Protein-coupled Receptor Targets Research Articles

Structural basis for activation of somatostatin receptor 5 by cyclic neuropeptide agonists

Somatostatin receptor 5 (SSTR5) is an important G protein-coupled receptor and drug target for neuroendocrine tumors and pituitary disorders. This study presents two high-resolution cryogenicelectron microscope structures of the SSTR5-Gi complexes bound to the cyclic neuropeptide agonists, cortistatin-17 (CST17) and octreotide, with resolutions of 2.7 Å and 2.9 Å, respectively. The structures reveal that binding of these peptides causes rearrangement of a "hydrophobic lock", consisting of residues from transmembrane helices TM3 and TM6. This rearrangement triggers outward movement of TM6, enabling Gαi protein engagement and receptor activation. In addition to hydrophobic interactions, CST17 forms conserved polar contacts similar to somatostatin-14 binding to SSTR2, while further structural and functional analysis shows that extracellular loops differently recognize CST17 and octreotide. These insights elucidate agonist selectivity and activation mechanisms of SSTR5, providing valuable guidance for structure-based drug development targeting this therapeutically relevant receptor.

OPUS-Rota5: A highly accurate protein side-chain modeling method with 3D-Unet and RotaFormer

Accurate protein side-chain modeling is crucial for protein folding and design. This is particularly true for molecular docking as ligands primarily interact with side chains. In this study, we introduce a two-stage side-chain modeling approach called OPUS-Rota5. It leverages a modified 3D-Unet to capture the local environmental features, including ligand information of each residue, and then employs the RotaFormer module to aggregate various types of features. Evaluation on three test sets, including recently released targets from CAMEO and CASP15, shows that OPUS-Rota5 significantly outperforms some other leading side-chain modeling methods. We also employ OPUS-Rota5 to refine the side chains of 25G protein-coupled receptor targets predicted by AlphaFold2 and achieve a significantly improved success rate in a subsequent "back" docking of their natural ligands. Therefore, OPUS-Rota5 is a useful and effective tool for molecular docking, particularly for targets with relatively accurate predicted backbones but not side chains such as high-homology targets.

RAMIHM generates fully human monoclonal antibodies by rapid mRNA immunization of humanized mice and BCR-seq

As a clinical vaccine, lipid nanoparticle (LNP) mRNA has demonstrated potent and broad antibody responses, leading to speculation about its potential for antibody discovery. Here, we developed RAMIHM, a highly efficient strategy for developing fully human monoclonal antibodies that employs rapid mRNA immunization of humanized mice followed by single B cell sequencing (scBCR-seq). We immunized humanized transgenic mice with RAMIHM and generated 15 top-ranked clones from peripheral blood, plasma B, and memory B cell populations, demonstrating a high rate of antigen-specificity (93.3%). Two Omicron-specific neutralizing antibodies with high potency and one broad-spectrum neutralizing antibody were discovered. Furthermore, we extended the application of RAMIHM to cancer immunotherapy targets, including a single transmembrane protein CD22 and a multi-transmembrane G protein-coupled receptor target, GPRC5D, which is difficult for traditional protein immunization methods. RAMIHM-scBCR-seq is a broadly applicable platform for the rapid and efficient development of fully human monoclonal antibodies against an assortment of targets.

Off-target pharmacological profiling of synthetic cannabinoid receptor agonists including AMB-FUBINACA, CUMYL-PINACA, PB-22, and XLR-11.

Synthetic cannabinoid receptor agonists (SCRAs) are a diverse class of new psychoactive substances that have been associated with multiple instances and types of toxicity. Some SCRAs appear to carry a greater toxicological burden than others, or compared to the prototypical cannabis-derived agonist Δ9-tetrahydrocannabinol (Δ9-THC), despite a common primary mechanism of action via cannabinoid 1 (CB1) receptors. "Off-target" (i.e., non-CB1 receptor) effects could underpin this differential toxicity, although there are limited data around the activity of SCRAs at such targets. A selection of 7 SCRAs (AMB-FUBINACA, XLR11, PB-22, AKB-48, AB-CHMINICA, CUMYL-PINACA, and 4F-MDMB-BUTINACA), representing several distinct chemotypes and toxicological profiles, underwent a 30 μM single-point screen against 241 G protein-coupled receptor (GPCR) targets in antagonist and agonist mode using a cellular β-arrestin recruitment assay. Strong screening "hits" at specific GPCRs were followed up in detail using concentration-response assays with AMB-FUBINACA, a SCRA with a particularly notable history of toxicological liability. The single-point screen yielded few hits in agonist mode for any compound aside from CB1 and CB2 receptors, but many hits in antagonist mode, including a range of chemokine receptors, the oxytocin receptor, and histamine receptors. Concentration-response experiments showed that AMB-FUBINACA inhibited most off-targets only at the highest 30 μM concentration, with inhibition of only a small subset of targets, including H1 histamine and α2B adrenergic receptors, at lower concentrations (≥1 μM). AMB-FUBINACA also produced concentration-dependent CB1 receptor signaling disruption at concentrations higher than 1 μM, but did not produce overt cytotoxicity beyond CP55,940 or Δ9-THC in CB1 expressing cells. These results suggest that while some "off-targets" could possibly contribute to the SCRA toxidrome, particularly at high concentrations, CB1-mediated cellular dysfunction provides support for hypotheses concerning on-target, rather than off-target, toxicity. Further investigation of non-GPCR off-targets is warranted.

In Vitro and In Silico Characterization of G-Protein Coupled Receptor (GPCR) Targets of Phlorofucofuroeckol-A and Dieckol.

Phlorotannins are polyphenolic compounds in marine alga, especially the brown algae. Among numerous phlorotannins, dieckol and phlorofucofuroeckol-A (PFF-A) are the major ones and despite a wider biological activity profile, knowledge of the G protein-coupled receptor (GPCR) targets of these phlorotannins is lacking. This study explores prime GPCR targets of the two phlorotannins. In silico proteocheminformatics modeling predicted twenty major protein targets and in vitro functional assays showed a good agonist effect at the α2C adrenergic receptor (α2CAR) and an antagonist effect at the adenosine 2A receptor (A2AR), δ-opioid receptor (δ-OPR), glucagon-like peptide-1 receptor (GLP-1R), and 5-hydroxytryptamine 1A receptor (5-TH1AR) of both phlorotannins. Besides, dieckol showed an antagonist effect at the vasopressin 1A receptor (V1AR) and PFF-A showed a promising agonist effect at the cannabinoid 1 receptor and an antagonist effect at V1AR. In silico molecular docking simulation enabled us to investigate and identify distinct binding features of these phlorotannins to the target proteins. The docking results suggested that dieckol and PFF-A bind to the crystal structures of the proteins with good affinity involving key interacting amino acid residues comparable to reference ligands. Overall, the present study suggests α2CAR, A2AR, δ-OPR, GLP-1R, 5-TH1AR, CB1R, and V1AR as prime receptor targets of dieckol and PFF-A.

Emulating Docking Results Using a Deep Neural Network: A New Perspective for Virtual Screening.

Docking is one of the most important steps in virtual screening pipelines, and it is an established method for examining potential interactions between ligands and receptors. However, this method is computationally expensive, and it is often among the last steps of the process of compound libraries evaluation. In this work, we investigate the feasibility of learning a deep neural network to predict the docking output directly from a two-dimensional compound structure. The developed protocol is orders of magnitude faster than typical docking software, and it returns ligand-receptor complexes encoded in the form of the interaction fingerprint. Its speed and efficiency unlock the application possibilities, such as screening compound libraries of vast size on the basis of contact patterns or docking score (derived on the basis of predicted interaction schemes). We tested our approach on several G protein-coupled receptor targets and 4 CYP enzymes in retrospective virtual screening experiments, and a variant of graph convolutional network appeared to be most effective in emulating docking results. The method can be easily used by the community based on the code available in the Supporting Information.

Addressing free fatty acid receptor 1 (FFAR1) activation using supervised molecular dynamics.

The free fatty acid receptor 1 (FFAR1, formerly GPR40), is a potential G protein-coupled receptor (GPCR) target for the treatment of type 2 diabetes mellitus (T2DM), as it enhances glucose-dependent insulin secretion upon activation by endogenous long-chain free fatty acids. The presence of two allosterically communicating binding sites and the lack of the conserved GPCR structural motifs challenge the general knowledge of its activation mechanism. To date, four X-ray crystal structures are available for computer-aided drug design. In this study, we employed molecular dynamics (MD) and supervised molecular dynamics (SuMD) to deliver insights into the (un)binding mechanism of the agonist MK-8666, and the allosteric communications between the two experimentally determined FFAR1 binding sites. We found that FFAR1 extracellular loop 2 (ECL2) mediates the binding of the partial agonist MK-8666. Moreover, simulations showed that the agonists MK-8666 and AP8 are reciprocally stabilized and that AP8 influences MK-8666 unbinding from FFAR1.

Membrane Protein Expression in Insect Cells Using the Baculovirus Expression Vector System.

Integral membrane proteins have a critical role in fundamental biological processes; they are major drug targets and therefore of high research interest. Recombinant protein production is the first step in the protein tool generation for biochemical and biophysical studies. Here, we provide simplified protocols that facilitate the generation of high-quality virus and initial expression analysis for integral membrane protein targets utilizing the baculovirus-mediated expression system in insect cells. The protocol steps include generation of viruses, virus quality control, and initial expression trials utilizing standard commercial baculovirus vector systems and are exemplified for G protein-coupled receptor targets. The viral quality, quantity, and recombinant protein expression are evaluated by microscopy, flow cytometry, fluorimetry, and SDS-PAGE, using either covalently fused fluorescent proteins or co-expressed fluorescence markers. Moreover, integral membrane protein expression levels, approximate molecular mass, and stability can be evaluated from small-scale expression and purification trials.

DeepCPI: A Deep Learning-based Framework for Large-scale in silico Drug Screening

Accurate identification of compound–protein interactions (CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development. Conventional similarity- or docking-based computational methods for predicting CPIs rarely exploit latent features from currently available large-scale unlabeled compound and protein data and often limit their usage to relatively small-scale datasets. In the present study, we propose DeepCPI, a novel general and scalable computational framework that combines effective feature embedding (a technique of representation learning) with powerful deep learning methods to accurately predict CPIs at a large scale. DeepCPI automatically learns the implicit yet expressive low-dimensional features of compounds and proteins from a massive amount of unlabeled data. Evaluations of the measured CPIs in large-scale databases, such as ChEMBL and BindingDB, as well as of the known drug–target interactions from DrugBank, demonstrated the superior predictive performance of DeepCPI. Furthermore, several interactions among small-molecule compounds and three G protein-coupled receptor targets (glucagon-like peptide-1 receptor, glucagon receptor, and vasoactive intestinal peptide receptor) predicted using DeepCPI were experimentally validated. The present study suggests that DeepCPI is a useful and powerful tool for drug discovery and repositioning. The source code of DeepCPI can be downloaded from https://github.com/FangpingWan/DeepCPI.

296-LB: Novel Analogs of the Mitochondrially Encoded Peptide CB5064 Improve Body Weight and Glucose Tolerance in Dio Mice, and Demonstrate Selective Agonism at the Apelin Receptor

Metabolic dysregulation is integral to the development and pathogenesis of many age-related diseases, including type 2 diabetes and obesity. Several mitochondrially encoded peptides have previously been shown to be secreted from cells and to act as regulatory signals for metabolic homeostasis, including MOTS-c, SHLP2, and humanin. Novel modified analogs of a newly identified mitochondrially encoded peptide, CB5064, have been synthesized and evaluated for plasma stability, activity in a broad screen of G protein-coupled receptor (GPCR) target engagement, and for potential biological activity in diet-induced obese (DIO) mice. In vitro evaluation demonstrated that CB5064 analogs interact with and activate the apelin receptor (APJ/AGTRL1/APLNR). The observed EC50 values of several novel CB5064 analogs at the apelin receptor were in the low µM range, in some cases achieving maximum responses similar to the natural substrate, Apelin-13. In DIO mice, once daily administration of the same series of CB5064 analogs demonstrated a range of effects on metabolic regulation, including significant reduction in body weight, selective fat mass loss, and improved glucose tolerance. The apelin receptor is widely expressed in tissues and appears to play a critical role in energy metabolism, cardiovascular function, fluid homeostasis, angiogenesis, and in diabetic complications. These mitochondria-based peptide analogs therefore represent an entirely new class of molecules with agonist activity at the apelin receptor and potential for use as a source of novel therapeutics for type 2 diabetes, cardiovascular disease, and other age-related disorders. Additional evaluation of the therapeutic potential of these peptides is ongoing. Disclosure K. Grindstaff: Employee; Self; CohBar, Inc. E. Stenger: None. R. Shang: Other Relationship; Self; CohBar Inc. T. Yu: None. W.C. Luo: None. V.V. Kim: None. D. Perez-Tilve: Research Support; Self; CohBar, Inc., Novo Nordisk A/S. K.C. Cundy: Employee; Self; CohBar, Inc. Stock/Shareholder; Self; CohBar, Inc.

Accelerating the Throughput of Affinity Mass Spectrometry-Based Ligand Screening toward a G Protein-Coupled Receptor.

Affinity mass spectrometry (MS) enables rapid screening of compound mixtures for ligands bound to a specific protein target, yet its current throughput is limited to individually assay pools of 400-2000 compounds. Typical affinity MS screens implemented in pharmaceutical industry laboratories identify putative ligands based on qualitative analysis of compound binding to the target whereas no quantitative information is acquired to discriminate high- and low-affinity ligands in the screening phase. Furthermore, these screens require purification of a stabilized form of the protein target, which poses a great challenge for membrane receptor targets. Here, we describe a new, potentially general affinity MS strategy that allows screening of 20,000 compounds in one pool for highly efficient ligand discovery toward a G protein-coupled receptor (GPCR) target. Quantitative measurement of compound binding to the receptor enables high-affinity ligand selection using both the purified receptor and receptor-embedded cell membranes. This high-throughput, label-free and quantitative affinity MS screen resulted in discovery of three new antagonists of the A2A adenosine receptor.

LakePharma, Twist form antibody service

LakePharma, a biologic-drugs contract service firm, and Twist Bioscience, a DNA synthesis provider, are combining services in antibody discovery and development. Under the agreement, LakePharma will have access to Twist’s antibody discovery and optimization platforms, including one that focuses on antibodies against G protein–coupled receptor targets. Twist will have access to LakePharma’s discovery and development services.

The differential actions of clozapine and other antipsychotic drugs on the translocation of dopamine D2 receptors to the cell surface

Most clinically available antipsychotic drugs (APDs) bind dopamine D2 receptors (D2R) at therapeutic concentrations, and it is thought that they suppress psychotic symptoms by serving as competitive antagonists of dopamine at D2R. Here, we present data that demonstrate that APDs act independently of dopamine at an intracellular pool of D2R to enhance transport of D2R to the cell surface and suggest that APDs can act as pharmacological chaperones at D2R. Among the first- and second-generation APDs that we tested, clozapine exhibited the lowest efficacy for translocating D2R to the cell surface. Thus, our observations could provide a cellular explanation for some of the distinct therapeutic characteristics of clozapine in schizophrenia. They also suggest that differential intracellular actions of APDs at their common G protein-coupled receptor (GPCR) target, D2R, could contribute to differences in their clinical profiles.

Activation Dynamics of the Neurotensin G Protein-Coupled Receptor 1

A replica-exchange protocol remarkably enhances the sampling of the activation dynamics of the neurotensin receptor type 1, a G protein-coupled receptor (GPCR) and important drug target. Our work highlights the dynamic communication between conformational changes of the agonist and the G protein-binding site, via contraction-oscillation of the orthosteric pocket. It also gives insights into the mechanism by which certain mutations diminish or stimulate activation. The replica-exchange protocol effectively enhances barrier crossing where standard brute-force molecular dynamics simulations fail. It is readily applicable to other GPCRs and represents a promising approach for virtual ligand screening, using the typical features of receptor activation as a benchmark.

A Combination of G Protein-Coupled Receptor Modulators Protects Photoreceptors from Degeneration.

Degeneration of retinal photoreceptor cells can arise from environmental and/or genetic causes. Since photoreceptor cells, the retinal pigment epithelium (RPE), neurons, and glial cells of the retina are intimately associated, all cell types eventually are affected by retinal degenerative diseases. Such diseases often originate either in rod and/or cone photoreceptor cells or the RPE. Of these, cone cells located in the central retina are especially important for daily human activity. Here we describe the protection of cone cells by a combination therapy consisting of the G protein-coupled receptor modulators metoprolol, tamsulosin, and bromocriptine. These drugs were tested in Abca4-/-Rdh8-/- mice, a preclinical model for retinal degeneration. The specificity of these drugs was determined with an essentially complete panel of human G protein-coupled receptors. Significantly, the combination of metoprolol, tamsulosin, and bromocriptine had no deleterious effects on electroretinographic responses of wild-type mice. Moreover, putative G protein-coupled receptor targets of these drugs were shown to be expressed in human and mouse eyes by RNA sequencing and quantitative polymerase chain reaction. Liquid chromatography together with mass spectrometry using validated internal standards confirmed that metoprolol, tamsulosin, and bromocriptine individually or together penetrate the eye after either intraperitoneal delivery or oral gavage. Collectively, these findings support human trials with combined therapy composed of lower doses of metoprolol, tamsulosin, and bromocriptine designed to safely impede retinal degeneration associated with certain genetic diseases (e.g., Stargardt disease). The same low-dose combination also could protect the retina against diseases with complex or unknown etiologies such as age-related macular degeneration.

Bias Factor and Therapeutic Window Correlate to Predict Safer Opioid Analgesics

Biased agonism has been proposed as a means to separate desirable and adverse drug responses downstream of G protein-coupled receptor (GPCR) targets. Herein, we describe structural features of a series of mu-opioid-receptor (MOR)-selective agonists that preferentially activate receptors to couple to G proteins or to recruit βarrestin proteins. By comparing relative bias for MOR-mediated signaling in each pathway, we demonstrate a strong correlation between the respiratory suppression/antinociception therapeutic window in a series of compounds spanning a wide range of signaling bias. We find that βarrestin-biased compounds, such as fentanyl, are more likely to induce respiratory suppression at weak analgesic doses, while G protein signaling bias broadens the therapeutic window, allowing for antinociception in the absence of respiratory suppression.

Abstract 3573: Novel G protein-coupled receptor targets in HER2+ breast cancer

Abstract Background: G protein-coupled receptors (GPCRs) are excellent drug targets. However, not many GPCRs have been identified as potential drug targets in breast cancer (BC). This may be due to two factors: (1) The primary mode of GPCR regulation is at the level of signaling rather than gene expression, which is the common method for large-scale target identification. (2) GPCR expression in BC stem cells or drug-resistant cells could have been overlooked because these cells exist in small fractions. In this study, we aimed to identify novel GPCR targets based on large panel functional screening and differential gene expression in HER2+ BC stem cells and drug resistant cells. Methods: (I) For the functional screening, each of the 370 GPCR genes was knocked down using targeted Silencer® Select siRNA library comprising of 3 individual siRNAs per target in BT474 and SKBR3 cell lines (N = 3). Candidate GPCRs were selected based on at least 20% alteration in cell growth by at least 2 out of 3 siRNAs. (II) For the expression screening, gene expression of 343 GPCRs was measured in ALDH+ BC stem cells vs. ALDH- cells as well as in anti-HER2 drug-resistant derivatives vs. parental cells of BT474 cells using Taqman RT-PCR arrays (N = 2-4). Melatonin MT1 receptor (MTNR1A) gene knockdown and pharmacological modulation were employed to functionally validate its role in anchorage-dependent (MTT assay) and -independent (soft agar colony formation assay) cell growth. Results: Gene knockdown of 11 GPCRs (ADORA, ADRA1D, CCR8, GNRHR, GPR26, GPR113, GPR120, LPAR3, MC5R, MTNR1A, NTSR2) altered cell growth of HER2+ BC cells by at least 20%. The MT1 receptor was selected as a candidate GPCR for this study because (1) its knockdown resulted in at least 20% inhibition of cell growth in both BT474 and SKBR3 cells, and (2) its expression was increased in ALDH+ vs. ALDH- as well as in drug-resistant cells vs. parental BT474 cells. MTNR1A knockdown resulted in 25-35% inhibition in the anchorage-dependent cell growth of both BT474 and SKBR3 cells. Furthermore, MTNR1A knockdown caused a marked (3-fold) decrease in the number of colonies in soft agar assay. MT1 receptor antagonist luzindole reduced the anchorage-dependent cell growth of BT474 cells in a concentration-dependent manner (IC50 = 20-55 nM). However, the efficacy of luzindole was only ∼50% of lapatinib efficacy in reducing cell growth. Luzindole (1μM) also significantly reduced (by 2-fold) anchorage-independent cell growth of BT474 cells. Neither MTNR1A knockdown nor luzindole significantly improved the efficacy of lapatinib (1nM) in inhibiting cell growth of BT474 cells. Conclusion: In summary, functional knockdown screening and differential gene expression of GPCRs is a powerful tool for identifying GPCRs as candidate targets in BC. Using the combination of these two approaches, we have discovered that melatonin MT1 receptor as a potential drug target in HER2+ BC cells. The place in therapy for MT1 receptor antagonists, however, still remains to be determined. Citation Format: Raksha Bhat, Puja Yadav, Pavel Christiny, Rachel Schiff, Meghana V. Trivedi. Novel G protein-coupled receptor targets in HER2+ breast cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3573. doi:10.1158/1538-7445.AM2015-3573

New G protein coupled receptor targets for 3-iodotyronamine

So far most in vivo effects of 3-iodothyronamine (3-T1AM) are thought to be mediated by binding at the trace amine-associated receptor 1 (TAAR1). Inconsistently, 3-T1AM effects still persist in Taar1 knockout mice, suggesting additional receptor targets. It has previously been reported that 3-T1AM also binds to the alpha-2A-adrenergic receptor (ADRA2A) modulating insulin secretion. We speculated that this effect is mediated by interaction with ADRA2A or with a ADRA2A/TAAR1 complex. To unravel the signaling properties of ADRA2A in interplay with 3-T1AM we first investigated on a potential general signaling modification by the label-free EPIC-technology and then specified changes in signaling by cAMP inhibition and MAPK (ERK1/2) assays. We found that 3-T1AM induces Gi/o activation at ADRA2A. In contrast to nor-epinephrine (NorEpi), 3-T1AM did not activate the MAPK pathway but reduced the NorEpi-induced MAPK activation. Interestingly, co-expression of TAAR1 and ADRA2A as it occurs e.g. in pancreatic β-cells, abolished NorEpi-induced Gi/o signaling at ADRA2A. In addition, in ADRA2A/TAAR1 hetero-oligomers application of NorEpi results in uncoupling of Gi/o signaling pathway, not affecting MAPK activation. RNA sequencing analyses showed significant co-expression of ADRA2A and TAAR1 in mouse pancreatic islets but chronical 3-T1AM application in mice over a period of six days had no significant effects on glucose homeostasis. Our study provides important insights into the 3-T1AM-mediated signaling spectrum. It revealed a direct influence of 3-T1AM on ADRA2A function activating Gi/o pathway and 3-T1AM-induced uncoupling of NorEpi-mediated ADRA2A signaling in ADRA2A/TAAR1 hetero-oligomers.

Toward more realistic drug-target interaction predictions.

A number of supervised machine learning models have recently been introduced for the prediction of drug–target interactions based on chemical structure and genomic sequence information. Although these models could offer improved means for many network pharmacology applications, such as repositioning of drugs for new therapeutic uses, the prediction models are often being constructed and evaluated under overly simplified settings that do not reflect the real-life problem in practical applications. Using quantitative drug–target bioactivity assays for kinase inhibitors, as well as a popular benchmarking data set of binary drug–target interactions for enzyme, ion channel, nuclear receptor and G protein-coupled receptor targets, we illustrate here the effects of four factors that may lead to dramatic differences in the prediction results: (i) problem formulation (standard binary classification or more realistic regression formulation), (ii) evaluation data set (drug and target families in the application use case), (iii) evaluation procedure (simple or nested cross-validation) and (iv) experimental setting (whether training and test sets share common drugs and targets, only drugs or targets or neither). Each of these factors should be taken into consideration to avoid reporting overoptimistic drug–target interaction prediction results. We also suggest guidelines on how to make the supervised drug–target interaction prediction studies more realistic in terms of such model formulations and evaluation setups that better address the inherent complexity of the prediction task in the practical applications, as well as novel benchmarking data sets that capture the continuous nature of the drug–target interactions for kinase inhibitors.

Mapping the Conformational Free-Energy Landscape of Classical Opioid Peptides using Extensive Molecular Dynamics and Bias Exchange Metadynamics Simulations

Opioid receptors are members of an important G protein-coupled receptor (GPCR) subfamily and targets for potent, widely used analgesics in the clinic. The main subtypes of this subfamily are the structurally related, highly homologous, mu-opioid (MOP), kappa-opioid (KOP), and delta-opioid (DOP) receptors. Although opioids targeting the MOP receptor have long been employed in clinical pain management, they display several serious adverse effects, including strong physical dependence. Selective DOP receptor agonists have been reported to exhibit strong antinociceptive activity with fewer side effects, making them ideal candidates for a more effective and safer analgesia. Among the most potent agonists of the DOP receptor are the linear and cyclic enkephalin analogs known as DADLE (Tyr-D-Ala-Gly-Phe-D-Leu) and DPDPE (Tyr-D-Pen-Gly-Phe-D-Pen), respectively. The structural properties of these penta-peptides have been studied extensively over the years, using both experimental and computational methods. However, to the best of our knowledge, a thorough examination of the conformational free-energy landscape of DADLE and DPDPE in an explicit solvent is missing from the literature. Here, we employ microsecond-scale molecular dynamics and bias-exchange metadynamics simulations of DADLE and DPDPE in an explicit solvent to provide such a structural and energetic characterization. Our results point to a small number of conformational minima in solution for both DADLE and DPDPE, separated by relatively small energy barriers. Among the different energy minima, putative bioactive conformations are identified based on rigid docking of each energy basin representative into the binding pocket of the newly released crystal structure of the DOP receptor. This information offers an unprecedented opportunity for the structure-based design of potent, selective peptidomimetics for the DOP receptor.