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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Published on: July 25, 2013
In silico study to identify novel NEK7 inhibitors from natural sources by a combination strategy
Heng Zhang1, Chenhong Lu1, Qilong Yao1
1State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, 210023, China.
Abstract:
Cancer poses a significant global health challenge and significantly contributes to mortality. NEK7, related to the NIMA protein kinase family, plays a crucial role in spindle assembly and cell division. The dysregulation of NEK7 is closely linked to the onset and progression of various cancers, especially colon and breast cancer, making it a promising target for cancer therapy. Nevertheless, the shortage of high-quality NEK7 inhibitors highlights the need for new therapeutic strategies. In this study, we utilized a multidisciplinary approach, including virtual screening, molecular docking, pharmacokinetics, molecular dynamics simulations (MDs), and MM/PBSA calculations, to evaluate natural compounds as NEK7 inhibitors comprehensively. Through various docking strategies, we identified three natural compounds: (-)-balanol, digallic acid, and scutellarin. Molecular docking revealed significant interactions at residues such as GLU112 and ALA114, with docking scores of -15.054, -13.059, and -11.547 kcal/mol, respectively, highlighting their potential as NEK7 inhibitors. MDs confirmed the stability of these compounds at the NEK7-binding site. Hydrogen bond analysis during simulations revealed consistent interactions, supporting their strong binding capacity. MM/PBSA analysis identified other crucial amino acids contributing to binding affinity, including ILE20, VAL28, ILE75, LEU93, ALA94, LYS143, PHE148, LEU160, and THR161, crucial for stabilizing the complex. This research demonstrated that these compounds exceeded dabrafenib in binding energy, according to MM/PBSA calculations, underscoring their effectiveness as NEK7 inhibitors. ADME/T predictions showed lower oral toxicity for these compounds, suggesting their potential for further development. This study highlights the promise of these natural compounds as bases for creating more potent derivatives with significant biological activities, paving the way for future experimental validation.
Insights
Three natural compounds show promise as NEK7 inhibitors for cancer therapy, exhibiting strong binding affinity and lower toxicity compared to existing drugs. Further development could lead to new cancer treatments.
Area of Science:
- Computational chemistry and drug discovery
- Molecular biology and cancer research
Background:
- Cancer is a leading cause of mortality worldwide.
- NEK7 kinase is implicated in cell division and cancer progression, particularly in colon and breast cancers.
- There is a critical need for novel, high-quality NEK7 inhibitors due to the limitations of current therapeutic strategies.
Purpose of the Study:
- To identify and evaluate natural compounds as potential inhibitors of NEK7.
- To assess the binding affinity, stability, and pharmacokinetic properties of identified compounds using computational methods.
Main Methods:
- Virtual screening and molecular docking to identify candidate natural compounds.
- Molecular dynamics simulations (MDs) to assess binding stability.
- MM/PBSA calculations for binding energy analysis and ADME/T predictions for pharmacokinetic properties.
Main Results:
- Identified three natural compounds—(-)-balanol, digallic acid, and scutellarin—as potent NEK7 inhibitors.
- Compounds demonstrated significant interactions with key NEK7 residues (e.g., GLU112, ALA114) and stable binding confirmed by MDs.
- MM/PBSA calculations indicated superior binding energy compared to dabrafenib, with favorable ADME/T profiles suggesting low oral toxicity.
Conclusions:
- The identified natural compounds represent promising lead structures for developing novel NEK7-targeted cancer therapies.
- These compounds exhibit strong binding affinity and favorable pharmacokinetic properties, warranting further experimental validation and derivatization.
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