Differential GLP-1R Binding and Activation by Peptide and Non-peptide Agonists.

Peptide drugs targeting class B1 G-protein-coupled receptors (GPCRs) can treat multiple diseases; however, there remains substantial interest in the development of orally delivered non-peptide drugs. Here, we reveal unexpected overlap between signaling and regulation of the glucagon-like peptide-1 (GLP-1) receptor by the non-peptide agonist PF 06882961 and GLP-1 that was not observed for another compound, CHU-128. Compounds from these patent series, including PF 06882961, are currently in clinical trials for treatment of type 2 diabetes. High-resolution cryoelectron microscopy (cryo-EM) structures reveal that the binding sites for PF 06882961 and GLP-1 substantially overlap, whereas CHU-128 adopts a unique binding mode with a more open receptor conformation at the extracellular face. Structural differences involving extensive water-mediated hydrogen bond networks could be correlated to functional data to understand how PF 06882961, but not CHU-128, can closely mimic the pharmacological properties of GLP-1. These findings will facilitate rational structure-based discovery of non-peptide agonists targeting class B GPCRs.

Structure of the class D GPCR Ste2 dimer coupled to two G proteins.

G-protein-coupled receptors (GPCRs) are divided phylogenetically into six classes1,2, denoted A to F. More than 370 structures of vertebrate GPCRs (belonging to classes A, B, C and F) have been determined, leading to a substantial understanding of their function3. By contrast, there are no structures of class D GPCRs, which are found exclusively in fungi where they regulate survival and reproduction. Here we determine the structure of a class D GPCR, the Saccharomyces cerevisiae pheromone receptor Ste2, in an active state coupled to the heterotrimeric G protein Gpa1-Ste4-Ste18. Ste2 was purified as a homodimer coupled to two G proteins. The dimer interface of Ste2 is formed by the N terminus, the transmembrane helices H1, H2 and H7, and the first extracellular loop ECL1. We establish a class D1 generic residue numbering system (CD1) to enable comparisons with orthologues and with other GPCR classes. The structure of Ste2 bears similarities in overall topology to class A GPCRs, but the transmembrane helix H4 is shifted by more than 20 Å and the G-protein-binding site is a shallow groove rather than a cleft. The structure provides a template for the design of novel drugs to target fungal GPCRs, which could be used to treat numerous intractable fungal diseases4.

GPCRdb in 2023: state-specific structure models using AlphaFold2 and new ligand resources.

G protein-coupled receptors (GPCRs) are physiologically abundant signaling hubs routing hundreds of extracellular signal substances and drugs into intracellular pathways. The GPCR database, GPCRdb supports >5000 interdisciplinary researchers every month with reference data, analysis, visualization, experiment design and dissemination. Here, we present our fifth major GPCRdb release setting out with an overview of the many resources for receptor sequences, structures, and ligands. This includes recently published additions of class D generic residue numbers, a comparative structure analysis tool to identify functional determinants, trees clustering GPCR structures by 3D conformation, and mutations stabilizing inactive/active states. We provide new state-specific structure models of all human non-olfactory GPCRs built using AlphaFold2-MultiState. We also provide a new resource of endogenous ligands along with a larger number of surrogate ligands with bioactivity, vendor, and physiochemical descriptor data. The one-stop-shop ligand resources integrate ligands/data from the ChEMBL, Guide to Pharmacology, PDSP Ki and PubChem database. The GPCRdb is available at https://gpcrdb.org.

Pharmacological Characterization of the Zebrafish (Danio Rerio) Histamine H1 Receptor Reveals the Involvement of the Second Extracellular Loop in the Binding of Histamine.

The zebrafish (Danio rerio) histamine H1 receptor gene (zfH1R) was cloned in 2007 and reported to be involved in fish locomotion. Yet, no detailed characterization of its pharmacology and signaling properties have so far been reported. In this study, we pharmacologically characterized the zfH1R expressed in HEK-293T cells by means of [3H]-mepyramine binding and G protein-signaling assays. The zfH1R [dissociation constant (KD), 0.7 nM] displayed similar affinity for the antagonist [3H]-mepyramine as the human histamine H1 receptor (hH1R) (KD, 1.5 nM), whereas the affinity for histamine is 100-fold higher than for the human H1R. The zfH1R couples to Gαq/11 proteins and activates several reporter genes, i.e., NFAT, NFÏ°B, CRE, VEGF, COX-2, SRE, and AP-1, and zfH1R-mediated signaling is prevented by the Gαq/11 inhibitor YM-254890 and the antagonist mepyramine. Molecular modeling of the zfH1R and human H1R shows that the binding pockets are identical, implying that variations along the ligand binding pathway could underly the differences in histamine affinity instead. Targeting differentially charged residues in extracellular loop 2 (ECL2) using site-directed mutagenesis revealed that Arg21045x55 is most likely involved in the binding process of histamine in zfH1R. This study aids the understanding of the pharmacological differences between H1R orthologs and the role of ECL2 in histamine binding and provides fundamental information for the understanding of the histaminergic system in the zebrafish. SIGNIFICANCE STATEMENT: The use of the zebrafish as in vivo models in neuroscience is growing exponentially, which asks for detailed characterization of the aminergic neurotransmitter systems in this model. This study is the first to pharmacologically characterize the zebrafish histamine H1 receptor after expression in HEK-293T cells. The results show a high pharmacological and functional resemblance with the human ortholog but also reveal interesting structural differences and unveils an important role of the second extracellular loop in histamine binding.

Predicting the target landscape of kinase inhibitors using 3D convolutional neural networks.

Many therapies in clinical trials are based on single drug-single target relationships. To further extend this concept to multi-target approaches using multi-targeted drugs, we developed a machine learning pipeline to unravel the target landscape of kinase inhibitors. This pipeline, which we call 3D-KINEssence, uses a new type of protein fingerprints (3D FP) based on the structure of kinases generated through a 3D convolutional neural network (3D-CNN). These 3D-CNN kinase fingerprints were matched to molecular Morgan fingerprints to predict the targets of each respective kinase inhibitor based on available bioactivity data. The performance of the pipeline was evaluated on two test sets: a sparse drug-target set where each drug is matched in most cases to a single target and also on a densely-covered drug-target set where each drug is matched to most if not all targets. This latter set is more challenging to train, given its non-exclusive character. Our model's root-mean-square error (RMSE) based on the two datasets was 0.68 and 0.8, respectively. These results indicate that 3D FP can predict the target landscape of kinase inhibitors at around 0.8 log units of bioactivity. Our strategy can be utilized in proteochemometric or chemogenomic workflows by consolidating the target landscape of kinase inhibitors.

The G protein database, GproteinDb.

Two-thirds of signaling substances, several sensory stimuli and over one-third of drugs act via receptors coupling to G proteins. Here, we present an online platform for G protein research with reference data and tools for analysis, visualization and design of scientific studies across disciplines and areas. This platform may help translate new pharmacological, structural and genomic data into insights on G protein signaling vital for human physiology and medicine. The G protein database is accessible at https://gproteindb.org.

Structure-Based Virtual Screening for Ligands of G Protein-Coupled Receptors: What Can Molecular Docking Do for You?

G protein-coupled receptors (GPCRs) constitute the largest family of membrane proteins in the human genome and are important therapeutic targets. During the last decade, the number of atomic-resolution structures of GPCRs has increased rapidly, providing insights into drug binding at the molecular level. These breakthroughs have created excitement regarding the potential of using structural information in ligand design and initiated a new era of rational drug discovery for GPCRs. The molecular docking method is now widely applied to model the three-dimensional structures of GPCR-ligand complexes and screen for chemical probes in large compound libraries. In this review article, we first summarize the current structural coverage of the GPCR superfamily and the understanding of receptor-ligand interactions at atomic resolution. We then present the general workflow of structure-based virtual screening and strategies to discover GPCR ligands in chemical libraries. We assess the state of the art of this research field by summarizing prospective applications of virtual screening based on experimental structures. Strategies to identify compounds with specific efficacy and selectivity profiles are discussed, illustrating the opportunities and limitations of the molecular docking method. Our overview shows that structure-based virtual screening can discover novel leads and will be essential in pursuing the next generation of GPCR drugs. SIGNIFICANCE STATEMENT: Extraordinary advances in the structural biology of G protein-coupled receptors have revealed the molecular details of ligand recognition by this large family of therapeutic targets, providing novel avenues for rational drug design. Structure-based docking is an efficient computational approach to identify novel chemical probes from large compound libraries, which has the potential to accelerate the development of drug candidates.

KLIFS: an overhaul after the first 5 years of supporting kinase research.

Kinases are a prime target of drug development efforts with >60 drug approvals in the past two decades. Due to the research into this protein family, a wealth of data has been accumulated that keeps on growing. KLIFS-Kinase-Ligand Interaction Fingerprints and Structures-is a structural database focusing on how kinase inhibitors interact with their targets. The aim of KLIFS is to support (structure-based) kinase research through the systematic collection, annotation, and processing of kinase structures. Now, 5 years after releasing the initial KLIFS website, the database has undergone a complete overhaul with a new website, new logo, and new functionalities. In this article, we start by looking back at how KLIFS has been used by the research community, followed by a description of the renewed KLIFS, and conclude with showcasing the functionalities of KLIFS. Major changes include the integration of approved drugs and inhibitors in clinical trials, extension of the coverage to atypical kinases, and a RESTful API for programmatic access. KLIFS is available at the new domain https://klifs.net.

GPCRdb in 2021: integrating GPCR sequence, structure and function.

G protein-coupled receptors (GPCRs) form both the largest family of membrane proteins and drug targets, mediating the action of one-third of medicines. The GPCR database, GPCRdb serves >4 000 researchers every month and offers reference data, analysis of own or literature data, experiment design and dissemination of published datasets. Here, we describe new and updated GPCRdb resources with a particular focus on integration of sequence, structure and function. GPCRdb contains all human non-olfactory GPCRs (and >27 000 orthologs), G-proteins and arrestins. It includes over 2 000 drug and in-trial agents and nearly 200 000 ligands with activity and availability data. GPCRdb annotates all published GPCR structures (updated monthly), which are also offered in a refined version (with re-modeled missing/distorted regions and reverted mutations) and provides structure models of all human non-olfactory receptors in inactive, intermediate and active states. Mutagenesis data in the GPCRdb spans natural genetic variants, GPCR-G protein interfaces, ligand sites and thermostabilising mutations. A new sequence signature tool for identification of functional residue determinants has been added and two data driven tools to design ligand site mutations and constructs for structure determination have been updated extending their coverage of receptors and modifications. The GPCRdb is available at https://gpcrdb.org.

KiSSim: Predicting Off-Targets from Structural Similarities in the Kinome.

Protein kinases are among the most important drug targets because their dysregulation can cause cancer, inflammatory and degenerative diseases, and many more. Developing selective inhibitors is challenging due to the highly conserved binding sites across the roughly 500 human kinases. Thus, detecting subtle similarities on a structural level can help explain and predict off-targets among the kinase family. Here, we present the kinase-focused, subpocket-enhanced KiSSim fingerprint (Kinase Structural Similarity). The fingerprint builds on the KLIFS pocket definition, composed of 85 residues aligned across all available protein kinase structures, which enables residue-by-residue comparison without a computationally expensive alignment. The residues' physicochemical and spatial properties are encoded within their structural context including key subpockets at the hinge region, the DFG motif, and the front pocket. Since structure was found to contain information complementary to sequence, we used the fingerprint to calculate all-against-all similarities within the structurally covered kinome. We could identify off-targets that are unexpected if solely considering the sequence-based kinome tree grouping; for example, Erlobinib's known kinase off-targets SLK and LOK show high similarities to the key target EGFR (TK group), although belonging to the STE group. KiSSim reflects profiling data better or at least as well as other approaches such as KLIFS pocket sequence identity, KLIFS interaction fingerprints (IFPs), or SiteAlign. To rationalize observed (dis)similarities, the fingerprint values can be visualized in 3D by coloring structures with residue and feature resolution. We believe that the KiSSim fingerprint is a valuable addition to the kinase research toolbox to guide off-target and polypharmacology prediction. The method is distributed as an open-source Python package on GitHub and as a conda package: https://github.com/volkamerlab/kissim.

Alkynamide phthalazinones as a new class of TbrPDEB1 inhibitors (Part 2).

Inhibitors against Trypanosoma brucei phosphodiesterase B1 (TbrPDEB1) and B2 (TbrPDEB2) have gained interest as new treatments for human African trypanosomiasis. The recently reported alkynamide tetrahydrophthalazinones, which show submicromolar activities against TbrPDEB1 and anti-T. brucei activity, have been used as starting point for the discovery of new TbrPDEB1 inhibitors. Structure-based design indicated that the alkynamide-nitrogen atom can be readily decorated, leading to the discovery of 37, a potent TbrPDEB1 inhibitor with submicromolar activities against T. brucei parasites. Furthermore, 37 is more potent against TbrPDEB1 than hPDE4 and shows no cytotoxicity on human MRC-5 cells. The crystal structures of the catalytic domain of TbrPDEB1 co-crystalized with several different alkynamides show a bidentate interaction with key-residue Gln874, but no interaction with the parasite-specific P-pocket, despite being (uniquely) a more potent inhibitor for the parasite PDE. Incubation of blood stream form trypanosomes by 37 increases intracellular cAMP levels and results in the distortion of the cell cycle and cell death, validating phosphodiesterase inhibition as mode of action.

Covalent Inhibition of the Histamine H3 Receptor.

Covalent binding of G protein-coupled receptors by small molecules is a useful approach for better understanding of the structure and function of these proteins. We designed, synthesized and characterized a series of 6 potential covalent ligands for the histamine H3 receptor (H3R). Starting from a 2-amino-pyrimidine scaffold, optimization of anchor moiety and warhead followed by fine-tuning of the required reactivity via scaffold hopping resulted in the isothiocyanate H3R ligand 44. It shows high reactivity toward glutathione combined with appropriate stability in water and reacts selectively with the cysteine sidechain in a model nonapeptide equipped with nucleophilic residues. The covalent interaction of 44 with H3R was validated with washout experiments and leads to inverse agonism on H3R. Irreversible binder 44 (VUF15662) may serve as a useful tool compound to stabilize the inactive H3R conformation and to study the consequences of prolonged inhibition of the H3R.

Synthesis and Characterization of a Bidirectional Photoswitchable Antagonist Toolbox for Real-Time GPCR Photopharmacology.

Noninvasive methods to modulate G protein-coupled receptors (GPCRs) with temporal and spatial precision are in great demand. Photopharmacology uses photons to control in situ the biological properties of photoswitchable small-molecule ligands, which bodes well for chemical biological precision approaches. Integrating the light-switchable configurational properties of an azobenzene into the ligand core, we developed a bidirectional antagonist toolbox for an archetypical family A GPCR, the histamine H3 receptor (H3R). From 16 newly synthesized photoswitchable compounds, VUF14738 (28) and VUF14862 (33) were selected as they swiftly and reversibly photoisomerize and show over 10-fold increased or decreased H3R binding affinities, respectively, upon illumination at 360 nm. Both ligands combine long thermal half-lives with fast and high photochemical trans-/ cis conversion, allowing their use in real-time electrophysiology experiments with oocytes to confirm dynamic photomodulation of H3R activation in repeated second-scale cycles. VUF14738 and VUF14862 are robust and fatigue-resistant photoswitchable GPCR antagonists suitable for spatiotemporal studies of H3R signaling.

KLIFS: a structural kinase-ligand interaction database.

Protein kinases play a crucial role in cell signaling and are important drug targets in several therapeutic areas. The KLIFS database contains detailed structural kinase-ligand interaction information derived from all (>2900) structures of catalytic domains of human and mouse protein kinases deposited in the Protein Data Bank in order to provide insights into the structural determinants of kinase-ligand binding and selectivity. The kinase structures have been processed in a consistent manner by systematically analyzing the structural features and molecular interaction fingerprints (IFPs) of a predefined set of 85 binding site residues with bound ligands. KLIFS has been completely rebuilt and extended (>65% more structures) since its first release as a data set, including: novel automated annotation methods for (i) the assessment of ligand-targeted subpockets and the analysis of (ii) DFG and (iii) αC-helix conformations; improved and automated protocols for (iv) the generation of sequence/structure alignments, (v) the curation of ligand atom and bond typing for accurate IFP analysis and (vi) weekly database updates. KLIFS is now accessible via a website (http://klifs.vu-compmedchem.nl) that provides a comprehensive visual presentation of different types of chemical, biological and structural chemogenomics data, and allows the user to easily access, compare, search and download the data.

3D-e-Chem-VM: Structural Cheminformatics Research Infrastructure in a Freely Available Virtual Machine.

3D-e-Chem-VM is an open source, freely available Virtual Machine ( http://3d-e-chem.github.io/3D-e-Chem-VM/ ) that integrates cheminformatics and bioinformatics tools for the analysis of protein-ligand interaction data. 3D-e-Chem-VM consists of software libraries, and database and workflow tools that can analyze and combine small molecule and protein structural information in a graphical programming environment. New chemical and biological data analytics tools and workflows have been developed for the efficient exploitation of structural and pharmacological protein-ligand interaction data from proteomewide databases (e.g., ChEMBLdb and PDB), as well as customized information systems focused on, e.g., G protein-coupled receptors (GPCRdb) and protein kinases (KLIFS). The integrated structural cheminformatics research infrastructure compiled in the 3D-e-Chem-VM enables the design of new approaches in virtual ligand screening (Chemdb4VS), ligand-based metabolism prediction (SyGMa), and structure-based protein binding site comparison and bioisosteric replacement for ligand design (KRIPOdb).

Structure-Based Prediction of G-Protein-Coupled Receptor Ligand Function: A ß-Adrenoceptor Case Study.

The spectacular advances in G-protein-coupled receptor (GPCR) structure determination have opened up new possibilities for structure-based GPCR ligand discovery. The structure-based prediction of whether a ligand stimulates (full/partial agonist), blocks (antagonist), or reduces (inverse agonist) GPCR signaling activity is, however, still challenging. A total of 31 ß1 (ß1R) and ß2 (ß2R) adrenoceptor crystal structures, including antagonist, inverse agonist, and partial/full agonist-bound structures, allowed us to explore the possibilities and limitations of structure-based prediction of GPCR ligand function. We used all unique protein-ligand interaction fingerprints (IFPs) derived from all ligand-bound ß-adrenergic crystal structure monomers to post-process the docking poses of known ß1R/ß2R partial/full agonists, antagonists/inverse agonists, and physicochemically similar decoys in each of the ß1R/ß2R structures. The systematic analysis of these 1920 unique IFP-structure combinations offered new insights into the relative impact of protein conformation and IFP scoring on selective virtual screening (VS) for ligands with a specific functional effect. Our studies show that ligands with the same function can be efficiently classified on the basis of their protein-ligand interaction profile. Small differences between the receptor conformation (used for docking) and reference IFP (used for scoring of the docking poses) determine, however, the enrichment of specific ligand types in VS hit lists. Interestingly, the selective enrichment of partial/full agonists can be achieved by using agonist IFPs to post-process docking poses in agonist-bound as well as antagonist-bound structures. We have identified optimal structure-IFP combinations for the identification and discrimination of antagonists/inverse agonist and partial/full agonists, and defined a predicted IFP for the small full agonist norepinephrine that gave the highest retrieval rate of agonists over antagonists for all structures (with an enrichment factor of 46 for agonists and 8 for antagonists on average at a 1% false-positive rate). This ß-adrenoceptor case study provides new insights into the opportunities for selective structure-based discovery of GPCR ligands with a desired function and emphasizes the importance of IFPs in scoring docking poses.

Analyzing multitarget activity landscapes using protein-ligand interaction fingerprints: interaction cliffs.

Activity landscape modeling is mostly a descriptive technique that allows rationalizing continuous and discontinuous SARs. Nevertheless, the interpretation of some landscape features, especially of activity cliffs, is not straightforward. As the nature of activity cliffs depends on the ligand and the target, information regarding both should be included in the analysis. A specific way to include this information is using protein-ligand interaction fingerprints (IFPs). In this paper we report the activity landscape modeling of 507 ligand-kinase complexes (from the KLIFS database) including IFP, which facilitates the analysis and interpretation of activity cliffs. Here we introduce the structure-activity-interaction similarity (SAIS) maps that incorporate information on ligand-target contact similarity. We also introduce the concept of interaction cliffs defined as ligand-target complexes with high structural and interaction similarity but have a large potency difference of the ligands. Moreover, the information retrieved regarding the specific interaction allowed the identification of activity cliff hot spots, which help to rationalize activity cliffs from the target point of view. In general, the information provided by IFPs provides a structure-based understanding of some activity landscape features. This paper shows examples of analyses that can be carried out when IFPs are added to the activity landscape model.

Combinatorial Consensus Scoring for Ligand-Based Virtual Fragment Screening: A Comparative Case Study for Serotonin 5-HT(3)A, Histamine H(1), and Histamine H(4) Receptors.

In the current study we have evaluated the applicability of ligand-based virtual screening (LBVS) methods for the identification of small fragment-like biologically active molecules using different similarity descriptors and different consensus scoring approaches. For this purpose, we have evaluated the performance of 14 chemical similarity descriptors in retrospective virtual screening studies to discriminate fragment-like ligands of three membrane-bound receptors from fragments that are experimentally determined to have no affinity for these proteins (true inactives). We used a complete fragment affinity data set of experimentally determined ligands and inactives for two G protein-coupled receptors (GPCRs), the histamine H1 receptor (H1R) and the histamine H4 receptor (H4R), and one ligand-gated ion channel (LGIC), the serotonin receptor (5-HT3AR), to validate our retrospective virtual screening studies. We have exhaustively tested consensus scoring strategies that combine the results of multiple actives (group fusion) or combine different similarity descriptors (similarity fusion), and for the first time systematically evaluated different combinations of group fusion and similarity fusion approaches. Our studies show that for these three case study protein targets both consensus scoring approaches can increase virtual screening enrichments compared to single chemical similarity search methods. Our cheminformatics analyses recommend to use a combination of both group fusion and similarity fusion for prospective ligand-based virtual fragment screening.

The receptor concept in 3D: from hypothesis and metaphor to GPCR-ligand structures.

The first mentioning of the word "receptor" for the structure with which a bioactive compound should react for obtaining its specific influence on a physiological system goes back to the years around 1900. The receptor concept was adapted from the lock and key theory for the enzyme substrate and blockers interactions. Through the years the concept, in the beginning rather being a metaphor, not a model, was refined and became reality in recent years. Not only the structures of receptors were elucidated, also the receptor machineries were unraveled. Following a brief historical review we will describe how the recent breakthroughs in the experimental determination of G protein-coupled receptor (GPCR) crystal structures can be complemented by computational modeling, medicinal chemistry, biochemical, and molecular pharmacological studies to obtain new insights into the molecular determinants of GPCR-ligand binding and activation. We will furthermore discuss how this information can be used for structure-based discovery of novel GPCR ligands that bind specific (allosteric) binding sites with desired effects on GPCR functional activity.