GOMoDo: A GPCRs online modeling and docking webserver

The development of diphenyleneiodonium analogs as GPR3 agonists

G protein-coupled receptor 3 (GPR3) is an orphan receptor potentially involved in many important physiological processes such as drug abuse, neuropathic pain, and anxiety and depression related disorders. Pharmacological studies of GPR3 have been limited due to the restricted number of known agonists and inverse agonists for this constitutively active receptor. In this medicinal chemistry study, we report the discovery of GPR3 agonists based off the diphenyleneiodonium (DPI) scaffold. The most potent full agonist was the 3-trifluoromethoxy analog (32) with an EC50 of 260 nM and 90% efficacy compared to DPI. Investigation of a homology model of GPR3 from multiple sequence alignment resulted in the finding of a binding site rich in potential π-π and π-cation interactions stabilizing DPI-scaffold agonists. MMGBSA free energy analysis showed a good correlation with trends in observed EC50s. DPI analogs retained the same high receptor selectivity for GPR3 over GPR6 and GPR12 as observed with DPI. Collectively, the DPI analog series shows that order of magnitude improvements in potency with the scaffold were attainable; however, attempts to replace the iodonium ion to make the scaffold more druggable failed.

Protons taken hostage: Dynamic H-bond networks of the pH-sensing GPR68

Proton-sensing G Protein Coupled Receptors (GPCRs) sense changes in the extracellular pH to effect cell signaling for cellular homeostasis. They tend to be overexpressed in solid tumors associated with acidic extracellular pH, and are of direct interest as drug targets. How proton-sensing GPCRs sense extracellular acidification and activate upon protonation change is important to understand, because it may guide the design of therapeutics. Lack of publicly available experimental structures make it challenging to discriminate between conflicting mechanisms proposed for proton-binding, as main roles have been assigned to either an extracellular histidine cluster or to an internal carboxylic triad. Here we present a protocol to derive and evaluate structural models of the proton-sensing GPR68. This approach integrates state-of-the-art homology modeling with microsecond-timescale atomistic simulations, and with a detailed assessment of the compatibility of the structural models with known structural features of class A GPCRs. To decipher structural elements of potential interest for protonation-coupled conformational changes of GPR68, we used the best-compatible model as a starting point for independent atomistic simulations of GPR68 with different protonation states, and graph computations to characterize the response of GPR68 to changes in protonation. We found that GPR68 hosts an extended hydrogen-bond network that inter-connects the extracellular histidine cluster to the internal carboxylic triad, and which can even reach groups at the cytoplasmic G-protein binding site. Taken together, results suggest that GPR68 relies on dynamic, hydrogen-bond networks to inter-connect extracellular and internal proton-binding sites, and to elicit conformational changes at the cytoplasmic G-protein binding site.

Application of computational methods for class A GPCR Ligand discovery

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.

PacDOCK: A Web Server for Positional Distance-Based and Interaction-Based Analysis of Docking Results

Molecular docking is a key method for structure-based drug design used to predict the conformations assumed by small drug-like ligands when bound to their target. However, the evaluation of molecular docking studies can be hampered by the lack of a free and easy to use platform for the complete analysis of results obtained by the principal docking programs. To this aim, we developed PacDOCK, a freely available and user-friendly web server that comprises a collection of tools for positional distance-based and interaction-based analysis of docking results, which can be provided in several file formats. PacDOCK allows a complete analysis of molecular docking results through root mean square deviation (RMSD) calculation, molecular visualization, and cluster analysis of docked poses. The RMSD calculation compares docked structures with a reference structure, also when atoms are randomly labelled, and their conformational and positional differences can be visualised. In addition, it is possible to visualise a ligand into the target binding pocket and investigate the key receptor–ligand interactions. Moreover, PacDOCK enables the clustering of docking results by identifying a restrained number of clusters from many docked poses. We believe that PacDOCK will contribute to facilitating the analysis of docking results to improve the efficiency of computer-aided drug design.

Computational and experimental approaches to probe GPCR activation and signaling

G protein-coupled receptors (GPCRs) regulate different physiological functions, e.g., sensation, growth, digestion, reproductivity, nervous and immune systems response, and many others. In eukaryotes, they are also responsible for intercellular communication in response to pathogens. The major primary messengers binding to these cell-surface receptors constitute small-molecule or peptide hormones and neurotransmitters, nucleotides, lipids as well as small proteins. The simplicity of the way how GPCR signaling can be regulated by their endogenous agonists prompted the usage of GPCRs as major drug targets in modern pharmacology. Drugs targeting GPCRs inhibit pathological processes at the very beginning. This enables to significantly reduce the occurrence of morphological changes caused by diseases. Until recently, X-ray crystallography was the method of the first choice to obtain high-resolution structural information about GPCRs. Following X-ray crystallography, cryo-EM gained attention in GPCR studies as a quick and low-cost alternative. FRET microscopy is also widely used for GPCRs in the analysis of protein-protein interactions (PPIs) in intact cells as well as for screening purposes. Regarding computational methods, molecular dynamics (MD) for many years has proven its usefulness in studying the GPCR activation. MODELLER and Rosetta were widely used to generate preliminary homology models of GPCRs for MD simulation systems. Apart from the conventional all-atom approach with explicitly defined solvent, also other techniques have been applied to GPCRs, e.g., MARTINI or hybrid methods involving the coarse-grained representation, less demanding regarding computational resources, and thus offering much larger simulation timescales.

Binding site identification of G protein-coupled receptors through a 3D Zernike polynomials-based method: application to C. elegans olfactory receptors

Studying the binding processes of G protein-coupled receptors (GPCRs) proteins is of particular interest both to better understand the molecular mechanisms that regulate the signaling between the extracellular and intracellular environment and for drug design purposes. In this study, we propose a new computational approach for the identification of the binding site for a specific ligand on a GPCR. The method is based on the Zernike polynomials and performs the ligand-GPCR association through a shape complementarity analysis of the local molecular surfaces. The method is parameter-free and it can distinguish, working on hundreds of experimentally GPCR-ligand complexes, binding pockets from randomly sampled regions on the receptor surface, obtaining an Area Under ROC curve of 0.77. Given its importance both as a model organism and in terms of applications, we thus investigated the olfactory receptors of the C. elegans, building a list of associations between 21 GPCRs belonging to its olfactory neurons and a set of possible ligands. Thus, we can not only carry out rapid and efficient screenings of drugs proposed for GPCRs, key targets in many pathologies, but also we laid the groundwork for computational mutagenesis processes, aimed at increasing or decreasing the binding affinity between ligands and receptors.

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.

Functional molecular switches of mammalian G protein-coupled bitter-taste receptors

Bitter taste receptors (TAS2Rs) are a poorly understood subgroup of G protein-coupled receptors (GPCRs). The experimental structure of these receptors has yet to be determined, and key-residues controlling their function remain mostly unknown. We designed an integrative approach to improve comparative modeling of TAS2Rs. Using current knowledge on class A GPCRs and existing experimental data in the literature as constraints, we pinpointed conserved motifs to entirely re-align the amino-acid sequences of TAS2Rs. We constructed accurate homology models of human TAS2Rs. As a test case, we examined the accuracy of the TAS2R16 model with site-directed mutagenesis and in vitro functional assays. This combination of in silico and in vitro results clarifies sequence-function relationships and proposes functional molecular switches that encode agonist sensing and downstream signaling mechanisms within mammalian TAS2Rs sequences.

Nonantimicrobial Actions of Macrolides: Overview and Perspectives for Future Development

Macrolides are among the most widely prescribed broad spectrum antibacterials, particularly for respiratory infections. It is now recognized that these drugs, in particular azithromycin, also exert time-dependent immunomodulatory actions that contribute to their therapeutic benefit in both infectious and other chronic inflammatory diseases. Their increased chronic use in airway inflammation and, more recently, of azithromycin in COVID-19, however, has led to a rise in bacterial resistance. An additional crucial aspect of chronic airway inflammation, such as chronic obstructive pulmonary disease, as well as other inflammatory disorders, is the loss of epithelial barrier protection against pathogens and pollutants. In recent years, azithromycin has been shown with time to enhance the barrier properties of airway epithelial cells, an action that makes an important contribution to its therapeutic efficacy. In this article, we review the background and evidence for various immunomodulatory and time-dependent actions of macrolides on inflammatory processes and on the epithelium and highlight novel nonantibacterial macrolides that are being studied for immunomodulatory and barrier-strengthening properties to circumvent the risk of bacterial resistance that occurs with macrolide antibacterials. We also briefly review the clinical effects of macrolides in respiratory and other inflammatory diseases associated with epithelial injury and propose that the beneficial epithelial effects of nonantibacterial azithromycin derivatives in chronic inflammation, even given prophylactically, are likely to gain increasing attention in the future. SIGNIFICANCE STATEMENT: Based on its immunomodulatory properties and ability to enhance the protective role of the lung epithelium against pathogens, azithromycin has proven superior to other macrolides in treating chronic respiratory inflammation. A nonantibiotic azithromycin derivative is likely to offer prophylactic benefits against inflammation and epithelial damage of differing causes while preserving the use of macrolides as antibiotics.

A New World Monkey Resembles Human in Bitter Taste Receptor Evolution and Function via a Single Parallel Amino Acid Substitution

Bitter taste receptors serve as a vital component in the defense system against toxin intake by animals, and the family of genes encoding these receptors has been demonstrated, usually by family size variance, to correlate with dietary preference. However, few systematic studies of specific Tas2R to unveil their functional evolution have been conducted. Here, we surveyed Tas2R16 across all major clades of primates and reported a rare case of a convergent change to increase sensitivity to β-glucopyranosides in human and a New World monkey, the white-faced saki. Combining analyses at multiple levels, we demonstrate that a parallel amino acid substitution (K172N) shared by these two species is responsible for this functional convergence of Tas2R16. Considering the specialized feeding preference of the white-faced saki, the K172N change likely played an important adaptive role in its early evolution to avoid potentially toxic cyanogenic glycosides, as suggested for the human TAS2R16 gene.

GPCRsignal: webserver for analysis of the interface between G-protein-coupled receptors and their effector proteins by dynamics and mutations

GPCRsignal (https://gpcrsignal.biomodellab.eu/) is a webserver devoted to signaling complexes of G-protein–coupled receptors (GPCRs). The recent improvement in cryo-electron microscopy resulted in the determination of a large number of high-resolution structures of GPCRs bound to their effector proteins: G proteins or arrestins. Analyzing the interfaces between receptor and an effector protein is of high importance since a selection of proper G protein or specific conformation of arrestin leads to changes of signaling that can significantly affect action of drugs. GPCRsignal provides a possibility of running molecular dynamics simulations of all currently available GPCR-effector protein complexes for curated structures: wild-type, with crystal/cryo-EM mutations, or with mutations introduced by the user. The simulations are performed in an implicit water-membrane environment, so they are rather fast. User can run several simulations to obtain statistically valid results. The simulations can be analyzed separately using dynamic FlarePlots for particular types of interactions. One can also compare groups of simulations in Interaction frequency analysis as HeatMaps and also in interaction frequency difference analysis as sticks, linking the interacting residues, of different color and size proportional to differences in contact frequencies.

PanGPCR: predictions for multiple targets, repurposing and side effects

SUMMARY

Drug discovery targeting G protein-coupled receptors (GPCRs), the largest known class of therapeutic targets, is challenging. To facilitate the rapid discovery and development of GPCR drugs, we built a system, PanGPCR, to predict multiple potential GPCR targets and their expression locations in the tissues, side effects and possible repurposing of GPCR drugs. With PanGPCR, the compound of interest is docked to a library of 36 experimentally determined crystal structures comprising of 46 docking sites for human GPCRs, and a ranked list is generated from the docking studies to assess all GPCRs and their binding affinities. Users can determine a given compound's GPCR targets and its repurposing potential accordingly. Moreover, potential side effects collected from the SIDER (Side-Effect Resource) database and mapped to 45 tissues and organs are provided by linking predicted off-targets and their expressed sequence tag profiles. With PanGPCR, multiple targets, repurposing potential and side effects can be determined by simply uploading a small ligand.

AVAILABILITY AND IMPLEMENTATION

PanGPCR is freely accessible at https://gpcrpanel.cmdm.tw/index.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Lowered sensitivity of bitter taste receptors to β-glucosides in bamboo lemurs: an instance of parallel and adaptive functional decline in TAS2R16?

Bitter taste facilitates the detection of potentially harmful substances and is perceived via bitter taste receptors (TAS2Rs) expressed on the tongue and oral cavity in vertebrates. In primates, TAS2R16 specifically recognizes β-glucosides, which are important in cyanogenic plants' use of cyanide as a feeding deterrent. In this study, we performed cell-based functional assays for investigating the sensitivity of TAS2R16 to β-glucosides in three species of bamboo lemurs (Prolemur simus, Hapalemur aureus and H. griseus), which primarily consume high-cyanide bamboo. TAS2R16 receptors from bamboo lemurs had lower sensitivity to β-glucosides, including cyanogenic glucosides, than that of the closely related ring-tailed lemur (Lemur catta). Ancestral reconstructions of TAS2R16 for the bamboo-lemur last common ancestor (LCA) and that of the Hapalemur LCA showed an intermediate sensitivity to β-glucosides between that of the ring-tailed lemurs and bamboo lemurs. Mutagenetic analyses revealed that P. simus and H. griseus had separate species-specific substitutions that led to reduced sensitivity. These results indicate that low sensitivity to β-glucosides at the cellular level—a potentially adaptive trait for feeding on cyanogenic bamboo—evolved independently after the Prolemur–Hapalemur split in each species.

BIO-GATS: A Tool for Automated GPCR Template Selection Through a Biophysical Approach for Homology Modeling

G protein-coupled receptors (GPCRs) are the largest family of membrane proteins with more than 800 members. GPCRs are involved in numerous physiological functions within the human body and are the target of more than 30% of the United States Food and Drug Administration (FDA) approved drugs. At present, over 400 experimental GPCR structures are available in the Protein Data Bank (PDB) representing 76 unique receptors. The absence of an experimental structure for the majority of GPCRs demand homology models for structure-based drug discovery workflows. The generation of good homology models requires appropriate templates. The commonly used methods for template selection are based on sequence identity. However, there exists low sequence identity among the GPCRs. Sequences with similar patterns of hydrophobic residues are often structural homologs, even with low sequence identity. Extending this, we propose a biophysical approach for template selection based principally on hydrophobicity correspondence between the target and the template. Our approach takes into consideration other relevant parameters, including resolution, similarity within the orthosteric binding pocket of GPCRs, and structure completeness, for template selection. The proposed method was implemented in the form of a free tool called Bio-GATS, to provide the user with easy selection of the appropriate template for a query GPCR sequence. Bio-GATS was successfully validated with recent published benchmarking datasets. An application to an olfactory receptor to select an appropriate template has also been provided as a case study.

Bitter taste in silico: A review on virtual ligand screening and characterization methods for TAS2R-bitterant interactions

The growing pharmaceutical interest in the human bitter taste receptors (hTAS2Rs) has two dimensions; i) evaluation of the bitterness of active pharmaceutical compounds, in order to develop strategies for improving patients' adherence to medication, and ii) application of ligands for extra-cellular hTAS2Rs for potential preventive therapeutic achievements. The result is an increasing demand on robust tools for bitterness assessment and screening the receptor-ligand affinity. In silico tools are useful for aiding experimental-screening, as well as to elucide ligand-receptor interactions. In this review, the ligand-based and structure-based approaches are described as the two main in silico tools for bitter taste analysis. The strengths and weaknesses of each approach are discussed. Both approaches provide key tools for understanding and exploiting bitter taste for human health applications.

CGMD Platform: Integrated Web Servers for the Preparation, Running, and Analysis of Coarse-Grained Molecular Dynamics Simulations

Advances in coarse-grained molecular dynamics (CGMD) simulations have extended the use of computational studies on biological macromolecules and their complexes, as well as the interactions of membrane protein and lipid complexes at a reduced level of representation, allowing longer and larger molecular dynamics simulations. Here, we present a computational platform dedicated to the preparation, running, and analysis of CGMD simulations. The platform is built on a completely revisited version of our Martini coarsE gRained MembrAne proteIn Dynamics (MERMAID) web server, and it integrates this with other three dedicated services. In its current version, the platform expands the existing implementation of the Martini force field for membrane proteins to also allow the simulation of soluble proteins using the Martini and the SIRAH force fields. Moreover, it offers an automated protocol for carrying out the backmapping of the coarse-grained description of the system into an atomistic one.

The G protein coupled receptor CXCR4 designed by the QTY code becomes more hydrophilic and retains cell signaling activity

G protein-coupled receptors (GPCRs) are vital for diverse biological functions, including vision, smell, and aging. They are involved in a wide range of diseases, and are among the most important targets of medicinal drugs. Tools that facilitate GPCR studies or GPCR-based technologies or therapies are thus critical to develop. Here we report using our QTY (glutamine, threonine, tyrosine) code to systematically replace 29 membrane-facing leucine, isoleucine, valine, and phenylalanine residues in the transmembrane α-helices of the GPCR CXCR4. This variant, CXCR4^QTY29, became more hydrophilic, while retaining the ability to bind its ligand CXCL12. When transfected into HEK293 cells, it inserted into the cell membrane, and initiated cellular signaling. This QTY code has the potential to improve GPCR and membrane protein studies by making it possible to design functional hydrophilic receptors. This tool can be applied to diverse α-helical membrane proteins, and may aid in the development of other applications, including clinical therapies.

Improving homology modeling from low-sequence identity templates in Rosetta: A case study in GPCRs

As sequencing methodologies continue to advance, the availability of protein sequences far outpaces the ability of structure determination. Homology modeling is used to bridge this gap but relies on high-identity templates for accurate model building. G-protein coupled receptors (GPCRs) represent a significant target class for pharmaceutical therapies in which homology modeling could fill the knowledge gap for structure-based drug design. To date, only about 17% of druggable GPCRs have had their structures characterized at atomic resolution. However, modeling of the remaining 83% is hindered by the low sequence identity between receptors. Here we test key inputs in the model building process using GPCRs as a focus to improve the pipeline in two critical ways: Firstly, we use a blended sequence- and structure-based alignment that accounts for structure conservation in loop regions. Secondly, by merging multiple template structures into one comparative model, the best possible template for every region of a target can be used expanding the conformational space sampled in a meaningful way. This optimization allows for accurate modeling of receptors using templates as low as 20% sequence identity, which accounts for nearly the entire druggable space of GPCRs. A model database of all non-odorant GPCRs is made available at www.rosettagpcr.org. Additionally, all protocols are made available with insights into modifications that may improve accuracy at new targets.

Hybrid MM/CG Webserver: Automatic Set Up of Molecular Mechanics/Coarse-Grained Simulations for Human G Protein-Coupled Receptor/Ligand Complexes

Hybrid Molecular Mechanics/Coarse-Grained (MM/CG) simulations help predict ligand poses in human G protein-coupled receptors (hGPCRs), the most important protein superfamily for pharmacological applications. This approach allows the description of the ligand, the binding cavity, and the surrounding water molecules at atomistic resolution, while coarse-graining the rest of the receptor. Here, we present the Hybrid MM/CG Webserver (mmcg.grs.kfa-juelich.de) that automatizes and speeds up the MM/CG simulation setup of hGPCR/ligand complexes. Initial structures for such complexes can be easily and efficiently generated with other webservers. The Hybrid MM/CG server also allows for equilibration of the systems, either fully automatically or interactively. The results are visualized online (using both interactive 3D visualizations and analysis plots), helping the user identify possible issues and modify the setup parameters accordingly. Furthermore, the prepared system can be downloaded and the simulation continued locally.

Ligand Pose Predictions for Human G Protein-Coupled Receptors: Insights from the Amber-Based Hybrid Molecular Mechanics/Coarse-Grained Approach

Human G protein-coupled receptors (hGPCRs) are the most frequent targets of Food and Drug Administration (FDA)-approved drugs. Structural bioinformatics, along with molecular simulation, can support structure-based drug design targeting hGPCRs. In this context, several years ago, we developed a hybrid molecular mechanics (MM)/coarse-grained (CG) approach to predict ligand poses in low-resolution hGPCR models. The approach was based on the GROMOS96 43A1 and PRODRG united-atom force fields for the MM part. Here, we present a new MM/CG implementation using, instead, the Amber 14SB and GAFF all-atom potentials for proteins and ligands, respectively. The new implementation outperforms the previous one, as shown by a variety of applications on models of hGPCR/ligand complexes at different resolutions, and it is also more user-friendly. Thus, it emerges as a useful tool to predict poses in low-resolution models and provides insights into ligand binding similarly to all-atom molecular dynamics, albeit at a lower computational cost.

Docking of peptides to GPCRs using a combination of CABS-dock with FlexPepDock refinement

The structural description of peptide ligands bound to G protein-coupled receptors (GPCRs) is important for the discovery of new drugs and deeper understanding of the molecular mechanisms of life. Here we describe a three-stage protocol for the molecular docking of peptides to GPCRs using a set of different programs: (1) CABS-dock for docking fully flexible peptides; (2) PD2 method for the reconstruction of atomistic structures from C-alpha traces provided by CABS-dock and (3) Rosetta FlexPepDock for the refinement of protein–peptide complex structures and model scoring. We evaluated the proposed protocol on the set of seven different GPCR–peptide complexes (including one containing a cyclic peptide), for which crystallographic structures are available. We show that CABS-dock produces high resolution models in the sets of top-scored models. These sets of models, after reconstruction to all-atom representation, can be further improved by Rosetta high-resolution refinement and/or minimization, leading in most of the cases to sub-Angstrom accuracy in terms of interface root-mean-square-deviation measure.

Web-ARM: A Web-Based Interface for the Automatic Construction of QM/MM Models of Rhodopsins

This article introduces Web-ARM, a specialized tool, online available, designed to build quantum mechanical/molecular mechanical models of rhodopsins, a widely spread family of light-responsive proteins. Web-ARM allows the rapidly building of models of rhodopsins with a documented quality and the prediction of trends in UV-vis absorption maximum wavelengths, based on their excitation energies computed at the CASPT2//CASSCF/Amber level of theory. Web-ARM builds upon the recently reported, python-based a-ARM protocol [J. Chem. Theory Comput., 2019, 15, 3134-3152] and, as such, necessitates only a crystallographic structure or a comparative model in PDB format and a very basic knowledge of the studied rhodopsin system. The user-friendly web interface uses such input to generate congruous, gas-phase models of rhodopsins and, if requested, their mutants. We present two possible applications of Web-ARM, which showcase how the interface can be employed to assist both research and educational activities in fields at the interface between chemistry and biology. The first application shows how, through Web-ARM, research projects (e.g., rhodopsin and rhodopsin mutant screening) can be carried out in significantly less time with respect to using the required computational photochemistry tools via a command line. The second application documents the use of Web-ARM in a real-life educational/training activity, through a hands-on experience illustrating the concepts of rhodopsin color tuning.

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

GPCRM: a homology modeling web service with triple membrane-fitted quality assessment of GPCR models

Due to the involvement of G protein-coupled receptors (GPCRs) in most of the physiological and pathological processes in humans they have been attracting a lot of attention from pharmaceutical industry as well as from scientific community. Therefore, the need for new, high quality structures of GPCRs is enormous. The updated homology modeling service GPCRM (http://gpcrm.biomodellab.eu/) meets those expectations by greatly reducing the execution time of submissions (from days to hours/minutes) with nearly the same average quality of obtained models. Additionally, due to three different scoring functions (Rosetta, Rosetta-MP, BCL::Score) it is possible to select accurate models for the required purposes: the structure of the binding site, the transmembrane domain or the overall shape of the receptor. Currently, no other web service for GPCR modeling provides this possibility. GPCRM is continually upgraded in a semi-automatic way and the number of template structures has increased from 20 in 2013 to over 90 including structures the same receptor with different ligands which can influence the structure not only in the on/off manner. Two types of protein viewers can be used for visual inspection of obtained models. The extended sortable tables with available templates provide links to external databases and display ligand-receptor interactions in visual form.

Toward the Identification of Extra-Oral TAS2R Agonists as Drug Agents for Muscle Relaxation Therapies via Bioinformatics-Aided Screening of Bitter Compounds in Traditional Chinese Medicine

Significant advances have been made in the past decade in mapping the distributions and the physiological functions of extra-oral bitter taste receptors (TAS2Rs) in non-gustatory tissues. In particular, it has been found that TAS2Rs are expressed in various muscle tissues and activation of TAS2Rs can lead to muscle cell relaxation, which suggests that TAS2Rs may be important new targets in muscle relaxation therapy for various muscle-related diseases. So far, however, there is a lack of potent extra-oral TAS2R agonists that can be used as novel drug agents in muscle relaxation therapies. Interestingly, traditional Chinese medicine (TCM) often characterizes a drug’s property in terms of five distinct flavors (bitter, sweet, sour, salty, and pungent) according to its taste and function, and commonly regards “bitterness” as an intrinsic property of “good medicine.” In addition, many bitter flavored TCM are known in practice to cause muscle relaxation after long term use, and in lab experiments the compounds identified from some bitter flavored TCM do activate TAS2Rs and thus relax muscle cells. Therefore, it is highly possible to discover very useful extra-oral TAS2R agonists for muscle relaxation therapies among the abundant bitter compounds used in bitter flavored TCM. With this perspective, we reviewed in literature the distribution of TAS2Rs in different muscle systems with a focus on the map of bitter flavored TCM which can regulate muscle contractility and related functional chemical components. We also reviewed the recently established databases of TCM chemical components and the bioinformatics software which can be used for high-throughput screening and data mining of the chemical components associated with bitter flavored TCM. All together, we aim to present a knowledge-based approach and technological platform for identification or discovery of extra-oral TAS2R agonists that can be used as novel drug agents for muscle relaxation therapies through screening and evaluation of chemical compounds used in bitter flavored TCM.

GPCR-SSFE 2.0-a fragment-based molecular modeling web tool for Class A G-protein coupled receptors

G-protein coupled receptors (GPCRs) are key players in signal transduction and therefore a large proportion of pharmaceutical drugs target these receptors. Structural data of GPCRs are sparse yet important for elucidating the molecular basis of GPCR-related diseases and for performing structure-based drug design. To ameliorate this problem, GPCR-SSFE 2.0 (http://www.ssfa-7tmr.de/ssfe2/), an intuitive web server dedicated to providing three-dimensional Class A GPCR homology models has been developed. The updated web server includes 27 inactive template structures and incorporates various new functionalities. Uniquely, it uses a fingerprint correlation scoring strategy for identifying the optimal templates, which we demonstrate captures structural features that sequence similarity alone is unable to do. Template selection is carried out separately for each helix, allowing both single-template models and fragment-based models to be built. Additionally, GPCR-SSFE 2.0 stores a comprehensive set of pre-calculated and downloadable homology models and also incorporates interactive loop modeling using the tool SL2, allowing knowledge-based input by the user to guide the selection process. For visual analysis, the NGL viewer is embedded into the result pages. Finally, blind-testing using two recently published structures shows that GPCR-SSFE 2.0 performs comparably or better than other state-of-the art GPCR modeling web servers.

Dual binding mode of "bitter sugars" to their human bitter taste receptor target

The 25 human bitter taste receptors (hTAS2Rs) are responsible for detecting bitter molecules present in food, and they also play several physiological and pathological roles in extraoral compartments. Therefore, understanding their ligand specificity is important both for food research and for pharmacological applications. Here we provide a molecular insight into the exquisite molecular recognition of bitter β-glycopyranosides by one of the members of this receptor subclass, hTAS2R16. Most of its agonists have in common the presence of a β-glycopyranose unit along with an extremely structurally diverse aglycon moiety. This poses the question of how hTAS2R16 can recognize such a large number of "bitter sugars". By means of hybrid molecular mechanics/coarse grained molecular dynamics simulations, here we show that the three hTAS2R16 agonists salicin, arbutin and phenyl-β-D-glucopyranoside interact with the receptor through a previously unrecognized dual binding mode. Such mechanism may offer a seamless way to fit different aglycons inside the binding cavity, while maintaining the sugar bound, similar to the strategy used by several carbohydrate-binding lectins. Our prediction is validated a posteriori by comparison with mutagenesis data and also rationalizes a wealth of structure-activity relationship data. Therefore, our findings not only provide a deeper molecular characterization of the binding determinants for the three ligands studied here, but also give insights applicable to other hTAS2R16 agonists. Together with our results for other hTAS2Rs, this study paves the way to improve our overall understanding of the structural determinants of ligand specificity in bitter taste receptors.

Understanding Ligand Binding to G-Protein Coupled Receptors Using Multiscale Simulations

Human G-protein coupled receptors (GPCRs) convey a wide variety of extracellular signals inside the cell and they are one of the main targets for pharmaceutical intervention. Rational drug design requires structural information on these receptors; however, the number of experimental structures is scarce. This gap can be filled by computational models, based on homology modeling and docking techniques. Nonetheless, the low sequence identity across GPCRs and the chemical diversity of their ligands may limit the quality of these models and hence refinement using molecular dynamics simulations is recommended. This is the case for olfactory and bitter taste receptors, which constitute the first and third largest GPCR groups and show sequence identities with the available GPCR templates below 20%. We have developed a molecular dynamics approach, based on the combination of molecular mechanics and coarse grained (MM/CG), tailored to study ligand binding in GPCRs. This approach has been applied so far to bitter taste receptor complexes, showing significant predictive power. The protein/ligand interactions observed in the simulations were consistent with extensive mutagenesis and functional data. Moreover, the simulations predicted several binding residues not previously tested, which were subsequently verified by carrying out additional experiments. Comparison of the simulations of two bitter taste receptors with different ligand selectivity also provided some insights into the binding determinants of bitter taste receptors. Although the MM/CG approach has been applied so far to a limited number of GPCR/ligand complexes, the excellent agreement of the computational models with the mutagenesis and functional data supports the applicability of this method to other GPCRs for which experimental structures are missing. This is particularly important for the challenging case of GPCRs with low sequence identity with available templates, for which molecular docking shows limited predictive power.

GABAA receptor family: overview on structural characterization

The pentameric γ-aminobutyric acid type A receptors are ion channels activated by ligands, which intervene in the rapid inhibitory transmission in the mammalian CNS. Due to their rich pharmacology and therapeutic potential, it is essential to understand their structure and function thoroughly. This deep characterization was hampered by the lack of experimental structural information for many years. Thus, computational techniques have been extensively combined with experimental data, in order to undertake the study of γ-aminobutyric acid type A receptors and their interaction with drugs. Here, we review the exciting journey made to assess the structures of these receptors and outline major outcomes. Finally, we discuss the brand new structure of the α1β2γ2 subtype and the amazing advances it brings to the field.

Potential off-target effects of beta-blockers on gut hormone receptors: In silico study including GUT-DOCK-A web service for small-molecule docking

The prolonged use of many currently available drugs results in the severe side effect of the disruption of glucose metabolism leading to type 2 diabetes mellitus (T2DM. Gut hormone receptors including glucagon receptor (GCGR) and the incretin hormone receptors: glucagon-like peptide 1 receptor (GLP1R) and gastric inhibitory polypeptide receptor (GIPR) are important drug targets for the treatment of T2DM, as they play roles in the regulation of glucose and insulin levels and of food intake. In this study, we hypothesized that we could compensate for the negative influences of specific drugs on glucose metabolism by the positive incretin effect enhanced by the off-target interactions with incretin GPCR receptors. As a test case, we chose to examine beta-blockers because beta-adrenergic receptors and incretin receptors are expressed in a similar location, making off-target interactions possible. The binding affinity of drugs for incretin receptors was approximated by using two docking scoring functions of Autodock VINA (GUT-DOCK) and Glide (Schrodinger) and juxtaposing these values with the medical information on drug-induced T2DM. We observed that beta-blockers with the highest theoretical binding affinities for gut hormone receptors were reported as the least harmful to glucose homeostasis in clinical trials. Notably, a recently discovered beta-blocker compound 15 ([4-((2S)-3-(((S)-3-(3-bromophenyl)-1-(methylamino)-1-oxopropan-2-yl)amino)-2-(2-cyclohexyl-2-phenylacetamido)-3-oxopropyl)benzamide was among the top-scoring drugs, potentially supporting its use in the treatment of hypertension in diabetic patients. Our recently developed web service GUT-DOCK (gut-dock.miningmembrane.com) allows for the execution of similar studies for any drug-like molecule. Specifically, users can compute the binding affinities for various class B GPCRs, gut hormone receptors, VIPR1 and PAC1R.

QTY code enables design of detergent-free chemokine receptors that retain ligand-binding activities

Structure and function studies of membrane proteins, particularly G protein-coupled receptors and multipass transmembrane proteins, require detergents. We have devised a simple tool, the QTY code (glutamine, threonine, and tyrosine), for designing hydrophobic domains to become water soluble without detergents. Here we report using the QTY code to systematically replace the hydrophobic amino acids leucine, valine, isoleucine, and phenylalanine in the seven transmembrane α-helices of CCR5, CXCR4, CCR10, and CXCR7. We show that QTY code-designed chemokine receptor variants retain their thermostabilities, α-helical structures, and ligand-binding activities in buffer and 50% human serum. CCR5QTY, CXCR4QTY, and CXCR7QTY also bind to HIV coat protein gp41-120. Despite substantial transmembrane domain changes, the detergent-free QTY variants maintain stable structures and retain their ligand-binding activities. We believe the QTY code will be useful for designing water-soluble variants of membrane proteins and other water-insoluble aggregated proteins.

Allosteric sodium binding cavity in GPR3: a novel player in modulation of Aβ production

The orphan G-protein coupled receptor 3 (GPR3) belongs to class A G-protein coupled receptors (GPCRs) and is highly expressed in central nervous system neurons. Among other functions, it is likely associated with neuron differentiation and maturation. Recently, GPR3 has also been linked to the production of Aβ peptides in neurons. Unfortunately, the lack of experimental structural information for this receptor hampers a deep characterization of its function. Here, using an in-silico and in-vitro combined approach, we describe, for the first time, structural characteristics of GPR3 receptor underlying its function: the agonist binding site and the allosteric sodium binding cavity. We identified and validated by alanine-scanning mutagenesis the role of three functionally relevant residues: Cys267^6.55, Phe120^3.36 and Asp^2.50. The latter, when mutated into alanine, completely abolished the constitutive and agonist-stimulated adenylate cyclase activity of GPR3 receptor by disrupting its sodium binding cavity. Interestingly, this is correlated with a decrease in Aβ production in a model cell line. Taken together, these results suggest an important role of the allosteric sodium binding site for GPR3 activity and open a possible avenue for the modulation of Aβ production in the Alzheimer’s Disease.

MIRATE: MIps RATional dEsign Science Gateway

Molecularly imprinted polymers (MIPs) are high affinity robust synthetic receptors, which can be optimally synthesized and manufactured more economically than their biological equivalents (i.e. antibody). In MIPs production, rational design based on molecular modeling is a commonly employed technique. This mostly aids in (i) virtual screening of functional monomers (FMs), (ii) optimization of monomer-template ratio, and (iii) selectivity analysis. We present MIRATE, an integrated science gateway for the intelligent design of MIPs. By combining and adapting multiple state-of-the-art bioinformatics tools into automated and innovative pipelines, MIRATE guides the user through the entire process of MIPs' design. The platform allows the user to fully customize each stage involved in the MIPs' design, with the main goal to support the synthesis in the wet-laboratory. Availability: MIRATE is freely accessible with no login requirement at http://mirate.di.univr.it/. All major browsers are supported.

Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design

The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the "golden age for GPCR structural biology." Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand- and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.

Predicting ligand binding poses for low-resolution membrane protein models: Perspectives from multiscale simulations

Membrane receptors constitute major targets for pharmaceutical intervention. Drug design efforts rely on the identification of ligand binding poses. However, the limited experimental structural information available may make this extremely challenging, especially when only low-resolution homology models are accessible. In these cases, the predictions may be improved by molecular dynamics simulation approaches. Here we review recent developments of multiscale, hybrid molecular mechanics/coarse-grained (MM/CG) methods applied to membrane proteins. In particular, we focus on our in-house MM/CG approach. It is especially tailored for G-protein coupled receptors, the largest membrane receptor family in humans. We show that our MM/CG approach is able to capture the atomistic details of the receptor/ligand binding interactions, while keeping the computational cost low by representing the protein frame and the membrane environment in a highly simplified manner. We close this review by discussing ongoing improvements and challenges of the current implementation of our MM/CG code.

Characterization of Ligand Binding to GPCRs Through Computational Methods

The recent increase in available G protein-coupled receptor structures now contributes decisively to the structure-based ligand design. In this context, computational approaches in combination with medicinal chemistry and pharmacology are extremely helpful. Here, we provide an update on our structure-based computational protocols, used to answer key questions related to GPCR-ligand binding. All combined, these techniques can shed light on ligand binding modes, determine the molecular basis of conformational selection, for agonists and antagonists, as well as of subtype selectivity. To illustrate each of these questions, we will consider examples from existing projects on three families of class A (rhodopsin-like) GPCRs: one small-molecule (nucleotide-like) family, i.e., the adenosine receptors, and two peptide-binding receptors: neuropeptide-Y and angiotensin II receptors. The successful application of the same computational protocols to investigate this diverse group of receptor families gives an idea of the general applicability of our methodology in the characterization of GPCR-ligand binding.

Evidence of Alternative Splicing as a Regulatory Mechanism for Kissr2 in Pejerrey Fish

Kisspeptin receptors are G-Protein-Coupled Receptors that regulate GnRH synthesis and release in vertebrates. Here, we report the gene structure of two kisspeptin receptors (kissr2 and kissr3) in pejerrey fish. Genomic analysis exposed a gene structure with 5 exons and 4 introns for kissr2 and 6 exons and 5 introns for kissr3. Two alternative variants for both genes, named kissr2_v1 and _v2, and kissr3_v1 and v2, were revealed by gene expression analyses of several tissues. For both receptors, these variants were originated by alternative splicing retaining intron 3 and intron 4 for kissr2_v2 and kissr3_v2, respectively. In the case of kissr2, the intron retention introduced two stop codons leading to a putatively truncated protein whereas for kissr3, the intron retention produced a reading shift leading to a stop codon in exon 5. Modeling and structural analysis of Kissr2 and Kissr3 spliced variants revealed that truncation of the proteins may lead to non-functional proteins, as the structural elements missing are critical for receptor function. To understand the functional significance of splicing variants, the expression pattern for kissr2 was characterized on fish subjected to different diets. Fasting induced an up-regulation of kissr2_v1 in the hypothalamus, a brain region implicated in control of reproduction and food intake, with no expression of kissr2_v2. On the other hand, fasting did not elicit differential expression in testes and habenula. These results suggest that alternative splicing may play a role in regulating Kissr2 function in pejerrey.

Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis

Human G-protein coupled receptors (hGPCRs) constitute a large and highly pharmaceutically relevant membrane receptor superfamily. About half of the hGPCRs' family members are chemosensory receptors, involved in bitter taste and olfaction, along with a variety of other physiological processes. Hence these receptors constitute promising targets for pharmaceutical intervention. Molecular modeling has been so far the most important tool to get insights on agonist binding and receptor activation. Here we investigate both aspects by bioinformatics-based predictions across all bitter taste and odorant receptors for which site-directed mutagenesis data are available. First, we observe that state-of-the-art homology modeling combined with previously used docking procedures turned out to reproduce only a limited fraction of ligand/receptor interactions inferred by experiments. This is most probably caused by the low sequence identity with available structural templates, which limits the accuracy of the protein model and in particular of the side-chains' orientations. Methods which transcend the limited sampling of the conformational space of docking may improve the predictions. As an example corroborating this, we review here multi-scale simulations from our lab and show that, for the three complexes studied so far, they significantly enhance the predictive power of the computational approach. Second, our bioinformatics analysis provides support to previous claims that several residues, including those at positions 1.50, 2.50, and 7.52, are involved in receptor activation.

Rosetta Broker for membrane protein structure prediction: concentrative nucleoside transporter 3 and corticotropin-releasing factor receptor 1 test cases

Background

Membrane proteins are difficult targets for structure prediction due to the limited structural data deposited in Protein Data Bank. Most computational methods for membrane protein structure prediction are based on the comparative modeling. There are only few de novo methods targeting that distinct protein family. In this work an example of such de novo method was used to structurally and functionally characterize two representatives of distinct membrane proteins families of solute carrier transporters and G protein-coupled receptors. The well-known Rosetta program and one of its protocols named Broker was used in two test cases. The first case was de novo structure prediction of three N-terminal transmembrane helices of the human concentrative nucleoside transporter 3 (hCNT3) homotrimer belonging to the solute carrier 28 family of transporters (SLC28). The second case concerned the large scale refinement of transmembrane helices of a homology model of the corticotropin-releasing factor receptor 1 (CRFR1) belonging to the G protein-coupled receptors family.

Results

The inward-facing model of the hCNT3 homotrimer was used to propose the functional impact of its single nucleotide polymorphisms. Additionally, the 100 ns molecular dynamics simulation of the unliganded hCNT3 model confirmed its validity and revealed mobility of the selected binding site and homotrimer interface residues. The large scale refinement of transmembrane helices of the CRFR1 homology model resulted in the significant improvement of its accuracy with respect to the crystal structure of CRFR1, especially in the binding site area. Consequently, the antagonist CP-376395 could be docked with Autodock VINA to the CRFR1 model without any steric clashes.

Conclusions

The presented work demonstrated that Rosetta Broker can be a versatile tool for solving various issues referring to protein biology. Two distinct examples of de novo membrane protein structure prediction presented here provided important insights into three major areas of protein biology. Namely, the dynamics of the inward-facing hCNT3 homotrimer system, the structural changes of the CRFR1 receptor upon the antagonist binding and finally, the role of single nucleotide polymorphisms in both, hCNT3 and CRFR1 proteins, were investigated.

Electronic supplementary material

The online version of this article (doi:10.1186/s12900-017-0078-8) contains supplementary material, which is available to authorized users.

All muscarinic acetylcholine receptors (M1-M5) are expressed in murine brain microvascular endothelium

Clinical and experimental studies indicate that muscarinic acetylcholine receptors are potential pharmacological targets for the treatment of neurological diseases. Although these receptors have been described in human, bovine and rat cerebral microvascular tissue, a subtype functional characterization in mouse brain endothelium is lacking. Here, we show that all muscarinic acetylcholine receptors (M₁-M₅) are expressed in mouse brain microvascular endothelial cells. The mRNA expression of M₂, M₃, and M₅ correlates with their respective protein abundance, but a mismatch exists for M₁ and M₄ mRNA versus protein levels. Acetylcholine activates calcium transients in brain endothelium via muscarinic, but not nicotinic, receptors. Moreover, although M₁ and M₃ are the most abundant receptors, only a small fraction of M₁ is present in the plasma membrane and functions in ACh-induced Ca²⁺ signaling. Bioinformatic analyses performed on eukaryotic muscarinic receptors demonstrate a high degree of conservation of the orthosteric binding site and a great variability of the allosteric site. In line with previous studies, this result indicates muscarinic acetylcholine receptors as potential pharmacological targets in future translational studies. We argue that research on drug development should especially focus on the allosteric binding sites of the M₁ and M₃ receptors.

Behavioral Effects of a Chemorepellent Receptor Knockout Mutation in Tetrahymena thermophila

Although many single-cell eukaryotes have served as classical model systems for chemosensory studies for decades, the major emphasis has been on chemoattraction and no chemorepellent receptor gene has been identified in any unicellular eukaryote. This is the first description of a gene that codes for a chemorepellent receptor in any protozoan. Integration of both depolarizing chemorepellent pathways and hyperpolarizing chemoattractant pathways is as important to chemoresponses of motile unicells as excitatory and inhibitory neurotransmitter pathways are to neurons. Therefore, both chemoattractant and chemorepellent pathways should be represented in a useful unicellular model system. Tetrahymena cells provide such a model system because simple behavioral bioassays, gene knockouts, biochemical analysis, and other approaches can be used with these eukaryotic model cells. This work can contribute to the basic understanding of unicellular sensory responses and provide insights into the evolution of chemoreceptors and possible chemorepellent approaches for preventing infections by some pathogenic protozoa.

A conditioned supernatant from Tetrahymena thermophila contains a powerful chemorepellent for wild-type cells, and a gene called G37 is required for this response. This is the first genomic identification of a chemorepellent receptor in any eukaryotic unicellular organism. This conditioned supernatant factor (CSF) is small (<1 kDa), and its repellent effect is resistant to boiling, protease treatment, and nuclease digestion. External BAPTA eliminated the CSF response, suggesting that Ca²⁺ entry is required for the classical avoiding reactions (AR) used for chemorepulsion. A macronuclear G37 gene knockout (G37-KO) mutant is both nonresponsive to the CSF and overresponsive to other repellents such as quinine, lysozyme, GTP, and high potassium concentrations. All of these mutant phenotypes were reversed by overexpression of the wild-type G37 gene in a G37 overexpression mutant. Overexpression of G37 in the wild type caused increased responsiveness to the CSF and underresponsiveness to high K⁺ concentrations. Behavioral adaptation (by prolonged exposure to the CSF) caused decreases in responsiveness to all of the stimuli used in the wild type and the overexpression mutant but not in the G37-KO mutant. We propose that the constant presence of the CSF causes a decreased basal excitability of the wild type due to chemosensory adaptation through G37 and that all of the G37-KO phenotypes are due to an inability to detect the CSF. Therefore, the G37 protein may be the CSF receptor. The physiological role of these G37-mediated responses may be to both moderate basal excitability and detect the CSF as an indicator of high cell density growth.

IMPORTANCE Although many single-cell eukaryotes have served as classical model systems for chemosensory studies for decades, the major emphasis has been on chemoattraction and no chemorepellent receptor gene has been identified in any unicellular eukaryote. This is the first description of a gene that codes for a chemorepellent receptor in any protozoan. Integration of both depolarizing chemorepellent pathways and hyperpolarizing chemoattractant pathways is as important to chemoresponses of motile unicells as excitatory and inhibitory neurotransmitter pathways are to neurons. Therefore, both chemoattractant and chemorepellent pathways should be represented in a useful unicellular model system. Tetrahymena cells provide such a model system because simple behavioral bioassays, gene knockouts, biochemical analysis, and other approaches can be used with these eukaryotic model cells. This work can contribute to the basic understanding of unicellular sensory responses and provide insights into the evolution of chemoreceptors and possible chemorepellent approaches for preventing infections by some pathogenic protozoa.

Molecular Docking at a Glance

The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.

Molecular Modelling Approaches for the Analysis of Histamine Receptors and Their Interaction with Ligands

Several experimental techniques to analyse histamine receptors are available, e.g. pharmacological characterisation of known or new compounds by different types of assays or mutagenesis studies. To obtain insights into the histamine receptors on a molecular and structural level, crystal structures have to be determined and molecular modelling studies have to be performed. It is widely accepted to generate homology models of the receptor of interest based on an appropriate crystal structure as a template and to refine the resulting models by molecular dynamic simulations. A lot of modelling techniques, e.g. docking, QSAR or interaction fingerprint methods, are used to predict binding modes of ligands and pharmacological data, e.g. affinity or even efficacy. However, within the last years, molecular dynamic simulations got more and more important: First of all, molecular dynamic simulations are very helpful to refine the binding mode of a ligand to a histamine receptor, obtained by docking studies. Furthermore, with increasing computational performance it got possible to simulate complete binding pathways of ions or ligands from the aqueous extracellular phase into the allosteric or orthosteric binding pocket of histamine receptors.

GPCRdb: the G protein-coupled receptor database - an introduction

GPCRs make up the largest family of human membrane proteins and of drug targets. Recent advances in GPCR pharmacology and crystallography have shed new light on signal transduction, allosteric modulation and biased signalling, translating into new mechanisms and principles for drug design. The GPCR database, GPCRdb, has served the community for over 20 years and has recently been extended to include a more multidisciplinary audience. This review is intended to introduce new users to the services in GPCRdb, which meets three overall purposes: firstly, to provide reference data in an integrated, annotated and structured fashion, with a focus on sequences, structures, single‐point mutations and ligand interactions. Secondly, to equip the community with a suite of web tools for swift analysis of structures, sequence similarities, receptor relationships, and ligand target profiles. Thirdly, to facilitate dissemination through interactive diagrams of, for example, receptor residue topologies, phylogenetic relationships and crystal structure statistics. Herein, these services are described for the first time; visitors and guides are provided with good practices for their utilization. Finally, we describe complementary databases cross‐referenced by GPCRdb and web servers with corresponding functionality.

GPCR-ModSim: A comprehensive web based solution for modeling G-protein coupled receptors

GPCR-ModSim (http://open.gpcr-modsim.org) is a centralized and easy to use service dedicated to the structural modeling of G-protein Coupled Receptors (GPCRs). 3D molecular models can be generated from amino acid sequence by homology-modeling techniques, considering different receptor conformations. GPCR-ModSim includes a membrane insertion and molecular dynamics (MD) equilibration protocol, which can be used to refine the generated model or any GPCR structure uploaded to the server, including if desired non-protein elements such as orthosteric or allosteric ligands, structural waters or ions. We herein revise the main characteristics of GPCR-ModSim and present new functionalities. The templates used for homology modeling have been updated considering the latest structural data, with separate profile structural alignments built for inactive, partially-active and active groups of templates. We have also added the possibility to perform multiple-template homology modeling in a unique and flexible way. Finally, our new MD protocol considers a series of distance restraints derived from a recently identified conserved network of helical contacts, allowing for a smoother refinement of the generated models which is particularly advised when there is low homology to the available templates. GPCR- ModSim has been tested on the GPCR Dock 2013 competition with satisfactory results.

The Dynamic Process of Drug-GPCR Binding at Either Orthosteric or Allosteric Sites Evaluated by Metadynamics

Major advances in G Protein-Coupled Receptor (GPCR) structural biology over the past few years have yielded a significant number of high-resolution crystal structures for several different receptor subtypes. This dramatic increase in GPCR structural information has underscored the use of automated docking algorithms for the discovery of novel ligands that can eventually be developed into improved therapeutics. However, these algorithms are often unable to discriminate between different, yet energetically similar, poses because of their relatively simple scoring functions. Here, we describe a metadynamics-based approach to study the dynamic process of ligand binding to/unbinding from GPCRs with a higher level of accuracy and yet satisfying efficiency.

Galaxy7TM: flexible GPCR-ligand docking by structure refinement

G-protein-coupled receptors (GPCRs) play important physiological roles related to signal transduction and form a major group of drug targets. Prediction of GPCR-ligand complex structures has therefore important implications to drug discovery. With previously available servers, it was only possible to first predict GPCR structures by homology modeling and then perform ligand docking on the model structures. However, model structures generated without explicit consideration of specific ligands of interest can be inaccurate because GPCR structures can be affected by ligand binding. The Galaxy7TM server, freely accessible at http://galaxy.seoklab.org/7TM, improves an input GPCR structure by simultaneous ligand docking and flexible structure refinement using GALAXY methods. The server shows better performance in both ligand docking and GPCR structure refinement than commonly used programs AutoDock Vina and Rosetta MPrelax, respectively.