Predicting Binding Affinities for GPCR Ligands Using Free-Energy Perturbation

Abstract

The rapid growth of structural information for G-protein-coupled receptors (GPCRs) has led to a greater understanding of their structure, function, selectivity, and ligand binding. Although novel ligands have been identified using methods such as virtual screening, computationally driven lead optimization has been possible only in isolated cases because of challenges associated with predicting binding free energies for related compounds. Here, we provide a systematic characterization of the performance of free-energy perturbation (FEP) calculations to predict relative binding free energies of congeneric ligands binding to GPCR targets using a consistent protocol and no adjustable parameters. Using the FEP+ package, first we validated the protocol, which includes a full lipid bilayer and explicit solvent, by predicting the binding affinity for a total of 45 different ligands across four different GPCRs (adenosine A2AAR, β1 adrenergic, CXCR4 chemokine, and δ opioid receptors). Comparison with experimental binding affinity measurements revealed a highly predictive ranking correlation (average spearman ρ = 0.55) and low root-mean-square error (0.80 kcal/mol). Next, we applied FEP+ in a prospective project, where we predicted the affinity of novel, potent adenosine A2A receptor (A2AR) antagonists. Four novel compounds were synthesized and tested in a radioligand displacement assay, yielding affinity values in the nanomolar range. The affinity of two out of the four novel ligands (plus three previously reported compounds) was correctly predicted (within 1 kcal/mol), including one compound with approximately a tenfold increase in affinity compared to the starting compound. Detailed analyses of the simulations underlying the predictions provided insights into the structural basis for the two cases where the affinity was overpredicted. Taken together, these results establish a protocol for systematically applying FEP+ to GPCRs and provide guidelines for identifying potent molecules in drug discovery lead optimization projects.

DeltaDelta neural networks for lead optimization of small molecule potency

“…It is therefore common to focus on relative binding free energy (RBFE) simulation methods, [4][5][6][7][8][9][10][11][12][13] where the difference in affinity between two ligands is computed using a thermodynamic cycle that alchemically perturbs only the small region associated with the changing atoms. RBFE methods have shown good results in several studies, with accuracy close to 1 kcal mol À1 and reasonable correlations.…”

Relative Binding Free Energy Calculations Applied to Protein Homology Models

“…For example, Steinbrecher et al 30 applied FEP+ to fragment-based optimization and shown success, which is promising given the importance and challenges in the fragment-based drug design field. Recently, Lenselink et al 31 reported the application to GPCRs in a retrospective as well as prospective fashion, where a novel and highly potent A 2A inhibitor was discovered. While the above studies reported relative binding free energy predictions, which is usually sufficient in a drug design context, recent progress in absolute binding free energy calculations 32 as well as conformational transition pathways 33 has also been reported.…”

Efficient sampling of puckering states of monosaccharides through replica exchange with solute tempering and bond softening

“…REST2 has been used extensively in many biomolecular simulations, [17][18][19] particularly for protein-ligand binding simulations, [20][21][22][23][24][25][26][27][28][29][30] where it was shown to yield a superior sampling efficiency over other sampling methods. 23 However, the different puckering conformations of monosaccharide rings are separated by energy barriers as high as ⇠20 kcal/mol, and the brute force application of REST2 on these systems gives poor sampling of the puckering conformations.…”