Structure-function studies of muscarinic acetylcholine receptors

Understanding Central Nervous System Effects of Deliriant Hallucinogenic Drugs through Experimental Animal Models

Hallucinogenic drugs potently alter human behavior and have a millennia-long history of use for medicinal and religious purposes. Interest is rapidly growing in their potential as CNS modulators and therapeutic agents for brain conditions. Antimuscarinic cholinergic drugs, such as atropine and scopolamine, induce characteristic hyperactivity and dream-like hallucinations and form a separate group of hallucinogens known as "deliriants". Although atropine and scopolamine are relatively well-studied drugs in cholinergic physiology, deliriants represent the least-studied class of hallucinogens in terms of their behavioral and neurological phenotypes. As such, novel approaches and new model organisms are needed to investigate the CNS effects of these compounds. Here, we comprehensively evaluate the preclinical effects of deliriant hallucinogens in various animal models, their mechanisms of action, and potential interplay with other signaling pathways. We also parallel experimental and clinical findings on deliriant agents and outline future directions of translational research in this field.

Lifetime of muscarinic receptor-G-protein complexes determines coupling efficiency and G-protein subtype selectivity

G-protein-coupled receptors (GPCRs) are essential for the detection of extracellular stimuli by cells and transfer the encoded information via the activation of functionally distinct subsets of heterotrimeric G proteins into intracellular signals. Despite enormous achievements toward understanding GPCR structures, major aspects of the GPCR-G-protein selectivity mechanism remain unresolved. As this can be attributed to the lack of suitable and broadly applicable assays, we set out to develop a quantitative FRET-based assay to study kinetics and affinities of G protein binding to activated GPCRs in membranes of permeabilized cells in the absence of nucleotides. We measured the association and dissociation kinetics of agonist-induced binding of Gi/o, Gq/11, Gs, and G12/13 proteins to muscarinic M1, M2, and M3 receptors in the absence of nucleotides between fluorescently labeled G proteins and receptors expressed in mammalian cells. Our results show a strong quantitative correlation between not the on-rates of G-protein-M3-R interactions but rather the affinities of Gq and Go proteins to M3-Rs, their GPCR-G-protein lifetime and their coupling efficiencies determined in intact cells, suggesting that the G-protein subtype-specific affinity to the activated receptor in the absence of nucleotides is, in fact, a major determinant of the coupling efficiency. Our broadly applicable FRET-based assay represents a fast and reliable method to quantify the intrinsic affinity and relative coupling selectivity of GPCRs toward all G-protein subtypes.

Extremely Randomized Machine Learning Methods for Compound Activity Prediction

Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called ‘extremely randomized methods’—Extreme Entropy Machine and Extremely Randomized Trees—for their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their ‘non-extreme’ competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure.

Robust optimization of SVM hyperparameters in the classification of bioactive compounds

Background

Support Vector Machine has become one of the most popular machine learning tools used in virtual screening campaigns aimed at finding new drug candidates. Although it can be extremely effective in finding new potentially active compounds, its application requires the optimization of the hyperparameters with which the assessment is being run, particularly the C and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}γ values. The optimization requirement in turn, establishes the need to develop fast and effective approaches to the optimization procedure, providing the best predictive power of the constructed model.

Results

In this study, we investigated the Bayesian and random search optimization of Support Vector Machine hyperparameters for classifying bioactive compounds. The effectiveness of these strategies was compared with the most popular optimization procedures—grid search and heuristic choice. We demonstrated that Bayesian optimization not only provides better, more efficient classification but is also much faster—the number of iterations it required for reaching optimal predictive performance was the lowest out of the all tested optimization methods. Moreover, for the Bayesian approach, the choice of parameters in subsequent iterations is directed and justified; therefore, the results obtained by using it are constantly improved and the range of hyperparameters tested provides the best overall performance of Support Vector Machine. Additionally, we showed that a random search optimization of hyperparameters leads to significantly better performance than grid search and heuristic-based approaches.

Conclusions

The Bayesian approach to the optimization of Support Vector Machine parameters was demonstrated to outperform other optimization methods for tasks concerned with the bioactivity assessment of chemical compounds. This strategy not only provides a higher accuracy of classification, but is also much faster and more directed than other approaches for optimization. It appears that, despite its simplicity, random search optimization strategy should be used as a second choice if Bayesian approach application is not feasible.Graphical abstract

A constitutively active G protein-coupled acetylcholine receptor regulates motility of larval Schistosoma mansoni

The neuromuscular system of helminths controls a variety of essential biological processes and therefore represents a good source of novel drug targets. The neuroactive substance, acetylcholine controls movement of Schistosoma mansoni but the mode of action is poorly understood. Here, we present first evidence of a functional G protein-coupled acetylcholine receptor in S. mansoni, which we have named SmGAR. A bioinformatics analysis indicated that SmGAR belongs to a clade of invertebrate GAR-like receptors and is related to vertebrate muscarinic acetylcholine receptors. Functional expression studies in yeast showed that SmGAR is constitutively active but can be further activated by acetylcholine and, to a lesser extent, the cholinergic agonist, carbachol. Anti-cholinergic drugs, atropine and promethazine, were found to have inverse agonist activity towards SmGAR, causing a significant decrease in the receptor's basal activity. An RNAi phenotypic assay revealed that suppression of SmGAR activity in early-stage larval schistosomulae leads to a drastic reduction in larval motility. In sum, our results provide the first molecular evidence that cholinergic GAR-like receptors are present in schistosomes and are required for proper motor control in the larvae. The results further identify SmGAR as a possible candidate for antiparasitic drug targeting.

What can crystal structures of aminergic receptors tell us about designing subtype-selective ligands?

G protein-coupled receptors (GPCRs) are integral membrane proteins that represent an important class of drug targets. In particular, aminergic GPCRs interact with a significant portion of drugs currently on the market. However, most drugs that target these receptors are associated with undesirable side effects, which are due in part to promiscuous interactions with close homologs of the intended target receptors. Here, based on a systematic analysis of all 37 of the currently available high-resolution crystal structures of aminergic GPCRs, we review structural elements that contribute to and can be exploited for designing subtype-selective compounds. We describe the roles of secondary binding pockets (SBPs), as well as differences in ligand entry pathways to the orthosteric binding site, in determining selectivity. In addition, using the available crystal structures, we have identified conformational changes in the SBPs that are associated with receptor activation and explore the implications of these changes for the rational development of selective ligands with tailored efficacy.

Advances in G protein-coupled receptor allostery: from function to structure

It is now widely accepted that G protein-coupled receptors (GPCRs) are highly dynamic proteins that adopt multiple active states linked to distinct functional outcomes. Furthermore, these states can be differentially stabilized not only by orthosteric ligands but also by allosteric ligands acting at spatially distinct binding sites. The key pharmacologic characteristics of GPCR allostery include improved selectivity due to either greater sequence divergence between receptor subtypes and/or subtype-selective cooperativity, a ceiling level to the effect, probe dependence (whereby the magnitude and direction of the allosteric effect change with the nature of the interacting ligands), and the potential for biased signaling. Recent chemical biology developments are beginning to demonstrate how the incorporation of analytical pharmacology and operational modeling into the experimental workflow can enrich structure-activity studies of allostery and bias, and have also led to the discovery of a new class of hybrid orthosteric/allosteric (bitopic) molecules. The potential for endogenous allosteric modulators to play a role in physiology and disease remains to be fully appreciated but will likely represent an important area for future studies. Finally, breakthroughs in structural and computational biology are beginning to unravel the mechanistic basis of GPCR allosteric modulation at the molecular level.

Extrasynaptic muscarinic acetylcholine receptors on neuronal cell bodies regulate presynaptic function in Caenorhabditis elegans

Acetylcholine (ACh) is a potent neuromodulator in the brain, and its effects on cognition and memory formation are largely performed through muscarinic acetylcholine receptors (mAChRs). mAChRs are often preferentially distributed on specialized membrane regions in neurons, but the significance of mAChR localization in modulating neuronal function is not known. Here we show that the Caenorhabditis elegans homolog of the M1/M3/M5 family of mAChRs, gar-3, is expressed in cholinergic motor neurons, and GAR-3-GFP fusion proteins localize to cell bodies where they are enriched at extrasynaptic regions that are in contact with the basal lamina. The GAR-3 N-terminal extracellular domain is necessary and sufficient for this asymmetric distribution, and mutation of a predicted N-linked glycosylation site within the N-terminus disrupts GAR-3-GFP localization. In transgenic animals expressing GAR-3 variants that are no longer asymmetrically localized, synaptic transmission at neuromuscular junctions is impaired and there is a reduction in the abundance of the presynaptic protein sphingosine kinase at release sites. Finally, GAR-3 can be activated by endogenously produced ACh released from neurons that do not directly contact cholinergic motor neurons. Together, our results suggest that humoral activation of asymmetrically localized mAChRs by ACh is an evolutionarily conserved mechanism by which ACh modulates neuronal function.

Insights into the structural biology of G-protein coupled receptors impacts drug design for central nervous system neurodegenerative processes

In the last few years, there have been important new insights into the structural biology of G-protein coupled receptors. It is now known that allosteric binding sites are involved in the affinity and selectivity of ligands for G-protein coupled receptors, and that signaling by these receptors involves both G-protein dependent and independent pathways. The present review outlines the physiological and pharmacological implications of this perspective for the design of new drugs to treat disorders of the central nervous system. Specifically, new possibilities are explored in relation to allosteric and orthosteric binding sites on dopamine receptors for the treatment of Parkinson's disease, and on muscarinic receptors for Alzheimer's disease. Future research can seek to identify ligands that can bind to more than one site on the same receptor, or simultaneously bind to two receptors and form a dimer. For example, the design of bivalent drugs that can reach homo/hetero-dimers of D2 dopamine receptor holds promise as a relevant therapeutic strategy for Parkinson's disease. Regarding the treatment of Alzheimer's disease, the design of dualsteric ligands for mono-oligomeric rinic receptors could increase therapeutic effectiveness by generating potent compounds that could activate more than one signaling pathway.

Approaches for probing allosteric interactions at 7 transmembrane spanning receptors

In recent years, allosteric modulation of 7 transmembrane spanning receptors (7TMRs) has become a highly productive and exciting field of receptor pharmacology and drug discovery efforts. Positive and negative allosteric modulators (PAMs and NAMs, respectively) present a number of pharmacological and therapeutic advantages over conventional orthosteric ligands, including improved receptor-subtype selectivity, a lower propensity to induce receptor desensitization, the preservation of endogenous temporal and spatial activation of receptors, greater chemical flexibility for optimization of drug metabolism and pharmacokinetic parameters, and saturability of effect at target receptors, thus improving safety concerns and risk of overdose. Additionally, the relatively new concept of allosteric modulator-mediated receptor signal bias opens up a number of intriguing possibilities for PAMs, NAMs, and allosteric agonists, including the potential to selectively activate therapeutically beneficial signaling cascades, which could yield a superior tissue selectivity and side effect profile of allosteric modulators. However, there are a number of considerations and caveats that must be addressed when screening for and characterizing the properties of 7TMR allosteric modulators. Mode of pharmacology, methodology used to monitor receptor activity, detection of appropriate downstream analytes, selection of orthosteric probe, and assay time-course must all be considered when implementing any high-throughput screening campaign or when characterizing the properties of active compounds. Yet compared to conventional agonist/antagonist drug discovery programs, these elements of assay design are often a great deal more complicated when working with 7TMRs allosteric modulators. Moreover, for classical pharmacological methodologies and analyses, like radioligand binding and the assessment of compound affinity, the properties of allosteric modulators yield data that are more nuanced than orthosteric ligand-receptor interactions. In this review, we discuss the current methodologies being used to identify and characterize allosteric modulators, lending insight into the approaches that have been most successful in accurately and robustly identifying hit compounds. New label-free technologies capable of detecting phenotypic cellular changes in response to receptor activation are powerful tools well suited for assessing subtle or potentially masked cellular responses to allosteric modulation of 7TMRs. Allosteric modulator-induced receptor signal bias and the assay systems available to probe the various downstream signaling outcomes of receptor activation are also discussed.