Discovering allatostatin type-C receptor specific agonists.

Currently, there is no pesticide available for the selective control of the pine processionary moth (Thaumetopoea pityocampa-specific), and conventional methods typically rely on mechanical techniques such as pheromone traps or broad-spectrum larvicidal chemicals. As climate change increases the range and dispersion capacity of crop and forest pests, outbreaks of the pine processionary occur with greater frequency and significantly impact forestry and public health. Our study is carried out to provide a T. pityocampa-specific pesticide targeting the Allatostatin Type-C Receptor (AlstR-C). We use a combination of computational biology methods, a cell-based screening assay, and in vivo toxicity and side effect assays to identify, for the first time, a series of AlstR-C ligands suitable for use as T. pityocampa-specific insecticides. We further demonstrate that the novel AlstR-C targeted agonists are specific to lepidopteran larvae, with no harmful effects on coleopteran larvae or adults. Overall, our study represents an important initial advance toward an insect GPCR-targeted next-generation pesticide design. Our approach may apply to other invertebrate GPCRs involved in vital metabolic pathways.

Gaining Insights into Key Structural Hotspots within the Allosteric Binding Pockets of Protein Kinases.

Protein kinases are essential regulators of cell function and represent one of the largest and most diverse protein families. They are particularly influential in signal transduction and coordinating complex processes like the cell cycle. Out of the 518 human protein kinases identified, 478 are part of a single superfamily sharing catalytic domains that are related in sequence. The dysregulation of protein kinases due to certain mutations has been associated with various diseases, including cancer. Although most of the protein kinase inhibitors identified as type I or type II primarily target the ATP-binding pockets of kinases, the structural and sequential resemblances among these pockets pose a significant challenge for selective inhibition. Therefore, targeting allosteric pockets that are beside highly conserved ATP pockets has emerged as a promising strategy to prevail current limitations, such as poor selectivity and drug resistance. In this article, we compared the binding pockets of various protein kinases for which allosteric (type III) inhibitors have already been developed. Additionally, understanding the structure and shape of existing ligands could aid in identifying key interaction sites within the allosteric pockets of kinases. This comprehensive review aims to facilitate the design of more effective and selective allosteric inhibitors.

Unveiling molecular insights: in silico exploration of TLR4 antagonist for management of dry eye syndrome.

BACKGROUND: Dry eye disease is the most commonplace multifractional ocular complication, which has already affected millions of people in the world. It is identified by the excessive buildup of reactive oxygen species, leading to substantial corneal epithelial cell demise and ocular surface inflammation attributed to TLR4. In this study, we aimed to identify potential compounds to treat of dry eye syndrome by exploring in silico methods. METHODS: In this research, molecular docking and dynamics simulation tests were used to examine the effects of selected compounds on TLR4 receptor. Compounds were extracted from different databases and were prepared and docked against TLR4 receptor via Autodock Vina. Celastrol, lumacaftor and nilotinib were selected for further molecular dynamics studies for a deeper understanding of molecular systems consisting of protein and ligands by using the Desmond module of the Schrodinger Suite. RESULTS: The docking results revealed that the compounds are having binding affinity in the range of -5.1 to -8.78 based on the binding affinity and three-dimensional interactions celastrol, lumacaftor and nilotinib were further studied for their activity by molecular dynamics. Among the three compounds, celastrol was the most stable based on molecular dynamics trajectory analysis from 100 ns in the catalytic pockets of 2Z63.pdb.pdb. Root mean square deviation of celastrol/2Z63 was in the range of 1.8-4.8 Å. CONCLUSION: In particular, Glu376 of TLR4 receptor is crucial for the identification and binding of lipopolysaccharides (LPS), which are part of Gram-negative bacteria's outer membrane. In our investigation, celastrol binds to Glu376, suggesting that celastrol may prevent the dry eye syndrome by inhibiting LPS's binding to TLR4.

Treatment of flexibility of protein backbone in simulations of protein-ligand interactions using steered molecular dynamics.

To ensure that an external force can break the interaction between a protein and a ligand, the steered molecular dynamics simulation requires a harmonic restrained potential applied to the protein backbone. A usual practice is that all or a certain number of protein's heavy atoms or Cα atoms are fixed, being restrained by a small force. This present study reveals that while fixing both either all heavy atoms and or all Cα atoms is not a good approach, while fixing a too small number of few atoms sometimes cannot prevent the protein from rotating under the influence of the bulk water layer, and the pulled molecule may smack into the wall of the active site. We found that restraining the Cα atoms under certain conditions is more relevant. Thus, we would propose an alternative solution in which only the Cα atoms of the protein at a distance larger than 1.2 nm from the ligand are restrained. A more flexible, but not too flexible, protein will be expected to lead to a more natural release of the ligand.

Chemogenetic activation of mammalian brain neurons expressing insect Ionotropic Receptors by systemic ligand precursor administration.

Chemogenetic approaches employing ligand-gated ion channels are advantageous regarding manipulation of target neuronal population functions independently of endogenous second messenger pathways. Among them, Ionotropic Receptor (IR)-mediated neuronal activation (IRNA) allows stimulation of mammalian neurons that heterologously express members of the insect chemosensory IR repertoire in response to their cognate ligands. In the original protocol, phenylacetic acid, a ligand of the IR84a/IR8a complex, was locally injected into a brain region due to its low permeability of the blood-brain barrier. To circumvent this invasive injection, we sought to develop a strategy of peripheral administration with a precursor of phenylacetic acid, phenylacetic acid methyl ester, which is efficiently transferred into the brain and converted to the mature ligand by endogenous esterase activities. This strategy was validated by electrophysiological, biochemical, brain-imaging, and behavioral analyses, demonstrating high utility of systemic IRNA technology in the remote activation of target neurons in the brain.

Identification of Helicobacter pylori-carcinogenic TNF-alpha-inducing protein inhibitors via daidzein derivatives through computational approaches.

Gastric cancer is considered a class 1 carcinogen that is closely linked to infection with Helicobacter pylori (H. pylori), which affects over 1 million people each year. However, the major challenge to fight against H. pylori and its associated gastric cancer due to drug resistance. This research gap had led our research team to investigate a potential drug candidate targeting the Helicobacter pylori-carcinogenic TNF-alpha-inducing protein. In this study, a total of 45 daidzein derivatives were investigated and the best 10 molecules were comprehensively investigated using in silico approaches for drug development, namely pass prediction, quantum calculations, molecular docking, molecular dynamics simulations, Lipinski rule evaluation, and prediction of pharmacokinetics. The molecular docking study was performed to evaluate the binding affinity between the target protein and the ligands. In addition, the stability of ligand-protein complexes was investigated by molecular dynamics simulations. Various parameters were analysed, including root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), hydrogen bond analysis, principal component analysis (PCA) and dynamic cross-correlation matrix (DCCM). The results has confirmed that the ligand-protein complex CID: 129661094 (07) and 129664277 (08) formed stable interactions with the target protein. It was also found that CID: 129661094 (07) has greater hydrogen bond occupancy and stability, while the ligand-protein complex CID 129664277 (08) has greater conformational flexibility. Principal component analysis revealed that the ligand-protein complex CID: 129661094 (07) is more compact and stable. Hydrogen bond analysis revealed favourable interactions with the reported amino acid residues. Overall, this study suggests that daidzein derivatives in particular show promise as potential inhibitors of H. pylori.

RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning method.

Small molecule drugs can be used to target nucleic acids (NA) to regulate biological processes. Computational modeling methods, such as molecular docking or scoring functions, are commonly employed to facilitate drug design. However, the accuracy of the scoring function in predicting the closest-to-native docking pose is often suboptimal. To overcome this problem, a machine learning model, RmsdXNA, was developed to predict the root-mean-square-deviation (RMSD) of ligand docking poses in NA complexes. The versatility of RmsdXNA has been demonstrated by its successful application to various complexes involving different types of NA receptors and ligands, including metal complexes and short peptides. The predicted RMSD by RmsdXNA was strongly correlated with the actual RMSD of the docked poses. RmsdXNA also outperformed the rDock scoring function in ranking and identifying closest-to-native docking poses across different structural groups and on the testing dataset. Using experimental validated results conducted on polyadenylated nuclear element for nuclear expression triplex, RmsdXNA demonstrated better screening power for the RNA-small molecule complex compared to rDock. Molecular dynamics simulations were subsequently employed to validate the binding of top-scoring ligand candidates selected by RmsdXNA and rDock on MALAT1. The results showed that RmsdXNA has a higher success rate in identifying promising ligands that can bind well to the receptor. The development of an accurate docking score for a NA-ligand complex can aid in drug discovery and development advancements. The code to use RmsdXNA is available at the GitHub repository https://github.com/laiheng001/RmsdXNA.

Ligand Profiling as a Diagnostic Tool to Differentiate Patient-Derived α-Synuclein Polymorphs.

Amyloid fibrils are characteristic of many neurodegenerative diseases, including Alzheimer's and Parkinson's diseases. While different diseases may have fibrils formed of the same protein, the supramolecular morphology of these fibrils is disease-specific. Here, a method is reported to distinguish eight morphologically distinct amyloid fibrils based on differences in ligand binding properties. Eight fibrillar polymorphs of α-synuclein (αSyn) were investigated: five generated de novo using recombinant αSyn and three generated using protein misfolding cyclic amplification (PMCA) of recombinant αSyn seeded with brain homogenates from deceased patients diagnosed with Parkinson's disease (PD), multiple system atrophy (MSA), and dementia with Lewy bodies (DLB). Fluorescence binding assays were carried out for each fibril using a toolkit of six different ligands. The fibril samples were separated into five categories based on a binary classification of whether they bound specific ligands or not. Quantitative binding measurements then allowed every fibrillar polymorph to be uniquely identified, and the PMCA fibrils derived from PD, MSA, and DLB patients could be unambiguously distinguished. This approach constitutes a novel and operationally simple method to differentiate amyloid fibril morphologies and to identify disease states using PMCA fibrils obtained by seeding with patient samples.

SEnSCA: Identifying possible ligand-receptor interactions and its application in cell-cell communication inference.

Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.

Chemical manipulation of an activation/inhibition switch in the nuclear receptor PXR.

Nuclear receptors are ligand-activated transcription factors that can often be useful drug targets. Unfortunately, ligand promiscuity leads to two-thirds of receptors remaining clinically untargeted. PXR is a nuclear receptor that can be activated by diverse compounds to elevate metabolism, negatively impacting drug efficacy and safety. This presents a barrier to drug development because compounds designed to target other proteins must avoid PXR activation while retaining potency for the desired target. This problem could be avoided by using PXR antagonists, but these compounds are rare, and their molecular mechanisms remain unknown. Here, we report structurally related PXR-selective agonists and antagonists and their corresponding co-crystal structures to describe mechanisms of antagonism and selectivity. Structural and computational approaches show that antagonists induce PXR conformational changes incompatible with transcriptional coactivator recruitment. These results guide the design of compounds with predictable agonist/antagonist activities and bolster efforts to generate antagonists to prevent PXR activation interfering with other drugs.

Exploring the Interactions between two Ligands, UCB-J and UCB-F, and Synaptic Vesicle Glycoprotein 2 Isoforms.

In silico modeling was applied to study the efficiency of two ligands, namely, UCB-J and UCB-F, to bind to isoforms of the synaptic vesicle glycoprotein 2 (SV2) that are involved in the regulation of synaptic function in the nerve terminals, with the ultimate goal to understand the selectivity of the interaction between UCB-J and UCB-F to different isoforms of SV2. Docking and large-scale molecular dynamics simulations were carried out to unravel various binding patterns, types of interactions, and binding free energies, covering hydrogen bonding and nonspecific hydrophobic interactions, water bridge, π-π, and cation-π interactions. The overall preference for bonding types of UCB-J and UCB-F with particular residues in the protein pockets can be disclosed in detail. A unique interaction fingerprint, namely, hydrogen bonding with additional cation-π interaction with the pyridine moiety of UCB-J, could be established as an explanation for its high selectivity over the SV2 isoform A (SV2A). Other molecular details, primarily referring to the presence of π-π interactions and hydrogen bonding, could also be analyzed as sources of selectivity of the UCB-F tracer for the three isoforms. The simulations provide atomic details to support future development of new selective tracers targeting synaptic vesicle glycoproteins and their associated diseases.

Surface ligand-regulated renal clearance of MRI/SPECT dual-modality nanoprobes for tumor imaging.

BACKGROUND: The general sluggish clearance kinetics of functional inorganic nanoparticles tend to raise potential biosafety concerns for in vivo applications. Renal clearance is a possible elimination pathway for functional inorganic nanoparticles delivered through intravenous injection, but largely depending on the surface physical chemical properties of a given particle apart from its size and shape. RESULTS: In this study, three small-molecule ligands that bear a diphosphonate (DP) group, but different terminal groups on the other side, i.e., anionic, cationic, and zwitterionic groups, were synthesized and used to modify ultrasmall Fe3O4 nanoparticles for evaluating the surface structure-dependent renal clearance behaviors. Systematic studies suggested that the variation of the surface ligands did not significantly increase the hydrodynamic diameter of ultrasmall Fe3O4 nanoparticles, nor influence their magnetic resonance imaging (MRI) contrast enhancement effects. Among the three particle samples, Fe3O4 nanoparticle coated with zwitterionic ligands, i.e., Fe3O4@DMSA, exhibited optimal renal clearance efficiency and reduced reticuloendothelial uptake. Therefore, this sample was further labeled with 99mTc through the DP moieties to achieve a renal-clearable MRI/single-photon emission computed tomography (SPECT) dual-modality imaging nanoprobe. The resulting nanoprobe showed satisfactory imaging capacities in a 4T1 xenograft tumor mouse model. Furthermore, the biocompatibility of Fe3O4@DMSA was evaluated both in vitro and in vivo through safety assessment experiments. CONCLUSIONS: We believe that the current investigations offer a simple and effective strategy for constructing renal-clearable nanoparticles for precise disease diagnosis.

A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.

Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.

Quantum mechanics insights into melatonin and analogs binding to melatonin MT1 and MT2 receptors.

Melatonin receptors MT1 and MT2 are G protein-coupled receptors that mediate the effects of melatonin, a hormone involved in circadian rhythms and other physiological functions. Understanding the molecular interactions between these receptors and their ligands is crucial for developing novel therapeutic agents. In this study, we used molecular docking, molecular dynamics simulations, and quantum mechanics calculation to investigate the binding modes and affinities of three ligands: melatonin (MLT), ramelteon (RMT), and 2-phenylmelatonin (2-PMT) with both receptors. Based on the results, we identified key amino acids that contributed to the receptor-ligand interactions, such as Gln181/194, Phe179/192, and Asn162/175, which are conserved in both receptors. Additionally, we described new meaningful interactions with Gly108/Gly121, Val111/Val124, and Val191/Val204. Our results provide insights into receptor-ligand recognition's structural and energetic determinants and suggest potential strategies for designing more optimized molecules. This study enhances our understanding of receptor-ligand interactions and offers implications for future drug development.

Structural and dynamic insights into the activation of the µ-opioid receptor by an allosteric modulator.

G-protein-coupled receptors (GPCRs) play pivotal roles in various physiological processes. These receptors are activated to different extents by diverse orthosteric ligands and allosteric modulators. However, the mechanisms underlying these variations in signaling activity by allosteric modulators remain largely elusive. Here, we determine the three-dimensional structure of the µ-opioid receptor (MOR), a class A GPCR, in complex with the Gi protein and an allosteric modulator, BMS-986122, using cryogenic electron microscopy. Our results reveal that BMS-986122 binding induces changes in the map densities corresponding to R1673.50 and Y2545.58, key residues in the structural motifs conserved among class A GPCRs. Nuclear magnetic resonance analyses of MOR in the absence of the Gi protein reveal that BMS-986122 binding enhances the formation of the interaction between R1673.50 and Y2545.58, thus stabilizing the fully-activated conformation, where the intracellular half of TM6 is outward-shifted to allow for interaction with the Gi protein. These findings illuminate that allosteric modulators like BMS-986122 can potentiate receptor activation through alterations in the conformational dynamics in the core region of GPCRs. Together, our results demonstrate the regulatory mechanisms of GPCRs, providing insights into the rational development of therapeutics targeting GPCRs.

Maintenance of homeostasis by TLR4 ligands.

Immunotherapy is renowned for its capacity to elicit anti-infective and anti-cancer effects by harnessing immune responses to microbial components and bolstering innate healing mechanisms through a cascade of immunological reactions. Specifically, mammalian Toll-like receptors (TLRs) have been identified as key receptors responsible for detecting microbial components. The discovery of these mammalian Toll-like receptors has clarified antigen recognition by the innate immune system. It has furnished a molecular foundation for comprehending the interplay between innate immunity and its anti-tumor or anti-infective capabilities. Moreover, accumulating evidence highlights the crucial role of TLRs in maintaining tissue homeostasis. It has also become evident that TLR-expressing macrophages play a central role in immunity by participating in the clearance of foreign substances, tissue repair, and the establishment of new tissue. This macrophage network, centered on macrophages, significantly contributes to innate healing. This review will primarily delve into innate immunity, specifically focusing on substances targeting TLR4.

Synthesis, characterization, biological potency, and molecular docking of Co2+, Ni2+ and Cu2+ complexes of a benzoyl isothiocyanate based ligand.

The primary objective of the present study was to produce metal complexes of H4DAP ligand (N,N'-((pyridine-2,6-diylbis(azanediyl))bis(carbonothioyl))dibenzamide) derived from 2,6-diaminopyridine and benzoyl isothiocyanate with either ML or M2L stoichiometry. There are three distinct coordination complexes obtained with the formulas [Co(H2DAP)]·H2O, [Ni2(H2DAP)Cl2(H2O)2]·H2O, and [Cu(H4DAP)Cl2]·3H2O. The confirmation of the structures of all derivatives was achieved through the utilization of several analytical techniques, including FT-IR, UV-Vis, NMR, GC-MS, PXRD, SEM, TEM analysis, and QM calculations. Aiming to analyze various noncovalent interactions, topological methods such as QTAIM, NCI, ELF, and LOL were performed. Furthermore, the capacity of metal-ligand binding was examined by fluorescence emission spectroscopy. An in vitro investigation showed that the viability of MDA-MB-231 and HepG-2 cells was lower when exposed to the manufactured Cu2+ complex, in comparison to the normal cis-platin medication. The compounds were further evaluated for their in vitro antibacterial activity. The Ni2+ complex has shown promising activity against all tested pathogens, comparable to the reference drugs Gentamycin and Ketoconazole. Furthermore, a computational docking investigation was conducted to further examine the orientation, interaction, and conformation of the recently created compounds on the active site of the Bcl-2 protein.

GSScore: a novel Graphormer-based shell-like scoring method for protein-ligand docking.

Protein-ligand interactions (PLIs) are essential for cellular activities and drug discovery. But due to the complexity and high cost of experimental methods, there is a great demand for computational approaches to recognize PLI patterns, such as protein-ligand docking. In recent years, more and more models based on machine learning have been developed to directly predict the root mean square deviation (RMSD) of a ligand docking pose with reference to its native binding pose. However, new scoring methods are pressingly needed in methodology for more accurate RMSD prediction. We present a new deep learning-based scoring method for RMSD prediction of protein-ligand docking poses based on a Graphormer method and Shell-like graph architecture, named GSScore. To recognize near-native conformations from a set of poses, GSScore takes atoms as nodes and then establishes the docking interface of protein-ligand into multiple bipartite graphs within different shell ranges. Benefiting from the Graphormer and Shell-like graph architecture, GSScore can effectively capture the subtle differences between energetically favorable near-native conformations and unfavorable non-native poses without extra information. GSScore was extensively evaluated on diverse test sets including a subset of PDBBind version 2019, CASF2016 as well as DUD-E, and obtained significant improvements over existing methods in terms of RMSE, $R$ (Pearson correlation coefficient), Spearman correlation coefficient and Docking power.

Cytotoxic sigma-2 ligands trigger cancer cell death via cholesterol-induced-ER-stress.

Sigma-2-ligands (S2L) are characterized by high binding affinities to their cognate sigma-2 receptor, overexpressed in rapidly proliferating tumor cells. As such, S2L were developed as imaging probes (ISO1) or as cancer therapeutics, alone (SV119 [C6], SW43 [C10]) and as delivery vehicles for cytotoxic drug cargoes (C6-Erastin, C10-SMAC). However, the exact mechanism of S2L-induced cytotoxicity remains to be fully elucidated. A series of high-affinity S2L were evaluated regarding their cytotoxicity profiles across cancer cell lines. While C6 and C10 displayed distinct cytotoxicities, C0 and ISO1 were essentially non-toxic. Confocal microscopy and lipidomics analysis in cellular and mouse models revealed that C10 induced increases in intralysosomal free cholesterol and in cholesterol esters, suggestive of unaltered intracellular cholesterol trafficking. Cytotoxicity was caused by cholesterol excess, a phenomenon that contrasts the effects of NPC1 inhibition. RNA-sequencing revealed gene clusters involved in cholesterol homeostasis and ER stress response exclusively by cytotoxic S2L. ER stress markers were confirmed by qPCR and their targeted modulation inhibited or enhanced cytotoxicity of C10 in a predicted manner. Moreover, C10 increased sterol regulatory element-binding protein 2 (SREBP2) and low-density lipoprotein receptor (LDLR), both found to be pro-survival factors activated by ER stress. Furthermore, inhibition of downstream processes of the adaptive response to S2L with simvastatin resulted in synergistic treatment outcomes in combination with C10. Of note, the S2L conjugates retained the ER stress response of the parental ligands, indicative of cholesterol homeostasis being involved in the overall cytotoxicity of the drug conjugates. Based on these findings, we conclude that S2L-mediated cell death is due to free cholesterol accumulation that leads to ER stress. Consequently, the cytotoxic profiles of S2L drug conjugates are proposed to be enhanced via concurrent ER stress inducers or simvastatin, strategies that could be instrumental on the path toward tumor eradication.

The modern human aryl hydrocarbon receptor is more active when ancestralized by genome editing.

The aryl hydrocarbon receptor (AHR) is a transcription factor that has many functions in mammals. Its best known function is that it binds aromatic hydrocarbons and induces the expression of cytochrome P450 genes, which encode enzymes that metabolize aromatic hydrocarbons and other substrates. All present-day humans carry an amino acid substitution at position 381 in the AHR that occurred after the divergence of modern humans from Neandertals and Denisovans. Previous studies that have expressed the ancestral and modern versions of AHR from expression vectors have yielded conflicting results with regard to their activities. Here, we use genome editing to modify the endogenous AHR gene so that it encodes to the ancestral, Neandertal-like AHR protein in human cells. In the absence of exogenous ligands, the expression of AHR target genes is higher in cells expressing the ancestral AHR than in cells expressing the modern AHR, and similar to the expression in chimpanzee cells. Furthermore, the modern human AHR needs higher doses of three ligands than the ancestral AHR to induce the expression of target genes. Thus, the ability of AHR to induce the expression of many of its target genes is reduced in modern humans.