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

Influence of Trp-Cage on the Function and Stability of GLP-1R Agonist Exenatide Derivatives

Exenatide (Ex4), a GLP-1 incretin mimetic polypeptide, is an effective therapeutic agent against diabetes and obesity. We highlight the indirect role of Ex4's structure-stabilizing Trp-cage (Tc) motif in governing GLP-1 receptor (GLP-1R) signal transduction. We use various Ex4 derivatives to explore how Tc compactness influences thermal stability, aggregation, enhancement of insulin secretion, and GLP-1R binding. We found that Ex4 variants decorated with fortified Tc motifs exhibit increased resistance to unfolding and aggregation but show an inverse relationship between the bioactivity and stability. Molecular dynamics simulations coupled with a rigid-body segmentation protocol to analyze dynamic interconnectedness revealed that the constrained Tc motifs remain intact within the receptor-ligand complexes but interfere with one of the major stabilizing contacts and recognition loci on the extracellular side of GLP-1R, dislodging the N-terminal activating region of the hormone mimetics, and restrict the free movement of TM6, the main signal transduction device of GLP-1R.

Receptor Pharmacogenomics: Deciphering Genetic Influence on Drug Response

The paradigm "one drug fits all" or "one dose fits all" will soon be challenged by pharmacogenetics research and application. Drug response-efficacy or safety-depends on interindividual variability. The current clinical practice does not include genetic screening as a routine procedure and does not account for genetic variation. Patients with the same illness receive the same treatment, yielding different responses. Integrating pharmacogenomics in therapy would provide critical information about how a patient will respond to a certain drug. Worldwide, great efforts are being made to achieve a personalized therapy-based approach. Nevertheless, a global harmonized guideline is still needed. Plasma membrane proteins, like receptor tyrosine kinase (RTK) and G protein-coupled receptors (GPCRs), are ubiquitously expressed, being involved in a diverse array of physiopathological processes. Over 30% of drugs approved by the FDA target GPCRs, reflecting the importance of assessing the genetic variability among individuals who are treated with these drugs. Pharmacogenomics of transmembrane protein receptors is a dynamic field with profound implications for precision medicine. Understanding genetic variations in these receptors provides a framework for optimizing drug therapies, minimizing adverse reactions, and advancing the paradigm of personalized healthcare.

Impact of 2-hydroxypropyl-β-cyclodextrin inclusion complex formation on dopamine receptor-ligand interaction - A case study

The octanol-water distribution coefficient (logP), used as a measure of lipophilicity, plays a major role in the drug design and discovery processes. While average logP values remain unchanged in approved oral drugs since 1983, current medicinal chemistry trends towards increasingly lipophilic compounds that require adapted analytical workflows and drug delivery systems. Solubility enhancers like cyclodextrins (CDs), especially 2-hydroxypropyl-β-CD (2-HP-β-CD), have been studied in vitro and in vivo investigating their ADMET (adsorption, distribution, metabolism, excretion and toxicity)-related properties. However, data is scarce regarding the applicability of CD inclusion complexes (ICs) in vitro compared to pure compounds. In this study, dopamine receptor (DR) ligands were used as a case study, utilizing a combined in silico/in vitro workflow. Media-dependent solubility and IC stoichiometry were investigated using HPLC. NMR was used to observe IC formation-caused chemical shift deviations while in silico approaches utilizing basin hopping global minimization were used to propose putative IC binding modes. A cell-based in vitro homogeneous time-resolved fluorescence (HTRF) assay was used to quantify ligand binding affinity at the DR subtype 2 (D2R). While all ligands showed increased solubility using 2-HP-β-CD, they differed regarding IC stoichiometry and receptor binding affinity. This case study shows that IC-formation was ligand-dependent and sometimes altering in vitro binding. Therefore, IC complex formation can't be recommended as a general means of improving compound solubility for in vitro studies as they may alter ligand binding.

Tumor-Homing Peptides as Crucial Component of Magnetic-Based Delivery Systems: Recent Developments and Pharmacoeconomical Perspective

According to data from the World Health Organization (WHO), cancer is considered to be one of the leading causes of death worldwide, and new therapeutic approaches, especially improved novel cancer treatment regimens, are in high demand. Considering that many chemotherapeutic drugs tend to have poor pharmacokinetic profiles, including rapid clearance and limited on-site accumulation, a combined approach with tumor-homing peptide (THP)-functionalized magnetic nanoparticles could lead to remarkable improvements. This is confirmed by an increasing number of papers in this field, showing that the on-target peptide functionalization of magnetic nanoparticles improves their penetration properties and ensures tumor-specific binding, which results in an increased clinical response. This review aims to highlight the potential applications of THPs in combination with magnetic carriers across various fields, including a pharmacoeconomic perspective.

Systematic characterization of multi-omics landscape between gut microbial metabolites and GPCRome in Alzheimer's disease

Shifts in the magnitude and nature of gut microbial metabolites have been implicated in Alzheimer's disease (AD), but the host receptors that sense and respond to these metabolites are largely unknown. Here, we develop a systems biology framework that integrates machine learning and multi-omics to identify molecular relationships of gut microbial metabolites with non-olfactory G-protein-coupled receptors (termed the "GPCRome"). We evaluate 1.09 million metabolite-protein pairs connecting 408 human GPCRs and 335 gut microbial metabolites. Using genetics-derived Mendelian randomization and integrative analyses of human brain transcriptomic and proteomic profiles, we identify orphan GPCRs (i.e., GPR84) as potential drug targets in AD and that triacanthine experimentally activates GPR84. We demonstrate that phenethylamine and agmatine significantly reduce tau hyperphosphorylation (p-tau181 and p-tau205) in AD patient induced pluripotent stem cell-derived neurons. This study demonstrates a systems biology framework to uncover the GPCR targets of human gut microbiota in AD and other complex diseases if broadly applied.

A fluorescently-tagged tick kinin neuropeptide triggers peristalsis and labels tick midgut muscles

Ticks are blood-feeding arthropods that require heme for their successful reproduction. During feeding they also acquire pathogens that are subsequently transmitted to humans, wildlife and/or livestock. Understanding the regulation of tick midgut is important for blood meal digestion, heme and nutrient absorption processes and for aspects of pathogen biology in the host. We previously demonstrated the activity of tick kinins on the cognate G protein-coupled receptor. Herein we uncovered the physiological role of the kinin receptor in the tick midgut. A fluorescently-labeled kinin peptide with the endogenous kinin 8 sequence (TMR-RK8), identical in the ticks Rhipicephalus microplus and R. sanguineus, activated and labeled the recombinant R. microplus receptor expressed in CHO-K1 cells. When applied to the live midgut the TMR-RK8 labeled the kinin receptor in muscles while the labeled peptide with the scrambled-sequence of kinin 8 (TMR-Scrambled) did not. The unlabeled kinin 8 peptide competed TMR-RK8, decreasing confocal microscopy signal intensity, indicating TMR-RK8 specificity to muscles. TMR-RK8 was active, inducing significant midgut peristalsis that was video-recorded and evaluated with video tracking software. The TMR-Scrambled peptide used as a negative control did not elicit peristalsis. The myotropic function of kinins in eliciting tick midgut peristalsis was established.

The orphan G protein-coupled receptor 141 expressed in myeloid cells functions as an inflammation suppressor

G protein-coupled receptors (GPCRs) regulate many cellular processes in response to various stimuli, including light, hormones, neurotransmitters, and odorants, some of which play critical roles in innate and adaptive immune responses. However, the physiological functions of many GPCRs and the involvement of them in autoimmune diseases of the central nervous system remain unclear. Here, we demonstrate that GPR141, an orphan GPCR belonging to the class A receptor family, suppresses immune responses. High GPR141 messenger RNA levels were expressed in myeloid-lineage cells, including neutrophils (CD11b + Gr1+), monocytes (CD11b + Gr1-Ly6C+ and CD11b + Gr1-Ly6C-), macrophages (F4/80+), and dendritic cells (CD11c+). Gpr141  -/- mice, which we independently generated, displayed almost no abnormalities in myeloid cell differentiation and compartmentalization in the spleen and bone marrow under steady-state conditions. However, Gpr141 deficiency exacerbated disease conditions of experimental autoimmune encephalomyelitis, an autoimmune disease model for multiple sclerosis, with increased inflammation in the spinal cord. Gpr141  -/- mice showed increased CD11b + Gr1+ neutrophils, CD11b + Gr1- monocytes, CD11c+ dendritic cells, and CD4+ T cell infiltration into the experimental autoimmune encephalomyelitis-induced spinal cord compared with littermate control mice. Lymphocytes enriched from Gpr141  -/- mice immunized with myelin oligodendrocyte glycoprotein 35-55 produced high amounts of interferon-γ, interleukin-17A, and interleukin-6 compared with those from wild-type mice. Moreover, CD11c+ dendritic cells (DCs) purified from Gpr141  -/- mice increased cytokine production of myelin oligodendrocyte glycoprotein 35-55-specific T cells. These findings suggest that GPR141 functions as a negative regulator of immune responses by controlling the functions of monocytes and dendritic cells and that targeting GPR141 may be a possible therapeutic intervention for modulating chronic inflammatory diseases.

A Bright Future? A Perspective on Class C GPCR Based Genetically Encoded Biosensors

One of the major challenges in molecular neuroscience today is to accurately monitor neurotransmitters, neuromodulators, peptides, and various other biomolecules in the brain with high temporal and spatial resolution. Only a comprehensive understanding of neuromodulator dynamics, their release probability, and spatial distribution will unravel their ultimate role in cognition and behavior. This Perspective offers an overview of potential design strategies for class C GPCR-based biosensors. It briefly highlights current applications of GPCR-based biosensors, with a primary focus on class C GPCRs and their unique structural characteristics compared with other GPCR subfamilies. The discussion offers insights into plausible future design approaches for biosensor development targeting members of this specific GPCR subfamily. It is important to note that, at this stage, we are contemplating possibilities rather than presenting a concrete guide, as the pipeline is still under development.

Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A3 Receptor

A structure-based drug design pipeline that considers both thermodynamic and kinetic binding data of ligands against a receptor will enable the computational design of improved drug molecules. For unresolved GPCR-ligand complexes, a workflow that can apply both thermodynamic and kinetic binding data in combination with alpha-fold (AF)-derived or other homology models and experimentally resolved binding modes of relevant ligands in GPCR-homologs needs to be tested. Here, as test case, we studied a congeneric set of ligands that bind to a structurally unresolved G protein-coupled receptor (GPCR), the inactive human adenosine A3 receptor (hA3R). We tested three available homology models from which two have been generated from experimental structures of hA1R or hA2AR and one model was a multistate alphafold 2 (AF2)-derived model. We applied alchemical calculations with thermodynamic integration coupled with molecular dynamics (TI/MD) simulations to calculate the experimental relative binding free energies and residence time (τ)-random accelerated MD (τ-RAMD) simulations to calculate the relative residence times (RTs) for antagonists. While the TI/MD calculations produced, for the three homology models, good Pearson correlation coefficients, correspondingly, r = 0.74, 0.62, and 0.67 and mean unsigned error (mue) values of 0.94, 1.31, and 0.81 kcal mol-1, the τ-RAMD method showed r = 0.92 and 0.52 for the first two models but failed to produce accurate results for the multistate AF2-derived model. With subsequent optimization of the AF2-derived model by reorientation of the side chain of R1735.34 located in the extracellular loop 2 (EL2) that blocked ligand's unbinding, the computational model showed r = 0.84 for kinetic data and improved performance for thermodynamic data (r = 0.81, mue = 0.56 kcal mol-1). Overall, after refining the multistate AF2 model with physics-based tools, we were able to show a strong correlation between predicted and experimental ligand relative residence times and affinities, achieving a level of accuracy comparable to an experimental structure. The computational workflow used can be applied to other receptors, helping to rank candidate drugs in a congeneric series and enabling the prioritization of leads with stronger binding affinities and longer residence times.

Design of an effective small expression tag to enhance GPCR production in E. coli-based cell-free and whole cell expression systems

G protein-coupled receptors (GPCRs) play crucial roles in sensory, immune, and tumor metastasis processes, making them valuable targets for pharmacological and sensing applications in various industries. However, most GPCRs have low production yields in Escherichia coli (E. coli) expression systems. To overcome this limitation, we introduced AT10 tag, an effective fusion tag that could significantly enhance expression levels of various GPCRs in E. coli and its derived cell-free protein synthesis (CFPS) system. This AT10 tag consisted of an A/T-rich gene sequence designed via optimization of translation initiation rate. It is translated into a short peptide sequence of 10 amino acids at the N-terminus of GPCRs. Additionally, effector proteins could be utilized to suppress cytotoxicity caused by membrane protein expression, further boosting GPCR production in E. coli. Enhanced expression of various GPCRs using this AT10 tag is a promising approach for large-scale production of functional GPCRs in E. coli-based CFPS and whole cell systems, enabling their potential utilization across a wide range of industrial applications.

Oncoinformatic screening of the gene clusters involved in the HER2-positive breast cancer formation along with the in silico pharmacodynamic profiling of selective long-chain omega-3 fatty acids as the metastatic antagonists

The HER2-positive patients occupy ~ 30% of the total breast cancer patients globally where no prevalent drugs are available to mitigate the frequent metastasis clinically except lapatinib and neratinib. This scarcity reinforced researchers' quest for new medications where natural substances are significantly considered. Valuing the aforementioned issues, this research aimed to study the ERBB2-mediated string networks that work behind the HER2-positive breast cancer formation regarding co-expression, gene regulation, GAMA-receptor-signaling pathway, cellular polarization, and signal inhibition. Following the overexpression, promotor methylation, and survivability profiles of ERBB2, the super docking position of HER2 was identified using the quantum tunneling algorithm. Supramolecular docking was conducted to study the target specificity of EPA and DHA fatty acids followed by a comprehensive molecular dynamic simulation (100 ns) to reveal the RMSD, RMSF, Rg, SASA, H-bonds, and MM/GBSA values. Finally, potential drug targets for EPA and DHA in breast cancer were constructed to determine the drug-protein interactions (DPI) at metabolic stages. Considering the values resulting from the combinational models of the oncoinformatic, pharmacodynamic, and metabolic parameters, long-chain omega-3 fatty acids like EPA and DHA can be considered as potential-targeted therapeutics for HER2-positive breast cancer treatment.

GPCR molecular dynamics forecasting using recurrent neural networks

G protein-coupled receptors (GPCRs) are a large superfamily of cell membrane proteins that play an important physiological role as transmitters of extracellular signals. Signal transmission through the cell membrane depends on conformational changes in the transmembrane region of the receptor, which makes the investigation of the dynamics in these regions particularly relevant. Molecular dynamics (MD) simulations provide a wealth of data about the structure, dynamics, and physiological function of biological macromolecules by modelling the interactions between their atomic constituents. In this study, a Recurrent and Convolutional Neural Network (RNN) model, namely Long Short-Term Memory (LSTM), is used to predict the dynamics of two GPCR states and three specific simulations of each one, through their activation path and focussing on specific receptor regions. Active and inactive states of the GPCRs are analysed in six scenarios involving APO, Full Agonist (BI 167107) and Partial Inverse Agonist (carazolol) of the receptor. Four Machine Learning models with increasing complexity in terms of neural network architecture are evaluated, and their results discussed. The best method achieves an overall RMSD lower than 0.139 Å and the transmembrane helices are the regions showing the minimum prediction errors and minimum relative movements of the protein.

The Lateral Metalation of Isoxazolo[3,4-d]pyridazinones towards Hit-to-Lead Development of Selective Positive Modulators of Metabotropic Glutamate Receptors

Isoxazolo[3,4-d] pyridazinones ([3,4-d]s) were previously shown to have selective positive modulation at the metabotropic glutamate receptor (mGluR) Subtypes 2 and 4, with no functional cross-reactivity at mGluR1a, mGluR5, or mGluR8. Additional analogs were prepared to access more of the allosteric pocket and achieve higher binding affinity, as suggested by homology modeling. Two different sets of analogs were generated. One uses the fully formed [3,4-d] with an N6-aryl with and without halogens. These underwent successful selective lateral metalation and electrophilic quenching (LM&EQ) at the C3 of the isoxazole. In a second set of analogs, a phenyl group was introduced at the C4 position of the [3,4-d] ring via a condensation of 4-phenylacetyl-3-ethoxcarbonyl-5-methyl isoxazole with the corresponding hydrazine to generate the 3,4-ds 2b and 2j to 2n.

An Atomic Level Investigation of Sodium Ions Regulating Agonist and Antagonist Binding in the Active Site of a Novel Target 5HT2BR Against Drug-Resistant Epilepsy

The study investigates the movement of sodium ions inside the ligand-binding pocket of the class-A GPCR serotonin receptor (5HT_2BR), a primary target for modern drugs. The available PDBs are mutant chimeras, so a 3D structure is modeled and validated by structural similarity (84.05%), Ramachandran favorable residues (93.01%), and clash score. Using MD simulations (500 ns), the ion active site is tracked in the presence and absence of ions and ligands. The ions enter the active site along helices III, VI, and VII, and the primary residue (ASP^3.32) interacts with ions via H-bond (stronger- ~2.4 Å). The radial distribution function around ASP^3.32 rises promptly at intermediate distances (2 Å < r < 4 Å), suggesting a higher probability of finding sodium ions at these distances. The ions stabilize the receptor at a better RMSD and promote stronger interactions (3-H-bonds, 1-π-bond~3.35 Å) with the agonist, and not the antagonist (no H-bond). Simulating unrestrained ligands further confirms this pattern, suggesting that ions might promote agonist binding but not be a prerequisite for antagonist action. The study highlights the mechanistic evaluation of sodium ions mobility in 5HT_2BR modulation and ligand binding, showing potential in drug development.

The crosstalk between 5-HT2AR and mGluR2 in schizophrenia

Schizophrenia is a severe brain disorder that usually produces a lifetime of disability. First generation or typical antipsychotics such as haloperidol and second generation or atypical antipsychotics such as clozapine and risperidone remain the current standard for schizophrenia treatment. In some patients with schizophrenia, antipsychotics produce complete remission of positive symptoms, such as hallucinations and delusions. However, antipsychotic drugs are ineffective against cognitive deficits and indeed treated schizophrenia patients have small improvements or even deterioration in several cognitive domains. This underlines the need for novel and more efficient therapeutic targets for schizophrenia treatment. Serotonin and glutamate have been identified as key parts of two neurotransmitter systems involved in fundamental brain processes. Serotonin (or 5-hydroxytryptamine) 5-HT2A receptor (5-HT2AR) and metabotropic glutamate 2 receptor (mGluR2) are G protein-coupled receptors (GPCRs) that interact at epigenetic and functional levels. These two receptors can form GPCR heteromeric complexes through which their pharmacology, function and trafficking becomes affected. Here we review past and current research on the 5-HT2AR-mGluR2 heterocomplex and its potential implication in schizophrenia and antipsychotic drug action. This article is part of the Special Issue on "The receptor-receptor interaction as a new target for therapy".

Activity Models of Key GPCR Families in the Central Nervous System: A Tool for Many Purposes

G protein-coupled receptors (GPCRs) are targets of many drugs, of which ∼25% are indicated for central nervous system (CNS) disorders. Drug promiscuity affects their efficacy and safety profiles. Predicting the polypharmacology profile of compounds against GPCRs can thus provide a basis for producing more precise therapeutics by considering the targets and the anti-targets in that family of closely related proteins. We provide a tool for predicting the polypharmacology of compounds within prominent GPCR families in the CNS: serotonin, dopamine, histamine, muscarinic, opioid, and cannabinoid receptors. Our in-house algorithm, "iterative stochastic elimination" (ISE), produces high-quality ligand-based models for agonism and antagonism at 31 GPCRs. The ISE models correctly predict 68% of CNS drug-GPCR interactions, while the "similarity ensemble approach" predicts only 33%. The activity models correctly predict 56% of reported activities of DrugBank molecules for these CNS receptors. We conclude that the combination of interactions and activity profiles generated by screening through our models form the basis for subsequent designing and discovering novel therapeutics, either single, multitargeting, or repurposed.

Dopamine Receptor Ligand Selectivity-An In Silico/In Vitro Insight

Different dopamine receptor (DR) subtypes are involved in pathophysiological conditions such as Parkinson's Disease (PD), schizophrenia and depression. While many DR-targeting drugs have been approved by the U.S. Food and Drug Administration (FDA), only a very small number are truly selective for one of the DR subtypes. Additionally, most of them show promiscuous activity at related G-protein coupled receptors, thus suffering from diverse side-effect profiles. Multiple studies have shown that combined in silico/in vitro approaches are a valuable contribution to drug discovery processes. They can also be applied to divulge the mechanisms behind ligand selectivity. In this study, novel DR ligands were investigated in vitro to assess binding affinities at different DR subtypes. Thus, nine D2R/D3R-selective ligands (micro- to nanomolar binding affinities, D3R-selective profile) were successfully identified. The most promising ligand exerted nanomolar D3R activity (Ki = 2.3 nM) with 263.7-fold D2R/D3R selectivity. Subsequently, ligand selectivity was rationalized in silico based on ligand interaction with a secondary binding pocket, supporting the selectivity data determined in vitro. The developed workflow and identified ligands could aid in the further understanding of the structural motifs responsible for DR subtype selectivity, thus benefitting drug development in D2R/D3R-associated pathologies such as PD.

Improving the Modeling of Extracellular Ligand Binding Pockets in RosettaGPCR for Conformational Selection

G protein-coupled receptors (GPCRs) are the largest class of drug targets and undergo substantial conformational changes in response to ligand binding. Despite recent progress in GPCR structure determination, static snapshots fail to reflect the conformational space of putative binding pocket geometries to which small molecule ligands can bind. In comparative modeling of GPCRs in the absence of a ligand, often a shrinking of the orthosteric binding pocket is observed. However, the exact prediction of the flexible orthosteric binding site is crucial for adequate structure-based drug discovery. In order to improve ligand docking and guide virtual screening experiments in computer-aided drug discovery, we developed RosettaGPCRPocketSize. The algorithm creates a conformational ensemble of biophysically realistic conformations of the GPCR binding pocket between the TM bundle, which is consistent with a knowledge base of expected pocket geometries. Specifically, tetrahedral volume restraints are defined based on information about critical residues in the orthosteric binding site and their experimentally observed range of Cα-Cα-distances. The output of RosettaGPCRPocketSize is an ensemble of binding pocket geometries that are filtered by energy to ensure biophysically probable arrangements, which can be used for docking simulations. In a benchmark set, pocket shrinkage observed in the default RosettaGPCR was reduced by up to 80% and the binding pocket volume range and geometric diversity were increased. Compared to models from four different GPCR homology model databases (RosettaGPCR, GPCR-Tasser, GPCR-SSFE, and GPCRdb), the here-created models showed more accurate volumes of the orthosteric pocket when evaluated with respect to the crystallographic reference structure. Furthermore, RosettaGPCRPocketSize was able to generate an improved realistic pocket distribution. However, while being superior to other homology models, the accuracy of generated model pockets was comparable to AlphaFold2 models. Furthermore, in a docking benchmark using small-molecule ligands with a higher molecular weight between 400 and 700 Da, a higher success rate in creating native-like binding poses was observed. In summary, RosettaGPCRPocketSize can generate GPCR models with realistic orthosteric pocket volumes, which are useful for structure-based drug discovery applications.

Where is the mind within the brain? Transient selection of subnetworks by metabotropic receptors and G protein-gated ion channels

Perhaps the most important question posed by brain research is: How the brain gives rise to the mind. To answer this question, we have primarily relied on the connectionist paradigm: The brain's entire knowledge and thinking skills are thought to be stored in the connections; and the mental operations are executed by network computations. I propose here an alternative paradigm: Our knowledge and skills are stored in metabotropic receptors (MRs) and the G protein-gated ion channels (GPGICs). Here, mental operations are assumed to be executed by the functions of MRs and GPGICs. As GPGICs have the capacity to close or open branches of dendritic trees and axon terminals, their states transiently re-route neural activity throughout the nervous system. First, MRs detect ligands that signal the need to activate GPGICs. Next, GPGICs transiently select a subnetwork within the brain. The process of selecting this new subnetwork is what constitutes a mental operation - be it in a form of directed attention, perception or making a decision. Synaptic connections and network computations play only a secondary role, supporting MRs and GPGICs. According to this new paradigm, the mind emerges within the brain as the function of MRs and GPGICs whose primary function is to continually select the pathways over which neural activity will be allowed to pass. It is argued that MRs and GPGICs solve the scaling problem of intelligence from which the connectionism paradigm suffers.

Deep-Learning-Enhanced Diffusion Imaging Assay for Resolving Local-Density Effects on Membrane Receptors

G-protein-coupled receptor (GPCR) density at the cell surface is thought to regulate receptor function. Spatially resolved measurements of local-density effects on GPCRs are needed but technically limited by density heterogeneity and mobility of membrane receptors. We now develop a deep-learning (DL)-enhanced diffusion imaging assay that can measure local-density effects on ligand-receptor interactions in the plasma membrane of live cells. In this method, the DL algorithm allows the transformation of 100 ms exposure images to density maps that report receptor numbers over any specified region with ∼95% accuracy by 1 s exposure images as ground truth. With the density maps, a diffusion assay is further established for spatially resolved measurements of receptor diffusion coefficient as well as to express relationships between receptor diffusivity and local density. By this assay, we scrutinize local-density effects on chemokine receptor CXCR4 interactions with various ligands, which reveals that an agonist prefers to act with CXCR4 at low density while an inverse agonist dominates at high density. This work suggests a new insight into density-dependent receptor regulation as well as provides an unprecedented assay that can be applicable to a wide variety of receptors in live cells.

Correlated evolution of social organization and lifespan in mammals

Discerning the relationship between sociality and longevity would permit a deeper understanding of how animal life history evolved. Here, we perform a phylogenetic comparative analysis of ~1000 mammalian species on three states of social organization (solitary, pair-living, and group-living) and longevity. We show that group-living species generally live longer than solitary species, and that the transition rate from a short-lived state to a long-lived state is higher in group-living than non-group-living species, altogether supporting the correlated evolution of social organization and longevity. The comparative brain transcriptomes of 94 mammalian species identify 31 genes, hormones and immunity-related pathways broadly involved in the association between social organization and longevity. Further selection features reveal twenty overlapping pathways under selection for both social organization and longevity. These results underscore a molecular basis for the influence of the social organization on longevity.

Hierarchical analysis of the target-based scoring function modification for the example of selected class A GPCRs

Computational methods, especially molecular docking-based calculations, have become indispensable in the modern drug discovery workflow. The constantly increasing chemical space requires fast, robust but most of all highly predictive methods to search for new bioactive agents. Thus, the scoring function (SF) is a useful and broadly applied energy-based element of docking software, allowing quick and effective evaluation of a ligand's propensity to bind to selected protein targets. Despite many spectacular successes of molecular docking applications in virtual screening (VS), the obtained results are often far from ideal, leading to incorrect selection of hit molecules and poor pose prediction. In our study we focused on docking calculation for the selected class A G-protein coupled receptors (GPCRs), with experimentally determined 3D structures and a sufficient set of known ligands with affinity values reported in the ChEMBL database. Our goal is to investigate how much the energy-based scoring function for this particular target class changes when changing from the default to the re-estimated weighting scheme on the specified energy terms in the SF definition. Additionally, we want to verify if indeed more accurate results are obtained when considering different levels of the biological hierarchy, namely: the whole class A GPCRs, sub-subfamilies, or just the individual proteins while applying default or specifically designed weighting coefficients. The performed calculation and evaluation factor values suggest a significant improvement of docking results for the designed SF definition. This individual approach improves the accuracy of binding affinity prediction and active compound recognition. The designed scoring function for classes, sub-subfamilies, or proteins leads to a significant improvement of molecular docking performance, especially at the level of individual proteins. Our results show that to increase the efficiency and predictive power of molecular docking calculations applied in classical VS, the strategy based on the individual approach for scoring function definition for selected proteins should be considered.

Class A and C GPCR Dimers in Neurodegenerative Diseases

Neurodegenerative diseases affect over 30 million people worldwide with an ascending trend. Most individuals suffering from these irreversible brain damages belong to the elderly population, with onset between 50 and 60 years. Although the pathophysiology of such diseases is partially known, it remains unclear upon which point a disease turns degenerative. Moreover, current therapeutics can treat some of the symptoms but often have severe side effects and become less effective in long-term treatment. For many neurodegenerative diseases, the involvement of G proteincoupled receptors (GPCRs), which are key players of neuronal transmission and plasticity, has become clearer and holds great promise in elucidating their biological mechanism. With this review, we introduce and summarize class A and class C GPCRs, known to form heterodimers or oligomers to increase their signalling repertoire. Additionally, the examples discussed here were shown to display relevant alterations in brain signalling and had already been associated with the pathophysiology of certain neurodegenerative diseases. Lastly, we classified the heterodimers into two categories of crosstalk, positive or negative, for which there is known evidence.

Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand–receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.

Short-Chain Fatty Acid Receptors and Blood Pressure Regulation: Council on Hypertension Mid-Career Award for Research Excellence 2021

The gut microbiome influences host physiology and pathophysiology through several pathways, one of which is microbial production of chemical metabolites which interact with host signaling pathways. Short-chain fatty acids (SCFAs) are a class of gut microbial metabolites known to activate multiple signaling pathways in the host. Growing evidence indicates that the gut microbiome is linked to blood pressure, that SCFAs modulate blood pressure regulation, and that delivery of exogenous SCFAs lowers blood pressure. Given that hypertension is a key risk factor for cardiovascular disease, the examination of novel contributors to blood pressure regulation has the potential to lead to novel approaches or treatments. Thus, this review will discuss SCFAs with a focus on their host G protein-coupled receptors including GPR41 (G protein-coupled receptor 41), GPR43, and GPR109A, as well as OLFR78 (olfactory receptor 78) and OLFR558. This includes a discussion of the ligand profiles, G protein coupling, and tissue distribution of each receptor. We will also review phenotypes relevant to blood pressure regulation which have been reported to date for Gpr41, Gpr43, Gpr109a, and Olfr78 knockout mice. In addition, we will consider how SCFA signaling influences physiology at baseline, and, how SCFA signaling may contribute to blood pressure regulation in settings of hypertension. In sum, this review will integrate current knowledge regarding how SCFAs and their receptors regulate blood pressure.

Bell-Evans model and steered molecular dynamics in uncovering the dissociation kinetics of ligands targeting G-protein-coupled receptors

Recently, academic and industrial scientific communities involved in kinetics-based drug development have become immensely interested in predicting the drug target residence time. Screening drug candidates in terms of their computationally predicted residence times, which is a measure of drug efficacy in vivo, and simultaneously assessing computational binding affinities are becoming inevitable. Non-equilibrium molecular simulation approaches are proven to be useful in this purpose. Here, we have implemented an optimized approach of combining the data derived from steered molecular dynamics simulations and the Bell-Evans model to predict the absolute residence times of the antagonist ZMA241385 and agonist NECA that target the A2A adenosine receptor of the G-protein-coupled receptor (GPCR) protein family. We have predicted the absolute ligand residence times on the timescale of seconds. However, our predictions were many folds shorter than those determined experimentally. Additionally, we calculated the thermodynamics of ligand binding in terms of ligand binding energies and the per-residue contribution of the receptor. Subsequently, binding pocket hotspot residues that would be important for further computational mutagenesis studies were identified. In the experiment, similar sets of residues were found to be in significant contact with both ligands under study. Our results build a strong foundation for further improvement of our approach by rationalizing the kinetics of ligand unbinding with the thermodynamics of ligand binding.

Implications of a Neuronal Receptor Family, Metabotropic Glutamate Receptors, in Cancer Development and Progression

Cancer is the second leading cause of death, and incidences are increasing globally. Simply defined, cancer is the uncontrolled proliferation of a cell, and depending on the tissue of origin, the cancer etiology, biology, progression, prognosis, and treatment will differ. Carcinogenesis and its progression are associated with genetic factors that can either be inherited and/or acquired and are classified as an oncogene or tumor suppressor. Many of these genetic factors converge on common signaling pathway(s), such as the MAPK and PI3K/AKT pathways. In this review, we will focus on the metabotropic glutamate receptor (mGluR) family, an upstream protein that transmits extracellular signals into the cell and has been shown to regulate many aspects of tumor development and progression. We explore the involvement of members of this receptor family in various cancers that include breast cancer, colorectal cancer, glioma, kidney cancer, melanoma, oral cancer, osteosarcoma, pancreatic cancer, prostate cancer, and T-cell cancers. Intriguingly, depending on the member, mGluRs can either be classified as oncogenes or tumor suppressors, although in general most act as an oncogene. The extensive work done to elucidate the role of mGluRs in various cancers suggests that it might be a viable strategy to therapeutically target glutamatergic signaling.

L-DOPA and Droxidopa: From Force Field Development to Molecular Docking into Human β2-Adrenergic Receptor

The increasing interest in the molecular mechanism of the binding of different agonists and antagonists to β₂-adrenergic receptor (β₂AR) inactive and active states has led us to investigate protein–ligand interactions using molecular docking calculations. To perform this study, the 3.2 Å X-ray crystal structure of the active conformation of human β₂AR in the complex with the endogenous agonist adrenaline has been used as a template for investigating the binding of two exogenous catecholamines to this adrenergic receptor. Here, we show the derivation of L-DOPA and Droxidopa OPLS all atom (AA) force field (FF) parameters via quantum mechanical (QM) calculations, molecular dynamics (MD) simulations in aqueous solutions of the two catecholamines and the molecular docking of both ligands into rigid and flexible β₂AR models. We observe that both ligands share with adrenaline similar experimentally observed binding anchor sites, which are constituted by Asp113/Asn312 and Ser203/Ser204/Ser207 side chains. Moreover, both L-DOPA and Droxidopa molecules exhibit binding affinities comparable to that predicted for adrenaline, which is in good agreement with previous experimental and computational results. L-DOPA and Droxidopa OPLS AA FFs have also been tested by performing MD simulations of these ligands docked into β₂AR proteins embedded in lipid membranes. Both hydrogen bonds and hydrophobic interaction networks observed over the 1 μs MD simulation are comparable with those derived from molecular docking calculations and MD simulations performed with the CHARMM FF.

Orphan GPR26 Counteracts Early Phases of Hyperglycemia-Mediated Monocyte Activation and Is Suppressed in Diabetic Patients

Diabetes is the ninth leading cause of death, with an estimated 1.5 million deaths worldwide. Type 2 diabetes (T2D) results from the body’s ineffective use of insulin and is largely the result of excess body weight and physical inactivity. T2D increases the risk of cardiovascular diseases, retinopathy, and kidney failure by two-to three-fold. Hyperglycemia, as a hallmark of diabetes, acts as a potent stimulator of inflammatory condition by activating endothelial cells and by dysregulating monocyte activation. G-protein couple receptors (GPCRs) can both exacerbate and promote inflammatory resolution. Genome-wide association studies (GWAS) indicate that GPCRs are differentially regulated in inflammatory and vessel cells from diabetic patients. However, most of these GPCRs are orphan receptors, for which the mechanism of action in diabetes is unknown. Our data indicated that orphan GPCR26 is downregulated in the PBMC isolated from T2D patients. In contrast, GPR26 was initially upregulated in human monocytes and PBMC treated with high glucose (HG) levels and then decreased upon chronic and prolonged HG exposure. GPR26 levels were decreased in T2D patients treated with insulin compared to non-insulin treated patients. Moreover, GPR26 inversely correlated with the BMI and the HbA1c of diabetic compared to non-diabetic patients. Knockdown of GPR26 enhanced monocyte ROS production, MAPK signaling, pro-inflammatory activation, monocyte adhesion to ECs, and enhanced the activity of Caspase 3, a pro-apoptotic molecule. The same mechanisms were activated by HG and exacerbated when GPR26 was knocked down. Hence, our data indicated that GPR26 is initially activated to protect monocytes from HG and is inhibited under chronic hyperglycemic conditions.

Identification of Novel Dopamine D2 Receptor Ligands—A Combined In Silico/In Vitro Approach

Diseases of the central nervous system are an alarming global problem showing an increasing prevalence. Dopamine receptor D2 (D2R) has been shown to be involved in central nervous system diseases. While different D2R-targeting drugs have been approved by the FDA, they all suffer from major drawbacks due to promiscuous receptor activity leading to adverse effects. Increasing the number of potential D2R-targeting drug candidates bears the possibility of discovering molecules with less severe side-effect profiles. In dire need of novel D2R ligands for drug development, combined in silico/in vitro approaches have been shown to be efficient strategies. In this study, in silico pharmacophore models were generated utilizing both ligand- and structure-based approaches. Subsequently, different databases were screened for novel D2R ligands. Selected virtual hits were investigated in vitro, quantifying their binding affinity towards D2R. This workflow successfully identified six novel D2R ligands exerting micro- to nanomolar (most active compound KI = 4.1 nM) activities. Thus, the four pharmacophore models showed prospective true-positive hit rates in between 4.5% and 12%. The developed workflow and identified ligands could aid in developing novel drug candidates for D2R-associated pathologies.

MC4R biased signalling and the conformational basis of biological function selections

The MC4R, a GPCR, has long been a major target for obesity treatment. As the most well‐studied melanocortin receptor subtype, the evolutionary knowledge pushes the drug development and structure–activity relationship (SAR) moving forward. The past decades have witnessed the evolution of scientists' view on GPCRs gradually from the control of a single canonical signalling pathway via a bilateral ‘active‐inactive’ model to a multi‐state alternative model where the ligands' binding affects the selection of the downstream signalling. This evolution brings the concept of biased signalling and the beginning of the next generation of peptide drug development, with the aim of turning from receptor subtype specificity to signalling pathway selectivity. The determination of the value structures of the MC4R revealed insights into the working mechanism of MC4R activation upon binding of agonists. However, new challenge has risen as we seek to unravel the mystery of MC4R signalling selection. Thus, more biased agonists and ligands with representative biological functions are needed to solve the rest of the puzzle.

What Makes GPCRs from Different Families Bind to the Same Ligand?

G protein-coupled receptors (GPCRs) are the largest class of cell-surface receptor proteins with important functions in signal transduction and often serve as therapeutic drug targets. With the rapidly growing public data on three dimensional (3D) structures of GPCRs and GPCR-ligand interactions, computational prediction of GPCR ligand binding becomes a convincing option to high throughput screening and other experimental approaches during the beginning phases of ligand discovery. In this work, we set out to computationally uncover and understand the binding of a single ligand to GPCRs from several different families. Three-dimensional structural comparisons of the GPCRs that bind to the same ligand revealed local 3D structural similarities and often these regions overlap with locations of binding pockets. These pockets were found to be similar (based on backbone geometry and side-chain orientation using APoc), and they correlate positively with electrostatic properties of the pockets. Moreover, the more similar the pockets, the more likely a ligand binding to the pockets will interact with similar residues, have similar conformations, and produce similar binding affinities across the pockets. These findings can be exploited to improve protein function inference, drug repurposing and drug toxicity prediction, and accelerate the development of new drugs.

Suppression of tumor progression by thioredoxin-interacting protein-dependent adenosine 2B receptor degradation in a PLAG-treated Lewis lung carcinoma-1 model of non-small cell lung cancer

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PLAG effectively inhibited excessive growth of LLC1 cells in an NSCLC model.

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PLAG inhibited tumor growth by inducing adenosine 2B receptor (A2BR) degradation.

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Unlike antagonists, PLAG terminates rather than suppresses signaling pathways.

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A2BR degradation by PLAG occurs through expression and re-localization of TXNIP.

Extracellular adenosine in the tumor microenvironment plays a vital role in cancer development. Specifically, activation of adenosine receptors affects tumor cell growth and adenosine release.

We examined the anti-tumor efficacy of 1-palmitoyl-2-linoleoyl-3-acetyl-rac-glycerol (PLAG) in animal models, revealing the role of PLAG in inhibiting tumor progression by promoting the degradation of adenosine 2B receptors (A2BRs) in tumors. PLAG induced the expression of thioredoxin-interacting protein (TXNIP), a type of α-arrestin that accelerates A2BR internalization by interacting with A2BR complexes containing β-arrestin. Engulfed receptors bound to TXNIP were rapidly degraded after E3 ligase recruitment and ubiquitination, resulting in early termination of intracellular signals that promote tumor overgrowth. However, in control cancer cells, A2BRs bound to protein phosphatase 2A and were returned to the cell membrane instead of being degraded, resulting in continuous receptor-mediated signaling by pathways including the Raf-Erk axis, which promotes tumor proliferation. A TXNIP-silenced cell-implanted mouse model and TXNIP knockout (KO) mice were used to verify that PLAG-mediated suppression of tumor progression is dependent on TXNIP expression. Increased tumor growth was observed in TXNIP-silenced cell-implanted mice, and the anti-tumor effects of PLAG, including delayed tumor overgrowth, were greatly reduced. However, the anti-tumor effects of PLAG were observed in cancer cell-implanted TXNIP-KO mice, which indicates that PLAG produces anti-tumor effects by enhancing TXNIP expression in tumor cells.

These essential functions of PLAG, including delaying tumor growth via A2BR degradation, suggest innovative directions for anticancer drug development.

Combining metabolic phenotype determination with metabolomics and transcriptional analyses to reveal pathways regulated by hydroxycarboxylic acid receptor 2

BACKGROUND

The adaptation of cellular metabolism is considered a hallmark of cancer. Oncogenic signaling pathways support tumorigenesis and cancer progression through the induction of certain metabolic phenotypes associated with altered regulation of key metabolic enzymes. Hydroxycarboxylic acid receptor 2 (HCA₂) is a G protein-coupled receptor previously shown to act as a tumor suppressor. Here, we aimed to unveil the connection between cellular metabolism and HCA₂ in BT-474 cells. Moreover, we intend to clarify how well this metabolic phenotype is reflected in transcriptional changes and metabolite levels as determined by global metabolomics analyses.

METHODS

We performed both, siRNA mediated knockdown of HCA₂ and stimulation with the HCA₂-specific agonist monomethyl fumarate. Seahorse technology was used to determine the role of HCA₂ in BT-474 breast cancer cell metabolism and its potential to induce a switch in the metabolic phenotype in the presence of different energy substrates. Changes in the mRNA expression of metabolic enzymes were detected with real-time quantitative PCR (RT-qPCR). Untargeted liquid chromatography-mass spectrometry (LC-MS) metabolic profiling was used to determine changes in metabolite levels.

RESULTS

Knockdown or stimulation of HCA₂ induced changes in the metabolic phenotype of BT474 cells dependent on the availability of energy substrates. The presence of HCA₂ was associated with increased glycolytic flux with no fatty acids available. This was reflected in the increased mRNA expression of the glycolytic enzymes PFKFB4 and PKM2, which are known to promote the Warburg effect and have been described as prognostic markers in different types of cancer. With exogenous palmitate present, HCA₂ caused elevated fatty acid oxidation and likely lipolysis. The increase in lipolysis was also detectable at the transcriptional level of ATGL and the metabolite levels of palmitic and stearic acid.

CONCLUSIONS

We combined metabolic phenotype determination with metabolomics and transcriptional analyses and identified HCA₂ as a regulator of glycolytic flux and fatty acid metabolism in BT-474 breast cancer cells. Thus, HCA₂, for which agonists are already widely used to treat diseases such as psoriasis or hyperlipidemia, may prove useful as a target in combination cancer therapy.

The Current Perspectives in Clinical Research: Computer-Assisted Drug Designing, Ethics, and Good Clinical Practice

In the era of emerging microbial and non-communicable diseases and re-emerging microbial infections, the medical fraternity and the public are plagued by under-preparedness. It is evident by the severity of the Coronavirus disease (COVID-19) pandemic that novel microbial diseases are a challenge and are challenging to control. This is mainly attributed to the lack of complete knowledge of the novel microbe’s biology and pathogenesis and the unavailability of therapeutic drugs and vaccines to treat and control the disease. Clinical research is the only answer utilizing which can handle most of these circumstances. In this review, we highlight the importance of computer-assisted drug designing (CADD) and the aspects of molecular docking, molecular superimposition, 3D-pharmacophore technology, ethics, and good clinical practice (GCP) for the development of therapeutic drugs, devices, and vaccines.

Prediction of GPCR activity using Machine Learning

GPCRs are the target for one-third of the FDA-approved drugs, however; the development of new drug molecules targeting GPCRs is limited by the lack of mechanistic understanding of the GPCR structure–activity-function relationship. To modulate the GPCR activity with highly specific drugs and minimal side-effects, it is necessary to quantitatively describe the important structural features in the GPCR and correlate them to the activation state of GPCR. In this study, we developed 3 ML approaches to predict the conformation state of GPCR proteins. Additionally, we predict the activity level of GPCRs based on their structure. We leverage the unique advantages of each of the 3 ML approaches, interpretability of XGBoost, minimal feature engineering for 3D convolutional neural network, and graph representation of protein structure for graph neural network. By using these ML approaches, we are able to predict the activation state of GPCRs with high accuracy (91%–95%) and also predict the activation state of GPCRs with low error (MAE of 7.15–10.58). Furthermore, the interpretation of the ML approaches allows us to determine the importance of each of the features in distinguishing between the GPCRs conformations.

Sodium or Not Sodium: Should Its Presence Affect the Accuracy of Pose Prediction in Docking GPCR Antagonists?

The function of the allosteric sodium ion in stabilizing the inactive form of GPCRs has been extensively described in the past decades. Its presence has been reported to be essential for the binding of antagonist molecules in the orthosteric site of these very important therapeutical targets. Among the GPCR–antagonist crystal structures available, in most cases, the sodium ion could not be experimentally resolved, obliging computational scientists using GPCRs as targets for virtual screening to ask: “Should the sodium ion affect the accuracy of pose prediction in docking GPCR antagonists?” In the present study, we examined the performance of three orthogonal docking programs in the self-docking of GPCR antagonists to try to answer this question. The results of the present work highlight that if the sodium ion is resolved in the crystal structure used as the target, it should also be taken into account during the docking calculations. If the crystallographic studies were not able to resolve the sodium ion then no advantage would be obtained if this is manually inserted in the virtual target. The outcomes of the present analysis are useful for researchers exploiting molecular docking-based virtual screening to efficiently identify novel GPCR antagonists.

The Impact of the Secondary Binding Pocket on the Pharmacology of Class A GPCRs

G-protein coupled receptors (GPCRs) are considered important therapeutic targets due to their pathophysiological significance and pharmacological relevance. Class A receptors represent the largest group of GPCRs that gives the highest number of validated drug targets. Endogenous ligands bind to the orthosteric binding pocket (OBP) embedded in the intrahelical space of the receptor. During the last 10 years, however, it has been turned out that in many receptors there is secondary binding pocket (SBP) located in the extracellular vestibule that is much less conserved. In some cases, it serves as a stable allosteric site harbouring allosteric ligands that modulate the pharmacology of orthosteric binders. In other cases it is used by bitopic compounds occupying both the OBP and SBP. In these terms, SBP binding moieties might influence the pharmacology of the bitopic ligands. Together with others, our research group showed that SBP binders contribute significantly to the affinity, selectivity, functional activity, functional selectivity and binding kinetics of bitopic ligands. Based on these observations we developed a structure-based protocol for designing bitopic compounds with desired pharmacological profile.

Precise druggability of the PTH type 1 receptor

Class B G protein-coupled receptors (GPCRs) are notoriously difficult to target by small molecules because their large orthosteric peptide-binding pocket embedded deep within the transmembrane domain limits the identification and development of nonpeptide small molecule ligands. Using the parathyroid hormone type 1 receptor (PTHR) as a prototypic class B GPCR target, and a combination of molecular dynamics simulations and elastic network model-based methods, we demonstrate that PTHR druggability can be effectively addressed. Here we found a key mechanical site that modulates the collective dynamics of the receptor and used this ensemble of PTHR conformers to identify selective small molecules with strong negative allosteric and biased properties for PTHR signaling in cell and PTH actions in vivo. This study provides a computational pipeline to detect precise druggable sites and identify allosteric modulators of PTHR signaling that could be extended to GPCRs to expedite discoveries of small molecules as novel therapeutic candidates.

In Silico Study Approach on a Series of 50 Polyphenolic Compounds in Plants; A Comparison on the Bioavailability and Bioactivity Data

Fifty (50) phytocompounds from several subclasses of polyphenols, chosen based on their abundance in the plant world, were analyzed through density functional methods, using computational tools to evaluate their oral availability and particular bioactivity on several cell modulators; key descriptors and molecular features related to the electron density and electrostatic potential for the lowest energy conformers of the investigated molecules were computed. An analysis of the bioactivity scores towards six cell modulators (GPCR ligand, ion channel modulator, kinase inhibitor, nuclear receptor ligand, protease inhibitor and enzyme inhibitor) was also achieved, in the context of investigating their potential side effects on the human digestive processes. Summarizing, computational results confirmed in vivo and in vitro data regarding the high bioavailability of soy isoflavones and better bioavailability of free aglycones in comparison with their esterified and glycosylated forms. However, by a computational approach analyzing Lipinski’s rule, apigenin and apigenin-7-O-rhamnoside, naringenin, hesperetin, genistein, daidzin, biochanin A and formonetin in the flavonoid series and all hydroxycinnamic acids and all hydroxybenzoic acids excepting ellagic acid were proved to have the best bioavailability data; rhamnoside derivatives, the predominant glycosides in green plants, which were reported to have the lowest bioavailability values by in vivo studies, were revealed to have the best bioavailability data among the studied flavonoids in the computational approach. Results of in silico screening on the phenolic derivatives series also revealed their real inhibitory potency on the six parameters studied, showing a remarkable similitude between the flavonoid series, while flavonoids were more powerful natural cell modulators than the phenyl carboxylic acids tested. Thus, it can be concluded that there is a need for supplementation with digestive enzymes, mainly in the case of individuals with low digestive efficiency, to obtain the best health benefits of polyphenols in humans.

Absolute Quantification of Plasma Membrane Receptors Via Quantitative Flow Cytometry

Plasma membrane receptors are transmembrane proteins that initiate cellular response following the binding of specific ligands (e.g., growth factors, hormones, and cytokines). The abundance of plasma membrane receptors can be a diagnostic or prognostic biomarker in many human diseases. One of the best techniques for measuring plasma membrane receptors is quantitative flow cytometry (qFlow). qFlow employs fluorophore-conjugated antibodies against the receptors of interest and corresponding fluorophore-loaded calibration beads offers standardized and reproducible measurements of plasma membrane receptors. More importantly, qFlow can achieve absolute quantification of plasma membrane receptors when phycoerythrin (PE) is the fluorophore of choice. Here we describe a detailed qFlow protocol to obtain absolute receptor quantities on the basis of PE calibration. This protocol is foundational for many previous and ongoing studies in quantifying tyrosine kinase receptors and G-protein-coupled receptors with in vitro cell models and ex vivo cell samples.

P17 induces chemotaxis and differentiation of monocytes via MRGPRX2-mediated mast cell-line activation

BACKGROUND

P17, a peptide isolated from Tetramorium bicarinatum ant venom, is known to induce an alternative phenotype of human monocyte-derived macrophages via activation of an unknown G protein-coupled receptor (GPCR).

OBJECTIVE

We sought to investigate the mechanism of action and the immunomodulatory effects of P17 mediated through MRGPRX2 (Mas-related G protein-coupled receptor X2).

METHODS

To identify the GPCR for P17, we screened 314 GPCRs. Upon identification of MRGPRX2, a battery of in silico, in vitro, ex vivo, and in vivo assays along with the receptor mutation studies were performed. In particular, to investigate the immunomodulatory actions, we used β-hexosaminidase release assay, cytokine releases, quantification of mRNA expression, cell migration and differentiation assays, immunohistochemical labeling, hematoxylin and eosin, and immunofluorescence staining.

RESULTS

P17 activated MRGPRX2 in a dose-dependent manner in β-arrestin recruitment assay. In LAD2 cells, P17 induced calcium and β-hexosaminidase release. Quercetin- and short hairpin RNA-mediated knockdown of MRGPRX2 reduced P17-evoked β-hexosaminidase release. In silico and in vitro mutagenesis studies showed that residue Lys8 of P17 formed a cation-π interaction with the Phe172 of MRGPRX2 and [Ala8]P17 lost its activity partially. P17 activated LAD2 cells to recruit THP-1 and human monocytes in Transwell migration assay, whereas MRGPRX2-impaired LAD2 cells cannot. In addition, P17-treated LAD2 cells stimulated differentiation of THP-1 and human monocytes, as indicated by the enhanced expression of macrophage markers cluster of differentiation 11b and TNF-α by quantitative RT-PCR. Immunohistochemical and immunofluorescent staining suggested monocyte recruitment in mice ears injected with P17.

CONCLUSIONS

Our data provide novel structural information regarding the interaction of P17 with MRGPRX2 and intracellular pathways for its immunomodulatory action.

Analysis of L-DOPA and droxidopa binding to human β2-adrenergic receptor

Over the last two decades, an increasing number of studies has been devoted to a deeper understanding of the molecular process involved in the binding of various agonists and antagonists to active and inactive conformations of β2-adrenergic receptor (β2AR). The 3.2 Å x-ray crystal structure of human β2AR active state in combination with the endogenous low affinity agonist adrenaline offers an ideal starting structure for studying the binding of various catecholamines to adrenergic receptors. We show that molecular docking of levodopa (L-DOPA) and droxidopa into rigid and flexible β2AR models leads for both ligands to binding anchor sites comparable to those experimentally reported for adrenaline, namely D113/N312 and S203/S204/S207 side chains. Both ligands have a hydrogen bond network that is extremely similar to those of noradrenaline and dopamine. Interestingly, redocking neutral and protonated versions of adrenaline to rigid and flexible β2AR models results in binding poses that are more energetically stable and distinct from the x-ray crystal structure. Similarly, lowest energy conformations of noradrenaline and dopamine generated by docking into flexible β2AR models had binding free energies lower than those of best poses in rigid receptor models. Furthermore, our findings show that L-DOPA and droxidopa molecules have binding affinities comparable to those predicted for adrenaline, noradrenaline, and dopamine, which are consistent with previous experimental and computational findings and supported by the molecular dynamics simulations of β2AR-ligand complexes performed here.

Sodium Glucose Transporter, Type 2 (SGLT2) Inhibitors (SGLT2i) and Glucagon-Like Peptide 1-Receptor Agonists: Newer Therapies in Whole-Body Glucose Stabilization

Diabetes is a worldwide epidemic that is increasing rapidly to become the seventh leading cause of death in the world. The increased incidence of this disease mirrors a similar uptick in obesity and metabolic syndrome, and, collectively, these conditions can cause deleterious effects on a number of organ systems including the renal and cardiovascular systems. Historically, treatment of type 2 diabetes has focused on decreasing hyperglycemia and glycated hemoglobin levels. However, it now is appreciated that there is more to the puzzle. Emerging evidence has indicated that newer classes of diabetes drugs, sodium-glucose co-transporter 2 inhibitors and glucagon-like peptide 1-receptor agonists, improve cardiovascular and renal function, while appropriately managing hyperglycemia. In this review, we highlight the recent clinical and preclinical studies that have shed light on sodium-glucose co-transporter 2 inhibitors and glucagon-like peptide 1-receptor agonists and their ability to stabilize blood glucose levels while offering whole-body protection in diabetic and nondiabetic patient populations.

Identification of novel positive allosteric modulators of GLP1R that stimulate its interaction with ligands and Gα subunits

Glucagon-like peptide-1 (GLP-1) is a major incretin hormone that enhances the release of insulin from pancreatic β-cells by activating the glucagon-like peptide-1 receptor (GLP1R), which belongs to secretin-like class B of G protein-coupled receptors (GPCRs). Owing to the absence of small molecule agonist drugs to GLP1R, focus has been placed on chemical modulators that bind to the allosteric site of GLP1R. In this study, we identified novel small-molecule positive allosteric modulators of GLP1R from a chemical library consisting of commercial drug compounds using an assay system that measures the direct interaction between a purified GLP1R and its ligand, exendin-4. Two newly identified compounds, benzethonium and tamoxifen, significantly enhanced the affinity of peptide ligands for GLP1R although they lacked agonist activity by themselves. In addition, benzethonium augmented the ligand-induced accumulation of cAMP in GLP1R-transfected HEK293T cells. These compounds significantly increased the affinity of GLP1R to the alpha-subunit of G proteins, suggesting that they stabilize GLP1R in a conformation with a higher affinity to peptide ligand as well as G proteins. These compounds may lead to the design of an orally active positive allosteric modulator for GLP1R.

Development of potential inhibitors of cell division protein kinase 2 by ligand based drug design

Cyclin-dependent kinases (CDKs) are commonly known by their role in cell cycle regulation which affects cancer mechanism. In many cancer types, CDKs show extreme activity or CDK inhibiting proteins are dysfunctional. Specifically, CDK2 plays an indispensable role in cell division especially in the G1/S phase and DNA damage repair. Therefore, it is important to find new potential CDK2 inhibitors. In this study, ligand-based drug design is used to design new potential CDK2 inhibitors. Y8 L ligand is obtained from the X-ray crystal structure of human CDK2 (PDB ID: 2XNB) (www.pdb.org) and used as a structure model. By adding hydrophilic and hydrophobic groups to the structure, a training set of 36 molecules is generated. Each molecule examined with Spartan’14 and optimized structures are used for docking to CDK2 structure by AutoDock and AutoDock Vina programs. Ligand-amino acid interactions are analysed with Discovery Studio Visualizer. Van der Waals, Pi-Pi T-shaped, alkyl, pi-alkyl, conventional hydrogen bond and carbon-hydrogen bond interactions are observed. By docking results and viewed interactions, some molecules are identified and discussed as potential CDK2 inhibitors. Additionally, 8 different QSAR descriptors obtained from Spartan’14, Preadmet and ALOGPS 2.1 programs are investigated with multiple linear regulation (MLR) analysis with SPSS program for their impact on affinity value.