The IUPHAR/BPS guide to PHARMACOLOGY in 2022: curating pharmacology for COVID-19, malaria and antibacterials

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

INTRODUCTION

Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology.

AREAS COVERED

This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples.

EXPERT OPINION

Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

Machine Learned Classification of Ligand Intrinsic Activities at Human μ-Opioid Receptor

Opioids are small-molecule agonists of μ-opioid receptor (μOR), while reversal agents such as naloxone are antagonists of μOR. Here, we developed machine learning (ML) models to classify the intrinsic activities of ligands at the human μOR based on the SMILES strings and two-dimensional molecular descriptors. We first manually curated a database of 983 small molecules with measured Emax values at the human μOR. Analysis of the chemical space allowed identification of dominant scaffolds and structurally similar agonists and antagonists. Decision tree models and directed message passing neural networks (MPNNs) were then trained to classify agonistic and antagonistic ligands. The hold-out test AUCs (areas under the receiver operator curves) of the extra-tree (ET) and MPNN models are 91.5 ± 3.9% and 91.8 ± 4.4%, respectively. To overcome the challenge of a small data set, a student-teacher learning method called tritraining with disagreement was tested using an unlabeled data set comprised of 15,816 ligands of human, mouse, and rat μOR, κOR, and δOR. We found that the tritraining scheme was able to increase the hold-out AUC of MPNN models to as high as 95.7%. Our work demonstrates the feasibility of developing ML models to accurately predict the intrinsic activities of μOR ligands, even with limited data. We envisage potential applications of these models in evaluating uncharacterized substances for public safety risks and discovering new therapeutic agents to counteract opioid overdoses.

Bioelectric pharmacology of cancer: A systematic review of ion channel drugs affecting the cancer phenotype

Cancer is a pernicious and pressing medical problem; moreover, it is a failure of multicellular morphogenesis that sheds much light on evolutionary developmental biology. Numerous classes of pharmacological agents have been considered as cancer therapeutics and evaluated as potential carcinogenic agents; however, these are spread throughout the primary literature. Here, we briefly review recent work on ion channel drugs as promising anti-cancer treatments and present a systematic review of the known cancer-relevant effects of 109 drugs targeting ion channels. The roles of ion channels in cancer are consistent with the importance of bioelectrical parameters in cell regulation and with the functions of bioelectric signaling in morphogenetic signals that act as cancer suppressors. We find that compounds that are well-known for having targets in the nervous system, such as voltage-gated ion channels, ligand-gated ion channels, proton pumps, and gap junctions are especially relevant to cancer. Our review suggests further opportunities for the repurposing of numerous promising candidates in the field of cancer electroceuticals.

The state of the art in secondary pharmacology and its impact on the safety of new medicines

Secondary pharmacology screening of investigational small-molecule drugs for potentially adverse off-target activities has become standard practice in pharmaceutical research and development, and regulatory agencies are increasingly requesting data on activity against targets with recognized adverse effect relationships. However, the screening strategies and target panels used by pharmaceutical companies may vary substantially. To help identify commonalities and differences, as well as to highlight opportunities for further optimization of secondary pharmacology assessment, we conducted a broad-ranging survey across 18 companies under the auspices of the DruSafe leadership group of the International Consortium for Innovation and Quality in Pharmaceutical Development. Based on our analysis of this survey and discussions and additional research within the group, we present here an overview of the current state of the art in secondary pharmacology screening. We discuss best practices, including additional safety-associated targets not covered by most current screening panels, and present approaches for interpreting and reporting off-target activities. We also provide an assessment of the safety impact of secondary pharmacology screening, and a perspective on opportunities and challenges in this rapidly developing field.

G Protein-Coupled Receptor-Ligand Pose and Functional Class Prediction

G protein-coupled receptor (GPCR) transmembrane protein family members play essential roles in physiology. Numerous pharmaceuticals target GPCRs, and many drug discovery programs utilize virtual screening (VS) against GPCR targets. Improvements in the accuracy of predicting new molecules that bind to and either activate or inhibit GPCR function would accelerate such drug discovery programs. This work addresses two significant research questions. First, do ligand interaction fingerprints provide a substantial advantage over automated methods of binding site selection for classical docking? Second, can the functional status of prospective screening candidates be predicted from ligand interaction fingerprints using a random forest classifier? Ligand interaction fingerprints were found to offer modest advantages in sampling accurate poses, but no substantial advantage in the final set of top-ranked poses after scoring, and, thus, were not used in the generation of the ligand-receptor complexes used to train and test the random forest classifier. A binary classifier which treated agonists, antagonists, and inverse agonists as active and all other ligands as inactive proved highly effective in ligand function prediction in an external test set of GPR31 and TAAR2 candidate ligands with a hit rate of 82.6% actual actives within the set of predicted actives.

Chemogenomics for NR1 nuclear hormone receptors

Nuclear receptors (NRs) regulate transcription in response to ligand binding and NR modulation allows pharmacological control of gene expression. Although some NRs are relevant as drug targets, the NR1 family, which comprises 19 NRs binding to hormones, vitamins, and lipid metabolites, has only been partially explored from a translational perspective. To enable systematic target identification and validation for this protein family in phenotypic settings, we present an NR1 chemogenomic (CG) compound set optimized for complementary activity/selectivity profiles and chemical diversity. Based on broad profiling of candidates for specificity, toxicity, and off-target liabilities, sixty-nine comprehensively annotated NR1 agonists, antagonists and inverse agonists covering all members of the NR1 family and meeting potency and selectivity standards are included in the final NR1 CG set. Proof-of-concept application of this set reveals effects of NR1 members in autophagy, neuroinflammation and cancer cell death, and confirms the suitability of the set for target identification and validation.

Pan-cancer proteogenomics expands the landscape of therapeutic targets

Fewer than 200 proteins are targeted by cancer drugs approved by the Food and Drug Administration (FDA). We integrate Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteogenomics data from 1,043 patients across 10 cancer types with additional public datasets to identify potential therapeutic targets. Pan-cancer analysis of 2,863 druggable proteins reveals a wide abundance range and identifies biological factors that affect mRNA-protein correlation. Integration of proteomic data from tumors and genetic screen data from cell lines identifies protein overexpression- or hyperactivation-driven druggable dependencies, enabling accurate predictions of effective drug targets. Proteogenomic identification of synthetic lethality provides a strategy to target tumor suppressor gene loss. Combining proteogenomic analysis and MHC binding prediction prioritizes mutant KRAS peptides as promising public neoantigens. Computational identification of shared tumor-associated antigens followed by experimental confirmation nominates peptides as immunotherapy targets. These analyses, summarized at https://targets.linkedomics.org, form a comprehensive landscape of protein and peptide targets for companion diagnostics, drug repurposing, and therapy development.

Machine Learned Classification of Ligand Intrinsic Activities at Human μ-Opioid Receptor

Opioids are small-molecule agonists of μ-opioid receptor (μOR), while reversal agents such as naloxone are antagonists of μOR. Here we developed machine learning (ML) models to classify the intrinsic activities of ligands at the human μOR based on the SMILE strings and two-dimensional molecular descriptors. We first manually curated a database of 983 small molecules with measured E max values at the human μOR. Analysis of the chemical space allowed identification of dominant scaffolds and structurally similar agonists and antagonists. Decision tree models and directed message passing neural networks (MPNNs) were then trained to classify agonistic and antagonistic ligands. The hold-out test AUCs (areas under the receiver operator curves) of the extra-tree (ET) and MPNN models are 91.5±3.9% and 91.8± 4.4%, respectively. To overcome the challenge of small dataset, a student-teacher learning method called tri-training with disagreement was tested using an unlabeled dataset comprised of 15,816 ligands of human, mouse, or rat μOR, κOR, or δOR. We found that the tri-training scheme was able to increase the hold-out AUC of MPNN to as high as 95.7%. Our work demonstrates the feasibility of developing ML models to accurately predict the intrinsic activities of μOR ligands, even with limited data. We envisage potential applications of these models in evaluating uncharacterized substances for public safety risks and discovering new therapeutic agents to counteract opioid overdoses.

Molecular Determinant Underlying Selective Coupling of Primary G-Protein by Class A GPCRs

G-protein-coupled receptors (GPCRs) transmit downstream signals predominantly via G-protein pathways. However, the conformational basis of selective coupling of primary G-protein remains elusive. Histamine receptors H2R and H3R couple with Gs- or Gi-proteins respectively. Here, three cryo-EM structures of H2R-Gs and H3R-Gi complexes are presented at a global resolution of 2.6-2.7 Å. These structures reveal the unique binding pose for endogenous histamine in H3R, wherein the amino group interacts with E2065.46 of H3R instead of the conserved D1143.32 of other aminergic receptors. Furthermore, comparative analysis of the H2R-Gs and H3R-Gi complexes reveals that the structural geometry of TM5/TM6 determines the primary G-protein selectivity in histamine receptors. Machine learning (ML)-based structuromic profiling and functional analysis of class A GPCR-G-protein complexes illustrate that TM5 length, TM5 tilt, and TM6 outward movement are key determinants of the Gs and Gi/o selectivity among the whole Class A family. Collectively, the findings uncover the common structural geometry within class A GPCRs that determines the primary Gs- and Gi/o-coupling selectivity.

MetalinksDB: a flexible and contextualizable resource of metabolite-protein interactions

From the catalytic breakdown of nutrients to signaling, interactions between metabolites and proteins play an essential role in cellular function. An important case is cell-cell communication, where metabolites, secreted into the microenvironment, initiate signaling cascades by binding to intra- or extracellular receptors of neighboring cells. Protein-protein cell-cell communication interactions are routinely predicted from transcriptomic data. However, inferring metabolite-mediated intercellular signaling remains challenging, partially due to the limited size of intercellular prior knowledge resources focused on metabolites. Here, we leverage knowledge-graph infrastructure to integrate generalistic metabolite-protein with curated metabolite-receptor resources to create MetalinksDB. MetalinksDB is an order of magnitude larger than existing metabolite-receptor resources and can be tailored to specific biological contexts, such as diseases, pathways, or tissue/cellular locations. We demonstrate MetalinksDB's utility in identifying deregulated processes in renal cancer using multi-omics bulk data. Furthermore, we infer metabolite-driven intercellular signaling in acute kidney injury using spatial transcriptomics data. MetalinksDB is a comprehensive and customizable database of intercellular metabolite-protein interactions, accessible via a web interface (https://metalinks.omnipathdb.org/) and programmatically as a knowledge graph (https://github.com/biocypher/metalinks). We anticipate that by enabling diverse analyses tailored to specific biological contexts, MetalinksDB will facilitate the discovery of disease-relevant metabolite-mediated intercellular signaling processes.

Evidence for solanidine as a dietary CYP2D6 biomarker: Significant correlation with risperidone metabolism

AIMS

The extensive variability in cytochrome P450 2D6 (CYP2D6) metabolism is mainly caused by genetic polymorphisms. However, there is large, unexplained variability in CYP2D6 metabolism within CYP2D6 genotype subgroups. Solanidine, a dietary compound found in potatoes, is a promising phenotype biomarker predicting individual CYP2D6 metabolism. The aim of this study was to investigate the correlation between solanidine metabolism and the CYP2D6-mediated metabolism of risperidone in patients with known CYP2D6 genotypes.

METHODS

The study included therapeutic drug monitoring (TDM) data from CYP2D6-genotyped patients treated with risperidone. Risperidone and 9-hydroxyrisperidone levels were determined during TDM, and reprocessing of the respective TDM full-scan high-resolution mass spectrometry files was applied for semi-quantitative measurements of solanidine and five metabolites (M402, M414, M416, M440 and M444). Spearman's tests determined the correlations between solanidine metabolic ratios (MRs) and the 9-hydroxyrisperidone-to-risperidone ratio.

RESULTS

A total of 229 patients were included. Highly significant, positive correlationswere observed between all solanidine MRs and the 9-hydroxyrisperidone-to-risperidone ratio (ρ > 0.6, P < .0001). The strongest correlation was observed for the M444-to-solanidine MR in patients with functional CYP2D6 metabolism, i.e., genotype activity scores of 1 and 1.5 (ρ 0.72-0.77, P < .0001).

CONCLUSION

The present study shows strong, positive correlations between solanidine metabolism and CYP2D6-mediated risperidone metabolism. The strong correlation within patients carrying CYP2D6 genotypes encoding functional CYP2D6 metabolism suggests that solanidine metabolism may predict individual CYP2D6 metabolism, and hence potentially improve personalized dosing of drugs metabolized by CYP2D6.

NeuroPep 2.0: An Updated Database Dedicated to Neuropeptide and Its Receptor Annotations

Neuropeptides not only work through nervous system but some of them also work peripherally to regulate numerous physiological processes. They are important in regulation of numerous physiological processes including growth, reproduction, social behavior, inflammation, fluid homeostasis, cardiovascular function, and energy homeostasis. The various roles of neuropeptides make them promising candidates for prospective therapeutics of different diseases. Currently, NeuroPep has been updated to version 2.0, it now holds 11,417 unique neuropeptide entries, which is nearly double of the first version of NeuroPep. When available, we collected information about the receptor for each neuropeptide entry and predicted the 3D structures of those neuropeptides without known experimental structure using AlphaFold2 or APPTEST according to the peptide sequence length. In addition, DeepNeuropePred and NeuroPred-PLM, two neuropeptide prediction tools developed by us recently, were also integrated into NeuroPep 2.0 to help to facilitate the identification of new neuropeptides. NeuroPep 2.0 is freely accessible at https://isyslab.info/NeuroPepV2/.

Hypothalamic tanycytes as mediators of maternally programmed seasonal plasticity

In mammals, maternal photoperiodic programming (MPP) provides a means whereby juvenile development can be matched to forthcoming seasonal environmental conditions.1,2,3,4 This phenomenon is driven by in utero effects of maternal melatonin5,6,7 on the production of thyrotropin (TSH) in the fetal pars tuberalis (PT) and consequent TSH receptor-mediated effects on tanycytes lining the 3rd ventricle of the mediobasal hypothalamus (MBH).8,9,10 Here we use LASER capture microdissection and transcriptomic profiling to show that TSH-dependent MPP controls the attributes of the ependymal region of the MBH in juvenile animals. In Siberian hamster pups gestated and raised on a long photoperiod (LP) and thereby committed to a fast trajectory for growth and reproductive maturation, the ependymal region is enriched for tanycytes bearing sensory cilia and receptors implicated in metabolic sensing. Contrastingly, in pups gestated and raised on short photoperiod (SP) and therefore following an over-wintering developmental trajectory with delayed sexual maturation, the ependymal region has fewer sensory tanycytes. Post-weaning transfer of SP-gestated pups to an intermediate photoperiod (IP), which accelerates reproductive maturation, results in a pronounced shift toward a ciliated tanycytic profile and formation of tanycytic processes. We suggest that tanycytic plasticity constitutes a mechanism to tailor metabolic development for extended survival in variable overwintering environments.

Drug repurposing for neurodegenerative diseases using Zebrafish behavioral profiles

Drug repurposing can accelerate drug development while reducing the cost and risk of toxicity typically associated with de novo drug design. Several disorders lacking pharmacological solutions and exhibiting poor results in clinical trials - such as Alzheimer's disease (AD) - could benefit from a cost-effective approach to finding new therapeutics. We previously developed a neural network model, Z-LaP Tracker, capable of quantifying behaviors in zebrafish larvae relevant to cognitive function, including activity, reactivity, swimming patterns, and optomotor response in the presence of visual and acoustic stimuli. Using this model, we performed a high-throughput screening of FDA-approved drugs to identify compounds that affect zebrafish larval behavior in a manner consistent with the distinct behavior induced by calcineurin inhibitors. Cyclosporine (CsA) and other calcineurin inhibitors have garnered interest for their potential role in the prevention of AD. We generated behavioral profiles suitable for cluster analysis, through which we identified 64 candidate therapeutics for neurodegenerative disorders.

A journey from molecule to physiology and in silico tools for drug discovery targeting the transient receptor potential vanilloid type 1 (TRPV1) channel

The heat and capsaicin receptor TRPV1 channel is widely expressed in nerve terminals of dorsal root ganglia (DRGs) and trigeminal ganglia innervating the body and face, respectively, as well as in other tissues and organs including central nervous system. The TRPV1 channel is a versatile receptor that detects harmful heat, pain, and various internal and external ligands. Hence, it operates as a polymodal sensory channel. Many pathological conditions including neuroinflammation, cancer, psychiatric disorders, and pathological pain, are linked to the abnormal functioning of the TRPV1 in peripheral tissues. Intense biomedical research is underway to discover compounds that can modulate the channel and provide pain relief. The molecular mechanisms underlying temperature sensing remain largely unknown, although they are closely linked to pain transduction. Prolonged exposure to capsaicin generates analgesia, hence numerous capsaicin analogs have been developed to discover efficient analgesics for pain relief. The emergence of in silico tools offered significant techniques for molecular modeling and machine learning algorithms to indentify druggable sites in the channel and for repositioning of current drugs aimed at TRPV1. Here we recapitulate the physiological and pathophysiological functions of the TRPV1 channel, including structural models obtained through cryo-EM, pharmacological compounds tested on TRPV1, and the in silico tools for drug discovery and repositioning.

FusionNW, a potential clinical impact assessment of kinases in pan-cancer fusion gene network

Kinase fusion genes are the most active fusion gene group in human cancer fusion genes. To help choose the clinically significant kinase so that the cancer patients that have fusion genes can be better diagnosed, we need a metric to infer the assessment of kinases in pan-cancer fusion genes rather than relying on the sample frequency expressed fusion genes. Most of all, multiple studies assessed human kinases as the drug targets using multiple types of genomic and clinical information, but none used the kinase fusion genes in their study. The assessment studies of kinase without kinase fusion gene events can miss the effect of one of the mechanisms that enhance the kinase function in cancer. To fill this gap, in this study, we suggest a novel way of assessing genes using a network propagation approach to infer how likely individual kinases influence the kinase fusion gene network composed of ~5K kinase fusion gene pairs. To select a better seed of propagation, we chose the top genes via dimensionality reduction like a principal component or latent layer information of six features of individual genes in pan-cancer fusion genes. Our approach may provide a novel way to assess of human kinases in cancer.

Network-Based Methods and Their Applications in Drug Discovery

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.

The IUPHAR/BPS Guide to PHARMACOLOGY in 2024

The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb; https://www.guidetopharmacology.org) is an open-access, expert-curated, online database that provides succinct overviews and key references for pharmacological targets and their recommended experimental ligands. It includes over 3039 protein targets and 12 163 ligand molecules, including approved drugs, small molecules, peptides and antibodies. Here, we report recent developments to the resource and describe expansion in content over the six database releases made during the last two years. The database update section of this paper focuses on two areas relating to important global health challenges. The first, SARS-CoV-2 COVID-19, remains a major concern and we describe our efforts to expand the database to include a new family of coronavirus proteins. The second area is antimicrobial resistance, for which we have extended our coverage of antibacterials in partnership with AntibioticDB, a collaboration that has continued through support from GARDP. We discuss other areas of curation and also focus on our external links to resources such as PubChem that bring important synergies to the resources.

SingPro: a knowledge base providing single-cell proteomic data

Single-cell proteomics (SCP) has emerged as a powerful tool for detecting cellular heterogeneity, offering unprecedented insights into biological mechanisms that are masked in bulk cell populations. With the rapid advancements in AI-based time trajectory analysis and cell subpopulation identification, there exists a pressing need for a database that not only provides SCP raw data but also explicitly describes experimental details and protein expression profiles. However, no such database has been available yet. In this study, a database, entitled 'SingPro', specializing in single-cell proteomics was thus developed. It was unique in (a) systematically providing the SCP raw data for both mass spectrometry-based and flow cytometry-based studies and (b) explicitly describing experimental detail for SCP study and expression profile of any studied protein. Anticipating a robust interest from the research community, this database is poised to become an invaluable repository for OMICs-based biomedical studies. Access to SingPro is unrestricted and does not mandate a login at: http://idrblab.org/singpro/.

DGIdb 5.0: rebuilding the drug-gene interaction database for precision medicine and drug discovery platforms

The Drug-Gene Interaction Database (DGIdb, https://dgidb.org) is a publicly accessible resource that aggregates genes or gene products, drugs and drug-gene interaction records to drive hypothesis generation and discovery for clinicians and researchers. DGIdb 5.0 is the latest release and includes substantial architectural and functional updates to support integration into clinical and drug discovery pipelines. The DGIdb service architecture has been split into separate client and server applications, enabling consistent data access for users of both the application programming interface (API) and web interface. The new interface was developed in ReactJS, and includes dynamic visualizations and consistency in the display of user interface elements. A GraphQL API has been added to support customizable queries for all drugs, genes, annotations and associated data. Updated documentation provides users with example queries and detailed usage instructions for these new features. In addition, six sources have been added and many existing sources have been updated. Newly added sources include ChemIDplus, HemOnc, NCIt (National Cancer Institute Thesaurus), Drugs@FDA, HGNC (HUGO Gene Nomenclature Committee) and RxNorm. These new sources have been incorporated into DGIdb to provide additional records and enhance annotations of regulatory approval status for therapeutics. Methods for grouping drugs and genes have been expanded upon and developed as independent modular normalizers during import. The updates to these sources and grouping methods have resulted in an improvement in FAIR (findability, accessibility, interoperability and reusability) data representation in DGIdb.

GproteinDb in 2024: new G protein-GPCR couplings, AlphaFold2-multimer models and interface interactions

G proteins are the major signal proteins of ∼800 receptors for medicines, hormones, neurotransmitters, tastants and odorants. GproteinDb offers integrated genomic, structural, and pharmacological data and tools for analysis, visualization and experiment design. Here, we present the first major update of GproteinDb greatly expanding its coupling data and structural templates, adding AlphaFold2 structure models of GPCR-G protein complexes and advancing the interactive analysis tools for their interfaces underlying coupling selectivity. We present insights on coupling agreement across datasets and parameters, including constitutive activity, agonist-induced activity and kinetics. GproteinDb is accessible at https://gproteindb.org.

Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing

Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a network's rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies.

Pan-cancer ion transport signature reveals functional regulators of glioblastoma aggression

Ion channels, transporters, and other ion-flux controlling proteins, collectively comprising the "ion permeome", are common drug targets, however, their roles in cancer remain understudied. Our integrative pan-cancer transcriptome analysis shows that genes encoding the ion permeome are significantly more often highly expressed in specific subsets of cancer samples, compared to pan-transcriptome expectations. To enable target selection, we identified 410 survival-associated IP genes in 33 cancer types using a machine-learning approach. Notably, GJB2 and SCN9A show prominent expression in neoplastic cells and are associated with poor prognosis in glioblastoma, the most common and aggressive brain cancer. GJB2 or SCN9A knockdown in patient-derived glioblastoma cells induces transcriptome-wide changes involving neuron projection and proliferation pathways, impairs cell viability and tumor sphere formation in vitro, perturbs tunneling nanotube dynamics, and extends the survival of glioblastoma-bearing mice. Thus, aberrant activation of genes encoding ion transport proteins appears as a pan-cancer feature defining tumor heterogeneity, which can be exploited for mechanistic insights and therapy development.

Enhancing Opioid Bioactivity Predictions through Integration of Ligand-Based and Structure-Based Drug Discovery Strategies with Transfer and Deep Learning Techniques

The opioid epidemic has cast a shadow over public health, necessitating immediate action to address its devastating consequences. To effectively combat this crisis, it is crucial to discover better opioid drugs with reduced addiction potential. Artificial intelligence-based and other machine learning tools, particularly deep learning models, have garnered significant attention in recent years for their potential to advance drug discovery. However, using these tools poses challenges, especially when training samples are insufficient to achieve adequate prediction performance. In this study, we investigate the effectiveness of transfer learning in building robust deep learning models to enhance ligand bioactivity prediction for each individual opioid receptor (OR) subtype. This is achieved by leveraging knowledge obtained from pretraining a model using supervised learning on a larger data set of bioactivity data combined with ligand-based and structure-based molecular descriptors related to the entire OR subfamily. Our studies hold the potential to advance opioid research by enabling the rapid identification of novel chemical probes with specific bioactivities, which can aid in the study of receptor function and contribute to the future development of improved opioid therapeutics.

Normalization of drug and therapeutic concepts with Thera-Py

Objective

The diversity of nomenclature and naming strategies makes therapeutic terminology difficult to manage and harmonize. As the number and complexity of available therapeutic ontologies continues to increase, the need for harmonized cross-resource mappings is becoming increasingly apparent. This study creates harmonized concept mappings that enable the linking together of like-concepts despite source-dependent differences in data structure or semantic representation.

Materials and Methods

For this study, we created Thera-Py, a Python package and web API that constructs searchable concepts for drugs and therapeutic terminologies using 9 public resources and thesauri. By using a directed graph approach, Thera-Py captures commonly used aliases, trade names, annotations, and associations for any given therapeutic and combines them under a single concept record.

Results

We highlight the creation of 16 069 unique merged therapeutic concepts from 9 distinct sources using Thera-Py and observe an increase in overlap of therapeutic concepts in 2 or more knowledge bases after harmonization using Thera-Py (9.8%-41.8%).

Conclusion

We observe that Thera-Py tends to normalize therapeutic concepts to their underlying active ingredients (excluding nondrug therapeutics, eg, radiation therapy, biologics), and unifies all available descriptors regardless of ontological origin.

Is the Functional Response of a Receptor Determined by the Thermodynamics of Ligand Binding?

For an effective drug, strong binding to the target protein is a prerequisite, but it is not enough. To produce a particular functional response, drugs need to either block the proteins' functions or modulate their activities by changing their conformational equilibrium. The binding free energy of a compound to its target is routinely calculated, but the timescales for the protein conformational changes are prohibitively long to be efficiently modeled via physics-based simulations. Thermodynamic principles suggest that the binding free energies of the ligands with different receptor conformations may infer their efficacy. However, this hypothesis has not been thoroughly validated. We present an actionable protocol and a comprehensive study to show that binding thermodynamics provides a strong predictor of the efficacy of a ligand. We apply the absolute binding free energy perturbation method to ligands bound to active and inactive states of eight G protein-coupled receptors and a nuclear receptor and then compare the resulting binding free energies. We find that carefully designed restraints are often necessary to efficiently model the corresponding conformational ensembles for each state. Our method achieves unprecedented performance in classifying ligands as agonists or antagonists across the various investigated receptors, all of which are important drug targets.

Attention Mechanism-Based Graph Neural Network Model for Effective Activity Prediction of SARS-CoV-2 Main Protease Inhibitors: Application to Drug Repurposing as Potential COVID-19 Therapy

Compared to de novo drug discovery, drug repurposing provides a time-efficient way to treat coronavirus disease 19 (COVID-19) that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 main protease (Mpro) has been proved to be an attractive drug target due to its pivotal involvement in viral replication and transcription. Here, we present a graph neural network-based deep-learning (DL) strategy to prioritize the existing drugs for their potential therapeutic effects against SARS-CoV-2 Mpro. Mpro inhibitors were represented as molecular graphs ready for graph attention network (GAT) and graph isomorphism network (GIN) modeling for predicting the inhibitory activities. The result shows that the GAT model outperforms the GIN and other competitive models and yields satisfactory predictions for unseen Mpro inhibitors, confirming its robustness and generalization. The attention mechanism of GAT enables to capture the dominant substructures and thus to realize the interpretability of the model. Finally, we applied the optimal GAT model in conjunction with molecular docking simulations to screen the Drug Repurposing Hub (DRH) database. As a result, 18 drug hits with best consensus prediction scores and binding affinity values were identified as the potential therapeutics against COVID-19. Both the extensive literature searching and evaluations on adsorption, distribution, metabolism, excretion, and toxicity (ADMET) illustrate the premium drug-likeness and pharmacokinetic properties of the drug candidates. Overall, our work not only provides an effective GAT-based DL prediction tool for inhibitory activity of SARS-CoV-2 Mpro inhibitors but also provides theoretical guidelines for drug discovery in the COVID-19 treatment.

Structural coverage of the human interactome

Complex biological processes in cells are embedded in the interactome, representing the complete set of protein-protein interactions. Mapping and analyzing the protein structures are essential to fully comprehending these processes' molecular details. Therefore, knowing the structural coverage of the interactome is important to show the current limitations. Structural modeling of protein-protein interactions requires accurate protein structures. In this study, we mapped all experimental structures to the reference human proteome. Later, we found the enrichment in structural coverage when complementary methods such as homology modeling and deep learning (AlphaFold) were included. We then collected the interactions from the literature and databases to form the reference human interactome, resulting in 117 897 non-redundant interactions. When we analyzed the structural coverage of the interactome, we found that the number of experimentally determined protein complex structures is scarce, corresponding to 3.95% of all binary interactions. We also analyzed known and modeled structures to potentially construct the structural interactome with a docking method. Our analysis showed that 12.97% of the interactions from HuRI and 73.62% and 32.94% from the filtered versions of STRING and HIPPIE could potentially be modeled with high structural coverage or accuracy, respectively. Overall, this paper provides an overview of the current state of structural coverage of the human proteome and interactome.

EDC-Predictor: A Novel Strategy for Prediction of Endocrine-Disrupting Chemicals by Integrating Pharmacological and Toxicological Profiles

Identification of endocrine-disrupting chemicals (EDCs) is crucial in the reduction of human health risks. However, it is hard to do so because of the complex mechanisms of the EDCs. In this study, we propose a novel strategy named EDC-Predictor to integrate pharmacological and toxicological profiles for the prediction of EDCs. Different from conventional methods that only focus on a few nuclear receptors (NRs), EDC-Predictor considers more targets. It uses computational target profiles from network-based and machine learning-based methods to characterize compounds, including both EDCs and non-EDCs. The best model constructed by these target profiles outperformed those models by molecular fingerprints. In a case study to predict NR-related EDCs, EDC-Predictor showed a wider applicability domain and higher accuracy than four previous tools. Another case study further demonstrated that EDC-Predictor could predict EDCs targeting other proteins rather than NRs. Finally, a free web server was developed to make EDC prediction easier (http://lmmd.ecust.edu.cn/edcpred/). In summary, EDC-Predictor would be a powerful tool in EDC prediction and drug safety assessment.

From CGRP to PACAP, VIP, and Beyond: Unraveling the Next Chapters in Migraine Treatment

Migraine is a neurovascular disorder that can be debilitating for individuals and society. Current research focuses on finding effective analgesics and management strategies for migraines by targeting specific receptors and neuropeptides. Nonetheless, newly approved calcitonin gene-related peptide (CGRP) monoclonal antibodies (mAbs) have a 50% responder rate ranging from 27 to 71.0%, whereas CGRP receptor inhibitors have a 50% responder rate ranging from 56 to 71%. To address the need for novel therapeutic targets, researchers are exploring the potential of another secretin family peptide, pituitary adenylate cyclase-activating polypeptide (PACAP), as a ground-breaking treatment avenue for migraine. Preclinical models have revealed how PACAP affects the trigeminal system, which is implicated in headache disorders. Clinical studies have demonstrated the significance of PACAP in migraine pathophysiology; however, a few clinical trials remain inconclusive: the pituitary adenylate cyclase-activating peptide 1 receptor mAb, AMG 301 showed no benefit for migraine prevention, while the PACAP ligand mAb, Lu AG09222 significantly reduced the number of monthly migraine days over placebo in a phase 2 clinical trial. Meanwhile, another secretin family peptide vasoactive intestinal peptide (VIP) is gaining interest as a potential new target. In light of recent advances in PACAP research, we emphasize the potential of PACAP as a promising target for migraine treatment, highlighting the significance of exploring PACAP as a member of the antimigraine armamentarium, especially for patients who do not respond to or contraindicated to anti-CGRP therapies. By updating our knowledge of PACAP and its unique contribution to migraine pathophysiology, we can pave the way for reinforcing PACAP and other secretin peptides, including VIP, as a novel treatment option for migraines.

The Emerging Concept of Transportome: State of the Art

The array of ion channels and transporters expressed in cell membranes, collectively referred to as the transportome, is a complex and multifunctional molecular machinery; in particular, at the plasma membrane level it finely tunes the exchange of biomolecules and ions, acting as a functionally adaptive interface that accounts for dynamic plasticity in the response to environmental fluctuations and stressors. The transportome is responsible for the definition of membrane potential and its variations, participates in the transduction of extracellular signals, and acts as a filter for most of the substances entering and leaving the cell, thus enabling the homeostasis of many cellular parameters. For all these reasons, physiologists have long been interested in the expression and functionality of ion channels and transporters, in both physiological and pathological settings and across the different domains of life. Today, thanks to the high-throughput technologies of the postgenomic era, the omics approach to the study of the transportome is becoming increasingly popular in different areas of biomedical research, allowing for a more comprehensive, integrated, and functional perspective of this complex cellular apparatus. This article represents a first effort for a systematic review of the scientific literature on this topic. Here we provide a brief overview of all those studies, both primary and meta-analyses, that looked at the transportome as a whole, regardless of the biological problem or the models they used. A subsequent section is devoted to the methodological aspect by reviewing the most important public databases annotating ion channels and transporters, along with the tools they provide to retrieve such information. Before conclusions, limitations and future perspectives are also discussed.

Kalium 3.0 is a comprehensive depository of natural, artificial, and labeled polypeptides acting on potassium channels

Here, we introduce the third release of Kalium database (http://kaliumdb.org/), a manually curated comprehensive depository that accumulates data on polypeptide ligands of potassium channels. The major goal of this amplitudinous update is to summarize findings for natural polypeptide ligands of K+ channels, as well as data for the artificial derivatives of these substances obtained over the decades of exploration. We manually analyzed more than 700 original manuscripts and systematized the information on mutagenesis, production of radio- and fluorescently labeled derivatives, and the molecular pharmacology of K+ channel ligands. As a result, data on more than 1200 substances were processed and added enriching the database content fivefold. We also included the electrophysiological data obtained on the understudied and neglected K+ channels including the heteromeric and concatenated channels. We associated target channels in Kalium with corresponding entries in the official database of the International Union of Basic and Clinical Pharmacology. Kalium was supplemented with an adaptive Statistics page, where users are able to obtain actual data output. Several other improvements were introduced, such as a color code to distinguish the range of ligand activity concentrations and advanced tools for filtration and sorting. Kalium is a fully open-access database, crosslinked to other databases of interest. It can be utilized as a convenient resource containing ample up-to-date information about polypeptide ligands of K+ channels.

Illuminating the structural basis of human neurokinin 1 receptor (NK1R) antagonism through classical all-atoms molecular dynamics simulations

Advances in structural biology have bestowed insights into the pleiotropic effects of neurokinin 1 receptors (NK1R) in diverse patho-physiological processes, thereby highlighting the potential therapeutic value of antagonists directed against NK1R. Herein, we investigate the mode of antagonist recognition to discern the obscure atomic facets germane for the function and molecular determinants of NK1R. To commence discernment of potent antagonists and the conformational changes in NK1R, induced upon antagonist binding, state-of-the-art classical all-atoms molecular dynamics (MD) simulations in lipid mimetic bilayers have been utilized. MD simulations of structural ensembles reveals the involvement of TM5 and TM6 in tight anchoring of antagonists through a network of interhelical hydrogen-bonds, while, the extracellular loop 2 (ECL2) governs the overall size and nature of the pocket, thereby modulating NK1R. Consistent comparison between experiments and MD simulation results discerns the predominant role of TM3, TM4, and TM6 in lipid-NK1R interaction. Correlation between hydrophobic index and helicity of TM domains elucidates their importance in maintaining the structural stability in addition to regulating NK1R antagonism. Taken together, we anticipate that our computational study marks a comprehensive structural basis of NK1R antagonism in lipid bilayers, which may facilitate designing of new therapeutics against associated diseases targeting human neurokinin receptors.

Data science in drug discovery safety: Challenges and opportunities

Early de-risking of drug targets and chemistry is essential to provide drug projects with the best chance of success. Target safety assessments (TSAs) use target biology, gene and protein expression data, genetic information from humans and animals, and competitor compound intelligence to understand the potential safety risks associated with modulating a drug target. However, there is a vast amount of information, updated daily that must be considered for each TSA. We have developed a data science-based approach that allows acquisition of relevant evidence for an optimal TSA. This is built on expert-led conventional and artificial intelligence-based mining of literature and other bioinformatics databases. Potential safety risks are identified according to an evidence framework, adjusted to the degree of target novelty. Expert knowledge is necessary to interpret the evidence and to take account of the nuances of drug safety, the modality, and the intended patient population for each TSA within each project. Overall, TSAs take full advantage of the most recent developments in data science and can be used within drug projects to identify and mitigate risks, helping with informed decision-making and resource management. These approaches should be used in the earliest stages of a drug project to guide decisions such as target selection, discovery chemistry options, in vitro assay choice, and end points for investigative in vivo studies.

Pulmonary hypertension associated with diazoxide: the SUR1 paradox

The ATP-sensitive potassium channels and their regulatory subunits, sulfonylurea receptor 1 (SUR1/Kir6.2) and SUR2/Kir6.1, contribute to the pathophysiology of pulmonary hypertension (PH). Loss-of-function pathogenic variants in the ABCC8 gene, which encodes for SUR1, have been associated with heritable pulmonary arterial hypertension. Conversely, activation of SUR1 and SUR2 leads to the relaxation of pulmonary arteries and reduces cell proliferation and migration. Diazoxide, a SUR1 activator, has been shown to alleviate experimental PH, suggesting its potential as a therapeutic option. However, there are paradoxical reports of diazoxide-induced PH in infants. This review explores the role of SUR1/2 in the pathophysiology of PH and the contradictory effects of diazoxide on the pulmonary vascular bed. Additionally, we conducted a comprehensive literature review of cases of diazoxide-associated PH and analysed data from the World Health Organization pharmacovigilance database (VigiBase). Significant disproportionality signals link diazoxide to PH, while no other SUR activators have been connected with pulmonary vascular disease. Diazoxide-associated PH seems to be dose-dependent and potentially related to acute effects on the pulmonary vascular bed. Further research is required to decipher the differing pulmonary vascular consequences of diazoxide in different age populations and experimental models.

Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing

Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a network's rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies.

A proteogenomics data-driven knowledge base of human cancer

By combining mass-spectrometry-based proteomics and phosphoproteomics with genomics, epi-genomics, and transcriptomics, proteogenomics provides comprehensive molecular characterization of cancer. Using this approach, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) has characterized over 1,000 primary tumors spanning 10 cancer types, many with matched normal tissues. Here, we present LinkedOmicsKB, a proteogenomics data-driven knowledge base that makes consistently processed and systematically precomputed CPTAC pan-cancer proteogenomics data available to the public through ∼40,000 gene-, protein-, mutation-, and phenotype-centric web pages. Visualization techniques facilitate efficient exploration and reasoning of complex, interconnected data. Using three case studies, we illustrate the practical utility of LinkedOmicsKB in providing new insights into genes, phosphorylation sites, somatic mutations, and cancer phenotypes. With precomputed results of 19,701 coding genes, 125,969 phosphosites, and 256 genotypes and phenotypes, LinkedOmicsKB provides a comprehensive resource to accelerate proteogenomics data-driven discoveries to improve our understanding and treatment of human cancer. A record of this paper's transparent peer review process is included in the supplemental information.

Finding Drug Repurposing Candidates for Neurodegenerative Diseases using Zebrafish Behavioral Profiles

Drug repurposing can accelerate drug development while reducing the cost and risk of toxicity typically associated with de novo drug design. Several disorders lacking pharmacological solutions and exhibiting poor results in clinical trials - such as Alzheimer's disease (AD) - could benefit from a cost-effective approach to finding new therapeutics. We previously developed a neural network model, Z-LaP Tracker, capable of quantifying behaviors in zebrafish larvae relevant to cognitive function, including activity, reactivity, swimming patterns, and optomotor response in the presence of visual and acoustic stimuli. Using this model, we performed a high-throughput screening of FDA-approved drugs to identify compounds that affect zebrafish larval behavior in a manner consistent with the distinct behavior induced by calcineurin inhibitors. Cyclosporine (CsA) and other calcineurin inhibitors have garnered interest for their potential role in the prevention of AD. We generated behavioral profiles suitable for cluster analysis, through which we identified 64 candidate therapeutics for neurodegenerative disorders.

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.

De Novo Design of κ-Opioid Receptor Antagonists Using a Generative Deep-Learning Framework

Likely effective pharmacological interventions for the treatment of opioid addiction include attempts to attenuate brain reward deficits during periods of abstinence. Pharmacological blockade of the κ-opioid receptor (KOR) has been shown to abolish brain reward deficits in rodents during withdrawal, as well as to reduce the escalation of opioid use in rats with extended access to opioids. Although KOR antagonists represent promising candidates for the treatment of opioid addiction, very few potent selective KOR antagonists are known to date and most of them exhibit significant safety concerns. Here, we used a generative deep-learning framework for the de novo design of chemotypes with putative KOR antagonistic activity. Molecules generated by models trained with this framework were prioritized for chemical synthesis based on their predicted optimal interactions with the receptor. Our models and proposed training protocol were experimentally validated by binding and functional assays.

Trace amine-associated receptor 1 (TAAR1) agonists for psychosis: protocol for a living systematic review and meta-analysis of human and non-human studies

BACKGROUND

There is an urgent need to develop more effective and safer antipsychotics beyond dopamine 2 receptor antagonists. An emerging and promising approach is TAAR1 agonism. Therefore, we will conduct a living systematic review and meta-analysis to synthesize and triangulate the evidence from preclinical animal experiments and clinical studies on the efficacy, safety, and underlying mechanism of action of TAAR1 agonism for psychosis.

METHODS

Independent searches will be conducted in multiple electronic databases to identify clinical and animal experimental studies comparing TAAR1 agonists with licensed antipsychotics or other control conditions in individuals with psychosis or animal models for psychosis, respectively. The primary outcomes will be overall psychotic symptoms and their behavioural proxies in animals. Secondary outcomes will include side effects and neurobiological measures. Two independent reviewers will conduct study selection, data extraction using predefined forms, and risk of bias assessment using suitable tools based on the study design. Ontologies will be developed to facilitate study identification and data extraction. Data from clinical and animal studies will be synthesized separately using random-effects meta-analysis if appropriate, or synthesis without meta-analysis. Study characteristics will be investigated as potential sources of heterogeneity. Confidence in the evidence for each outcome and source of evidence will be evaluated, considering the summary of the association, potential concerns regarding internal and external validity, and reporting biases. When multiple sources of evidence are available for an outcome, an overall conclusion will be drawn in a triangulation meeting involving a multidisciplinary team of experts. We plan trimonthly updates of the review, and any modifications in the protocol will be documented. The review will be co-produced by multiple stakeholders aiming to produce impactful and relevant results and bridge the gap between preclinical and clinical research on psychosis.

PROTOCOL REGISTRATION

PROSPERO-ID: CRD42023451628.

Pluripotential GluN1 (NMDA NR1): Functional Significance in Cellular Nuclei in Pain/Nociception

The N-methyl-D-aspartate (NMDA) glutamate receptors function as plasma membrane ionic channels and take part in very tightly controlled cellular processes activating neurogenic and inflammatory pathways. In particular, the NR1 subunit (new terminology: GluN1) is required for many neuronal and non-neuronal cell functions, including plasticity, survival, and differentiation. Physiologic levels of glutamate agonists and NMDA receptor activation are required for normal neuronal functions such as neuronal development, learning, and memory. When glutamate receptor agonists are present in excess, binding to NMDA receptors produces neuronal/CNS/PNS long-term potentiation, conditions of acute pain, ongoing severe intractable pain, and potential excitotoxicity and pathology. The GluNR1 subunit (116 kD) is necessary as the anchor component directing ion channel heterodimer formation, cellular trafficking, and the nuclear localization that directs functionally specific heterodimer formation, cellular trafficking, and nuclear functions. Emerging studies report the relevance of GluN1 subunit composition and specifically that nuclear GluN1 has major physiologic potential in tissue and/or subnuclear functioning assignments. The shift of the GluN1 subunit from a surface cell membrane to nuclear localization assigns the GluN1 promoter immediate early gene behavior with access to nuclear and potentially nucleolar functions. The present narrative review addresses the nuclear translocation of GluN1, focusing particularly on examples of the role of GluN1 in nociceptive processes.

Employing Molecular Conformations for Ligand-Based Virtual Screening with Equivariant Graph Neural Network and Deep Multiple Instance Learning

Ligand-based virtual screening (LBVS) is a promising approach for rapid and low-cost screening of potentially bioactive molecules in the early stage of drug discovery. Compared with traditional similarity-based machine learning methods, deep learning frameworks for LBVS can more effectively extract high-order molecule structure representations from molecular fingerprints or structures. However, the 3D conformation of a molecule largely influences its bioactivity and physical properties, and has rarely been considered in previous deep learning-based LBVS methods. Moreover, the relative bioactivity benchmark dataset is still lacking. To address these issues, we introduce a novel end-to-end deep learning architecture trained from molecular conformers for LBVS. We first extracted molecule conformers from multiple public molecular bioactivity data and consolidated them into a large-scale bioactivity benchmark dataset, which totally includes millions of endpoints and molecules corresponding to 954 targets. Then, we devised a deep learning-based LBVS called EquiVS to learn molecule representations from conformers for bioactivity prediction. Specifically, graph convolutional network (GCN) and equivariant graph neural network (EGNN) are sequentially stacked to learn high-order molecule-level and conformer-level representations, followed with attention-based deep multiple-instance learning (MIL) to aggregate these representations and then predict the potential bioactivity for the query molecule on a given target. We conducted various experiments to validate the data quality of our benchmark dataset, and confirmed EquiVS achieved better performance compared with 10 traditional machine learning or deep learning-based LBVS methods. Further ablation studies demonstrate the significant contribution of molecular conformation for bioactivity prediction, as well as the reasonability and non-redundancy of deep learning architecture in EquiVS. Finally, a model interpretation case study on CDK2 shows the potential of EquiVS in optimal conformer discovery. The overall study shows that our proposed benchmark dataset and EquiVS method have promising prospects in virtual screening applications.

Enhancing Opioid Bioactivity Predictions through Integration of Ligand-Based and Structure-Based Drug Discovery Strategies with Transfer and Deep Learning Techniques

The opioid epidemic has cast a shadow over public health, necessitating immediate action to address its devastating consequences. To effectively combat this crisis, it is crucial to discover better opioid drugs with reduced addiction potential. Artificial intelligence-based and other machine learning tools, particularly deep learning models, have garnered significant attention in recent years for their potential to advance drug discovery. However, utilizing these tools poses challenges, especially when training samples are insufficient to achieve adequate prediction performance. In this study, we investigate the effectiveness of transfer learning using combined ligand-based and structure-based molecular descriptors from the entire opioid receptor (OR) subfamily in building robust deep learning models for enhanced bioactivity prediction of opioid ligands at each individual OR subtype. Our studies hold the potential to greatly advance opioid research by enabling the rapid identification of novel chemical probes with specific bioactivities, which can aid in the study of receptor function and contribute to the future development of improved opioid therapeutics.

Developmental emergence of cortical neurogliaform cell diversity

GABAergic interneurons are key regulators of cortical circuit function. Among the dozens of reported transcriptionally distinct subtypes of cortical interneurons, neurogliaform cells (NGCs) are unique: they are recruited by long-range excitatory inputs, are a source of slow cortical inhibition and are able to modulate the activity of large neuronal populations. Despite their functional relevance, the developmental emergence and diversity of NGCs remains unclear. Here, by combining single-cell transcriptomics, genetic fate mapping, and electrophysiological and morphological characterization, we reveal that discrete molecular subtypes of NGCs, with distinctive anatomical and molecular profiles, populate the mouse neocortex. Furthermore, we show that NGC subtypes emerge gradually through development, as incipient discriminant molecular signatures are apparent in preoptic area (POA)-born NGC precursors. By identifying NGC developmentally conserved transcriptional programs, we report that the transcription factor Tox2 constitutes an identity hallmark across NGC subtypes. Using CRISPR-Cas9-mediated genetic loss of function, we show that Tox2 is essential for NGC development: POA-born cells lacking Tox2 fail to differentiate into NGCs. Together, these results reveal that NGCs are born from a spatially restricted pool of Tox2+ POA precursors, after which intra-type diverging molecular programs are gradually acquired post-mitotically and result in functionally and molecularly discrete NGC cortical subtypes.

Profiling of basal and ligand-dependent GPCR activities by means of a polyvalent cell-based high-throughput platform

Representing the most attractive and successful druggable receptors of the proteome, GPCRs regulate a myriad of physiological and pathophysiological functions. Although over half of present pharmaceuticals target GPCRs, the advancement of drug discovery is hampered by a lack of adequate screening tools, the majority of which are limited to probing agonist-induced G-protein and β-arrestin-2-mediated events as a measure of receptor activation. Here, we develop Tango-Trio, a comprehensive cell-based high-throughput platform comprising cumate-inducible expression of transducers, capable of the parallelized profiling of both basal and agonist-dependent GPCR activities. We capture the functional diversity of GPCRs, reporting β-arrestin-1/2 couplings, selectivities, and receptor internalization signatures across the GPCRome. Moreover, we present the construction of cumate-induced basal activation curves at approximately 200 receptors, including over 50 orphans. Overall, Tango-Trio's robustness is well-suited for the functional characterization and screening of GPCRs, especially for parallel interrogation, and is a valuable addition to the pharmacological toolbox.

MOKPE: drug-target interaction prediction via manifold optimization based kernel preserving embedding

BACKGROUND

In many applications of bioinformatics, data stem from distinct heterogeneous sources. One of the well-known examples is the identification of drug-target interactions (DTIs), which is of significant importance in drug discovery. In this paper, we propose a novel framework, manifold optimization based kernel preserving embedding (MOKPE), to efficiently solve the problem of modeling heterogeneous data. Our model projects heterogeneous drug and target data into a unified embedding space by preserving drug-target interactions and drug-drug, target-target similarities simultaneously.

RESULTS

We performed ten replications of ten-fold cross validation on four different drug-target interaction network data sets for predicting DTIs for previously unseen drugs. The classification evaluation metrics showed better or comparable performance compared to previous similarity-based state-of-the-art methods. We also evaluated MOKPE on predicting unknown DTIs of a given network. Our implementation of the proposed algorithm in R together with the scripts that replicate the reported experiments is publicly available at https://github.com/ocbinatli/mokpe .

Fatty acid-mediated signaling as a target for developing type 1 diabetes therapies

INTRODUCTION

Type 1 diabetes (T1D) is an autoimmune disease in which pro-inflammatory and cytotoxic signaling drive the death of the insulin-producing β cells. This complex signaling is regulated in part by fatty acids and their bioproducts, making them excellent therapeutic targets.

AREAS COVERED

We provide an overview of the fatty acid actions on β cells by discussing how they can cause lipotoxicity or regulate inflammatory response during insulitis. We also discuss how diet can affect the availability of fatty acids and disease development. Finally, we discuss development avenues that need further exploration.

EXPERT OPINION

Fatty acids, such as hydroxyl fatty acids, ω-3 fatty acids, and their downstream products, are druggable candidates that promote protective signaling. Inhibitors and antagonists of enzymes and receptors of arachidonic acid and free fatty acids, along with their derived metabolites, which cause pro-inflammatory and cytotoxic responses, have the potential to be developed as therapeutic targets also. Further, because diet is the main source of fatty acid intake in humans, balancing protective and pro-inflammatory/cytotoxic fatty acid levels through dietary therapy may have beneficial effects, delaying T1D progression. Therefore, therapeutic interventions targeting fatty acid signaling hold potential as avenues to treat T1D.

Histaminergic regulation of food intake

Histamine is a biogenic amine that acts as a neuromodulator within the brain. In the hypothalamus, histaminergic signaling contributes to the regulation of numerous physiological and homeostatic processes, including the regulation of energy balance. Histaminergic neurons project extensively throughout the hypothalamus and two histamine receptors (H1R, H3R) are strongly expressed in key hypothalamic nuclei known to regulate energy homeostasis, including the paraventricular (PVH), ventromedial (VMH), dorsomedial (DMH), and arcuate (ARC) nuclei. The activation of different histamine receptors is associated with differential effects on neuronal activity, mediated by their different G protein-coupling. Consequently, activation of H1R has opposing effects on food intake to that of H3R: H1R activation suppresses food intake, while H3R activation mediates an orexigenic response. The central histaminergic system has been implicated in atypical antipsychotic-induced weight gain and has been proposed as a potential therapeutic target for the treatment of obesity. It has also been demonstrated to interact with other major regulators of energy homeostasis, including the central melanocortin system and the adipose-derived hormone leptin. However, the exact mechanisms by which the histaminergic system contributes to the modification of these satiety signals remain underexplored. The present review focuses on recent advances in our understanding of the central histaminergic system's role in regulating feeding and highlights unanswered questions remaining in our knowledge of the functionality of this system.

Combined GWAS and single cell transcriptomics uncover the underlying genes and cell types in disorders of gut-brain interaction

Disorders of gut-brain interaction (DGBIs), formerly known as functional gastrointestinal disorders, are extremely common and historically difficult to manage. This is largely because their cellular and molecular mechanisms have remained poorly understood and understudied. One approach to unravel the molecular underpinnings of complex disorders such as DGBIs is performing genome wide association studies (GWASs). However, due to the heterogenous and non-specific nature of GI symptoms, it has been difficult to accurately classify cases and controls. Thus, to perform reliable studies, we need to access large patient populations which has been difficult to date. Here, we leveraged the UK Biobank (UKBB) database, containing genetic and medical record data of over half a million individuals, to perform GWAS for five DGBI categories: functional chest pain, functional diarrhea, functional dyspepsia, functional dysphagia, and functional fecal incontinence. By applying strict inclusion and exclusion criteria, we resolved patient populations and identified genes significantly associated with each condition. Leveraging multiple human single-cell RNA-sequencing datasets, we found that the disease associated genes were highly expressed in enteric neurons, which innervate and control GI functions. Further expression and association testing-based analyses revealed specific enteric neuron subtypes consistently linked with each DGBI. Furthermore, protein-protein interaction analysis of each of the disease associated genes revealed protein networks specific to each DGBI, including hedgehog signaling for functional chest pain and neuronal function and neurotransmission for functional diarrhea and functional dyspepsia. Finally, through retrospective medical record analysis we found that drugs that inhibit these networks are associated with an increased disease risk, including serine/threonine kinase 32B drugs for functional chest pain, solute carrier organic anion transporter family member 4C1, mitogen-activated protein kinase 6, and dual serine/threonine and tyrosine protein kinase drugs for functional dyspepsia, and serotonin transporter drugs for functional diarrhea. This study presents a robust strategy for uncovering the tissues, cell types, and genes involved in DGBIs, presenting novel predictions of the mechanisms underlying these historically intractable and poorly understood diseases.