Corrigendum: Crystal structure of the GLP-1 receptor bound to a peptide agonist.

This corrects the article DOI: 10.1038/nature22800.

Crystal structure of the GLP-1 receptor bound to a peptide agonist.

Glucagon-like peptide 1 (GLP-1) regulates glucose homeostasis through the control of insulin release from the pancreas. GLP-1 peptide agonists are efficacious drugs for the treatment of diabetes. To gain insight into the molecular mechanism of action of GLP-1 peptides, here we report the crystal structure of the full-length GLP-1 receptor bound to a truncated peptide agonist. The peptide agonist retains an α-helical conformation as it sits deep within the receptor-binding pocket. The arrangement of the transmembrane helices reveals hallmarks of an active conformation similar to that observed in class A receptors. Guided by this structural information, we design peptide agonists with potent in vivo activity in a mouse model of diabetes.

Intracellular allosteric antagonism of the CCR9 receptor.

Chemokines and their G-protein-coupled receptors play a diverse role in immune defence by controlling the migration, activation and survival of immune cells. They are also involved in viral entry, tumour growth and metastasis and hence are important drug targets in a wide range of diseases. Despite very significant efforts by the pharmaceutical industry to develop drugs, with over 50 small-molecule drugs directed at the family entering clinical development, only two compounds have reached the market: maraviroc (CCR5) for HIV infection and plerixafor (CXCR4) for stem-cell mobilization. The high failure rate may in part be due to limited understanding of the mechanism of action of chemokine antagonists and an inability to optimize compounds in the absence of structural information. CC chemokine receptor type 9 (CCR9) activation by CCL25 plays a key role in leukocyte recruitment to the gut and represents a therapeutic target in inflammatory bowel disease. The selective CCR9 antagonist vercirnon progressed to phase 3 clinical trials in Crohn's disease but efficacy was limited, with the need for very high doses to block receptor activation. Here we report the crystal structure of the CCR9 receptor in complex with vercirnon at 2.8 Å resolution. Remarkably, vercirnon binds to the intracellular side of the receptor, exerting allosteric antagonism and preventing G-protein coupling. This binding site explains the need for relatively lipophilic ligands and describes another example of an allosteric site on G-protein-coupled receptors that can be targeted for drug design, not only at CCR9, but potentially extending to other chemokine receptors.

Accurate Prediction of GPCR Ligand Binding Affinity with Free Energy Perturbation.

The computational prediction of relative binding free energies is a crucial goal for drug discovery, and G protein-coupled receptors (GPCRs) are arguably the most important drug target class. However, they present increased complexity to model compared to soluble globular proteins. Despite breakthroughs, experimental X-ray crystal and cryo-EM structures are challenging to attain, meaning computational models of the receptor and ligand binding mode are sometimes necessary. This leads to uncertainty in understanding ligand-protein binding induced changes such as, water positioning and displacement, side chain positioning, hydrogen bond networks, and the overall structure of the hydration shell around the ligand and protein. In other words, the very elements that define structure activity relationships (SARs) and are crucial for accurate binding free energy calculations are typically more uncertain for GPCRs. In this work we use free energy perturbation (FEP) to predict the relative binding free energies for ligands of two different GPCRs. We pinpoint the key aspects for success such as the important role of key water molecules, amino acid ionization states, and the benefit of equilibration with specific ligands. Initial calculations following typical FEP setup and execution protocols delivered no correlation with experiment, but we show how results are improved in a logical and systematic way. This approach gave, in the best cases, a coefficient of determination (R2) compared with experiment in the range of 0.6-0.9 and mean unsigned errors compared to experiment of 0.6-0.7 kcal/mol. We anticipate that our findings will be applicable to other difficult-to-model protein ligand data sets and be of wide interest for the community to continue improving FE binding energy predictions.

X-Ray Crystallography and Free Energy Calculations Reveal the Binding Mechanism of A2A Adenosine Receptor Antagonists.

We present a robust protocol based on iterations of free energy perturbation (FEP) calculations, chemical synthesis, biophysical mapping and X-ray crystallography to reveal the binding mode of an antagonist series to the A2A adenosine receptor (AR). Eight A2A AR binding site mutations from biophysical mapping experiments were initially analyzed with sidechain FEP simulations, performed on alternate binding modes. The results distinctively supported one binding mode, which was subsequently used to design new chromone derivatives. Their affinities for the A2A AR were experimentally determined and investigated through a cycle of ligand-FEP calculations, validating the binding orientation of the different chemical substituents proposed. Subsequent X-ray crystallography of the A2A AR with a low and a high affinity chromone derivative confirmed the predicted binding orientation. The new molecules and structures here reported were driven by free energy calculations, and provide new insights on antagonist binding to the A2A AR, an emerging target in immuno-oncology.

Identification of a novel allosteric GLP-1R antagonist HTL26119 using structure- based drug design.

A series of novel allosteric antagonists of the GLP-1 receptor (GLP-1R), exemplified by HTL26119, are described. SBDD approaches were employed to identify HTL26119, exploiting structural understanding of the allosteric binding site of the closely related Glucagon receptor (GCGR) (Jazayeri et al., 2016) and the homology relationships between GCGR and GLP-1R. The region around residue C3476.36b of the GLP-1R receptor represents a key difference from GCGR and was targeted for selectivity for GLP-1R.

Understanding Ligand Binding Selectivity in a Prototypical GPCR Family.

Adenosine receptors are involved in many pathological conditions and are thus promising drug targets. However, developing drugs that target this GPCR subfamily is a challenging task. A number of drug candidates fail due to lack of selectivity which results in unwanted side effects. The extensive structural similarity of adenosine receptors complicates the design of selective ligands. The problem of selective targeting is a general concern in GPCRs, and in this respect adenosine receptors are a prototypical example. Here we use enhanced sampling simulations to decipher the determinants of selectivity of ligands in A2a and A1 adenosine receptors. Our model shows how small differences in the binding pocket and in the water network around the ligand can be leveraged to achieve selectivity.

GPCR-Bench: A Benchmarking Set and Practitioners' Guide for G Protein-Coupled Receptor Docking.

Virtual screening is routinely used to discover new ligands and in particular new ligand chemotypes for G protein-coupled receptors (GPCRs). To prepare for a virtual screen, we often tailor a docking protocol that will enable us to select the best candidates for further screening. To aid this, we created GPCR-Bench, a publically available docking benchmarking set in the spirit of the DUD and DUD-E reference data sets for validation studies, containing 25 nonredundant high-resolution GPCR costructures with an accompanying set of diverse ligands and computational decoy molecules for each target. Benchmarking sets are often used to compare docking protocols; however, it is important to evaluate docking methods not by "retrospective" hit rates but by the actual likelihood that they will produce novel prospective hits. Therefore, docking protocols must not only rank active molecules highly but also produce good poses that a chemist will select for purchase and screening. Currently, no simple objective machine-scriptable function exists that can do this; instead, docking hit lists must be subjectively examined in a consistent way to compare between docking methods. We present here a case study highlighting considerations we feel are of importance when evaluating a method, intended to be useful as a practitioners' guide.

Decoding the Role of Water Dynamics in Ligand-Protein Unbinding: CRF1R as a Test Case.

The residence time of a ligand-protein complex is a crucial aspect in determining biological effect in vivo. Despite its importance, the prediction of ligand koff still remains challenging for modern computational chemistry. We have developed aMetaD, a fast and generally applicable computational protocol to predict ligand-protein unbinding events using a molecular dynamics (MD) method based on adiabatic-bias MD and metadynamics. This physics-based, fully flexible, and pose-dependent ligand scoring function evaluates the maximum energy (RTscore) required to move the ligand from the bound-state energy basin to the next. Unbinding trajectories are automatically analyzed and translated into atomic solvation factor (SF) values representing the water dynamics during the unbinding event. This novel computational protocol was initially tested on two M3 muscarinic receptor and two adenosine A2A receptor antagonists and then evaluated on a test set of 12 CRF1R ligands. The resulting RTscores were used successfully to classify ligands with different residence times. Additionally, the SF analysis was used to detect key differences in the degree of accessibility to water molecules during the predicted ligand unbinding events. The protocol provides actionable working hypotheses that are applicable in a drug discovery program for the rational optimization of ligand binding kinetics.

Water network perturbation in ligand binding: adenosine A(2A) antagonists as a case study.

Recent efforts in the computational evaluation of the thermodynamic properties of water molecules have resulted in the development of promising new in silico methods to evaluate the role of water in ligand binding. These methods include WaterMap, SZMAP, GRID/CRY probe, and Grand Canonical Monte Carlo simulations. They allow the prediction of the position and relative free energy of the water molecule in the protein active site and the analysis of the perturbation of an explicit water network (WNP) as a consequence of ligand binding. We have for the first time extended these approaches toward the prediction of kinetics for small molecules and of relative free energy of binding with a focus on the perturbation of the water network and application to large diverse data sets. Our results support a qualitative correlation between the residence time of 12 related triazine adenosine A(2A) receptor antagonists and the number and position of high energy trapped solvent molecules. From a quantitative viewpoint, we successfully applied these computational techniques as an implicit solvent alternative, in linear combination with a molecular mechanics force field, to predict the relative ligand free energy of binding (WNP-MMSA). The applicability of this linear method, based on the thermodynamics additivity principle, did not extend to 375 diverse A(2A) receptor antagonists. However, a fast but effective method could be enabled by replacing the linear approach with a machine learning technique using probabilistic classification trees, which classified the binding affinity correctly for 90% of the ligands in the training set and 67% in the test set.

High-throughput virtual screening of proteins using GRID molecular interaction fields.

A new computational algorithm for protein binding sites characterization and comparison has been developed, which uses a common reference framework of the projected ligand-space four-point pharmacophore fingerprints, includes cavity shape, and can be used with diverse proteins as no structural alignment is required. Protein binding sites are first described using GRID molecular interaction fields (GRID-MIFs), and the FLAP (fingerprints for ligands and proteins) method is then used to encode and compare this information. The discriminating power of the algorithm and its applicability for large-scale protein analysis was validated by analyzing various scenarios: clustering of kinase protein families in a relevant manner, predicting ligand activity across related targets, and protein-protein virtual screening. In all cases the results showed the effectiveness of the GRID-FLAP method and its potential use in applications such as identifying selectivity targets and tools/hits for new targets via the identification of other proteins with pharmacophorically similar binding sites.

Structure-Based Drug Discovery of N-((R)-3-(7-Methyl-1H-indazol-5-yl)-1-oxo-1-(((S)-1-oxo-3-(piperidin-4-yl)-1-(4-(pyridin-4-yl)piperazin-1-yl)propan-2-yl)amino)propan-2-yl)-2'-oxo-1',2'-dihydrospiro[piperidine-4,4'-pyrido[2,3-d][1,3]oxazine]-1-carboxamide (HTL22562): A Calcitonin Gene-Related Peptide Receptor Antagonist for Acute Treatment of Migraine.

Structure-based drug design enabled the discovery of 8, HTL22562, a calcitonin gene-related peptide (CGRP) receptor antagonist. The structure of 8 complexed with the CGRP receptor was determined at a 1.6 Å resolution. Compound 8 is a highly potent, selective, metabolically stable, and soluble compound suitable for a range of administration routes that have the potential to provide rapid systemic exposures with resultant high levels of receptor coverage (e.g., subcutaneous). The low lipophilicity coupled with a low anticipated clinically efficacious plasma exposure for migraine also suggests a reduced potential for hepatotoxicity. These properties have led to 8 being selected as a clinical candidate for acute treatment of migraine.

Comparison of Orexin 1 and Orexin 2 Ligand Binding Modes Using X-ray Crystallography and Computational Analysis.

The orexin system, which consists of the two G protein-coupled receptors OX1 and OX2, activated by the neuropeptides OX-A and OX-B, is firmly established as a key regulator of behavioral arousal, sleep, and wakefulness and has been an area of intense research effort over the past two decades. X-ray structures of the receptors in complex with 10 new antagonist ligands from diverse chemotypes are presented, which complement the existing structural information for the system and highlight the critical importance of lipophilic hotspots and water molecules for these peptidergic GPCR targets. Learnings from the structural information regarding the utility of pharmacophore models and how selectivity between OX1 and OX2 can be achieved are discussed.

Can we rationally design promiscuous drugs?

Structure-based drug design is now used widely in modern medicinal chemistry. The application of structural biology to medicinal chemistry has heralded the "rational drug design" vision of discovering exquisitely selective ligands. However, recent advances in post-genomic biology are indicating that polypharmacology may be a necessary trait for the efficacy of many drugs, therefore questioning the "one drug, one target" assumption of current rational drug design. By combining advances in chemoinformatics and structural biology, it might be possible to rationally design the next generation of promiscuous drugs with polypharmacology.

Global mapping of pharmacological space.

We present the global mapping of pharmacological space by the integration of several vast sources of medicinal chemistry structure-activity relationships (SAR) data. Our comprehensive mapping of pharmacological space enables us to identify confidently the human targets for which chemical tools and drugs have been discovered to date. The integration of SAR data from diverse sources by unique canonical chemical structure, protein sequence and disease indication enables the construction of a ligand-target matrix to explore the global relationships between chemical structure and biological targets. Using the data matrix, we are able to catalog the links between proteins in chemical space as a polypharmacology interaction network. We demonstrate that probabilistic models can be used to predict pharmacology from a large knowledge base. The relationships between proteins, chemical structures and drug-like properties provide a framework for developing a probabilistic approach to drug discovery that can be exploited to increase research productivity.

Analysis of tractable allosteric sites in G protein-coupled receptors.

Allosteric modulation of G protein-coupled receptors represent a promising mechanism of pharmacological intervention. Dramatic developments witnessed in the structural biology of membrane proteins continue to reveal that the binding sites of allosteric modulators are widely distributed, including along protein surfaces. Here we restrict consideration to intrahelical and intracellular sites together with allosteric conformational locks, and show that the protein mapping tools FTMap and FTSite identify 83% and 88% of such experimentally confirmed allosteric sites within the three strongest sites found. The methods were also able to find partially hidden allosteric sites that were not fully formed in X-ray structures crystallized in the absence of allosteric ligands. These results confirm that the intrahelical sites capable of binding druglike allosteric modulators are among the strongest ligand recognition sites in a large fraction of GPCRs and suggest that both FTMap and FTSite are useful tools for identifying allosteric sites and to aid in the design of such compounds in a range of GPCR targets.

Structure-Based Optimization Strategies for G Protein-Coupled Receptor (GPCR) Allosteric Modulators: A Case Study from Analyses of New Metabotropic Glutamate Receptor 5 (mGlu5) X-ray Structures.

Two interesting new X-ray structures of negative allosteric modulator (NAM) ligands for the mGlu5 receptor, M-MPEP (3) and fenobam (4), are reported. The new structures show how the binding of the ligands induces different receptor water channel conformations to previously published structures. The structure of fenobam, where a urea replaces the acetylenic linker in M-MPEP and mavoglurant, reveals a binding mode where the ligand is rotated by 180° compared to a previously proposed docking model. The need for multiple ligand structures for accurate GPCR structure-based drug design is demonstrated by the different growing vectors identified for the head groups of M-MPEP and mavoglurant and by the unexpected water-mediated receptor interactions of a new chemotype represented by fenobam. The implications of the new structures for ligand design are discussed, with extensive analysis of the energetics of the water networks of both pseudoapo and bound structures providing a new design strategy for allosteric modulators.

Methodologies for the Examination of Water in GPCRs.

The following chapter examines some of the current "state-of-the-art" tools for predicting, scoring, and examining explicit water molecules in proteins and protein/ligand complexes, highlighting some of the ways information can be readily examined in a manner that is useful in a drug discovery process.

Impact of protein-ligand solvation and desolvation on transition state thermodynamic properties of adenosine A2A ligand binding kinetics.

Ligand-protein binding kinetic rates are growing in importance as parameters to consider in drug discovery and lead optimization. In this study we analysed using surface plasmon resonance (SPR) the transition state (TS) properties of a set of six adenosine A2A receptor inhibitors, belonging to both the xanthine and the triazolo-triazine scaffolds. SPR highlighted interesting differences among the ligands in the enthalpic and entropic components of the TS energy barriers for the binding and unbinding events. To better understand at a molecular level these differences, we developed suMetaD, a novel molecular dynamics (MD)-based approach combining supervised MD and metadynamics. This method allows simulation of the ligand unbinding and binding events. It also provides the system conformation corresponding to the highest energy barrier the ligand is required to overcome to reach the final state. For the six ligands evaluated in this study their TS thermodynamic properties were linked in particular to the role of water molecules in solvating/desolvating the pocket and the small molecules. suMetaD identified kinetic bottleneck conformations near the bound state position or in the vestibule area. In the first case the barrier is mainly enthalpic, requiring the breaking of strong interactions with the protein. In the vestibule TS location the kinetic bottleneck is instead mainly of entropic nature, linked to the solvent behaviour.

Identification of Ligand Binding Hot Spots of the Histamine H1 Receptor following Structure-Based Fragment Optimization.

Developments in G protein-coupled receptor (GPCR) structural biology provide insights into GPCR-ligand binding. Compound 1 (4-(2-benzylphenoxy)piperidine) with high ligand efficiency for the histamine H1 receptor (H1R) was used to design derivatives to investigate the roles of (i) the amine-binding region, (ii) the upper and lower aromatic region, and (iii) binding site solvation. SAR analysis showed that the amine-binding region serves as the primary binding hot spot, preferably binding small tertiary amines. In silico prediction of water network energetics and mutagenesis studies indicated that the displacement of a water molecule from the amine-binding region is most likely responsible for the increased affinity of the N-methylated analog of 1. Deconstruction of 1 showed that the lower aromatic region serves as a secondary binding hot spot. This study demonstrates that an X-ray structure in combination with tool compounds, assessment of water energetics, and mutagenesis studies enables SAR exploration to map GPCR-ligand binding hot spots.