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.
We report the development of homology models of dopamine (D(2), D(3), and D(4)), serotonin (5-HT(1B), 5-HT(2A), 5-HT(2B), and 5-HT(2C)), histamine (H(1)), and muscarinic (M(1)) receptors, based on the high-resolution structure of the beta(2)-adrenergic receptor. The homology models were built and refined using Prime. We have addressed the required modeling of extracellular loop 2, which is often implicated in ligand binding. The orthosteric sites of the models were optimized using induced fit docking, to allow for side-chain flexibility, and the resulting receptor models have been evaluated using protein validation tools. Of the nine homology models developed, six models showed moderate to good enrichment in virtual screening experiments (5-HT(2A), 5-HT(1B), D(2), 5-HT(2C), D(3), and M(1)). The 5-HT(2A) receptor displayed the highest enrichment in virtual screening experiments with enrichment factors of 6.1, 6.9, and 5.9 at 2, 5, and 10%, respectively, of the screened database. However, three of the models require further refinement (5-HT(2B), D(4), and H(1)), due to difficulties in modeling some of the binding site residues as well as the extracellular loop 2. Our effort also aims to supplement the limited number of tested G protein-coupled receptor homology models based on the beta(2) crystal structure that are freely available to the research community.
To date all typical and atypical antipsychotics target the dopamine D(2) receptor. Clozapine represents the best-characterized atypical antipsychotic, although it displays only moderate (submicromolar) affinity for the dopamine D(2) receptor. Herein, we present the design, synthesis, and pharmacological evaluation of three series of homobivalent ligands of clozapine, differing in the length and nature of the spacer and the point of attachment to the pharmacophore. Attachment of the spacer at the N4' position of clozapine yielded a series of homobivalent ligands that displayed spacer-length-dependent gains in affinity and activity for the dopamine D(2) receptor. The 16 and 18 atom spacer bivalent ligands were the highlight compounds, displaying marked low nanomolar receptor binding affinity (1.41 and 1.35 nM, respectively) and functional activity (23 and 44 nM), which correspond to significant gains in affinity (75- and 79-fold) and activity (9- and 5-fold) relative to the original pharmacophore, clozapine. As such these ligands represent useful tools with which to investigate dopamine receptor dimerization and the atypical nature of clozapine.
Understanding the pharmacological similarity of G protein-coupled receptors (GPCRs) is paramount to predicting ligand off-target effects, drug repurposing, and ligand discovery for orphan receptors. Phylogenetic relationships do not always correctly capture pharmacological similarity. Previous family-wide attempts to define pharmacological relationships were based on three-dimensional structures and/or known receptor:ligand pairings, both unavailable for orphan GPCRs. Here, we present GPCR-CoINPocket, a novel contact-informed neighboring pocket metric of GPCR binding site similarity that is informed by patterns of ligand:residue interactions observed in crystallographically-characterized GPCRs. GPCR-CoINPocket is applicable to receptors with unknown structure or ligands and accurately captures known pharmacological relationships between GPCRs, even those undetected by phylogeny. When applied to orphan receptor GPR37L1, GPCR-CoINPocket identified its pharmacological neighbors, and transfer of their pharmacology aided discovery of the first surrogate ligands for this orphan with a 30% success rate. Although primarily designed for GPCRs, the method is easily transferable to other protein families.
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