Advances in GPCR modeling evaluated by the GPCR Dock 2013 assessment: meeting new challenges

Combined Approach of Patch-Surfer and PL-PatchSurfer for Protein-Ligand Binding Prediction in CSAR 2013 and 2014

The Community Structure-Activity Resource (CSAR) benchmark exercise provides a unique opportunity for researchers to objectively evaluate the performance of protein-ligand docking methods. Patch-Surfer and PL-PatchSurfer, molecular surface-based methods for predicting binding ligands of proteins developed in our group, were tested on both CSAR 2013 and 2014 benchmark exercises in combination with an empirical scoring function-based method, AutoDock, while we only participated in CSAR 2013 using Patch-Surfer. The prediction results for Phase 1 task in CSAR 2013 showed that Patch-Surfer was able to rank all the four designed binding proteins within top ranks, outperforming AutoDock Vina. In Phase 2 of 2013, PL-PatchSurfer correctly selected the correct ligand pose for two target proteins. PL-PatchSurfer performed reasonably well in ranking ligands according to their binding affinity and in selecting near-native ligand poses in 2013 Phase 3 and 2014 Phase 1, respectively, although AutoDock Vina showed better performance. Lastly, in the 2014 Phase 2 exercise, the PL-PatchSurfer scores computed for ligands to target protein pairs correlated well with their pIC50 values, which was better or comparable to results by other participants. Overall, our methods showed fairly good performance in CSAR 2013 and 2014. Unique characteristics of the methods are discussed in comparison with AutoDock.

New paradigms in GPCR drug discovery

G protein-coupled receptors (GPCRs) remain a major domain of pharmaceutical discovery. The identification of GPCR lead compounds and their optimization are now structure-based, thanks to advances in X-ray crystallography, molecular modeling, protein engineering and biophysical techniques. In silico screening provides useful hit molecules. New pharmacological approaches to tuning the pleotropic action of GPCRs include: allosteric modulators, biased ligands, GPCR heterodimer-targeted compounds, manipulation of polypharmacology, receptor antibodies and tailoring of drug molecules to fit GPCR pharmacogenomics. Measurements of kinetics and drug efficacy are factors influencing clinical success. With the exception of inhibitors of GPCR kinases, targeting of intracellular GPCR signaling or receptor cycling for therapeutic purposes remains a futuristic concept. New assay approaches are more efficient and multidimensional: cell-based, label-free, fluorescence-based assays, and biosensors. Tailoring GPCR drugs to a patient's genetic background is now being considered. Chemoinformatic tools can predict ADME-tox properties. New imaging technology visualizes drug action in vivo. Thus, there is reason to be optimistic that new technology for GPCR ligand discovery will help reverse the current narrowing of the pharmaceutical pipeline.

GPCRdb: an information system for G protein-coupled receptors

Recent developments in G protein-coupled receptor (GPCR) structural biology and pharmacology have greatly enhanced our knowledge of receptor structure-function relations, and have helped improve the scientific foundation for drug design studies. The GPCR database, GPCRdb, serves a dual role in disseminating and enabling new scientific developments by providing reference data, analysis tools and interactive diagrams. This paper highlights new features in the fifth major GPCRdb release: (i) GPCR crystal structure browsing, superposition and display of ligand interactions; (ii) direct deposition by users of point mutations and their effects on ligand binding; (iii) refined snake and helix box residue diagram looks; and (iii) phylogenetic trees with receptor classification colour schemes. Under the hood, the entire GPCRdb front- and back-ends have been re-coded within one infrastructure, ensuring a smooth browsing experience and development. GPCRdb is available at http://www.gpcrdb.org/ and it's open source code at https://bitbucket.org/gpcr/protwis.

Macromolecular Modelling and Docking Simulations for the Discovery of Selective GPER Ligands

Estrogens influence multiple physiological processes and are implicated in many diseases as well. Cellular responses to estrogens are mainly mediated by the estrogen receptors (ER)α and ERβ, which act as ligand-activated transcription factors. Recently, a member of the G protein-coupled receptor (GPCR) superfamily, namely GPER/GPR30, has been identified as a further mediator of estrogen signalling in different pathophysiological conditions, including cancer. Today, computational methods are commonly used in all areas of health science research. Among these methods, virtual ligand screening has become an established technique for hit discovery and optimization. The absence of an established three-dimensional structure of GPER promoted studies of structure-based drug design in order to build reliable molecular models of this receptor. Here, we discuss the results obtained through the structure-based virtual ligand screening for GPER, which allowed the identification and synthesis of different selective agonist and antagonist moieties. These compounds led significant advances in our understanding of the GPER function at the cellular, tissue, and organismal levels. In particular, selective GPER ligands were critical toward the evaluation of the role elicited by this receptor in several pathophysiological conditions, including cancer. Considering that structure-based approaches are fundamental in drug discovery, future research breakthroughs with the aid of computer-aided molecular design and chemo-bioinformatics could generate a new class of drugs that, acting through GPER, would be useful in a variety of diseases as well as in innovative anticancer strategies.

Mutation-Guided Unbiased Modeling of the Fat Sensor GPR119 for High-Yield Agonist Screening

Recent benchmark studies have demonstrated the difficulties in obtaining accurate predictions of ligand binding conformations to comparative models of G-protein-coupled receptors. We have developed a data-driven optimization protocol, which integrates mutational data and structural information from multiple X-ray receptor structures in combination with a fully flexible ligand docking protocol to determine the binding conformation of AR231453, a small-molecule agonist, in the GPR119 receptor. Resulting models converge to one conformation that explains the majority of data from mutation studies and is consistent with the structure-activity relationship for a large number of AR231453 analogs. Another key property of the refined models is their success in separating active ligands from decoys in a large-scale virtual screening. These results demonstrate that mutation-guided receptor modeling can provide predictions of practical value for describing receptor-ligand interactions and drug discovery.

Towards predictive docking at aminergic G-protein coupled receptors

G protein-coupled receptors (GPCRs) are hard to crystallize. However, attempts to predict their structure have boomed as a result of advancements in crystallographic techniques. This trend has allowed computer-aided molecular modeling of GPCRs. We analyzed the performance of four molecular modeling programs in pose evaluation of re-docked antagonists / inverse agonists to 11 original crystal structures of aminergic GPCRs using an induced fit-docking procedure. AutoDock and Glide were used for docking. AutoDock binding energy function, GlideXP, Prime MM-GB/SA, and YASARA binding function were used for pose scoring. Root mean square deviation (RMSD) of the best pose ranged from 0.09 to 1.58 Å, and median RMSD of the top 60 poses ranged from 1.47 to 3.83 Å. However, RMSD of the top pose ranged from 0.13 to 7.33 Å and ranking of the best pose ranged from the 1st to 60th out of 60 poses. Moreover, analysis of ligand-receptor interactions of top poses revealed substantial differences from interactions found in crystallographic structures. Bad ranking of top poses and discrepancies between top docked poses and crystal structures render current simple docking methods unsuitable for predictive modeling of receptor-ligand interactions. Prime MM-GB/SA optimized for 3NY9 by multiple linear regression did not work well at 3NY8 and 3NYA, structures of the same receptor with different ligands. However, 9 of 11 trajectories of molecular dynamics simulations by Desmond of top poses converged with trajectories of crystal structures. Key interactions were properly detected for all structures. This procedure also worked well for cross-docking of tested β2-adrenergic antagonists. Thus, this procedure represents a possible way to predict interactions of antagonists with aminergic GPCRs.

Mutational mapping of the transmembrane binding site of the G-protein coupled receptor TGR5 and binding mode prediction of TGR5 agonists

TGR5 (Gpbar-1, M-Bar) is a class A G-protein coupled bile acid-sensing receptor predominately expressed in brain, liver and gastrointestinal tract, and a promising drug target for the treatment of metabolic disorders. Due to the lack of a crystal structure of TGR5, the development of TGR5 agonists has been guided by ligand-based approaches so far. Three binding mode models of bile acid derivatives have been presented recently. However, they differ from one another in terms of overall orientation or with respect to the location and interactions of the cholane scaffold, or cannot explain all results from mutagenesis experiments. Here, we present an extended binding mode model based on an iterative and integrated computational and biological approach. An alignment of 68 TGR5 agonists based on this binding mode leads to a significant and good structure-based 3D QSAR model, which constitutes the most comprehensive structure-based 3D-QSAR study of TGR5 agonists undertaken so far and suggests that the binding mode model is a close representation of the "true" binding mode. The binding mode model is further substantiated in that effects predicted for eight mutations in the binding site agree with experimental analyses on the impact of these TGR5 variants on receptor activity. In the binding mode, the hydrophobic cholane scaffold of taurolithocholate orients towards the interior of the orthosteric binding site such that rings A and B are in contact with TM5 and TM6, the taurine side chain orients towards the extracellular opening of the binding site and forms a salt bridge with R79(EL1), and the 3-hydroxyl group forms hydrogen bonds with E169(5.44) and Y240(6.51). The binding mode thus differs in important aspects from the ones recently presented. These results are highly relevant for the development of novel, more potent agonists of TGR5 and should be a valuable starting point for the development of TGR5 antagonists, which could show antiproliferative effects in tumor cells.

Free fatty acid receptors: structural models and elucidation of ligand binding interactions

Background

The free fatty acid receptors (FFAs), including FFA1 (orphan name: GPR40), FFA2 (GPR43) and FFA3 (GPR41) are G protein-coupled receptors (GPCRs) involved in energy and metabolic homeostasis. Understanding the structural basis of ligand binding at FFAs is an essential step toward designing potent and selective small molecule modulators.

Results

We analyse earlier homology models of FFAs in light of the newly published FFA1 crystal structure co-crystallized with TAK-875, an ago-allosteric ligand, focusing on the architecture of the extracellular binding cavity and agonist-receptor interactions. The previous low-resolution homology models of FFAs were helpful in highlighting the location of the ligand binding site and the key residues for ligand anchoring. However, homology models were not accurate in establishing the nature of all ligand-receptor contacts and the precise ligand-binding mode. From analysis of structural models and mutagenesis, it appears that the position of helices 3, 4 and 5 is crucial in ligand docking. The FFA1-based homology models of FFA2 and FFA3 were constructed and used to compare the FFA subtypes. From docking studies we propose an alternative binding mode for orthosteric agonists at FFA1 and FFA2, involving the interhelical space between helices 4 and 5. This binding mode can explain mutagenesis results for residues at positions 4.56 and 5.42. The novel FFAs structural models highlight higher aromaticity of the FFA2 binding cavity and higher hydrophilicity of the FFA3 binding cavity. The role of the residues at the second extracellular loop used in mutagenesis is reanalysed. The third positively-charged residue in the binding cavity of FFAs, located in helix 2, is identified and predicted to coordinate allosteric modulators.

Conclusions

The novel structural models of FFAs provide information on specific modes of ligand binding at FFA subtypes and new suggestions for mutagenesis and ligand modification, guiding the development of novel orthosteric and allosteric chemical probes to validate the importance of FFAs in metabolic and inflammatory conditions. Using our FFA homology modelling experience, a strategy to model a GPCR, which is phylogenetically distant from GPCRs with the available crystal structures, is discussed.

Electronic supplementary material

The online version of this article (doi:10.1186/s12900-015-0044-2) contains supplementary material, which is available to authorized users.

A Molecular Pharmacologist's Guide to G Protein-Coupled Receptor Crystallography

G protein-coupled receptor (GPCR) structural biology has progressed dramatically in the last decade. There are now over 120 GPCR crystal structures deposited in the Protein Data Bank of 32 different receptors from families scattered across the phylogenetic tree, including class B, C, and Frizzled GPCRs. These structures have been obtained in combination with a wide variety of ligands and captured in a range of conformational states. This surge in structural knowledge has enlightened research into the molecular recognition of biologically active molecules, the mechanisms of receptor activation, the dynamics of functional selectivity, and fueled structure-based drug design efforts for GPCRs. Here we summarize the innovations in both protein engineering/molecular biology and crystallography techniques that have led to these advances in GPCR structural biology and discuss how they may influence the resulting structural models. We also provide a brief molecular pharmacologist's guide to GPCR X-ray crystallography, outlining some key aspects in the process of structure determination, with the goal to encourage noncrystallographers to interrogate structures at the molecular level. Finally, we show how chemogenomics approaches can be used to marry the wealth of existing receptor pharmacology data with the expanding repertoire of structures, providing a deeper understanding of the mechanistic details of GPCR function.

Evidence for a Transient Additional Ligand Binding Site in the TAS2R46 Bitter Taste Receptor

Most human G protein coupled receptors (GPCRs) are activated by small molecules binding to their 7-transmembrane (7-TM) helix bundle. They belong to basally diverging branches: the 25 bitter taste 2 receptors and most members of the very large rhodopsin-like/class A GPCRs subfamily. Some members of the class A branch have been suggested to feature not only an orthosteric agonist-binding site but also a more extracellular or "vestibular" site, involved in the binding process. Here we use a hybrid molecular mechanics/coarse-grained (MM/CG) molecular dynamics approach on a widely studied bitter taste receptor (TAS2R46) receptor in complex with its agonist strychnine. Three ∼1 μs molecular simulation trajectories find two sites hosting the agonist, which together elucidate experimental data measured previously and in this work. This mechanism shares similarities with the one suggested for the evolutionarily distant class A GPCRs. It might be instrumental for the remarkably broad but specific spectrum of agonists of these chemosensory receptors.

Modeling of the OX1R-orexin-A complex suggests two alternative binding modes

Background

Interactions between the orexin peptides and their cognate OX₁ and OX₂ receptors remain poorly characterized. Site-directed mutagenesis studies on orexin peptides and receptors have indicated amino acids important for ligand binding and receptor activation. However, a better understanding of specific pairwise interactions would benefit small molecule discovery.

Results

We constructed a set of three-dimensional models of the orexin 1 receptor based on the 3D-structures of the orexin 2 receptor (released while this manuscript was under review), neurotensin receptor 1 and chemokine receptor CXCR4, conducted an exhaustive docking of orexin-A_16–33 peptide fragment with ZDOCK and RDOCK, and analyzed a total of 4301 complexes through multidimensional scaling and clustering. The best docking poses reveal two alternative binding modes, where the C-terminus of the peptide lies deep in the binding pocket, on average about 5–6 Å above Tyr^6.48 and close to Gln^3.32. The binding modes differ in the about 100° rotation of the peptide; the peptide His26 faces either the receptor’s fifth transmembrane helix or the seventh helix. Both binding modes are well in line with previous mutation studies and partake in hydrogen bonding similar to suvorexant.

Conclusions

We present two binding modes for orexin-A into orexin 1 receptor, which help rationalize previous results from site-directed mutagenesis studies. The binding modes should serve small molecule discovery, and offer insights into the mechanism of receptor activation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12900-015-0036-2) contains supplementary material, which is available to authorized users.

Molecular recognition of ketamine by a subset of olfactory G protein-coupled receptors

Ketamine elicits various neuropharmacological effects, including sedation, analgesia, general anesthesia, and antidepressant activity. Through an in vitro screen, we identified four mouse olfactory receptors (ORs) that responded to ketamine. In addition to their presence in the olfactory epithelium, these G protein (heterotrimeric guanine nucleotide-binding protein)-coupled receptors (GPCRs) are distributed throughout the central nervous system. To better understand the molecular basis of the interactions between ketamine and ORs, we used sequence comparison and molecular modeling to design mutations that (i) increased, reduced, or abolished ketamine responsiveness in responding receptors, and (ii) rendered nonresponding receptors responsive to ketamine. We showed that olfactory sensory neurons (OSNs) that expressed distinct ORs responded to ketamine in vivo, suggesting that ORs may serve as functional targets for ketamine. The ability to both abolish and introduce responsiveness to ketamine in GPCRs enabled us to identify and confirm distinct interaction loci in the binding site, which suggested a signature ketamine-binding pocket that may guide exploration of additional receptors for this general anesthetic drug.

Molecular modeling of the human P2Y14 receptor: A template for structure-based design of selective agonist ligands

The P2Y14 receptor (P2Y14R) is a Gi protein-coupled receptor that is activated by uracil nucleotides UDP and UDP-glucose. The P2Y14R structure has yet to be solved through X-ray crystallography, but the recent agonist-bound crystal structure of the P2Y12R provides a potentially suitable template for its homology modeling for rational structure-based design of selective and high-affinity ligands. In this study, we applied ligand docking and molecular dynamics refinement to a P2Y14R homology model to qualitatively explain structure-activity relationships of previously published synthetic nucleotide analogues and to probe the quality of P2Y14R homology modeling as a template for structure-based design. The P2Y14R model supports the hypothesis of a conserved binding mode of nucleotides in the three P2Y12-like receptors involving functionally conserved residues. We predict phosphate group interactions with R253(6.55), K277(7.35), Y256(6.58) and Q260(6.62), nucleobase (anti-conformation) π-π stacking with Y102(3.33) and the role of F191(5.42) as a means for selectivity among P2Y12-like receptors. The glucose moiety of UDP-glucose docked in a secondary subpocket at the P2Y14R homology model. Thus, P2Y14R homology modeling may allow detailed prediction of interactions to facilitate the design of high affinity, selective agonists as pharmacological tools to study the P2Y14R.

The importance of ligands for G protein-coupled receptor stability

Traditionally, G protein-coupled receptor (GPCR) activity has been characterized by ligand properties including affinity (Ki), potency (IC50/EC50), efficacy (Emax), and kinetics (Kon/Koff). These properties are related to ligand residence time, a general index of drug-target interaction in vivo. Recent GPCR structure-function breakthroughs have all required ligand stabilization of the receptor in some manner, highlighting the natural instability of these important cell surface receptors. This research has initiated a new era of discovery that highlights the importance of ligand-receptor interactions beyond the traditional mindset. We propose that receptor stability is related to receptor folding and residence in the cell membrane, affording a new dimension that should be considered when studying receptor function.

Experiment-Guided Molecular Modeling of Protein-Protein Complexes Involving GPCRs

Experimental structure determination for G protein-coupled receptors (GPCRs) and especially their complexes with protein and peptide ligands is at its infancy. In the absence of complex structures, molecular modeling and docking play a large role not only by providing a proper 3D context for interpretation of biochemical and biophysical data, but also by prospectively guiding experiments. Experimentally confirmed restraints may help improve the accuracy and information content of the computational models. Here we present a hybrid molecular modeling protocol that integrates heterogeneous experimental data with force field-based calculations in the stochastic global optimization of the conformations and relative orientations of binding partners. Some experimental data, such as pharmacophore-like chemical fields or disulfide-trapping restraints, can be seamlessly incorporated in the protocol, while other types of data are more useful at the stage of solution filtering. The protocol was successfully applied to modeling and design of a stable construct that resulted in crystallization of the first complex between a chemokine and its receptor. Examples from this work are used to illustrate the steps of the protocol. The utility of different types of experimental data for modeling and docking is discussed and caveats associated with data misinterpretation are highlighted.

Docking and Virtual Screening Strategies for GPCR Drug Discovery

Progress in structure determination of G protein-coupled receptors (GPCRs) has made it possible to apply structure-based drug design (SBDD) methods to this pharmaceutically important target class. The quality of GPCR structures available for SBDD projects fall on a spectrum ranging from high resolution crystal structures (<2 Å), where all water molecules in the binding pocket are resolved, to lower resolution (>3 Å) where some protein residues are not resolved, and finally to homology models that are built using distantly related templates. Each GPCR project involves a distinct set of opportunities and challenges, and requires different approaches to model the interaction between the receptor and the ligands. In this review we will discuss docking and virtual screening to GPCRs, and highlight several refinement and post-processing steps that can be used to improve the accuracy of these calculations. Several examples are discussed that illustrate specific steps that can be taken to improve upon the docking and virtual screening accuracy. While GPCRs are a unique target class, many of the methods and strategies outlined in this review are general and therefore applicable to other protein families.

Expanding the horizons of G protein-coupled receptor structure-based ligand discovery and optimization using homology models

We show the key role of structural homology models in GPCR structure-based lead discovery and optimization, highlighting methodological aspects, recent progress and future directions.

Discovery of trace amine-associated receptor 1 ligands by molecular docking screening against a homology model

An in silico screen of a TAAR1 homology model identifies novel ligands.

Structure-based virtual screening for fragment-like ligands of the G protein-coupled histamine H4 receptor

Structure-based virtual screening using H₁R- and β₂R-based histamine H₄R homology models identified 9 fragments with an affinity ranging from 0.14 to 6.3 μm for H₄R.

Generic GPCR residue numbers - aligning topology maps while minding the gaps

Generic residue numbers facilitate comparisons of, for example, mutational effects, ligand interactions, and structural motifs. The numbering scheme by Ballesteros and Weinstein for residues within the class A GPCRs (G protein-coupled receptors) has more than 1100 citations, and the recent crystal structures for classes B, C, and F now call for a community consensus in residue numbering within and across these classes. Furthermore, the structural era has uncovered helix bulges and constrictions that offset the generic residue numbers. The use of generic residue numbers depends on convenient access by pharmacologists, chemists, and structural biologists. We review the generic residue numbering schemes for each GPCR class, as well as a complementary structure-based scheme, and provide illustrative examples and GPCR database (GPCRDB) web tools to number any receptor sequence or structure.

Improving homology modeling of G-protein coupled receptors through multiple-template derived conserved inter-residue interactions

Evidenced by the three-rounds of G-protein coupled receptors (GPCR) Dock competitions, improving homology modeling methods of helical transmembrane proteins including the GPCRs, based on templates of low sequence identity, remains an eminent challenge. Current approaches addressing this challenge adopt the philosophy of "modeling first, refinement next". In the present work, we developed an alternative modeling approach through the novel application of available multiple templates. First, conserved inter-residue interactions are derived from each additional template through conservation analysis of each template-target pairwise alignment. Then, these interactions are converted into distance restraints and incorporated in the homology modeling process. This approach was applied to modeling of the human β2 adrenergic receptor using the bovin rhodopsin and the human protease-activated receptor 1 as templates and improved model quality was demonstrated compared to the homology model generated by standard single-template and multiple-template methods. This method of "refined restraints first, modeling next", provides a fast and complementary way to the current modeling approaches. It allows rational identification and implementation of additional conserved distance restraints extracted from multiple templates and/or experimental data, and has the potential to be applicable to modeling of all helical transmembrane proteins.

Achievements and challenges in structural bioinformatics and computational biophysics

Motivation: The field of structural bioinformatics and computational biophysics has undergone a revolution in the last 10 years. Developments that are captured annually through the 3DSIG meeting, upon which this article reflects.

Results: An increase in the accessible data, computational resources and methodology has resulted in an increase in the size and resolution of studied systems and the complexity of the questions amenable to research. Concomitantly, the parameterization and efficiency of the methods have markedly improved along with their cross-validation with other computational and experimental results.

Conclusion: The field exhibits an ever-increasing integration with biochemistry, biophysics and other disciplines. In this article, we discuss recent achievements along with current challenges within the field.

Contact:Rafael.Najmanovich@USherbrooke.ca

Sequence and conformational preferences at termini of α-helices in membrane proteins: role of the helix environment

α-Helices are amongst the most common secondary structural elements seen in membrane proteins and are packed in the form of helix bundles. These α-helices encounter varying external environments (hydrophobic, hydrophilic) that may influence the sequence preferences at their N and C-termini. The role of the external environment in stabilization of the helix termini in membrane proteins is still unknown. Here we analyze α-helices in a high-resolution dataset of integral α-helical membrane proteins and establish that their sequence and conformational preferences differ from those in globular proteins. We specifically examine these preferences at the N and C-termini in helices initiating/terminating inside the membrane core as well as in linkers connecting these transmembrane helices. We find that the sequence preferences and structural motifs at capping (Ncap and Ccap) and near-helical (N' and C') positions are influenced by a combination of features including the membrane environment and the innate helix initiation and termination property of residues forming structural motifs. We also find that a large number of helix termini which do not form any particular capping motif are stabilized by formation of hydrogen bonds and hydrophobic interactions contributed from the neighboring helices in the membrane protein. We further validate the sequence preferences obtained from our analysis with data from an ultradeep sequencing study that identifies evolutionarily conserved amino acids in the rat neurotensin receptor. The results from our analysis provide insights for the secondary structure prediction, modeling and design of membrane proteins.

Strategies for improved modeling of GPCR-drug complexes: blind predictions of serotonin receptors bound to ergotamine

The recent increase in the number of atomic-resolution structures of G protein-coupled receptors (GPCRs) has contributed to a deeper understanding of ligand binding to several important drug targets. However, reliable modeling of GPCR-ligand complexes for the vast majority of receptors with unknown structure remains to be one of the most challenging goals for computer-aided drug design. The GPCR Dock 2013 assessment, in which researchers were challenged to predict the crystallographic structures of serotonin 5-HT(1B) and 5-HT(2B) receptors bound to ergotamine, provided an excellent opportunity to benchmark the current state of this field. Our contributions to GPCR Dock 2013 accurately predicted the binding mode of ergotamine with RMSDs below 1.8 Å for both receptors, which included the best submissions for the 5-HT(1B) complex. Our models also had the most accurate description of the binding sites and receptor-ligand contacts. These results were obtained using a ligand-guided homology modeling approach, which combines extensive molecular docking screening with incorporation of information from multiple crystal structures and experimentally derived restraints. In this work, we retrospectively analyzed thousands of structures that were generated during the assessment to evaluate our modeling strategies. Major contributors to accuracy were found to be improved modeling of extracellular loop two in combination with the use of molecular docking to optimize the binding site for ligand recognition. Our results suggest that modeling of GPCR-drug complexes has reached a level of accuracy at which structure-based drug design could be applied to a large number of pharmaceutically relevant targets.

Structure-based discovery of selective serotonin 5-HT(1B) receptor ligands

The development of safe and effective drugs relies on the discovery of selective ligands. Serotonin (5-hydroxytryptamine [5-HT]) G protein-coupled receptors are therapeutic targets for CNS disorders but are also associated with adverse drug effects. The determination of crystal structures for the 5-HT1B and 5-HT2B receptors provided an opportunity to identify subtype selective ligands using structure-based methods. From docking screens of 1.3 million compounds, 22 molecules were predicted to be selective for the 5-HT1B receptor over the 5-HT2B subtype, a requirement for safe serotonergic drugs. Nine compounds were experimentally verified as 5-HT1B-selective ligands, with up to 300-fold higher affinities for this subtype. Three of the ligands were agonists of the G protein pathway. Analysis of state-of-the-art homology models of the two 5-HT receptors revealed that the crystal structures were critical for predicting selective ligands. Our results demonstrate that structure-based screening can guide the discovery of ligands with specific selectivity profiles.