UV-Irradiation- and Inflammation-Induced Skin Barrier Dysfunction Is Associated with the Expression of Olfactory Receptor Genes in Human Keratinocytes

Olfactory receptors (ORs) have diverse physiological roles in various cell types, beyond their function as odorant sensors in the olfactory epithelium. These previous findings have suggested that ORs could be diagnostic markers and promising therapeutic targets in several pathological conditions. In the current study, we sought to characterize the changes in the expression of ORs in the HaCaT human keratinocytes cell line exposed to ultraviolet (UV) light or inflammation, well-recognized stimulus for skin barrier disruption. We confirmed that major olfactory signaling components, including ORs, GNAL, Ric8b, and adenylate cyclase type 3, are highly expressed in HaCaT cells. We have also demonstrated that the 12 ectopic ORs detectable in HaCaT cells are more highly expressed in UV-irradiated or inflamed conditions than in normal conditions. We further assessed the specific OR-mediated biological responses of HaCaT cells in the presence of known odorant ligands of ORs and observed that specific ligand-activated ORs downregulate skin barrier genes in HaCaT cells. This study shows the potential of OR as a marker for skin barrier abnormalities. Further research is needed to explore how OR is implicated in the development and progression of barrier dysfunction.     

Automatic modeling of mammalian olfactory receptors and docking of odorants

We present a procedure that (i) automates the homology modeling of mammalian olfactory receptors (ORs) based on the six three-dimensional (3D) structures of G protein-coupled receptors (GPCRs) available so far and (ii) performs the docking of odorants on these models, using the concept of colony energy to score the complexes. ORs exhibit low-sequence similarities with other GPCR and current alignment methods often fail to provide a reliable alignment. Here, we use a fold recognition technique to obtain a robust initial alignment. We then apply our procedure to a human OR that we have previously functionally characterized. The analysis of the resulting in silico complexes, supported by receptor mutagenesis and functional assays in a heterologous expression system, suggests that antagonists dock in the upper part of the binding pocket whereas agonists dock in the narrow lower part. We propose that the potency of agonists in activating receptors depends on their ability to establish tight interactions with the floor of the binding pocket. We developed a web site that allows the user to upload a GPCR sequence, choose a ligand in a library and obtain the 3D structure of the free receptor and ligand-receptor complex (http://genome.jouy.inra.fr/GPCRautomodel).     

Exploring the Molecular Interactions of 7,8-Dihydroxyflavone and Its Derivatives with TrkB and VEGFR2 Proteins

7,8-Dihydroxyflavone (7,8-DHF) is a TrkB receptor agonist, and treatment with this flavonoid derivative brings about an enhanced TrkB phosphorylation and promotes downstream cellular signalling. Flavonoids are also known to exert an inhibitory effect on the vascular endothelial growth factor receptor (VEGFR) family of tyrosine kinase receptors. VEGFR2 is one of the important receptors involved in the regulation of vasculogenesis and angiogenesis and has also been implicated to exhibit various neuroprotective roles. Its upregulation and uncontrolled activity is associated with a range of pathological conditions such as age-related macular degeneration and various proliferative disorders. In this study, we investigated molecular interactions of 7,8-DHF and its derivatives with both the TrkB receptor as well as VEGFR2. Using a combination of molecular docking and computational mapping tools involving molecular dynamics approaches we have elucidated additional residues and binding energies involved in 7,8-DHF interactions with the TrkB Ig2 domain and VEGFR2. Our investigations have revealed for the first time that 7,8-DHF has dual biochemical action and its treatment may have divergent effects on the TrkB via its extracellular Ig2 domain and on the VEGFR2 receptor through the intracellular kinase domain. Contrary to its agonistic effects on the TrkB receptor, 7,8-DHF was found to downregulate VEGFR2 phosphorylation both in 661W photoreceptor cells and in retinal tissue.     

Olfactory Receptor OR7A17 Expression Correlates with All-Trans Retinoic Acid (ATRA)-Induced Suppression of Proliferation in Human Keratinocyte Cells

Olfactory receptors (ORs), which belong to the G-protein-coupled receptor family, have been widely studied as ectopically expressed receptors in various human tissues, including the skin. However, the physiological functions of only a few OR types have been elucidated in skin cells. All-trans retinoic acid (ATRA) is a well-known medication for various skin diseases. However, many studies have shown that ATRA can have adverse effects, resulting from the suppression of cell proliferation. Here, we investigated the involvement of OR7A17 in the ATRA-induced suppression of human keratinocyte (HaCaT) proliferation. We demonstrated that OR7A17 is expressed in HaCaT keratinocytes, and its expression was downregulated by ATRA. The ATRA-induced downregulation of OR7A17 was attenuated via RAR α or RAR γ antagonist treatment, indicating that the effects of ATRA on OR7A17 expression were mediated through nuclear retinoic acid receptor signaling. Moreover, we found that the overexpression of OR7A17 induced the proliferation of HaCaT cells while counteracting the antiproliferative effect of ATRA. Mechanistically, OR7A17 overexpression reversed the ATRA-induced attenuation of Ca²⁺ entry. Our findings indicated that ATRA suppresses cell proliferation through the downregulation of OR7A17 via RAR α- and γ-mediated retinoid signaling. Taken together, OR7A17 is a potential therapeutic target for ameliorating the anti-proliferative effects of ATRA.     

Ligand-Receptor Interactions and Machine Learning in GCGR and GLP-1R Drug Discovery

The large amount of data that has been collected so far for G protein-coupled receptors requires machine learning (ML) approaches to fully exploit its potential. Our previous ML model based on gradient boosting used for prediction of drug affinity and selectivity for a receptor subtype was compared with explicit information on ligand-receptor interactions from induced-fit docking. Both methods have proved their usefulness in drug response predictions. Yet, their successful combination still requires allosteric/orthosteric assignment of ligands from datasets. Our ligand datasets included activities of two members of the secretin receptor family: GCGR and GLP-1R. Simultaneous activation of two or three receptors of this family by dual or triple agonists is not a typical kind of information included in compound databases. A precise allosteric/orthosteric ligand assignment requires a continuous update based on new structural and biological data. This data incompleteness remains the main obstacle for current ML methods applied to class B GPCR drug discovery. Even so, for these two class B receptors, our ligand-based ML model demonstrated high accuracy (5-fold cross-validation Q² > 0.63 and Q² > 0.67 for GLP-1R and GCGR, respectively). In addition, we performed a ligand annotation using recent cryogenic-electron microscopy (cryo-EM) and X-ray crystallographic data on small-molecule complexes of GCGR and GLP-1R. As a result, we assigned GLP-1R and GCGR actives deposited in ChEMBL to four small-molecule binding sites occupied by positive and negative allosteric modulators and a full agonist. Annotated compounds were added to our recently released repository of GPCR data.     

Receptor‐Based Virtual Screening and Biological Characterization of Human Apurinic/Apyrimidinic Endonuclease (Ape1) Inhibitors

The endonucleolytic activity of human apurinic/apyrimidinic endonuclease (AP endo, Ape1) is a major factor in maintaining the integrity of the genome. Conversely, as an undesired effect, Ape1 overexpression has been linked to resistance to radio‐ and chemotherapeutic treatments in several human tumors. Inhibition of Ape1 using siRNA or the expression of a dominant negative form of the protein has been shown to sensitize cells to DNA‐damaging agents, including various chemotherapeutic agents. Therefore, inhibition of the enzymatic activity of Ape1 might result in a potent antitumor therapy. A number of small molecules have been described as Ape1 inhibitors; however, those compounds are in the early stages of development. Herein we report the identification of new compounds as potential Ape1 inhibitors through a docking‐based virtual screening technique. Some of the compounds identified have in vitro activities in the low‐to‐medium micromolar range. Interaction of these compounds with the Ape1 protein was observed by mass spectrometry. These molecules also potentiate the cytotoxicity of the chemotherapeutic agent methyl methanesulfonate in fibrosarcoma cells. This study demonstrates the power of docking and virtual screening techniques as initial steps in the design of new drugs, and opens the door to the development of a new generation of Ape1 inhibitors.     

Virtual Screening of Specific Insulin-Like Growth Factor 1 Receptor (IGF1R) Inhibitors from the National Cancer Institute (NCI) Molecular Database

Insulin-like growth factor 1 receptor (IGF1R) is an attractive drug target for cancer therapy and research on IGF1R inhibitors has had success in clinical trials. A particular challenge in the development of specific IGF1R inhibitors is interference from insulin receptor (IR), which has a nearly identical sequence. A few potent inhibitors that are selective for IGF1R have been discovered experimentally with the aid of computational methods. However, studies on the rapid identification of IGF1R-selective inhibitors using virtual screening and confidence-level inspections of ligands that show different interactions with IGF1R and IR in docking analysis are rare. In this study, we established virtual screening and binding-mode prediction workflows based on benchmark results of IGF1R and several kinase receptors with IGF1R-like structures. We used comprehensive analysis of the known complexes of IGF1R and IR with their binding ligands to screen specific IGF1R inhibitors. Using these workflows, 17 of 139,735 compounds in the NCI (National Cancer Institute) database were identified as potential specific inhibitors of IGF1R. Calculations of the potential of mean force (PMF) with GROMACS were further conducted for three of the identified compounds to assess their binding affinity differences towards IGF1R and IR.     

Cannabis sativa L. as a Natural Drug Meeting the Criteria of a Multitarget Approach to Treatment

Cannabis sativa L. turned out to be a valuable source of chemical compounds of various structures, showing pharmacological activity. The most important groups of compounds include phytocannabinoids and terpenes. The pharmacological activity of Cannabis (in epilepsy, sclerosis multiplex (SM), vomiting and nausea, pain, appetite loss, inflammatory bowel diseases (IBDs), Parkinson’s disease, Tourette’s syndrome, schizophrenia, glaucoma, and coronavirus disease 2019 (COVID-19)), which has been proven so far, results from the affinity of these compounds predominantly for the receptors of the endocannabinoid system (the cannabinoid receptor type 1 (CB₁), type two (CB₂), and the G protein-coupled receptor 55 (GPR₅₅)) but, also, for peroxisome proliferator-activated receptor (PPAR), glycine receptors, serotonin receptors (5-HT), transient receptor potential channels (TRP), and GPR, opioid receptors. The synergism of action of phytochemicals present in Cannabis sp. raw material is also expressed in their increased bioavailability and penetration through the blood–brain barrier. This review provides an overview of phytochemistry and pharmacology of compounds present in Cannabis extracts in the context of the current knowledge about their synergistic actions and the implications of clinical use in the treatment of selected diseases.     

Insect olfactory receptor responses to components of pheromone blends

Multicomponent pheromone systems are common in many insect species. As our knowledge about the number of different chemical compounds actually involved in a particular communication system increases, so too does the need for an efficient neural mechanism for the encoding of behaviorially relevant odor compounds. Here we consider the electrical activity of olfactory receptor neurons in a subset of the individual pheromone-sensitive sensilla on the antennae of male cabbage looper moths (Trichoplupia ni). Responses to single- and multiple-component stimuli, drawn from seven behaviorally active compounds, were obtained at several different intensities. Some blends elicited electrical responses which were not readily predicted from a knowledge of the receptor neuron's response to individual components.     

Computer-Assisted Selective Optimization of Side-Activities—from Cinalukast to a PPARα Modulator

Automated computational analogue design and scoring can speed up hit-to-lead optimization and appears particularly promising in selective optimization of side-activities (SOSA) where possible analogue diversity is confined. Probing this concept, we employed the cysteinyl leukotriene receptor 1 (CysLT₁R) antagonist cinalukast as lead for which we discovered peroxisome proliferator-activated receptor α (PPARα) modulatory activity. We automatically generated a virtual library of close analogues and classified these roughly 8000 compounds for PPARα agonism and CysLT₁R antagonism using automated affinity scoring and machine learning. A computationally preferred analogue for SOSA was synthesized, and in vitro characterization indeed revealed a marked activity shift toward enhanced PPARα activation and diminished CysLT₁R antagonism. Thereby, this prospective application study highlights the potential of automating SOSA.     

PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features

Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyrosine is a prerequisite to understanding the molecular function of nitrated proteins. Thanks to the progress of machine learning, computational prediction can play a vital role before the biological experimentation. Herein, we developed a computational predictor PredNTS by integrating multiple sequence features including K-mer, composition of k-spaced amino acid pairs (CKSAAP), AAindex, and binary encoding schemes. The important features were selected by the recursive feature elimination approach using a random forest classifier. Finally, we linearly combined the successive random forest (RF) probability scores generated by the different, single encoding-employing RF models. The resultant PredNTS predictor achieved an area under a curve (AUC) of 0.910 using five-fold cross validation. It outperformed the existing predictors on a comprehensive and independent dataset. Furthermore, we investigated several machine learning algorithms to demonstrate the superiority of the employed RF algorithm. The PredNTS is a useful computational resource for the prediction of nitrotyrosine sites. The web-application with the curated datasets of the PredNTS is publicly available.     

Target Hopping as a Useful Tool for the Identification of Novel EphA2 Protein–Protein Antagonists

Lithocholic acid (LCA), a physiological ligand for the nuclear receptor FXR and the G‐protein‐coupled receptor TGR5, has been recently described as an antagonist of the EphA2 receptor, a key member of the ephrin signalling system involved in tumour growth. Given the ability of LCA to recognize FXR, TGR5, and EphA2 receptors, we hypothesized that the structural requirements for a small molecule to bind each of these receptors might be similar. We therefore selected a set of commercially available FXR or TGR5 ligands and tested them for their ability to inhibit EphA2 by targeting the EphA2‐ephrin‐A1 interface. Among the selected compounds, the stilbene carboxylic acid GW4064 was identified as an effective antagonist of EphA2, being able to block EphA2 activation in prostate carcinoma cells, in the micromolar range. This finding proposes the “target hopping” approach as a new effective strategy to discover new protein–protein interaction inhibitors.     

Chemical Space Overlap with Critical Protein–Protein Interface Residues in Commercial and Specialized Small-Molecule Libraries

There is growing interest in the use of structure-based virtual screening to identify small molecules that inhibit challenging protein–protein interactions (PPIs). In this study, we investigated how effectively chemical library members docked at the PPI interface mimic the position of critical side-chain residues known as “hot spots”. Three compound collections were considered, a commercially available screening collection (ChemDiv), a collection of diversity-oriented synthesis (DOS) compounds that contains natural-product-like small molecules, and a library constructed using established reactions (the “screenable chemical universe based on intuitive data organization”, SCUBIDOO). Three different tight PPIs for which hot-spot residues have been identified were selected for analysis: uPAR⋅uPA, TEAD4⋅Yap1, and Ca_Vα⋅Ca_Vβ. Analysis of library physicochemical properties was followed by docking to the PPI receptors. A pharmacophore method was used to measure overlap between small-molecule substituents and hot-spot side chains. Fragment-like conformationally restricted small molecules showed better hot-spot overlap for interfaces with well-defined pockets such as uPAR⋅uPA, whereas better overlap was observed for more complex DOS compounds in interfaces lacking a well-defined binding site such as TEAD4⋅Yap1. Virtual screening of conformationally restricted compounds targeting uPAR⋅uPA and TEAD4⋅Yap1 followed by experimental validation reinforce these findings, as the best hits were fragment-like and had few rotatable bonds for the former, while no hits were identified for the latter. Overall, such studies provide a framework for understanding PPIs in the context of additional chemical matter and new PPI definitions.