Modeling MEK4 Kinase Inhibitors through Perturbed Electrostatic Potential Charges
Rama K Mishra1,2, Kristine K Deibler3, Matthew R Clutter4,5
1Center for Molecular Innovation and Drug Discovery , Northwestern University , 2145 Sheridan Road , Evanston , Illinois 60208 , United States.
Abstract:
MEK4, mitogen-activated protein kinase kinase 4, is overexpressed and induces metastasis in advanced prostate cancer lesions. However, the value of MEK4 as an oncology target has not been pharmacologically validated because selective chemical probes targeting MEK4 have not been developed. With advances in both computer and biological high-throughput screening, selective chemical entities can be discovered. Structure-based quantitative structure-activity relationship (QSAR) modeling often fails to generate accurate models due to poor alignment of training sets containing highly diverse compounds. Here we describe a highly predictive, nonalignment based robust QSAR model based on a data set of strikingly diverse MEK4 inhibitors. We computed the electrostatic potential (ESP) charges using a density functional theory (DFT) formalism of the donor and acceptor atoms of the ligands and hinge residues. Novel descriptors were then generated from the perturbation of the charge densities of the donor and acceptor atoms and were used to model a diverse set of 84 compounds, from which we built a robust predictive model.
Insights
Researchers developed a robust quantitative structure-activity relationship (QSAR) model to discover novel mitogen-activated protein kinase kinase 4 (MEK4) inhibitors, crucial for targeting prostate cancer metastasis.
Area of Science:
- Oncology
- Medicinal Chemistry
- Computational Biology
Background:
- Mitogen-activated protein kinase kinase 4 (MEK4) is overexpressed in advanced prostate cancer, promoting metastasis.
- Pharmacological validation of MEK4 as an oncology target is hindered by the lack of selective chemical probes.
- Advances in high-throughput screening enable the discovery of selective chemical entities.
Purpose of the Study:
- To develop a highly predictive, non-alignment-based quantitative structure-activity relationship (QSAR) model for MEK4 inhibitors.
- To address limitations of traditional structure-based QSAR modeling with diverse compound sets.
- To facilitate the discovery of novel MEK4-targeting oncology drugs.
Main Methods:
- Computed electrostatic potential (ESP) charges using density functional theory (DFT) for ligands and hinge residues.
- Generated novel descriptors from charge density perturbations of donor and acceptor atoms.
- Developed a robust QSAR model using a diverse dataset of 84 MEK4 inhibitors.
Main Results:
- A highly predictive, non-alignment-based QSAR model was successfully built.
- The model effectively modeled a diverse set of 84 MEK4 inhibitors.
- Novel descriptors derived from charge density perturbations proved valuable for QSAR modeling.
Conclusions:
- The developed QSAR model offers a robust platform for identifying novel MEK4 inhibitors.
- This approach overcomes challenges associated with modeling diverse chemical structures.
- The findings pave the way for the pharmacological validation of MEK4 as an oncology target.
