Structure-Based Design of a Chemical Probe Set for the 5-HT5A Serotonin Receptor

Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery

Understanding protein structures can facilitate the development of therapeutic drugs. Traditionally, protein structures have been determined through experimental approaches such as X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy. While these methods are effective and are considered the gold standard, they are very resource-intensive and time-consuming, ultimately limiting their scalability. However, with recent developments in computational biology and artificial intelligence (AI), the field of protein prediction has been revolutionized. Innovations like AlphaFold and RoseTTAFold enable protein structure predictions to be made directly from amino acid sequences with remarkable speed and accuracy. Despite the enormous enthusiasm associated with these newly developed AI-approaches, their true potential in structure-based drug discovery remains uncertain. In fact, although these algorithms generally predict overall protein structures well, essential details for computational ligand docking, such as the exact location of amino acid side chains within the binding pocket, are not predicted with the necessary accuracy. Additionally, docking methodologies are considered more as a hypothesis generator rather than a precise predictor of ligand-target interactions, and thus, usually identify many false-positive hits among only a few correctly predicted interactions. In this paper, we are reviewing the latest development in this cutting-edge field with emphasis on the GPCR target class to assess the potential role of AI approaches in structure-based drug discovery.

Current development in sulfonamide derivatives to enable CNS-drug discovery

The encouraging therapeutic potential of sulfonamide-based derivatives has been unraveled by breakthrough discovery of Paul Ehrlich, who pointed out the possibility of fighting microbes with chemicals. Over the decades, the utility of sulfonamides has expanded beyond antimicrobial agents, revealing their usefulness in many areas of pharmacotherapy, including the treatment of central nervous system (CNS) diseases. Through a detailed analysis of preclinical and clinical data, we identify key sulfonamide-based compounds that have demonstrated significant CNS activity. We also discuss the challenges in the development of sulfonamide derivatives as enzyme/ion channel inhibitors or receptor ligands for CNS applications, describing their mode of action and therapeutic significance. This is followed by the characteristics of pharmacological targets, structure-activity relationships, ADMET properties, efficacy in experimental animal models, and outcomes from clinical trials. Overall, the versatile nature of arylsulfonamides makes them a valuable motif in drug discovery, offering diverse opportunities for the development of novel agents for treating CNS disorders.

Uncovering the unique characteristics of different groups of 5-HT5AR ligands with reference to their interaction with the target protein

BACKGROUND

The serotonin 5-HT5A receptor has attracted much more research attention, due to the therapeutic potential of its ligands being increasingly recognized, and the possibilities that lie ahead of these findings. There is a growing body of evidence indicating that these ligands have procognitive, pro-social, and anti-depressant properties, which offers new avenues for the development of treatments that could address socially important conditions related to the malfunctioning of the central nervous system. The aim of our study was to unravel the molecular determinants for 5-HT5AR ligands that govern their activity towards the receptor.

METHODS

In response to the need for identification of molecular determinants for 5-HT5AR activity, we prepared a comprehensive collection of 5-HT5AR ligands, carefully gathering literature and patent data. Leveraging molecular modeling techniques, such as pharmacophore hypothesis development, docking, and molecular dynamics simulations enables to gain valuable insights into the specific interactions of 5-HT5AR ligand groups with the receptor.

RESULTS

The obtained comprehensive set of 2160 compounds was divided into dozens of subsets, and a pharmacophore model was developed for each group. The results from the docking and molecular dynamics simulations have enabled the identification of crucial ligand-protein interactions that are essential for the compound's activity towards 5-HT5AR.

CONCLUSIONS

The findings from the molecular modeling study provide valuable insights that can guide medicinal chemists in the development of new 5-HT5AR ligands. Considering the pharmacological significance of these compounds, they have the potential to become impactful treatments for individuals and communities in the future. Understanding how different crystal/cryo-EM structures of 5-HT5AR affect molecular modeling experiments could have major implications for future computational studies on this receptor.

Molecular glues as potential GPCR therapeutics

"Molecular Glues" are defined as small molecules that can either be endogenous or synthetic which promote interactions between proteins at their interface. Allosteric modulators, specifically GPCR allosteric modulators, can promote both the association and the dissociation of a given receptor's transducer but accomplishes this "at a distance" from the interface. However, recent structures of GPCR G protein complexes in the presence of allosteric modulators indicate that some GPCR allosteric modulators can act as "molecular glues" interacting with both the receptor and the transducer at the interface biasing transducer signaling in both a positive and negative manner depending on the transducer. Given these phenomena we discuss the implications for this class of allosteric modulators to be used as molecular tools and for future drug development.

Exploration of prognostic and treatment markers in hepatocellular carcinoma via GPCR-related genes analysis

Background

G protein-coupled receptors (GPCRs), the biggest family of signaling receptors, account for 34 % of all the drug targets approved by the Food and Drug Administration (FDA). It has been gradually recognized that GPCRs are of significance for tumorigenesis, but in-depth studies are still required to explore specific mechanisms. In this study, the role of GPCRs in hepatocellular carcinoma (HCC) was elucidated, and GPCR-related genes were employed for building a risk-score model for the prognosis and treatment efficacy prediction of HCC patients.

Methods

Patients' data on HCC were sourced from the Liver Hepatocellular Carcinoma-Japan (LIRI-JP) and The Cancer Genome Atlas (TCGA) databases, while GPCR-related genes were obtained from the Molecular Signatures Database (MSigDB). Univariant and multivariant Cox regression analyses, as well as least absolute shrinkage and selection operator (LASSO) were performed with the aim of identifying differentially expressed GPCR-related genes and grouping patients. Differential expression and functional enrichment analyses were performed; protein-protein interaction (PPI) mechanisms were explored; hub genes and micro ribonucleic acid (miRNA)-target gene regulatory networks were constructed. The tumor immune dysfunction and exclusion (TIDE) algorithm was utilized to evaluate immune infiltration levels and genetic variations. Sensitivity to immunotherapy and common antitumor drugs was predicted via the database Genomics of Drug Sensitivity in Cancer (GDSC).

Results

A GPCR-related risk score containing eight GPCR-related genes (atypical chemokine receptor 3 (ACKR3), C-C chemokine receptor type 3 (CCR3), CCR7, frizzled homolog 5 (FZD5), metabotropic glutamate receptor 8 (GRM8), hydroxycarboxylic acid receptor 1 (HCAR1), 5-hydroxytryptamine receptor 5A (HTR5A) and nucleotide-binding oligomerization domain-like receptor family pyrin domain containing 6 (NLRP6)) was set up. In addition, patients were classified into groups with high and low risks. Patients in the high-risk group exhibited a worse prognosis but demonstrated a more favorable immunotherapy response rate compared with those in the low-risk group. Distinct sensitivity to chemotherapeutic drugs was observed. A clinical prediction model on the basis of GPCR-related risk scores was constructed. Areas under the curves (AUC) corresponding to one-, three- and five-year survival were 0.731, 0.765 and 0.731, respectively.

Conclusions

In this study, an efficient HCC prognostic prediction model was constructed by only GPCR-related genes, which are all potential targets for HCC treatment.

SerotoninAI: Serotonergic System Focused, Artificial Intelligence-Based Application for Drug Discovery

SerotoninAI is an innovative web application for scientific purposes focused on the serotonergic system. By leveraging SerotoninAI, researchers can assess the affinity (pKi value) of a molecule to all main serotonin receptors and serotonin transporters based on molecule structure introduced as SMILES. Additionally, the application provides essential insights into critical attributes of potential drugs such as blood-brain barrier penetration and human intestinal absorption. The complexity of the serotonergic system demands advanced tools for accurate predictions, which is a fundamental requirement in drug development. SerotoninAI addresses this need by providing an intuitive user interface that generates predictions of pKi values for the main serotonergic targets. The application is freely available on the Internet at https://serotoninai.streamlit.app/, implemented in Streamlit with all major web browsers supported. Currently, to the best of our knowledge, there is no tool that allows users to access affinity predictions for serotonergic targets without registration or financial obligations. SerotoninAI significantly increases the scope of drug development activities worldwide. The source code of the application is available at https://github.com/nczub/SerotoninAI_streamlit.

Illuminating the understudied GPCR-ome

G-protein-coupled receptors (GPCRs) are the target of >30% of approved drugs. Despite their popularity, many of the >800 human GPCRs remain understudied. The Illuminating the Druggable Genome (IDG) project has generated many tools leading to important insights into the function and druggability of these so-called 'dark' receptors. These tools include assays, such as PRESTO-TANGO and TRUPATH, billions of small molecules made available via the ZINC virtual library, solved orphan GPCR structures, GPCR knock-in mice, and more. Together, these tools are illuminating the remaining 'dark' GPCRs.

Psychedelics: preclinical insights provide directions for future research

Recently, psychedelics have emerged as promising therapeutics for numerous neuropsychiatric disorders. While their potential in the clinic has yet to be fully elucidated, understanding their molecular and biological mechanisms is imperative as these compounds are becoming widely used both in therapeutic and recreational contexts. This review examines the current understanding of basic biology, pharmacology, and structural biology in an attempt to reveal both the knowns and unknowns within the field.

Structural studies of serotonin receptor family

Serotonin receptors, also known as 5-HT receptors, belong to the G protein-coupled receptors (GPCRs) superfamily. They mediate the effects of serotonin, a neurotransmitter that plays a key role in a wide range of functions including mood regulation, cognition and appetite. The functions of serotonin are mediated by a family of 5-HT receptors including 12 GPCRs belonging to six major families: 5-HT1, 5-HT2, 5-HT4, 5-HT5, 5-HT6 and 5-HT7. Despite their distinct characteristics and functions, these receptors' subtypes share common structural features and signaling mechanisms. Understanding the structure, functions and pharmacology of the serotonin receptor family is essential for unraveling the complexities of serotonin signaling and developing targeted therapeutics for neuropsychiatric disorders. However, developing drugs that selectively target specific receptor subtypes is challenging due to the structural similarities in their orthosteric binding sites. This review focuses on the recent advancements in the structural studies of 5-HT receptors, highlighting the key structural features of each subtype and shedding light on their potential as targets for mental health and neurological disorders (such as depression, anxiety, schizophrenia, and migraine) drugs. [BMB Reports 2023; 56(10): 527-536].

Large library docking for novel SARS-CoV-2 main protease non-covalent and covalent inhibitors

Antiviral therapeutics to treat SARS-CoV-2 are needed to diminish the morbidity of the ongoing COVID-19 pandemic. A well-precedented drug target is the main viral protease (MPro ), which is targeted by an approved drug and by several investigational drugs. Emerging viral resistance has made new inhibitor chemotypes more pressing. Adopting a structure-based approach, we docked 1.2 billion non-covalent lead-like molecules and a new library of 6.5 million electrophiles against the enzyme structure. From these, 29 non-covalent and 11 covalent inhibitors were identified in 37 series, the most potent having an IC50 of 29 and 20 μM, respectively. Several series were optimized, resulting in low micromolar inhibitors. Subsequent crystallography confirmed the docking predicted binding modes and may template further optimization. While the new chemotypes may aid further optimization of MPro inhibitors for SARS-CoV-2, the modest success rate also reveals weaknesses in our approach for challenging targets like MPro versus other targets where it has been more successful, and versus other structure-based techniques against MPro itself.

G protein-coupled receptors as targets for transformative neuropsychiatric therapeutics

G protein-coupled receptors (GPCRs) constitute the largest family of druggable genes in the human genome. Even though perhaps 30% of approved medications target GPCRs, they interact with only a small number of them. Here, we consider whether there might be new opportunities for transformative therapeutics for neuropsychiatric disorders by specifically targeting both known and understudied GPCRs. Using psychedelic drugs that target serotonin receptors as an example, we show how recent insights into the structure, function, signaling, and cell biology of these receptors have led to potentially novel therapeutics. We next focus on the possibility that nonpsychedelic 5-HT2A receptor agonists might prove to be safe and rapidly acting antidepressants. Finally, we examine understudied and orphan GPCRs using the MRGPR family of receptors as an example.

Probing the orphan receptors: Tools and directions

The endogenous ligands activating a large fraction of the G Protein Coupled Receptor (GPCR) family members have yet to be identified. These receptors are commonly labeled as orphans (oGPCRs), and because of the absence of available pharmacological tools they are currently understudied. Nonetheless, genome wide association studies, together with research using animal models identified many physiological functions regulated by oGPCRs. Similarly, mutations in some oGPCRs have been associated with rare genetic disorders or with an increased risk of developing pathologies. The once underestimated pharmacological potential of targeting oGPCRs is increasingly being exploited by the development of novel tools to understand their biology and by drug discovery endeavors aimed at identifying new modulators of their activity. Here, we summarize recent advancements in the field of oGPCRs and future directions.

A Comprehensive Survey of Prospective Structure-Based Virtual Screening for Early Drug Discovery in the Past Fifteen Years

Structure-based virtual screening (SBVS), also known as molecular docking, has been increasingly applied to discover small-molecule ligands based on the protein structures in the early stage of drug discovery. In this review, we comprehensively surveyed the prospective applications of molecular docking judged by solid experimental validations in the literature over the past fifteen years. Herein, we systematically analyzed the novelty of the targets and the docking hits, practical protocols of docking screening, and the following experimental validations. Among the 419 case studies we reviewed, most virtual screenings were carried out on widely studied targets, and only 22% were on less-explored new targets. Regarding docking software, GLIDE is the most popular one used in molecular docking, while the DOCK 3 series showed a strong capacity for large-scale virtual screening. Besides, the majority of identified hits are promising in structural novelty and one-quarter of the hits showed better potency than 1 μM, indicating that the primary advantage of SBVS is to discover new chemotypes rather than highly potent compounds. Furthermore, in most studies, only in vitro bioassays were carried out to validate the docking hits, which might limit the further characterization and development of the identified active compounds. Finally, several successful stories of SBVS with extensive experimental validations have been highlighted, which provide unique insights into future SBVS drug discovery campaigns.

Systematic Investigation of Docking Failures in Large-Scale Structure-Based Virtual Screening

In recent years, large-scale structure-based virtual screening has attracted increasing levels of interest for identification of novel compounds corresponding to potential drug targets. It is critical to understand the strengths and weaknesses of docking algorithms to increase the success rate in practical applications. Here, we systematically investigated the docking successes and failures of two representative docking programs: UCSF DOCK 3.7 and AutoDock Vina. DOCK 3.7 performed better in early enrichment on the Directory of Useful Decoys: Enhanced (DUD-E) data set, although both docking methods were roughly comparable in overall enrichment performance. DOCK 3.7 also showed superior computational efficiency. Intriguingly, the Vina scoring function showed a bias toward compounds with higher molecular weights. Both the tested docking approaches yielded incorrectly predicted ligand binding poses caused by the limitations of torsion sampling. Based on a careful analysis of docking results from six representative cases, we propose the reasons underlying docking failures; furthermore, we provide a few solutions, representing practical guidance for large-scale virtual screening campaigns and future docking algorithm development.

Structure-based discovery of nonopioid analgesics acting through the α2A-adrenergic receptor

Because nonopioid analgesics are much sought after, we computationally docked more than 301 million virtual molecules against a validated pain target, the α2A-adrenergic receptor (α2AAR), seeking new α2AAR agonists chemotypes that lack the sedation conferred by known α2AAR drugs, such as dexmedetomidine. We identified 17 ligands with potencies as low as 12 nanomolar, many with partial agonism and preferential Gi and Go signaling. Experimental structures of α2AAR complexed with two of these agonists confirmed the docking predictions and templated further optimization. Several compounds, including the initial docking hit '9087 [mean effective concentration (EC50) of 52 nanomolar] and two analogs, '7075 and PS75 (EC50 4.1 and 4.8 nanomolar), exerted on-target analgesic activity in multiple in vivo pain models without sedation. These newly discovered agonists are interesting as therapeutic leads that lack the liabilities of opioids and the sedation of dexmedetomidine.

Inactive and active state structures template selective tools for the human 5-HT5A receptor

Serotonin receptors are important targets for established therapeutics and drug development as they are expressed throughout the human body and play key roles in cell signaling. There are 12 serotonergic G protein-coupled receptor members encoded in the human genome, of which the 5-hydroxytryptamine (5-HT)5A receptor (5-HT5AR) is the least understood and lacks selective tool compounds. Here, we report four high-resolution (2.73-2.80 Å) structures of human 5-HT5ARs, including an inactive state structure bound to an antagonist AS2674723 by crystallization and active state structures bound to a partial agonist lisuride and two full agonists, 5-carboxamidotryptamine (5-CT) and methylergometrine, by cryo-EM. Leveraging the new structures, we developed a highly selective and potent antagonist for 5-HT5AR. Collectively, these findings both enhance our understanding of this enigmatic receptor and provide a roadmap for structure-based drug discovery for 5-HT5AR.

Structural insights into the ligand binding and Gi coupling of serotonin receptor 5-HT5A

5-hydroxytryptamine receptor 5A (5-HT_5A) belongs to the 5-HT receptor family and signals through the G_i/o protein. It is involved in nervous system regulation and an attractive target for the treatment of psychosis, depression, schizophrenia, and neuropathic pain. 5-HT_5A is the only G_i/o-coupled 5-HT receptor subtype lacking a high-resolution structure, which hampers the mechanistic understanding of ligand binding and G_i/o coupling for 5-HT_5A. Here we report a cryo-electron microscopy structure of the 5-HT_5A–G_i complex bound to 5-Carboxamidotryptamine (5-CT). Combined with functional analysis, this structure reveals the 5-CT recognition mechanism and identifies the receptor residue at 6.55 as a determinant of the 5-CT selectivity for G_i/o-coupled 5-HT receptors. In addition, 5-HT_5A shows an overall conserved G_i protein coupling mode compared with other G_i/o-coupled 5-HT receptors. These findings provide comprehensive insights into the ligand binding and G protein coupling of G_i/o-coupled 5-HT receptors and offer a template for the design of 5-HT_5A-selective drugs.