Uni-FEP Case Study: mGluR5 (GPCR) inhibitors

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Introduction

With the increasing amount of structural information available on G protein-coupled receptors (GPCRs), our understanding of their structure, function, selectivity, and ligand binding has deepened, making it feasible to identify potential active compounds through computational methods. However, as GPCRs are membrane proteins with deeply located orthosteric pockets and ligands that include both agonists and antagonists, calculating the binding free energy for GPCR-ligand interactions is complex, and successful cases are currently rare.

In this case study of the Uni-FEP, we focus on Class C GPCR mGluR5 as the target protein. We selected representative compounds from the literature as ligands and used Uni-FEP to calculate their binding free energies with mGluR5, exploring and validating the applicability and accuracy of Uni-FEP within the mGluR5 system.

Glutamate is the primary excitatory neurotransmitter in the central nervous system, acting through ionotropic glutamate (iGlu) receptors and metabotropic glutamate (mGlu) receptors. Glutamate ion channels regulate the transport rate of glutamate, while modulation of mGluR can control the strength of synaptic transmission. Metabotropic glutamate receptor 5 (mGluR5) is a member of the G protein-coupled receptor family (GPCR), specifically classified under Class C GPCRs. Unlike ordinary water-soluble proteins, GPCRs are embedded within the phospholipid bilayer, which presents a different chemical environment and increases the computational complexity.

Free Energy Perturbation (FEP) can calculate the binding free energy between compounds and targets with chemical precision, primarily used during the lead compound optimization phase. In this case study, we selected 12 compounds with an activity range spanning over 10,000-fold from the work of Christopher, J.A., et al., to explore the ability of Hermite? Uni-FEP in predicting the binding affinity of mGluR5 inhibitors and guiding lead compound optimization.

In the article by Christopher, J.A., et al. [3], a detailed process for the discovery and optimization of mGluR5 inhibitors using fragment-based drug design is described. The authors conducted extensive biochemical and biological experiments to verify the binding modes and activities of the compounds.

This study selected 12 representative compounds (Compound 6 to 17) and used Uni-FEP to rapidly validate their binding free energy with mGluR5 (PDB ID: 5CGC) to assess the accuracy of Uni-FEP in calculating compound binding free energy.

The structures of the selected compounds are shown in Figure 1:

User-friendly Experience

Hermite? Uni-FEP provides a comprehensive, automated FEP calculation process and result analysis guidance. The entire workflow includes: protein preparation, ligand preparation, compound alignment (supporting rigid alignment, flexible alignment, and restrained docking), automatic mapping/perturbation of compound pairs, and result analysis.

Accurate Result

Table 1 compares the Hermite? Uni-FEP calculated ΔG with experimental ΔG. Among the 12 compounds, the prediction results for 9 compounds, excluding Compounds 10, 11, and 15, deviated from the experimental results by less than 1 kcal/mol, demonstrating high computational accuracy. (Note: For FEP calculations, a deviation of less than 1.4 kcal/mol between predicted and experimental values is considered acceptable as it corresponds to approximately a 10-fold difference in compound activity). Further analysis revealed that Compounds 10 and 11 had substituents on the benzene ring differing significantly from other molecules, leading to larger computational errors. Analyzing the results for each compound individually showed that Uni-FEP’s predictions for highly active compounds were consistent with experimental results.

Further analysis of the correlation between calculated ΔG and experimental ΔG (Figure 4) showed a correlation coefficient (R2) of 0.73 and a root mean square error (RMSE) of 1.16 kcal/mol, indicating that Uni-FEP has a good capability to reflect experimental affinity in the mGluR5 system.

Reference:

[1] akanishi, S. Molecular diversity of glutamate receptors and implications for brain function [J]. Science. 1992, 258, 597?603.

[2] ?Conn P J , Pin J P . Conn JP, Pin J-P. ?Pharmacology and functions of metabotropic glutamate receptors. Annu. Rev. Pharmacol. Toxicol.1997, 37, 205?237.

[3] Christopher J A , Aves S J , Bennett K A , et al. Fragment and Structure-Based Drug Discovery for a Class C GPCR: Discovery of the mGlu5 Negative Allosteric Modulator HTL14242 (3-Chloro-5-[6-(5-fluoropyridin-2-yl)pyrimidin-4-yl]benzonitrile) [J]. J. Med. Chem. 2015, 6653-6664.