In drug design, the binding free energy of small molecules to a target and the ability to predict the functional consequences of binding are crucial. We study the dynamics and energetics of the nuclear receptor REV-ERBα with its co-repressor NCoR and thirty-five novel agonists. Our in silico approach combines molecular docking, molecular dynamics, solvent-accessible surface area (SASA), and molecular mechanics Poisson Boltzmann surface area (MMPBSA) calculations. While docking yielded initial hints on the binding modes, their stability was assessed by MD. The SASA calculations revealed that the presence of a ligand led to a higher exposure of hydrophobic REV-ERB residues for NCoR recruitment. The ligand binding free energies of six calibration compounds were then calculated and ranked using MMPBSA. For this retrospective set, a good correlation between the calculated free energies and the pEC50 values from a FRET assay and a clear tendency with values from a Mammalian 2-Hybrid (M2H) assay was observed. As a validation, four molecules out of twenty-nine were chosen based on synthetic feasibility and synthesized. Three molecules were predicted as being active and one as being less active. The pEC50 values from the M2H assay confirmed our predictions. Thus, good binders were identified by correlating the experimentally determined pEC50 values with the calculated free energies from MMPBSA. In the REVERB-NCoR-agonist systems studied, our MMPBSA approach was promising both retrospectively and prospectively. The analysis of the heavy atom RMSDs of the REV-ERB binding site residues revealed that low values for three phenylalanines led to a better biological activity. An ether linker helped to ideally position the group which intercalates between these residues and presumably contributes to the recruitment of NCoR. We then used both the RMSDs of the phenylalanines and the calculated free energies for establishing criteria that allowed to successfully separating active from less active or inactive compounds. For the calibration set, five out of six molecules were confirmed to be good recruiters based on the RMSDs and good binders based on the MMPBSA results. In conclusion, the binding modes, free energies, and hints on NCoR recruitment obtained from our MD/MMPBSA approach can be useful for prioritizing chemical series for synthesis and biological testing.

Binding mode prediction and MD/MMPBSA-based free energy ranking for agonists of REV-ERBα/NCoR

255th National Meeting (2018)

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