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.

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.

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.

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.

An online GPCR structure analysis platform.

We present an online, interactive platform for comparative analysis of all available G-protein coupled receptor (GPCR) structures while correlating to functional data. The comprehensive platform encompasses structure similarity, secondary structure, protein backbone packing and movement, residue-residue contact networks, amino acid properties and prospective design of experimental mutagenesis studies. This lets any researcher tap the potential of sophisticated structural analyses enabling a plethora of basic and applied receptor research studies.

GPCR activation mechanisms across classes and macro/microscales.

Two-thirds of human hormones and one-third of clinical drugs activate ~350 G-protein-coupled receptors (GPCR) belonging to four classes: A, B1, C and F. Whereas a model of activation has been described for class A, very little is known about the activation of the other classes, which differ by being activated by endogenous ligands bound mainly or entirely extracellularly. Here we show that, although they use the same structural scaffold and share several 'helix macroswitches', the GPCR classes differ in their 'residue microswitch' positions and contacts. We present molecular mechanistic maps of activation for each GPCR class and methods for contact analysis applicable for any functional determinants. This provides a superfamily residue-level rationale for conformational selection and allosteric communication by ligands and G proteins, laying the foundation for receptor-function studies and drugs with the desired modality.

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.

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 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.

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.

Discovery of Diaryl Ether Substituted Tetrahydrophthalazinones as TbrPDEB1 Inhibitors Following Structure-Based Virtual Screening.

Several members of the 3',5'-cyclic nucleotide phosphodiesterase (PDE) family play an essential role in cellular processes, which has labeled them as interesting targets for various diseases. The parasitic protozoan Trypanosoma brucei, causative agent of human African trypanosomiasis, contains several cyclic AMP specific PDEs from which TbrPDEB1 is validated as a drug target. The recent discovery of selective TbrPDEB1 inhibitors has increased their potential for a novel treatment for this disease. Compounds characterized by a rigid biphenyl tetrahydrophthalazinone core structure were used as starting point for the exploration of novel TbrPDEB1 inhibitors. Using a virtual screening campaign and structure-guided design, diaryl ether substituted phthalazinones were identified as novel TbrPDEB1 inhibitors with IC50 values around 1 µM against T. brucei. This study provides important structure-activity relationship (SAR) information for the future design of effective parasite-specific PDE inhibitors.

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.

The Landscape of Atypical and Eukaryotic Protein Kinases.

Kinases are attractive anticancer targets due to their central role in the growth, survival, and therapy resistance of tumor cells. This review explores the two primary kinase classes, the eukaryotic protein kinases (ePKs) and the atypical protein kinases (aPKs), and provides a structure-centered comparison of their sequences, structures, hydrophobic spines, mutation and SNP hotspots, and inhibitor interaction patterns. Despite the limited sequence similarity between these two classes, atypical kinases commonly share the archetypical kinase fold but lack conserved eukaryotic kinase motifs and possess altered hydrophobic spines. Furthermore, atypical kinase inhibitors explore only a limited number of binding modes both inside and outside the orthosteric binding site. The distribution of genetic variations in both classes shows multiple ways they can interfere with kinase inhibitor binding. This multilayered review provides a research framework bridging the eukaryotic and atypical kinase classes.

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.

Route to Prolonged Residence Time at the Histamine H1 Receptor: Growing from Desloratadine to Rupatadine.

Drug-target binding kinetics are an important predictor of in vivo drug efficacy, yet the relationship between ligand structures and their binding kinetics is often poorly understood. We show that both rupatadine (1) and desloratadine (2) have a long residence time at the histamine H1 receptor (H1R). Through development of a [3H]levocetirizine radiolabel, we find that the residence time of 1 exceeds that of 2 more than 10-fold. This was further explored with 22 synthesized rupatadine and desloratadine analogues. Methylene-linked cycloaliphatic or ß-branched substitutions of desloratadine increase the residence time at the H1R, conveying a longer duration of receptor antagonism. However, cycloaliphatic substituents directly attached to the piperidine amine (i.e., lacking the spacer) have decreased binding affinity and residence time compared to their methylene-linked structural analogues. Guided by docking studies, steric constraints within the binding pocket are hypothesized to explain the observed differences in affinity and binding kinetics between analogues.

Aminergic GPCR-Ligand Interactions: A Chemical and Structural Map of Receptor Mutation Data.

The aminergic family of G protein-coupled receptors (GPCRs) plays an important role in various diseases and represents a major drug discovery target class. Structure determination of all major aminergic subfamilies has enabled structure-based ligand design for these receptors. Site-directed mutagenesis data provides an invaluable complementary source of information for elucidating the structural determinants of binding of different ligand chemotypes. The current study provides a comparative analysis of 6692 mutation data points on 34 aminergic GPCR subtypes, covering the chemical space of 540 unique ligands from mutagenesis experiments and information from experimentally determined structures of 52 distinct aminergic receptor-ligand complexes. The integrated analysis enables detailed investigation of structural receptor-ligand interactions and assessment of the transferability of combined binding mode and mutation data across ligand chemotypes and receptor subtypes. An overview is provided of the possibilities and limitations of using mutation data to guide the design of novel aminergic receptor ligands.

Structural insights into serotonin receptor ligands polypharmacology.

Identifying desired interactions with a target receptor is often the first step when designing new active compounds. However, attention should also be focused on contacts with other proteins that result in either selective or polypharmacological compounds. Here, the search for the structural determinants of selectivity between selected serotonin receptor subtypes was carried out. Special attention was focused on 5-HT7R and the cross-interactions between its ligands and the 5-HT1AR, 5-HT1BR, 5-HT2AR, 5-HT2BR, and 5-HT6R subtypes. Selective and non-selective compounds for each pair of 5-HT7/5-HTx receptors were docked to the respective 5-HTR homology models and 5-HT1B/5-HT2BR crystal structures. The contacts present in the ligand-receptor complexes obtained by docking were characterized by the structural interaction fingerprint and statistically analyzed in terms of their frequency. The results allowed for the identification of amino acids that discriminate between selective and non-selective compounds for each 5-HT7/5-HTx receptor pair, which was further compared with available mutagenesis data. Interaction pattern characteristics for compounds with particular activity profiles can constitute the basis for the coherent selectivity theory within a considered set of proteins, supporting the ongoing development of new ligands targeting these receptors. The in silico results were used to analyze prospective virtual screening results towards the 5-HT7 receptor in which compounds of different chemotypes to known 5-HT7R ligands, with micromolar level activities were identified. The findings in this study not only confirm the legitimacy of the approach but also constitute a great starting point for further studies on 5-HT7R ligands selectivity.