Estradiol dimers (EDs) possess significant anticancer activity by targeting tubulin dynamics. In this study, we synthesised 12 EDs variants via copper-catalysed azide-alkyne cycloaddition (CuAAC) reaction, focusing on structural modifications within the aromatic bridge connecting two estradiol moieties. In vitro testing of these EDs revealed a marked improvement in selectivity towards cancerous cells, particularly for ED1-8. The most active compounds, ED3 (IC50 = 0.38 μM in CCRF-CEM) and ED5 (IC50 = 0.71 μM in CCRF-CEM) demonstrated cytotoxic effects superior to 2-methoxyestradiol (IC50 = 1.61 μM in CCRF-CEM) and exhibited anti-angiogenic properties in an endothelial cell tube-formation model. Cell-based experiments and in vitro assays revealed that EDs interfere with mitotic spindle assembly. Additionally, we proposed an in silico model illustrating the probable binding modes of ED3 and ED5, suggesting that dimers with a simple linker and a single substituent on the aromatic central ring possess enhanced characteristics compared to more complex dimers.

• Keywords
• Estradiol, cancer cell, dimer, in silico, tubulin,
• MeSH
• Click Chemistry MeSH
• Dimerization MeSH
• Estradiol * pharmacology   chemistry   chemical synthesis   MeSH
• Humans MeSH
• Molecular Structure MeSH
• Cell Line, Tumor MeSH
• Cell Proliferation * drug effects   MeSH
• Antineoplastic Agents * pharmacology   chemical synthesis   chemistry   MeSH
• Drug Screening Assays, Antitumor * MeSH
• Dose-Response Relationship, Drug * MeSH
• Structure-Activity Relationship MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Estradiol * MeSH
• Antineoplastic Agents * MeSH

Molecular dynamics simulations serve as a prevalent approach for investigating the dynamic behaviour of proteins and protein-ligand complexes. Due to its versatility and speed, GROMACS stands out as a commonly utilized software platform for executing molecular dynamics simulations. However, its effective utilization requires substantial expertise in configuring, executing, and interpreting molecular dynamics trajectories. Existing automation tools are constrained in their capability to conduct simulations for large sets of compounds with minimal user intervention, or in their ability to distribute simulations across multiple servers. To address these challenges, we developed a Python-based tool that streamlines all phases of molecular dynamics simulations, encompassing preparation, execution, and analysis. This tool minimizes the required knowledge for users engaging in molecular dynamics simulations and can efficiently operate across multiple servers within a network or a cluster. Notably, the tool not only automates trajectory simulation but also facilitates the computation of free binding energies for protein-ligand complexes and generates interaction fingerprints across the trajectory. Our study demonstrated the applicability of this tool on several benchmark datasets. Additionally, we provided recommendations for end-users to effectively utilize the tool.Scientific contributionThe developed tool, StreaMD, is applicable to different systems (proteins, ligands and their complexes including co-factors) and requires a little user knowledge to setup and run molecular dynamics simulations. Other features of StreaMD are seamless integration with calculation of MM-GBSA/PBSA binding free energies and protein-ligand interaction fingerprints, and running of simulations within distributed environments. All these will facilitate routine and massive molecular dynamics simulations.

• Keywords
• Distributed simulations, GROMACS, High-throughput molecular dynamics, Molecular dynamics,
• Publication type
• Journal Article MeSH