Targeting cyclin-dependent kinase 2 CDK2: Insights from molecular docking and dynamics simulation - A systematic
Bharath Kumar Chagaleti1, Shantha Kumar B1, Anjana G V1
1Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology, Chengalpattu District, Kattankulathur, Tamil Nadu 603203, India.
Abstract:
Global public health is confronted with significant challenges due to the prevalence of cancer and the emergence of treatment resistance. This work focuses on the identification of cyclin-dependent kinase 2 (CDK2) through a systematic computational approach to discover novel cancer therapeutics. A ligand-based pharmacophore model was initially developed using a training set of seven potent CDK2 inhibitors. The obtained most robust model was characterized by three features: one donor (|Don|) and two acceptors (|Acc|). Screening this model against the ZINC database resulted in identifying 108 hits, which underwent further molecular docking studies. The docking results indicated binding affinity, with energy values ranging from -6.59 kcal mol⁻¹ to -7.40 kcal mol⁻¹ compared to the standard Roscovitine. The top 10 compounds (Z1-Z10) selected from the docking data were further screened for ADMET profiling, ensuring their compliance with pharmacokinetic and toxicological criteria. The top 3 compounds (Z1-Z3) chosen from the docking were subjected to Density Functional Theory (DFT) studies. They revealed significant variations in electronic properties, providing insights into the reactivity, stability, and polarity of these compounds. Molecular dynamics simulations confirmed the stability of the ligand-protein complexes, with acceptable RMSD and RMSF values. Specifically, compound Z1 demonstrated stability, around 2.4 Å, and maintained throughout the 100 ns simulation period with minimal conformational changes, stable RMSD, and consistent protein-ligand interactions.
Insights
This study identifies novel cancer drug candidates targeting cyclin-dependent kinase 2 (CDK2). Computational methods screened compounds, revealing promising therapeutic agents with favorable stability and drug-like properties for cancer treatment.
Area of Science:
- Computational chemistry and drug discovery
- Medicinal chemistry and pharmacology
Background:
- Cancer remains a major global health challenge, exacerbated by increasing treatment resistance.
- Targeting specific kinases like cyclin-dependent kinase 2 (CDK2) is a key strategy for novel cancer therapeutics.
Purpose of the Study:
- To identify novel small molecules as potential inhibitors of CDK2 using a systematic computational approach.
- To evaluate the drug-likeness and stability of identified compounds for therapeutic development.
Main Methods:
- Development of a ligand-based pharmacophore model for CDK2 inhibitors.
- Virtual screening of the ZINC database against the pharmacophore model.
- Molecular docking, ADMET profiling, Density Functional Theory (DFT), and molecular dynamics simulations for hit validation.
Main Results:
- A pharmacophore model with one donor and two acceptor features was generated.
- 108 potential CDK2 inhibitors were identified, with docking energies comparable to Roscovitine.
- Top compounds exhibited favorable ADMET profiles, electronic properties via DFT, and stable interactions via molecular dynamics simulations (e.g., Compound Z1 showed 2.4 Å stability over 100 ns).
Conclusions:
- The computational strategy successfully identified promising novel compounds targeting CDK2.
- The validated compounds (Z1-Z3) possess desirable drug-like properties and stability, warranting further investigation as potential anti-cancer agents.
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