Covalent allosteric modulation: An emerging strategy for GPCRs drug discovery

Photoswitchable positive allosteric modulators of metabotropic glutamate receptor 4 to improve selectivity

Metabotropic glutamate receptors (mGlu) regulate multiple functions in the nervous systems and are involved in several neurological disorders. However, selectively targeting individual mGlu subtypes with spatiotemporal precision is still an unmet need. Photopharmacology can address this concern through the utilization of photoswitchable compounds such as optogluram, which is a positive allosteric modulator (PAM) of mGlu4 that enables the precise control of physiological responses using light but does not have an optimal selectivity profile. Optogluram analogs were developed to obtain photoswitchable PAMs of mGlu4 receptor with an improved selectivity. Among them, optogluram-2 emerged as a photoswitchable ligand for mGlu4 receptor with activity as both PAM and allosteric agonists. It presents a higher selectivity and offers improved photoswitching of mGlu4 activity. These improved properties make optogluram-2 an excellent candidate to study the role of mGlu4 with a high spatiotemporal precision in systems where mGlu4 can be co-expressed with other mGlu receptors.

G protein-coupled receptors (GPCRs): advances in structures, mechanisms, and drug discovery

G protein-coupled receptors (GPCRs), the largest family of human membrane proteins and an important class of drug targets, play a role in maintaining numerous physiological processes. Agonist or antagonist, orthosteric effects or allosteric effects, and biased signaling or balanced signaling, characterize the complexity of GPCR dynamic features. In this study, we first review the structural advancements, activation mechanisms, and functional diversity of GPCRs. We then focus on GPCR drug discovery by revealing the detailed drug-target interactions and the underlying mechanisms of orthosteric drugs approved by the US Food and Drug Administration in the past five years. Particularly, an up-to-date analysis is performed on available GPCR structures complexed with synthetic small-molecule allosteric modulators to elucidate key receptor-ligand interactions and allosteric mechanisms. Finally, we highlight how the widespread GPCR-druggable allosteric sites can guide structure- or mechanism-based drug design and propose prospects of designing bitopic ligands for the future therapeutic potential of targeting this receptor family.

Advances of Predicting Allosteric Mechanisms Through Protein Contact in New Technologies and Their Application

Allostery is an intriguing phenomenon wherein the binding activity of a biological macromolecule is modulated via non-canonical binding site, resulting in synchronized functional changes. The mechanics underlying allostery are relatively complex and this review is focused on common methodologies used to study allostery, such as X-ray crystallography, NMR spectroscopy, and HDXMS. Different methodological approaches are used to generate data in different scenarios. For example, X-ray crystallography provides high-resolution structural information, NMR spectroscopy offers dynamic insights into allosteric interactions in solution, and HDXMS provides information on protein dynamics. The residue transition state (RTS) approach has emerged as a critical tool in understanding the energetics and conformational changes associated with allosteric regulation. Allostery has significant implications in drug discovery, gene transcription, disease diagnosis, and enzyme catalysis. Enzymes' catalytic activity can be modulated by allosteric regulation, offering opportunities to develop novel therapeutic alternatives. Understanding allosteric mechanisms associated with infectious organisms like SARS-CoV and bacterial pathogens can aid in the development of new antiviral drugs and antibiotics. Allosteric mechanisms are crucial in the regulation of a variety of signal transduction and cell metabolism pathways, which in turn govern various cellular processes. Despite progress, challenges remain in identifying allosteric sites and characterizing their contribution to a variety of biological processes. Increased understanding of these mechanisms can help develop allosteric systems specifically designed to modulate key biological mechanisms, providing novel opportunities for the development of targeted therapeutics. Therefore, the current review aims to summarize common methodologies that are used to further our understanding of allosteric mechanisms. In conclusion, this review provides insights into the methodologies used for the study of allostery, its applications in in silico modeling, the mechanisms underlying antibody allostery, and the ongoing challenges and prospects in advancing our comprehension of this intriguing phenomenon.

Allosteric Regulation of G-Protein-Coupled Receptors: From Diversity of Molecular Mechanisms to Multiple Allosteric Sites and Their Ligands

Allosteric regulation is critical for the functioning of G protein-coupled receptors (GPCRs) and their signaling pathways. Endogenous allosteric regulators of GPCRs are simple ions, various biomolecules, and protein components of GPCR signaling (G proteins and β-arrestins). The stability and functional activity of GPCR complexes is also due to multicenter allosteric interactions between protomers. The complexity of allosteric effects caused by numerous regulators differing in structure, availability, and mechanisms of action predetermines the multiplicity and different topology of allosteric sites in GPCRs. These sites can be localized in extracellular loops; inside the transmembrane tunnel and in its upper and lower vestibules; in cytoplasmic loops; and on the outer, membrane-contacting surface of the transmembrane domain. They are involved in the regulation of basal and orthosteric agonist-stimulated receptor activity, biased agonism, GPCR-complex formation, and endocytosis. They are targets for a large number of synthetic allosteric regulators and modulators, including those constructed using molecular docking. The review is devoted to the principles and mechanisms of GPCRs allosteric regulation, the multiplicity of allosteric sites and their topology, and the endogenous and synthetic allosteric regulators, including autoantibodies and pepducins. The allosteric regulation of chemokine receptors, proteinase-activated receptors, thyroid-stimulating and luteinizing hormone receptors, and beta-adrenergic receptors are described in more detail.

Covalent cannabinoid receptor ligands - structural insight and selectivity challenges

X-ray crystallography and cryogenic electronic microscopy have provided significant advancement in the knowledge of GPCR structure and have allowed the rational design of GPCR ligands. The class A GPCRs cannabinoid receptor type 1 and type 2 are implicated in many pathophysiological processes and thus rational design of drug and tool compounds is of great interest. Recent structural insight into cannabinoid receptors has already led to a greater understanding of ligand binding sites and receptor residues that likely contribute to ligand selectivity. Herein, classes of heterocyclic covalent cannabinoid receptor ligands are reviewed in light of the recent advances in structural knowledge of cannabinoid receptors, with particular discussion regarding covalent ligand selectivity and rationale design.

Fast and Effective Prediction of the Absolute Binding Free Energies of Covalent Inhibitors of SARS-CoV-2 Main Protease and 20S Proteasome

The COVID-19 pandemic has been a public health emergency with continuously evolving deadly variants around the globe. Among many preventive and therapeutic strategies, the design of covalent inhibitors targeting the main protease (M^pro) of SARS-CoV-2 that causes COVID-19 has been one of the hotly pursued areas. Currently, about 30% of marketed drugs that target enzymes are covalent inhibitors. Such inhibitors have been shown in recent years to have many advantages that counteract past reservation of their potential off-target activities, which can be minimized by modulation of the electrophilic warhead and simultaneous optimization of nearby noncovalent interactions. This process can be greatly accelerated by exploration of binding affinities using computational models, which are not well-established yet due to the requirement of capturing the chemical nature of covalent bond formation. Here, we present a robust computational method for effective prediction of absolute binding free energies (ABFEs) of covalent inhibitors. This is done by integrating the protein dipoles Langevin dipoles method (in the PDLD/S-LRA/β version) with quantum mechanical calculations of the energetics of the reaction of the warhead and its amino acid target, in water. This approach evaluates the combined effects of the covalent and noncovalent contributions. The applicability of the method is illustrated by predicting the ABFEs of covalent inhibitors of SARS-CoV-2 M^pro and the 20S proteasome. Our results are found to be reliable in predicting ABFEs for cases where the warheads are significantly different. This computational protocol might be a powerful tool for designing effective covalent inhibitors especially for SARS-CoV-2 M^pro and for targeted protein degradation.

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.

Boosting the Discovery of Small Molecule Inhibitors of Glucose-6-Phosphate Dehydrogenase for the Treatment of Cancer, Infectious Diseases, and Inflammation

We present an overview of small molecule glucose-6-phosphate dehydrogenase (G6PD) inhibitors that have potential for use in the treatment of cancer, infectious diseases, and inflammation. Both steroidal and nonsteroidal inhibitors have been identified with steroidal inhibitors lacking target selectivity. The main scaffolds encountered in nonsteroidal inhibitors are quinazolinones and benzothiazinones/benzothiazepinones. Three molecules show promise for development as antiparasitic (25 and 29) and anti-inflammatory (32) agents. Regarding modality of inhibition (MOI), steroidal inhibitors have been shown to be uncompetitive and reversible. Nonsteroidal small molecules have exhibited all types of MOI. Strategies to boost the discovery of small molecule G6PD inhibitors include exploration of structure-activity relationships (SARs) for established inhibitors, employment of high-throughput screening (HTS), and fragment-based drug discovery (FBDD) for the identification of new hits. We discuss the challenges and gaps associated with drug discovery efforts of G6PD inhibitors from in silico, in vitro, and in cellulo to in vivo studies.

Artificial Intelligent Deep Learning Molecular Generative Modeling of Scaffold-Focused and Cannabinoid CB2 Target-Specific Small-Molecule Sublibraries

Design and generation of high-quality target- and scaffold-specific small molecules is an important strategy for the discovery of unique and potent bioactive drug molecules. To achieve this goal, authors have developed the deep-learning molecule generation model (DeepMGM) and applied it for the de novo molecular generation of scaffold-focused small-molecule libraries. In this study, a recurrent neural network (RNN) using long short-term memory (LSTM) units was trained with drug-like molecules to result in a general model (g-DeepMGM). Sampling practices on indole and purine scaffolds illustrate the feasibility of creating scaffold-focused chemical libraries based on machine intelligence. Subsequently, a target-specific model (t-DeepMGM) for cannabinoid receptor 2 (CB2) was constructed following the transfer learning process of known CB2 ligands. Sampling outcomes can present similar properties to the reported active molecules. Finally, a discriminator was trained and attached to the DeepMGM to result in an in silico molecular design-test circle. Medicinal chemistry synthesis and biological validation was performed to further investigate the generation outcome, showing that XIE9137 was identified as a potential allosteric modulator of CB2. This study demonstrates how recent progress in deep learning intelligence can benefit drug discovery, especially in de novo molecular design and chemical library generation.

New Chemical Biology Tools for the Histamine Receptor Family

The histamine research community has in the last decade been very active and generated a number of exciting new chemical biology tools for the study of histamine receptors, their ligands, and their pharmacology. In this paper we describe the development of histamine receptor structural biology, the use of receptor conformational biosensors, and the development of new ligands for covalent or fluorescent labeling or for photopharmacological approaches (photocaging and photoswitching). These new tools allow new approaches to study histamine receptors and hopefully will lead to better insights in the molecular aspects of histamine receptors and their ligands.

Search for Structural Basis of Interactions of Biogenic Amines with Human TAAR1 and TAAR6 Receptors

The identification and characterization of ligand-receptor binding sites are important for drug development. Trace amine-associated receptors (TAARs, members of the class A GPCR family) can interact with different biogenic amines and their metabolites, but the structural basis for their recognition by the TAARs is not well understood. In this work, we have revealed for the first time a group of conserved motifs (fingerprints) characterizing TAARs and studied the docking of aromatic (β-phenylethylamine, tyramine) and aliphatic (putrescine and cadaverine) ligands, including gamma-aminobutyric acid, with human TAAR1 and TAAR6 receptors. We have identified orthosteric binding sites for TAAR1 (Asp68, Asp102, Asp284) and TAAR6 (Asp78, Asp112, Asp202). By analyzing the binding results of 7500 structures, we determined that putrescine and cadaverine bind to TAAR1 at one site, Asp68 + Asp102, and to TAAR6 at two sites, Asp78 + Asp112 and Asp112 + Asp202. Tyramine binds to TAAR6 at the same two sites as putrescine and cadaverine and does not bind to TAAR1 at the selected Asp residues. β-Phenylethylamine and gamma-aminobutyric acid do not bind to the TAAR1 and TAAR6 receptors at the selected Asp residues. The search for ligands targeting allosteric and orthosteric sites of TAARs has excellent pharmaceutical potential.

Integrated Multi-Class Classification and Prediction of GPCR Allosteric Modulators by Machine Learning Intelligence

G-protein-coupled receptors (GPCRs) are the largest and most diverse group of cell surface receptors that respond to various extracellular signals. The allosteric modulation of GPCRs has emerged in recent years as a promising approach for developing target-selective therapies. Moreover, the discovery of new GPCR allosteric modulators can greatly benefit the further understanding of GPCR cell signaling mechanisms. It is critical but also challenging to make an accurate distinction of modulators for different GPCR groups in an efficient and effective manner. In this study, we focus on an 11-class classification task with 10 GPCR subtype classes and a random compounds class. We used a dataset containing 34,434 compounds with allosteric modulators collected from classical GPCR families A, B, and C, as well as random drug-like compounds. Six types of machine learning models, including support vector machine, naïve Bayes, decision tree, random forest, logistic regression, and multilayer perceptron, were trained using different combinations of features including molecular descriptors, Atom-pair fingerprints, MACCS fingerprints, and ECFP6 fingerprints. The performances of trained machine learning models with different feature combinations were closely investigated and discussed. To the best of our knowledge, this is the first work on the multi-class classification of GPCR allosteric modulators. We believe that the classification models developed in this study can be used as simple and accurate tools for the discovery and development of GPCR allosteric modulators.

Bisavenathramide Analogues as Nrf2 Inductors and Neuroprotectors in In Vitro Models of Oxidative Stress and Hyperphosphorylation

Oxidative stress is crucial to the outbreak and advancement of neurodegenerative diseases and is a common factor to many of them. We describe the synthesis of a library of derivatives of the 4-arylmethylen-2-pyrrolin-5-one framework by sequential application of a three-component reaction of primary amines, β-dicarbonyl compounds, and α-haloketones and a Knoevenagel condensation. These compounds can be viewed as cyclic amides of caffeic and ferulic acids, and are also structurally related to the bisavenanthramide family of natural antioxidants. Most members of the library showed low cytotoxicity and good activity as inductors of Nrf2, a transcription factor that acts as the master regulator of the antioxidant response associated with activation of the antioxidant response element (ARE). Nrf2-dependent protein expression was also proved by the significant increase in the levels of the HMOX1 and NQO1 proteins. Some compounds exerted neuroprotective properties in oxidative stress situations, such as rotenone/oligomycin-induced toxicity, and also against protein hyperphosphorylation induced by the phosphatase inhibitor okadaic acid. Compound 3i, which can be considered a good candidate for further hit-to-lead development against neurodegenerative diseases due to its well-balanced multitarget profile, was further characterized by proving its ability to reduce phosphorylated Tau levels.

Fragment-based covalent ligand discovery

Targeted covalent inhibitors have regained widespread attention in drug discovery and have emerged as powerful tools for basic biomedical research. Fueled by considerable improvements in mass spectrometry sensitivity and sample processing, chemoproteomic strategies have revealed thousands of proteins that can be covalently modified by reactive small molecules. Fragment-based drug discovery, which has traditionally been used in a target-centric fashion, is now being deployed on a proteome-wide scale thereby expanding its utility to both the discovery of novel covalent ligands and their cognate protein targets. This powerful approach is allowing ‘high-throughput’ serendipitous discovery of cryptic pockets leading to the identification of pharmacological modulators of proteins previously viewed as “undruggable”. The reactive fragment toolkit has been enabled by recent advances in the development of new chemistries that target residues other than cysteine including lysine and tyrosine. Here, we review the emerging area of covalent fragment-based ligand discovery, which integrates the benefits of covalent targeting and fragment-based medicinal chemistry. We discuss how the two strategies synergize to facilitate the efficient discovery of new pharmacological modulators of established and new therapeutic target proteins.

Covalent fragment-based ligand discovery greatly facilitates the discovery of useful fragments for drug discovery and helps unveil chemical-tractable biological targets in native biological systems.