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Exploration of Novel PDEδ Inhibitor Based on Pharmacophore and Molecular Docking against KRAS Mutant in Colorectal
Mohammed Mouhcine1, Youness Kadil1, Imane Rahmoune1
1Laboratory of Pharmacology-Toxicology, Faculty of Medicine and Pharmacy of Casablanca, Hassan II University of Casablanca, Casablanca, Morocco.
Aim:
The prenyl-binding protein, phosphodiesterase-δ (PDEδ), is essential for the localization of prenylated KRas to the plasma membrane for its signaling in cancer.
Introduction:
The general objective of this work was to develop virtually new potential inhibitors of the PDEδ protein that prevent Ras enrichment at the plasma membrane.
Methods:
All computational molecular modeling studies were performed by Molecular Operating Environment (MOE). In this study, sixteen crystal structures of PDEδ in complex with fifteen different fragment inhibitors were used in the protein-ligand interaction fingerprints (PLIF) study to identify the chemical features responsible for the inhibition of the PDEδ protein. Based on these chemical characteristics, a pharmacophore with representative characteristics was obtained for screening the BindingDB database. Compounds that matched the pharmacophore model were filtered by the Lipinski filter. The ADMET properties of the compounds that passed the Lipinski filter were predicted by the Swiss ADME webserver and by the ProTox-II-Prediction of Toxicity of Chemicals web server. The selected compounds were subjected to a molecular docking study.
Results:
In the PLIF study, it was shown that the fifteen inhibitors formed interactions with residues Met20, Trp32, Ile53, Cys56, Lys57, Arg61, Gln78, Val80, Glu88, Ile109, Ala11, Met117, Met118, Ile129, Thr131, and Tyr149 of the prenyl-binding pocket of PDEδ. Based on these chemical features, a pharmacophore with representative characteristics was composed of three bond acceptors, two hydrophobic elements, and one hydrogen bond donor. When the pharmacophore model was used in the virtual screening of the Binding DB database, 2532 compounds were selected. Then, the 2532 compounds were screened by the Lipinski rule filter. Among the 2532 compounds, two compounds met the Lipinski's rule. Subsequently, a comparison of the ADMET properties and the drug properties of the two compounds was performed. Finally, compound 2 was selected for molecular docking analysis and as a potential inhibitor against PDEδ.
Conclusion:
The hit found by the combination of structure-based pharmacophore generation, pharmacophore- based virtual screening, and molecular docking showed interaction with key amino acids in the hydrophobic pocket of PDEδ, leading to the discovery of a novel scaffold as a potential inhibitor of PDEδ.
Insights
Researchers identified novel inhibitors for phosphodiesterase-δ (PDEδ) by virtually screening compounds. This work aims to block Ras enrichment at the plasma membrane, a key process in cancer signaling.
Area of Science:
- Computational chemistry
- Drug discovery
- Molecular modeling
Background:
- Phosphodiesterase-δ (PDEδ) is crucial for prenylated KRas localization to the plasma membrane, driving cancer signaling.
- Targeting PDEδ offers a strategy to inhibit oncogenic KRas signaling.
Purpose of the Study:
- To computationally develop novel PDEδ inhibitors.
- To identify compounds that prevent Ras enrichment at the plasma membrane.
Main Methods:
- Utilized molecular modeling (MOE) and protein-ligand interaction fingerprints (PLIF).
- Generated a pharmacophore model for virtual screening of the BindingDB database.
- Applied Lipinski filters and ADMET/toxicity predictions, followed by molecular docking.
Main Results:
- Identified key PDEδ residues interacting with inhibitors (Met20, Trp32, Ile53, etc.).
- Developed a pharmacophore model comprising acceptors, hydrophobic elements, and a hydrogen bond donor.
- Virtual screening yielded two drug-like compounds, with compound 2 selected for further analysis.
Conclusions:
- A novel scaffold inhibitor for PDEδ was discovered.
- The combined approach of pharmacophore modeling and molecular docking is effective for identifying potential drug candidates.
