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.

Emulating Docking Results Using a Deep Neural Network: A New Perspective for Virtual Screening.

Docking is one of the most important steps in virtual screening pipelines, and it is an established method for examining potential interactions between ligands and receptors. However, this method is computationally expensive, and it is often among the last steps of the process of compound libraries evaluation. In this work, we investigate the feasibility of learning a deep neural network to predict the docking output directly from a two-dimensional compound structure. The developed protocol is orders of magnitude faster than typical docking software, and it returns ligand-receptor complexes encoded in the form of the interaction fingerprint. Its speed and efficiency unlock the application possibilities, such as screening compound libraries of vast size on the basis of contact patterns or docking score (derived on the basis of predicted interaction schemes). We tested our approach on several G protein-coupled receptor targets and 4 CYP enzymes in retrospective virtual screening experiments, and a variant of graph convolutional network appeared to be most effective in emulating docking results. The method can be easily used by the community based on the code available in the Supporting Information.

GPCRdb in 2018: adding GPCR structure models and ligands.

G protein-coupled receptors are the most abundant mediators of both human signalling processes and therapeutic effects. Herein, we report GPCRome-wide homology models of unprecedented quality, and roughly 150 000 GPCR ligands with data on biological activities and commercial availability. Based on the strategy of 'Less model - more Xtal', each model exploits both a main template and alternative local templates. This achieved higher similarity to new structures than any of the existing resources, and refined crystal structures with missing or distorted regions. Models are provided for inactive, intermediate and active states-except for classes C and F that so far only have inactive templates. The ligand database has separate browsers for: (i) target selection by receptor, family or class, (ii) ligand filtering based on cross-experiment activities (min, max and mean) or chemical properties, (iii) ligand source data and (iv) commercial availability. SMILES structures and activity spreadsheets can be downloaded for further processing. Furthermore, three recent landmark publications on GPCR drugs, G protein selectivity and genetic variants have been accompanied with resources that now let readers view and analyse the findings themselves in GPCRdb. Altogether, this update will enable scientific investigation for the wider GPCR community. GPCRdb is available at http://www.gpcrdb.org.

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.

From Homology Models to a Set of Predictive Binding Pockets-a 5-HT1A Receptor Case Study.

Despite its remarkable importance in the arena of drug design, serotonin 1A receptor (5-HT1A) has been elusive to the X-ray crystallography community. This lack of direct structural information not only hampers our knowledge regarding the binding modes of many popular ligands (including the endogenous neurotransmitter-serotonin), but also limits the search for more potent compounds. In this paper we shed new light on the 3D pharmacological properties of the 5-HT1A receptor by using a ligand-guided approach (ALiBERO) grounded in the Internal Coordinate Mechanics (ICM) docking platform. Starting from a homology template and set of known actives, the method introduces receptor flexibility via Normal Mode Analysis and Monte Carlo sampling, to generate a subset of pockets that display enriched discrimination of actives from inactives in retrospective docking. Here, we thoroughly investigated the repercussions of using different protein templates and the effect of compound selection on screening performance. Finally, the best resulting protein models were applied prospectively in a large virtual screening campaign, in which two new active compounds were identified that were chemically distinct from those described in the literature.

A new crystal structure fragment-based pharmacophore method for G protein-coupled receptors.

We have developed a new method for the building of pharmacophores for G protein-coupled receptors, a major drug target family. The method is a combination of the ligand- and target-based pharmacophore methods and founded on the extraction of structural fragments, interacting ligand moiety and receptor residue pairs, from crystal structure complexes. We describe the procedure to collect a library with more than 250 fragments covering 29 residue positions within the generic transmembrane binding pocket. We describe how the library fragments are recombined and inferred to build pharmacophores for new targets. A validating retrospective virtual screening of histamine H1 and H3 receptor pharmacophores yielded area-under-the-curves of 0.88 and 0.82, respectively. The fragment-based method has the unique advantage that it can be applied to targets for which no (homologous) crystal structures or ligands are known. 47% of the class A G protein-coupled receptors can be targeted with at least four-element pharmacophores. The fragment libraries can also be used to grow known ligands or for rotamer refinement of homology models. Researchers can download the complete fragment library or a subset matching their receptor of interest using our new tool in GPCRDB.

Multi-Step Protocol for Automatic Evaluation of Docking Results Based on Machine Learning Methods--A Case Study of Serotonin Receptors 5-HT(6) and 5-HT(7).

Molecular docking, despite its undeniable usefulness in computer-aided drug design protocols and the increasing sophistication of tools used in the prediction of ligand-protein interaction energies, is still connected with a problem of effective results analysis. In this study, a novel protocol for the automatic evaluation of numerous docking results is presented, being a combination of Structural Interaction Fingerprints and Spectrophores descriptors, machine-learning techniques, and multi-step results analysis. Such an approach takes into consideration the performance of a particular learning algorithm (five machine learning methods were applied), the performance of the docking algorithm itself, the variety of conformations returned from the docking experiment, and the receptor structure (homology models were constructed on five different templates). Evaluation using compounds active toward 5-HT6 and 5-HT7 receptors, as well as additional analysis carried out for beta-2 adrenergic receptor ligands, proved that the methodology is a viable tool for supporting virtual screening protocols, enabling proper discrimination between active and inactive compounds.

An application of machine learning methods to structural interaction fingerprints--a case study of kinase inhibitors.

In this Letter, we present a novel methodology of searching for biologically active compounds, which is based on the combination of docking experiments and analysis of the results by machine learning methods. The study was performed for 5 different protein kinases, and several sets of compounds (active, inactive and assumed inactives) were docked into their targets. The resulting ligand-protein complexes were represented by the means of structural interaction fingerprints profiles (SIFts profiles) that constituted an input for ML methods. The developed protocol was found to be superior to the combination of classification algorithms with the standard fingerprint MACCSFP.

Impact of template choice on homology model efficiency in virtual screening.

Homology modeling is a reliable method of predicting the three-dimensional structures of proteins that lack NMR or X-ray crystallographic data. It employs the assumption that a structural resemblance exists between closely related proteins. Despite the availability of many crystal structures of possible templates, only the closest ones are chosen for homology modeling purposes. To validate the aforementioned approach, we performed homology modeling of four serotonin receptors (5-HT1AR, 5-HT2AR, 5-HT6R, 5-HT7R) for virtual screening purposes, using 10 available G-Protein Coupled Receptors (GPCR) templates with diverse evolutionary distances to the targets, with various approaches to alignment construction and model building. The resulting models were further validated in two steps by means of ligand docking and enrichment calculation, using Glide software. The final quality of the models was determined in virtual screening-like experiments by the AUROC score of the resulting ROC curves. The outcome of this research showed that no correlation between sequence identity and model quality was found, leading to the conclusion that the closest phylogenetic relative is not always the best template for homology modeling.

Simulations in evolution. III. Randomness as a generator of opportunities.

In Neo-Darwinism, variation and natural selection are the two evolutionary mechanisms which propel biological evolution. Our previous reports presented a histogram model to simulate the evolution of populations of individuals classified into bins according to an unspecified, quantifiable phenotypic character, and whose number in each bin changed generation after generation under the influence of fitness, while the total population was maintained constant. The histogram model also allowed Shannon entropy (SE) to be monitored continuously as the information content of the total population decreased or increased. Here, a simple Perl (Practical Extraction and Reporting Language) application was developed to carry out these computations, with the critical feature of an added random factor in the percent of individuals whose offspring moved to a vicinal bin. The results of the simulations demonstrate that the random factor mimicking variation increased considerably the range of values covered by Shannon entropy, especially when the percentage of changed offspring was high. This increase in information content is interpreted as facilitated adaptability of the population.

Homology Modeling of the G Protein-Coupled Receptors.

G protein-coupled receptors (GPCRs) are therapeutically important family of membrane proteins. Despite growing number of experimental structures available for GPCRs, homology modeling remains a relevant method for studying these receptors and for discovering new ligands for them. Here we describe the state-of-the-art methods for modeling GPCRs, starting from template selection, through fine-tuning sequence alignment to model refinement.

Gαi-derived peptide binds the µ-opioid receptor.

BACKGROUND: G protein-coupled receptors (GPCRs) transduce external stimuli into the cell by G proteins via an allosteric mechanism. Agonist binding to the receptor stimulates GDP/GTP exchange within the heterotrimeric G protein complex, whereas recent structures of GPCR-G protein complexes revealed that the H5, S1 and S2 domains of Gα are involved in binding the active receptor, earlier studies showed that a short peptide analog derived from the C-terminus (H5) of the G protein transducin (Gt) is sufficient to stabilize rhodopsin in an active form. METHODS: We have used Molecular Dynamics simulations along with biological evaluation by means of radio-ligand binding assay to study the interactions between Gαi-derived peptide (G-peptide) and the µ-opioid receptor (µOR). RESULTS: Here, we show that a Gαi-derived peptide of 12 amino acids binds the µ-opioid receptor and acts as an allosteric modulator. The Gαi-derived peptide increases µOR affinity for its agonist morphine in a dose-dependent way. CONCLUSIONS: These results indicate that the GPCR-Gα peptide interaction observed so far for only rhodopsin can be extrapolated to µOR. In addition, we show that the C-terminal peptide of the Gαi subunit is sufficient to stabilize the active conformation of the receptor. Our approach opens the possibility to investigate the GPCR-G protein interface with peptide modification.

Protein binding site analysis by means of structural interaction fingerprint patterns.

We introduce a new approach to the known concept of interaction profiles, based on Structural Interaction Fingerprints (SIFt), for precise and rapid binding site description. A set of scripts for batch generation and analysis of SIFt were prepared, and the implementation is computationally efficient and supports parallelization. It is based on a 9-digit binary interaction pattern that describes physical ligand-protein interactions in structures and models of ligand-protein complexes. The tool performs analysis and identifies binding site residues (crucial and auxiliary) and classifies interactions according to type (hydrophobic, aromatic, charge, polar, side chain, and backbone). It is convenient and easy to use, and gives manageable output data for both, interpretation and further processing. In the presented Letter, SIFts are applied to analyze binding sites in models of antagonist-5-HT7 receptor complexes and structures of cyclin dependent kinase 2-ligand complexes.

2D SIFt: a matrix of ligand-receptor interactions.

Depicting a ligand-receptor complex via Interaction Fingerprints has been shown to be both a viable data visualization and an analysis tool. The spectrum of its applications ranges from simple visualization of the binding site through analysis of molecular dynamics runs, to the evaluation of the homology models and virtual screening. Here we present a novel tool derived from the Structural Interaction Fingerprints providing a detailed and unique insight into the interactions between receptor and specific regions of the ligand (grouped into pharmacophore features) in the form of a matrix, a 2D-SIFt descriptor. The provided implementation is easy to use and extends the python library, allowing the generation of interaction matrices and their manipulation (reading and writing as well as producing the average 2D-SIFt). The library for handling the interaction matrices is available via repository http://bitbucket.org/zchl/sift2d .

Rationally designed N-phenylsulfonylindoles as a tool for the analysis of the non-basic 5-HT6R ligands binding mode.

Among all of the monoaminergic receptors, the 5-HT6R has the highest number of non-basic ligands (approximately 5% of compounds stored in 25th version of ChEMBL database have the strongest basic pKa below 5, calculated using the Instant JChem calculator plugin). These compounds, when devoid of a basic nitrogen, exhibit high affinity and remarkable selectivity. Despite a decade of research, no clues have been given for explanation of such an intriguing phenomenon. Here, a series of analogs of four known 5-HT6R ligands, has been rationally designed to approach this issue. For each of the synthesized 42 compounds, a binding affinity for 5-HT6R has been measured, together with a selectivity profile against 5-HT1AR, 5-HT2AR, 5-HT7R and D2R. Performed induced fit docking and molecular dynamics experiments revealed that no particular interaction was responsible for the activity of non-basic compounds. In fact, a plain N-phenylsulfonylindole (1e) was found to possess a moderate (5-HT6R, Ki = 159 nM) affinity. No other monoaminergic receptor has as simple and selective ligand as this one. Thus, it is stated that it binds to the receptor solely based on its conformation and as such, possesses a minimum amount of features, required for binding. Also, any functional group able to form an additional interaction with the receptor increase the binding affinity, like in the case of two highly active non-basic compounds 3e and 5g (5-HT6R, Ki = 65 nM and 38 nM, respectively).

The European Research Network on Signal Transduction (ERNEST): Toward a Multidimensional Holistic Understanding of G Protein-Coupled Receptor Signaling.

G protein-coupled receptors (GPCRs) are intensively studied due to their therapeutic potential as drug targets. Members of this large family of transmembrane receptor proteins mediate signal transduction in diverse cell types and play key roles in human physiology and health. In 2013 the research consortium GLISTEN (COST Action CM1207) was founded with the goal of harnessing the substantial growth in knowledge of GPCR structure and dynamics to push forward the development of molecular modulators of GPCR function. The success of GLISTEN, coupled with new findings and paradigm shifts in the field, led in 2019 to the creation of a related consortium called ERNEST (COST Action CA18133). ERNEST broadens focus to entire signaling cascades, based on emerging ideas of how complexity and specificity in signal transduction are not determined by receptor-ligand interactions alone. A holistic approach that unites the diverse data and perspectives of the research community into a single multidimensional map holds great promise for improved drug design and therapeutic targeting.

The effect of the intramolecular C-H⋯O interactions on the conformational preferences of bis-arylsulfones - 5-HT6 receptor antagonists and beyond.

The development of compounds with enhanced activity and selectivity by a conserved spatial orientation of the pharmacophore elements has a long history in medicinal chemistry. Rigidified compounds are an example of this concept. However, the intramolecular interactions were seldom used as a basis for conformational restraints. Here, we show the weak intramolecular interactions that contribute to the relatively well-conserved geometry of N1-arylsulfonyl indole derivatives. The structure analysis along with quantum mechanics calculations revealed a crucial impact of the sulfonyl group on the compound geometry. The weak intramolecular C-H⋯O interaction stabilizes the mutual "facing" orientation of two aromatic fragments. These findings extend the pharmacological interpretation of the sulfonyl group role from the double hydrogen bond acceptor to the conformational scaffold based on intramolecular forces. This feature has, to date, been omitted in in silico drug discovery. Our results should increase the awareness of researchers to consider the conformational preference when designing new compounds or improving computational methods.

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.

Reaction mechanism of sterol hydroxylation by steroid C25 dehydrogenase - Homology model, reactivity and isoenzymatic diversity.

Steroid C25 dehydrogenase (S25DH) is a molybdenum-containing oxidoreductase isolated from the anaerobic Sterolibacterium denitrificans Chol-1S. S25DH is classified as 'EBDH-like' enzyme (EBDH, ethylbenzene dehydrogenase) and catalyzes the introduction of an OH group to the C25 atom of a sterol aliphatic side-chain. Due to its regioselectivity, S25DH is proposed as a catalyst in production of pharmaceuticals: calcifediol or 25-hydroxycholesterol. The aim of presented research was to obtain structural model of catalytic subunit α and investigate the reaction mechanism of the O2-independent tertiary carbon atom activation. Based on homology modeling and theoretical calculations, a S25DH α subunit model was for the first time characterized and compared to other S25DH-like isoforms. The molecular dynamics simulations of the enzyme-substrate complexes revealed two stable binding modes of a substrate, which are stabilized predominantly by van der Waals forces in the hydrophobic substrate channel. However, H-bond interactions involving polar residues with C3=O/C3-OH in the steroid ring appear to be responsible for positioning the substrate. These results may explain the experimental kinetic results which showed that 3-ketosterols are hydroxylated 5-10-fold faster than 3-hydroxysterols. The reaction mechanism was studied using QM:MM and QM-only cluster models. The postulated mechanism involves homolytic CH cleavage by the MoO ligand, giving rise to a radical intermediate with product obtained in an OH rebound process. The hypothesis was supported by kinetic isotopic effect (KIE) experiments involving 25,26,26,26-[2H]-cholesterol (4.5) and the theoretically predicted intrinsic KIE (7.0-7.2). Finally, we have demonstrated that the recombinant S25DH-like isoform catalyzes the same reaction as S25DH.