{"title":"Quantum mechanical and molecular dynamics insights into the binding mechanism of tubulin-stabilizing agents at the taxol site of β-tubulin","authors":"Yonatan Dillu, Shatrughan Yadav, Ramachandran Vinayagam, Umesh Yadava","doi":"10.1007/s00894-026-06982-3","DOIUrl":null,"url":null,"abstract":"<div><h3>Context</h3><p>Microtubules, composed of α- and β-tubulin heterodimers, play a crucial role in cell division and are well-established targets for anticancer drugs known as tubulin-binding agents (TBAs). Targeting the Taxol-binding site of β-tubulin has been particularly effective in disrupting microtubule dynamics and inhibiting cancer cell proliferation. In this study, selected TBAs were evaluated to identify potential candidates capable of stabilizing microtubules through interactions at this binding site.</p><h3>Methods</h3><p>An integrated computational strategy combining molecular docking, density functional theory (DFT), and molecular dynamics (MD) simulations was employed to investigate the binding characteristics of TBAs. Initially, 97 compounds were screened using a stringent molecular docking approach. Based on binding affinity and interaction analysis, four compounds (TBA79, TBA80, TBA81, and TBA41) were identified as promising candidates. Interaction profiling assessed hydrogen bonding and hydrophobic interactions with key residues. MD simulations (500 ns each) were conducted to evaluate the stability of the protein–ligand complexes. Furthermore, MMGBSA binding free energy calculations and DFT analyses were performed to examine binding stability, reactivity, and charge transfer properties of the selected compounds.</p></div>","PeriodicalId":651,"journal":{"name":"Journal of Molecular Modeling","volume":"32 11","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2026-10-07","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/s00894-026-06982-3","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
Microtubules, composed of α- and β-tubulin heterodimers, play a crucial role in cell division and are well-established targets for anticancer drugs known as tubulin-binding agents (TBAs). Targeting the Taxol-binding site of β-tubulin has been particularly effective in disrupting microtubule dynamics and inhibiting cancer cell proliferation. In this study, selected TBAs were evaluated to identify potential candidates capable of stabilizing microtubules through interactions at this binding site.
Methods
An integrated computational strategy combining molecular docking, density functional theory (DFT), and molecular dynamics (MD) simulations was employed to investigate the binding characteristics of TBAs. Initially, 97 compounds were screened using a stringent molecular docking approach. Based on binding affinity and interaction analysis, four compounds (TBA79, TBA80, TBA81, and TBA41) were identified as promising candidates. Interaction profiling assessed hydrogen bonding and hydrophobic interactions with key residues. MD simulations (500 ns each) were conducted to evaluate the stability of the protein–ligand complexes. Furthermore, MMGBSA binding free energy calculations and DFT analyses were performed to examine binding stability, reactivity, and charge transfer properties of the selected compounds.
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.