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Updated: Jan 16, 2026
Real-Time Monitoring of Aurora kinase A Activation using Conformational FRET Biosensors in Live Cells
Published on: July 30, 2020
Targeting Aurora A kinase: Computational discovery of potent inhibitors through integrated pharmacophore and
Bhuvaneswari Sivaraman1, Kathiravan Muthukumaradoss2
1Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu District, Tamil Nadu 603203, India.
Abstract:
Cancer currently ranks as the second most common cause of mortality worldwide, primarily due to uncontrolled cell growth driven by aberrant mitotic processes. Aurora A kinase (AURKA), a key regulator of mitosis involved in centrosome maturation, bipolar spindle formation, and cytokinesis, has been identified as a promising anticancer target. This study employs a comprehensive computational approach to identify new AURKA inhibitors. Using MOE software, a ligand-based pharmacophore model was developed based on six potent AURKA inhibitors. The model, consisting of three features-Aro/HydA, Acc, and Don/Acc-at an 80 % threshold, demonstrated strong discriminative power with a sensitivity of 69.8 %, specificity of 63.6 %, and accuracy of 60.4 %. Screening of the ZINC database yielded 774 hits, from which A1 (ZINC63106872) and A2 (ZINC39272872) were identified as the top candidates, with superior docking scores (-9.24 and -8.97 kcal/mol) compared to the reference MK-5108 (-7.49 kcal/mol). These hits satisfied Lipinski's rule and exhibited favourable ADMET profiles. DFT analysis revealed higher dipole moments (A1: 6.15 D, A2:6.39 D) and narrower HOMO-LUMO gaps (A1: 0.33 eV, A2: 0.38 eV), indicating enhanced polarity and reactivity. MEP plots showed defined donor-acceptor zones for both compounds, having a balanced surface. Molecular dynamics simulations over 500 ns confirmed complex stability, with protein backbone RMSD around 2.8 Å and ligand RMSD of 4.0 Å (A1) and 6.0 Å (A2). RMSF values remained below 2.4 Å. The most favourable binding energy for A1 (-75.34 kcal/mol) in MM-GBSA analysis confirms its strong interaction and therapeutic potential.
Insights
This study identifies novel Aurora A kinase (AURKA) inhibitors for cancer therapy using computational methods. Top candidates A1 and A2 show promising binding affinity and stability, suggesting potential as new anticancer drugs.
Area of Science:
- Computational chemistry and drug discovery
- Molecular modeling and simulation
- Oncology and cancer therapeutics
Background:
- Cancer is a leading cause of death globally, often driven by uncontrolled cell proliferation.
- Aurora A kinase (AURKA) is crucial for cell division and a validated target for cancer treatment.
- Developing novel AURKA inhibitors is essential for advancing cancer therapy.
Purpose of the Study:
- To computationally identify and characterize novel inhibitors of Aurora A kinase (AURKA).
- To evaluate the potential of identified compounds as anticancer agents.
- To explore the binding interactions and stability of lead compounds with AURKA.
Main Methods:
- Development of a ligand-based pharmacophore model for AURKA using MOE software.
- Virtual screening of the ZINC database against the pharmacophore model.
- Docking studies, ADMET profiling, DFT analysis, molecular dynamics, and MM-GBSA calculations.
Main Results:
- A pharmacophore model with good discriminative power was established.
- Two top candidate inhibitors, A1 (ZINC63106872) and A2 (ZINC39272872), were identified with superior docking scores.
- Molecular dynamics and MM-GBSA confirmed the stability and strong binding affinity of A1 and A2 to AURKA.
Conclusions:
- The identified compounds A1 and A2 demonstrate significant potential as novel AURKA inhibitors.
- These compounds exhibit favorable drug-like properties and strong binding interactions.
- Further experimental validation is warranted to confirm their therapeutic efficacy against cancer.
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