Structure-based virtual screening discovers potent and selective adenosine A1 receptor antagonists.

Development of subtype-selective leads is essential in drug discovery campaigns targeting G protein-coupled receptors (GPCRs). Herein, a structure-based virtual screening approach to rationally design subtype-selective ligands was applied to the A1 and A2A adenosine receptors (A1R and A2AR). Crystal structures of these closely related subtypes revealed a non-conserved subpocket in the binding sites that could be exploited to identify A1R selective ligands. A library of 4.6 million compounds was screened computationally against both receptors using molecular docking and 20 A1R selective ligands were predicted. Of these, seven antagonized the A1R with micromolar activities and several compounds displayed slight selectivity for this subtype. Twenty-seven analogs of two discovered scaffolds were designed, resulting in antagonists with nanomolar potency and up to 76-fold A1R-selectivity. Our results show the potential of structure-based virtual screening to guide discovery and optimization of subtype-selective ligands, which could facilitate the development of safer drugs.

Structure-Guided Design of G-Protein-Coupled Receptor Polypharmacology.

Many diseases are polygenic and can only be treated efficiently with drugs that modulate multiple targets. However, rational design of compounds with multi-target profiles is rarely pursued because it is considered too difficult, in particular if the drug must enter the central nervous system. Here, a structure-based strategy to identify dual-target ligands of G-protein-coupled receptors is presented. We use this approach to design compounds that both antagonize the A2A adenosine receptor and activate the D2 dopamine receptor, which have excellent potential as antiparkinson drugs. Atomic resolution models of the receptors guided generation of a chemical library with compounds designed to occupy orthosteric and secondary binding pockets in both targets. Structure-based virtual screens identified ten compounds, of which three had affinity for both targets. One of these scaffolds was optimized to nanomolar dual-target activity and showed the predicted pharmacodynamic effect in a rat model of Parkinsonism.

Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity.

Rational drug design for G protein-coupled receptors (GPCRs) is limited by the small number of available atomic resolution structures. We assessed the use of homology modeling to predict the structures of two therapeutically relevant GPCRs and strategies to improve the performance of virtual screening against modeled binding sites. Homology models of the D2 dopamine (D2R) and serotonin 5-HT2A receptors (5-HT2AR) were generated based on crystal structures of 16 different GPCRs. Comparison of the homology models to D2R and 5-HT2AR crystal structures showed that accurate predictions could be obtained, but not necessarily using the most closely related template. Assessment of virtual screening performance was based on molecular docking of ligands and decoys. The results demonstrated that several templates and multiple models based on each of these must be evaluated to identify the optimal binding site structure. Models based on aminergic GPCRs showed substantial ligand enrichment and there was a trend toward improved virtual screening performance with increasing binding site accuracy. The best models even yielded ligand enrichment comparable to or better than that of the D2R and 5-HT2AR crystal structures. Methods to consider binding site plasticity were explored to further improve predictions. Molecular docking to ensembles of structures did not outperform the best individual binding site models, but could increase the diversity of hits from virtual screens and be advantageous for GPCR targets with few known ligands. Molecular dynamics refinement resulted in moderate improvements of structural accuracy and the virtual screening performance of snapshots was either comparable to or worse than that of the raw homology models. These results provide guidelines for successful application of structure-based ligand discovery using GPCR homology models.

Mapping the Interface of a GPCR Dimer: A Structural Model of the A2A Adenosine and D2 Dopamine Receptor Heteromer.

The A2A adenosine (A2AR) and D2 dopamine (D2R) receptors form oligomers in the cell membrane and allosteric interactions across the A2AR-D2R heteromer represent a target for development of drugs against central nervous system disorders. However, understanding of the molecular determinants of A2AR-D2R heteromerization and the allosteric antagonistic interactions between the receptor protomers is still limited. In this work, a structural model of the A2AR-D2R heterodimer was generated using a combined experimental and computational approach. Regions involved in the heteromer interface were modeled based on the effects of peptides derived from the transmembrane (TM) helices on A2AR-D2R receptor-receptor interactions in bioluminescence resonance energy transfer (BRET) and proximity ligation assays. Peptides corresponding to TM-IV and TM-V of the A2AR blocked heterodimer interactions and disrupted the allosteric effect of A2AR activation on D2R agonist binding. Protein-protein docking was used to construct a model of the A2AR-D2R heterodimer with a TM-IV/V interface, which was refined using molecular dynamics simulations. Mutations in the predicted interface reduced A2AR-D2R interactions in BRET experiments and altered the allosteric modulation. The heterodimer model provided insights into the structural basis of allosteric modulation and the technique developed to characterize the A2AR-D2R interface can be extended to study the many other G protein-coupled receptors that engage in heteroreceptor complexes.

Docking Screens for Dual Inhibitors of Disparate Drug Targets for Parkinson's Disease.

Modulation of multiple biological targets with a single drug can lead to synergistic therapeutic effects and has been demonstrated to be essential for efficient treatment of CNS disorders. However, rational design of compounds that interact with several targets is very challenging. Here, we demonstrate that structure-based virtual screening can guide the discovery of multi-target ligands of unrelated proteins relevant for Parkinson's disease. A library with 5.4 million molecules was docked to crystal structures of the A2A adenosine receptor (A2AAR) and monoamine oxidase B (MAO-B). Twenty-four compounds that were among the highest ranked for both binding sites were evaluated experimentally, resulting in the discovery of four dual-target ligands. The most potent compound was an A2AAR antagonist with nanomolar affinity ( Ki = 19 nM) and inhibited MAO-B with an IC50 of 100 nM. Optimization guided by the predicted binding modes led to the identification of a second potent dual-target scaffold. The two discovered scaffolds were shown to counteract 6-hydroxydopamine-induced neurotoxicity in dopaminergic neuronal-like SH-SY5Y cells. Structure-based screening can hence be used to identify ligands with specific polypharmacological profiles, providing new avenues for drug development against complex diseases.

Structure-Guided Screening for Functionally Selective D2 Dopamine Receptor Ligands from a Virtual Chemical Library.

Functionally selective ligands stabilize conformations of G protein-coupled receptors (GPCRs) that induce a preference for signaling via a subset of the intracellular pathways activated by the endogenous agonists. The possibility to fine-tune the functional activity of a receptor provides opportunities to develop drugs that selectively signal via pathways associated with a therapeutic effect and avoid those causing side effects. Animal studies have indicated that ligands displaying functional selectivity at the D2 dopamine receptor (D2R) could be safer and more efficacious drugs against neuropsychiatric diseases. In this work, computational design of functionally selective D2R ligands was explored using structure-based virtual screening. Molecular docking of known functionally selective ligands to a D2R homology model indicated that such compounds were anchored by interactions with the orthosteric site and extended into a common secondary pocket. A tailored virtual library with close to 13 000 compounds bearing 2,3-dichlorophenylpiperazine, a privileged orthosteric scaffold, connected to diverse chemical moieties via a linker was docked to the D2R model. Eighteen top-ranked compounds that occupied both the orthosteric and allosteric site were synthesized, leading to the discovery of 16 partial agonists. A majority of the ligands had comparable maximum effects in the G protein and ß-arrestin recruitment assays, but a subset displayed preference for a single pathway. In particular, compound 4 stimulated ß-arrestin recruitment (EC50 = 320 nM, Emax = 16%) but had no detectable G protein signaling. The use of structure-based screening and virtual libraries to discover GPCR ligands with tailored functional properties will be discussed.

Fragment optimization for GPCRs by molecular dynamics free energy calculations: Probing druggable subpockets of the A 2A adenosine receptor binding site.

Fragment-based lead discovery is becoming an increasingly popular strategy for drug discovery. Fragment screening identifies weakly binding compounds that require optimization to become high-affinity leads. As design of leads from fragments is challenging, reliable computational methods to guide optimization would be invaluable. We evaluated using molecular dynamics simulations and the free energy perturbation method (MD/FEP) in fragment optimization for the A2A adenosine receptor, a pharmaceutically relevant G protein-coupled receptor. Optimization of fragments exploring two binding site subpockets was probed by calculating relative binding affinities for 23 adenine derivatives, resulting in strong agreement with experimental data (R2 = 0.78). The predictive power of MD/FEP was significantly better than that of an empirical scoring function. We also demonstrated the potential of the MD/FEP to assess multiple binding modes and to tailor the thermodynamic profile of ligands during optimization. Finally, MD/FEP was applied prospectively to optimize three nonpurine fragments, and predictions for 12 compounds were evaluated experimentally. The direction of the change in binding affinity was correctly predicted in a majority of the cases, and agreement with experiment could be improved with rigorous parameter derivation. The results suggest that MD/FEP will become a powerful tool in structure-driven optimization of fragments to lead candidates.

Evolution of Pentameric Ligand-Gated Ion Channels: Pro-Loop Receptors.

Pentameric ligand-gated ion channels (pLGICs) are ubiquitous neurotransmitter receptors in Bilateria, with a small number of known prokaryotic homologues. Here we describe a new inventory and phylogenetic analysis of pLGIC genes across all kingdoms of life. Our main finding is a set of pLGIC genes in unicellular eukaryotes, some of which are metazoan-like Cys-loop receptors, and others devoid of Cys-loop cysteines, like their prokaryotic relatives. A number of such "Cys-less" receptors also appears in invertebrate metazoans. Together, those findings draw a new distribution of pLGICs in eukaryotes. A broader distribution of prokaryotic channels also emerges, including a major new archaeal taxon, Thaumarchaeota. More generally, pLGICs now appear nearly ubiquitous in major taxonomic groups except multicellular plants and fungi. However, pLGICs are sparsely present in unicellular taxa, suggesting a high rate of gene loss and a non-essential character, contrasting with their essential role as synaptic receptors of the bilaterian nervous system. Multiple alignments of these highly divergent sequences reveal a small number of conserved residues clustered at the interface between the extracellular and transmembrane domains. Only the "Cys-loop" proline is absolutely conserved, suggesting the more fitting name "Pro loop" for that motif, and "Pro-loop receptors" for the superfamily. The infered molecular phylogeny shows a Cys-loop and a Cys-less clade in eukaryotes, both containing metazoans and unicellular members. This suggests new hypotheses on the evolutionary history of the superfamily, such as a possible origin of the Cys-loop cysteines in an ancient unicellular eukaryote. Deeper phylogenetic relationships remain uncertain, particularly around the split between bacteria, archaea, and eukaryotes.