1-mediated DNA methylation: computational insights into breast cancer","authors":"Tripti Gangwar, Abhimanyu Kumar Jha","doi":"10.1007/s00894-026-06979-y","DOIUrl":null,"url":null,"abstract":"<div><h3>Context</h3><p>Aberrant DNA methylation, particularly DNMT1-mediated hypermethylation of tumor suppressor and DNA repair genes, contributes to breast cancer progression. The limitations associated with existing DNMT inhibitors highlight the need for alternative and potentially safer epigenetic modulators. Here, we developed a computational workflow to identify phytochemicals targeting the catalytic methyltransferase domain of DNMT1. Screening of the SelleckChem phytochemical library identified Echinacoside (-9.56 kcal/mol), 1,2,3,4,6-O-Pentagalloylglucose (-9.49 kcal/mol), and Complanatoside A (-9.24 kcal/mol) as promising candidates based on favourable docking and interaction profiles. Among them, 1,2,3,4,6-O-Pentagalloylglucose exhibited most favorable predicted binding free energy (− 105.31 ± 5.51 kcal/mol) and maintained stable interactions throughout 500 ns molecular dynamics simulation. DFT, PCA, FEL, and QM/MM analyses further supported its favorable electronic characteristics and conformational stability within the DNMT1 catalytic pocket.</p><h3>Methods</h3><p>Structure-based virtual screening, molecular docking, DFT, 500 ns molecular dynamics simulations, MM/GBSA, PCA, FEL, and QM/MM analyses were integrated to characterize ligand binding, stability, and electronic interactions. These findings represent purely in silico predictions and require experimental validation to establish the DNMT1 inhibitory potential of the identified compounds and their therapeutic relevance.</p></div>","PeriodicalId":651,"journal":{"name":"Journal of Molecular Modeling","volume":"32 11","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Molecular Modeling","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1007/s00894-026-06979-y","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Context
Aberrant DNA methylation, particularly DNMT1-mediated hypermethylation of tumor suppressor and DNA repair genes, contributes to breast cancer progression. The limitations associated with existing DNMT inhibitors highlight the need for alternative and potentially safer epigenetic modulators. Here, we developed a computational workflow to identify phytochemicals targeting the catalytic methyltransferase domain of DNMT1. Screening of the SelleckChem phytochemical library identified Echinacoside (-9.56 kcal/mol), 1,2,3,4,6-O-Pentagalloylglucose (-9.49 kcal/mol), and Complanatoside A (-9.24 kcal/mol) as promising candidates based on favourable docking and interaction profiles. Among them, 1,2,3,4,6-O-Pentagalloylglucose exhibited most favorable predicted binding free energy (− 105.31 ± 5.51 kcal/mol) and maintained stable interactions throughout 500 ns molecular dynamics simulation. DFT, PCA, FEL, and QM/MM analyses further supported its favorable electronic characteristics and conformational stability within the DNMT1 catalytic pocket.
Methods
Structure-based virtual screening, molecular docking, DFT, 500 ns molecular dynamics simulations, MM/GBSA, PCA, FEL, and QM/MM analyses were integrated to characterize ligand binding, stability, and electronic interactions. These findings represent purely in silico predictions and require experimental validation to establish the DNMT1 inhibitory potential of the identified compounds and their therapeutic relevance.
异常的DNA甲基化,特别是dnmt1介导的肿瘤抑制基因和DNA修复基因的高甲基化,有助于乳腺癌的进展。与现有DNMT抑制剂相关的局限性突出了对替代和潜在更安全的表观遗传调节剂的需求。在这里,我们开发了一个计算工作流程来识别靶向DNMT1催化甲基转移酶结构域的植物化学物质。SelleckChem植物化学文库筛选发现,基于良好的对接和相互作用谱,紫锥花苷(-9.56 kcal/mol)、1,2,3,4,6- o -五gallallylglucose (-9.49 kcal/mol)和planatoside A (-9.24 kcal/mol)是有希望的候选化合物。其中,1,2,3,4,6- o -五五烯基葡萄糖表现出最有利的预测结合自由能(- 105.31±5.51 kcal/mol),并在500 ns的分子动力学模拟中保持稳定的相互作用。DFT、PCA、FEL和QM/MM分析进一步支持其在DNMT1催化袋内良好的电子特性和构象稳定性。方法结合基于结构的虚拟筛选、分子对接、DFT、500 ns分子动力学模拟、MM/GBSA、PCA、FEL和QM/MM分析,表征配体结合、稳定性和电子相互作用。这些发现纯粹代表了计算机预测,需要实验验证来确定所鉴定化合物的DNMT1抑制潜力及其治疗相关性。
期刊介绍:
The Journal of Molecular Modeling focuses on "hardcore" modeling, publishing high-quality research and reports. Founded in 1995 as a purely electronic journal, it has adapted its format to include a full-color print edition, and adjusted its aims and scope fit the fast-changing field of molecular modeling, with a particular focus on three-dimensional modeling.
Today, the journal covers all aspects of molecular modeling including life science modeling; materials modeling; new methods; and computational chemistry.
Topics include computer-aided molecular design; rational drug design, de novo ligand design, receptor modeling and docking; cheminformatics, data analysis, visualization and mining; computational medicinal chemistry; homology modeling; simulation of peptides, DNA and other biopolymers; quantitative structure-activity relationships (QSAR) and ADME-modeling; modeling of biological reaction mechanisms; and combined experimental and computational studies in which calculations play a major role.