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Updated: Jul 31, 2025
Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
Published on: August 1, 2018
Dynamacophore model for breast cancer estrogen receptor alpha as an effective lead generation screening technique
Dhivya Shanmugarajan1, Anagha Biju1, Dona Sibi1
1Department of Biotechnology, Vignan's Foundation for Science, Technology and Research (Deemed to be University), Guntur, Andhra Pradesh, India.
Abstract:
Regardless to overwhelming quantum of cancer research worldwide, there are few drugs on the market to treat disease conditions. This is owing to multiple process inferences of drug targets in integrated pathways for invasion, growth, and metastasis. Over the past years, the death rate due to breast cancer has been increasing, that set the stage for improved better treatment. Therefore, there is a persistent and vital demand for innovative development of drugs to treat breast cancer. Many studies have reported that more than 60% of breast cancers are Estrogen receptor-α (ERα)-positive tumours and a key transcription factor, Estrogen receptor-α (ERα) was believed to promote proliferation of breast cancer cells. In this study, 150 ns of molecular dynamics was performed for protein-ligand complex to retrieve the potential stable conformations. The most populated dynamics cluster of 4-Hydroxytamoxifen intact with active site amino acid was selected to generate dynamacophore model (dynamic pharmacophore). Further, internal model validation with AU-ROC values ∼0.93 indicate the best model to screen library. The refined hits are funnelled in pharmacokinetics/dynamics, CDOCKER molecular docking, MM-GBSA and density functional theory to identify the promising ERα ligand candidates.Communicated by Ramaswamy H. Sarma.
Insights
Developing novel breast cancer drugs is crucial due to increasing mortality. This study identified promising Estrogen receptor-α (ERα) ligand candidates using molecular dynamics and computational screening for improved ERα-positive breast cancer treatments.
Area of Science:
- Oncology
- Pharmacology
- Computational Chemistry
Background:
- Despite extensive cancer research, effective treatments, especially for breast cancer, remain limited.
- Estrogen receptor-α (ERα)-positive tumors constitute over 60% of breast cancers, with ERα promoting cell proliferation.
- There is a critical need for innovative therapeutic strategies targeting ERα in breast cancer.
Purpose of the Study:
- To identify novel drug candidates targeting Estrogen receptor-α (ERα) for breast cancer treatment.
- To develop a robust computational model for screening potential ERα ligands.
- To validate and refine potential drug candidates through integrated computational analyses.
Main Methods:
- Performed 150 ns molecular dynamics simulations of protein-ligand complexes.
- Generated a dynamacophore model from the most populated cluster of 4-Hydroxytamoxifen and ERα active site.
- Validated the model using AU-ROC values (~0.93) and screened a library of potential ligands.
- Utilized CDOCKER molecular docking, MM-GBSA, and density functional theory for further analysis.
Main Results:
- A validated dynamacophore model with high predictive accuracy (AU-ROC ~0.93) was generated.
- Computational screening identified promising ligand candidates targeting ERα.
- Integrated analyses including molecular docking and binding energy calculations refined the selection of potential drugs.
Conclusions:
- The study successfully developed and validated a computational approach for identifying ERα ligands.
- Promising ERα ligand candidates were identified, offering potential for new breast cancer therapies.
- This research addresses the urgent need for innovative treatments for ERα-positive breast cancer.
