Probing non-peptide agonists binding at the human nociceptin/orphanin FQ receptor: a molecular modelling study

Abstract

The N/OFQ–NOP receptor is a fascinating peptidergic system with the potential to be exploited for the development of analgesic drugs devoid of side effects associated with classical opioid signalling modulation. To date, up to four X-ray and cryo-EM structures of the NOP receptor in complex with the endogenous peptide agonist N/OFQ and three small molecule antagonists have been solved and released. Despite the available structural information, the details of selective small molecule agonist binding to the NOP receptor in the active state remain elusive. In this study, by leveraging the available structural information and using N/OFQ(1–13)-NH₂ as a reference compound, we developed a computational protocol based on docking followed by short molecular dynamics (MD) simulations that can suggest small molecule agonist binding modes at the NOP receptor that are reproducible and stable over time in the solvated membrane-embedded receptor active state and in agreement with known structure–activity relationship (SAR) data.

---

Antonella Ciancetta is Associate Professor in Medicinal Chemistry at the University of Ferrara, Italy, and Principal Investigator of the Molecular Modelling Lab in the Department of Chemical, Pharmaceutical and Agricultural Sciences. She received her PhD in Drug Sciences with the additional “Doctor Europaeus” title from the School of Advanced Study at Chieti University, Italy, in 2010. Before taking up her current position, she was Junior Research Assistant at the University of Padua, Italy; Visiting Fellow at the National Institutes of Health, Bethesda-USA; Marie-Curie Research Fellow at Queen's University Belfast, UK; and Senior Scientist at Sygnature Discovery Limited, Nottingham, UK. In 2023, she was awarded the “Premio Divisione Chimica Farmaceutica” from the Medicinal Chemistry Division of the Italian Chemical Society. She is a current member of the ACS Journal of Medicinal Chemistry Early Career Board. She has co-authored 55 scientific publications and one internationally granted patent. Her research has attracted more than 1500 citations and has been featured on four covers of prestigious journals in the Medicinal Chemistry field.

---

Introduction

The nociceptin/orphanin FQ (N/OFQ) receptor (NOP) is a class A G protein-coupled receptor (GPCR) belonging to the opioid sub-family and is endogenously activated by the 17-mer peptide N/OFQ (FGGFTGARKSARKLANQ).^1–5 The NOP receptor primarily couples to Gi proteins and shares ∼40% and 60% sequence identity and similarity, respectively, with other family members. The N/OFQ–NOP system is widely distributed in the brain, spinal cord, and some peripheral organs (e.g., intestines, vas deferens, spleen, and liver), where N/OFQ acts as an inhibitory neurotransmitter by suppressing neuronal activity and releasing several neuro-transmitters. From a medicinal chemistry perspective, NOP agonists are actively sought as hypnoinducers^6,7 and as a treatment against chronic pain, while NOP antagonists could be beneficial for the treatment of depression⁸ and Parkinson's disease.⁹ Unlike other family members that feature promiscuous activation by classical opioid peptides, the N/OFQ–NOP system is characterised by mutually exclusive recognition, in which NOP does not bind classical opioid peptides and N/OFQ does not bind classical opioid receptors. Exploiting the exclusiveness of N/OFQ–NOP recognition represents one of the strategies pursued by drug discovery programs aimed at developing NOP selective ligands. Such compounds, indeed, hold the promise of being potentially devoid of side effects associated with classical opioid receptor activation.¹⁰ Over the past three decades, several selective small molecules acting as NOP receptor agonists and antagonists have been developed; although a few of them have been clinically investigated, none have reached the market.¹¹ Currently, two non-peptide agonists are under active investigation, namely, cebranopadol and sunobinop. Cebranopadol, a mixed NOP and opioid receptor agonist, has recently advanced to Phase III clinical trials for pain treatment and drug abuse;^12,13 sunobinop, a NOP receptor partial agonist, has currently reached Phase II for insomnia associated with alcohol cessation/alcohol use disorder (AUD/IAAC).⁶ As far as the molecular detail of ligand recognition at NOP is concerned, three X-ray structures of the receptor inactive state in complex with non-peptides, namely, compound-24 (ref. 14) (C-24, PDB ID: 4EA3), C-35 (ref. 15) (PDB ID: 5DHG), and SB-612111 (ref. 14) (PDB ID: 5DHH), unravelled antagonist binding. More recently, N/OFQ–NOP recognition was elucidated in the cryo-EM structure of the ternary complex with the Gi protein¹⁶ (PDB ID: 8F7X). From a structural point of view, however, the details of small molecule agonist binding remain elusive. By leveraging the available structural information and using the minimal sequence of the endogenous ligand known to retain full agonism, namely, N/OFQ(1–13)-NH₂,¹⁷ as a reference, we conducted a molecular modelling study aimed at elucidating the binding mode of two prototype non-peptide agonists, namely (R)-Ro65-6570 (ref. 18) and MCOPPB¹⁹ (Fig. 1), using a protocol based on molecular docking and short molecular dynamics simulations (MD). The analysis revealed that the applied workflow can suggest binding modes that are reproducible, stable over time, and characterised by favourable ligand–protein interaction energies. Moreover, the suggested binding modes are in line with the structure–activity relationship (SAR) data accrued over the past three decades on the scaffolds to which the selected prototype agonists belong. Given the underexploited drug discovery potential of NOP agonists, we believe that this study might shed light on the structural details of agonist binding and speed up the structure-based design of novel selective small molecule agonists.

Fig. 1 2D structures of NOP agonists considered in this study: peptide N/OFQ analogue N/OFQ(1–13)-NH₂ (top), non-peptide ligands (R)-Ro65-6570 (bottom left) and MCOPPB (bottom right). The common substructure in the two small molecule agonists is highlighted in teal, whereas positively charged nitrogen atoms at physiological pH, which could potentially mimic the N/OFQ charged N-terminus, are highlighted with a cyan sphere.

Results and discussion

1) Pharmacological evaluation of investigated agonists

The agonists evaluated in this study were tested in vitro owing to their propensity to stimulate the human NOP receptor in a calcium mobilization assay by taking advantage of the Gα_qi5 chimeric G protein²⁰ and Fluo-4 calcium-sensitive fluorescent dye.²¹ As shown in Table 1, N/OFQ evoked concentration-dependent stimulation of the NOP receptor with high potency (pEC₅₀ = 10.5) and maximal effects (E_max = 142%) (Fig. 2 and Table 1). N/OFQ(1–13)-NH₂ mimicked the stimulatory effect of N/OFQ with similar potency and maximal effects. With regard to the small molecule agonists, Ro65-6570 and MCOPPB displayed high maximal effects, with Ro65-6570 being 25-fold less potent than N/OFQ, while MCOPPB was only 6-fold less potent (Fig. 2 and Table 1). Notably, Ro65-6570 was synthetized and tested as a racemic mixture, while the modelling analysis focused on the known eutomer.¹⁸ Based on the results obtained herein, the rank order in terms of potency, i.e. N/OFQ ≥ N/OFQ(1–13)-NH₂ ≥ MCOPPB > Ro65-6570, is largely in line with what was previously shown with the same assay as well as with other in vitro pharmacological tests.²²

Table 1 Pharmacological parameters. Potency (pEC₅₀) and E_max of N/OFQ, N/OFQ(1–13)-NH₂, MCOPPB, and Ro65-6570 are reported (mean of 6 experiments)

Agonist	pEC50 [CL95%]	E max ± SEM
N/OFQ	10.55 (10.25–10.84)	142 ± 12
N/OFQ(1–13)-NH2	10.00 (9.90–10.10)	143 ± 19
MCOPPB	9.73 (9.18–10.28)	156 ± 15
Ro65-6570	9.14 (8.51–9.77)	147 ± 16

---

Fig. 2 Calcium mobilization experiments for N/OFQ, N/OFQ(1–13)-NH₂, MCOPPB, and Ro65-6570 were evaluated (1 pM–1 μM) in CHO cells stably expressing the human NOP receptor and Gα_qi5 chimeric G protein upon incubation with a calcium sensitive dye. Data are mean and SEM of 6 separate experiments performed in duplicates.

2) Molecular modelling analysis of reference analogue N/OFQ(1–13)-NH₂ cryo-EM structure analysis

The analysis of the cryo-EM structure of the N/OFQ–NOP receptor complex¹⁶ (PDB ID: 8F7X, Fig. 3A) and SAR data available in the literature²³ identified three key pharmacophoric features within the endogenous agonist message moiety (FGGF) and pinpointed the respective regions hosting these residues in the receptor orthosteric binding site:

Fig. 3 A) Initial conformation of N/OFQ(1–13)-NH₂ in the cryo-EM structure (PDB ID: 8F7X). B) Root mean square deviation (RMSD), C) root mean square fluctuation (RSMF), and D) interaction energy (IE) profiles of N/OFQ(1–13)-NH₂. RMSD and RMSF values were calculated for the receptor (solid lines) and the peptide (dashed lines) alpha carbon atoms (Cα) during 50 ns of unrestrained MD. Each trajectory was aligned on the receptor Cα atoms using the first frame of the production run as a reference. The total IE was calculated as the sum of the electrostatic and van der Waals contributions as implemented in the namdenergy function.

i) Phe¹ aromatic side chain is hosted in a lipophilic pocket located deeper in the TM bundle surrounded by F131^3x33, M134^3x36, F135^3x37, I219^5x43, W276^6x48, V279^6x51 (superscript numbers refer to the Ballesteros–Weinstein numbering system²⁴), that can accommodate bulky lipophilic groups. N/OFQ SAR data suggest that aromaticity is not mandatory in this position: indeed, ring saturation (e.g., as in [Cha¹] N/OFQ(1–13)-NH₂) or replacement with acyclic aliphatic residues (e.g., as in [Leu¹] N/OFQ(1–13)-NH₂) yields potent full agonists.²⁵

ii) Phe⁴ aromatic side chain occupies a pocket underneath the second extracellular loop (ECL2) enclosed by Q107^2x60, D110^2x63, W116^ECL1, V126^3x28, and I127^3x29, with a low permittivity in terms of size and perturbation of the ring electronic distribution. N/OFQ SAR data suggest that aromaticity in this position is essential for receptor affinity. Moreover, bulky side chain insertion in position 4 (e.g., with a naphthalene or biphenyl ring) is not tolerated, with the only derivatives retaining binding affinity and efficacy bearing small electron-withdrawing groups in the para-position of the Phe⁴ phenyl ring.²⁶

iii) The cationic nature of the N/OFQ N-terminus is crucial for receptor binding and activation.²⁷ In the cryo-EM complex, an ionic interaction between this moiety and the D130^3x32 side chain is detected, with a measured distance between the charged atoms of 3.32 Å.

Molecular dynamics of N/OFQ(1–13)-NH₂. The cryo-EM complex was subjected to short MD simulations to establish a baseline in terms of the stability of the reference peptide agonist and the NOP receptor and to determine the key interactions persisting over time. As the coordinates for N/OFQ residues spanning from Leu¹⁵ to Gln¹⁷ are lacking in the cryo-EM structure, we simulated and used N/OFQ(1–13)-NH₂, which represents the minimal sequence of the endogenous ligand retaining full agonist activity known to date,¹⁷ as a reference analogue. The simulations were run in triplicate, with each run showing average displacement values (root mean square deviation, RMSD) for both NOP and N/OFQ(1–13)-NH₂ below 2 Å (Table 2 and Fig. 3B), and minimal deviation from the initial N/OFQ(1–13)-NH₂ conformation, especially in the message moiety. Run3 showing the lowest average RMSD and root mean square fluctuation values (RMSF, see data in Table 2 and profiles in Fig. 3B and C and Fig. S1 in the ESI†) was chosen to further inspect the interactions between the peptide and receptor. As shown in Video S1,† in this trajectory, a network of hydrogen bonds links TM2-3-7 via Q107^2x60, D130^3x32, and Y309^7x43 side chains. These polar interactions are stabilised by the N/OFQ(1–13)-NH₂ N-terminus, which bridges D130^3x32 and Y309^7x43, by establishing a salt bridge with the former and a hydrogen bond with the latter. A more detailed analysis of the interaction carried out with PyContact²⁸ (see Table S1†) clearly indicates that this pattern is reproducible and enables highlighting the role played by specific N/OFQ(1–13)-NH₂ residues. In particular, higher median hydrophobic and hydrogen bond scores are associated with the N-Ter and the Phe⁴ moieties, thus suggesting that both the peptide charged N-terminus and Phe⁴ aromatic rings contribute to stabilising the connection among the above-mentioned TMs. The N/OFQ(1–13)-NH₂ hinge portion (GG) appears to be stabilised by a polar interaction between the peptide Thr⁵ residue and the receptor Q286^6x58 side chain, which assists in the correct positioning of Phe⁴. The highest median hydrogen bond score for this interaction (Table S1†) quantitatively confirms the persistence of the established interaction. However, with respect to the pattern established by the message moiety, the interactions anchoring the hinge region display higher inter-replica variability. As far as N/OFQ(1–13)-NH₂ address moiety (i.e., residues 5–13) is concerned, a series of receptor-specific interactions with negatively charged residues located on the ECL2 and the extracellular regions of TM2 and 7 were detected, which are in line with NOP receptor high selectivity and electrostatic complementarity for N/OFQ.¹⁶ In particular, the peptides Arg⁸, Lys⁹, Arg¹², and Lys¹³ establish salt bridges with D110^2x63, E199^ECL2, E194^ECL2, E295^7x28, and E197^ECL2, with Arg¹² switching between the last two residues during the selected trajectory (see Video S1†). Of these salt bridges, the ones with the highest median score and inter-replica reproducibility involve NOP D110^2x63, E199^ECL2, E194^ECL2, and E197^ECL2 (see Table S1 in the ESI†). In terms of interaction energies (IEs), the system exhibited values fluctuating between −500 and −800 kcal mol⁻¹, with an average of −653 kcal mol⁻¹ (see the data in Table 2 and the profiles in Fig. 3D). As expected, the primary energetic contribution to the total IE originates from N/OFQ(1–13)-NH₂ address moiety, which forms strong interactions with the receptor through the above-mentioned multiple salt bridges. The N/OFQ(1–13)-NH₂–NOP interaction was furthermore studied with MM/PBSA calculations (see ESI,† Table S2†), which returned favourable energies for all replicas with the most energetically favourable being the selected run (run3: −78.65 ± 18.86 kcal mol⁻¹), thus confirming the viability of our choice to use this replica as a reference for the subsequent small-molecule analysis.

Table 2 Average RMSD, RMSF and total IE values, with standard deviations, derived from three MD simulation replicas of the complex between the NOP receptor and N/OFQ(1–13)-NH₂, RBM01, RBM03, MBM04 and MBM05. The trajectories were aligned on the receptors Cα atoms using the first frame as a reference. Each receptor and N/OFQ(1–13)-NH₂, RMSD and RMSF values were calculated only for Cα atoms, whereas for the small molecule ligands, all heavy atoms were considered. Values of the lowest RMSD, RMSF and IE detected among the three runs for each considered component of the trajectory (ligand and receptor) are in bold

		RMSD		RMSF		IE
		RMSD_Lig_Ave ± σ [Å]	RMSD_Rec_Ave ± σ [Å]	RMSF_Lig_Ave ± σ [Å]	RMSF_Rec_Ave ± σ [Å]	Tot_Ave ± σ [kcal mol^−1]
N/OFQ(1–13)-NH2	Run1	1.63 ± 0.30	1.23 ± 0.10	1.08 ± 0.27	0.76 ± 0.11	−628.55 ± 64.36
	Run2	1.69 ± 0.42	1.68 ± 0.18	1.08 ± 0.32	0.92 ± 0.16	−634.67 ± 72.47
	Run3	1.24 ± 0.28	1.29 ± 0.15	0.81 ± 0.31	0.78 ± 0.12	−653.04 ± 42.98
RBM01	Run1	2.74 ± 0.38	1.68 ± 0.17	0.86 ± 0.48	1.09 ± 0.19	−106.34 ± 9.20
	Run2	2.64 ± 0.25	1.61 ± 0.16	0.77 ± 0.34	1.02 ± 0.17	−125.01 ± 8.83
	Run3	1.29 ± 0.61	1.64 ± 0.16	1.06 ± 0.37	1.04 ± 0.14	−123.13 ± 9.78
RBM03	Run1	1.04 ± 0.27	1.46 ± 0.15	0.71 ± 0.19	1.26 ± 0.23	−118.88 ± 6.86
	Run2	1.44 ± 0.24	1.36 ± 0.13	0.60 ± 0.22	0.93 ± 0.15	−119.71 ± 7.09
	Run3	0.86 ± 0.20	1.82 ± 0.44	0.65 ± 0.19	1.12 ± 0.19	−116.97 ± 8.09
MBM04	Run1	2.95 ± 0.26	1.71 ± 0.17	1.12 ± 0.38	1.36 ± 0.19	−217.90 ± 40.68
	Run2	1.81 ± 0.42	1.52 ± 0.20	0.83 ± 0.43	1.46 ± 0.15	−243.07 ± 43.47
	Run3	2.41 ± 0.37	1.70 ± 0.16	1.04 ± 0.34	1.51 ± 0.17	−230.00 ± 43.83
MBM05	Run1	0.87 ± 0.20	1.79 ± 0.17	0.61 ± 0.14	1.41 ± 0.17	−273.31 ± 32.79
	Run2	1.04 ± 0.20	1.52 ± 0.25	0.66 ± 0.21	1.41 ± 0.18	−285.38 ± 19.86
	Run3	1.32 ± 0.20	1.49 ± 0.18	0.63 ± 0.18	1.34 ± 0.14	−249.32 ± 26.02

---

3) Docking of small molecule agonists

Molecular docking at the cryo-EM structure. The small molecule NOP receptor agonists Ro65-6570 and MCOPPB contain a chiral centre in their structure (on the acenaphthenyl group and the 3-monosubstituted piperidine of Ro65-6570 and MCOPPB, respectively). For the molecular modelling analysis, we focused on the R configuration only, as this coincides with the eutomer for both compounds.^18,19 The binding modes (hereby denoted as “BMs”) obtained for the two small molecule agonists in the NOP cryo-EM structure are reported in Fig. S2.† In particular, the program suggested two alternative BMs for each compound, namely RBM01 and RBM02 for (R)-Ro65-6570 (Fig. S2A and B,† respectively), and MBM01 and MBM02 for MCOPPB (Fig. S2C and D,† respectively). These docking poses were analysed in light of known SAR data and the above-described analysis of the reference peptide N/OFQ(1–13)-NH₂. In particular, these BMs revealed the following issues:

i) In RBM02, MBM01, and MBM02 (Fig. S2B–D†), the distance between the positively charged piperidinyl nitrogen (1,4-disubstituted in MCOPPB), believed to play a role similar to the N/OFQ N-terminus, and NOP receptor D130^3x32, is not compatible with the formation of an ionic interaction (d_{N(Lig)---O(D130)} > 7.0 Å), while in RBM01 (Fig. S2A†), the distance, whilst lower, is nonetheless above the 4 Å limit conventionally considered for this bond type²⁹ (d_{N(Lig)---O(D130)} = 5.4 Å).

ii) Only one of the two pockets surrounding N/OFQ Phe¹ and Phe⁴ residues in the cryo-EM BMs¹⁶ is occupied by a lipophilic/aromatic group of (R)-Ro65–6570 and MCOPPB in RBM02 (Fig. S2B†), MBM01 (Fig. S2C†), and MBM02 (Fig. S2D†). Specifically, the pocket hosting N/OFQ Phe¹ is occupied by the phenyl ring in MBM01 and RBM02, and by the methyl-cyclo-octyl group in MBM02. In these conformations, the other lipophilic/aromatic group is consistently located in a region quite distant from the pocket hosting N/OFQ Phe⁴ side chain, which is located near the receptor extracellular side between TM4, TM5, and TM6.

iii) In RBM01, both pockets surrounding N/OFQ Phe¹ and Phe⁴ rings are occupied by an aromatic group (the phenyl ring for the former and the acenaphthenyl group for the latter pocket, respectively). This ligand placement is consistent with the positioning of N/OFQ Phe¹ and Phe⁴ side chains. However, it seems unlikely that a potent NOP agonist, such as (R)-Ro65-6570 (pEC₅₀ = 9.14 for the racemate), would occupy the narrow and non-permissive pocket surrounding N/OFQ Phe⁴ residue through such a bulky group as the acenaphthene.²⁶

iv) Another aspect to consider regarding RBM01 and RBM02 is the role played by the lactam nitrogen atom of (R)-Ro65-6570. In a study by Guerrini et al.,³⁰ position 3 of the 1,3,8-triaza-spirodecane moiety of the (R)-Ro65–6570 parent compound named NNC 63-0532 was functionalized with the N/OFQ(5–17) sequence, thus resulting in a chimeric compound derived from NNC 63-0532 and N/OFQ acting as a NOP receptor agonist. Consequently, it is highly unlikely that the lactam nitrogen of this class of NOP agonists, capable of being functionalized with a long peptide chain, is directed towards the intracellular side of the receptor.

These issues led us to deem the four obtained BMs unfeasible and prompted further investigation into (R)-Ro65-6570 and MCOPPB bioactive conformations.

Analysis of other available NOP receptor structures. To gain a better understanding of the reasons behind these unsatisfactory BMs, we decided to compare the cryo-EM structure in complex with the endogenous peptide agonist¹⁶ (PDB ID: 8F7X) with other available NOP receptor X-ray structures. Among the available constructs, we chose the one in complex with the non-peptide antagonist C-24,¹⁴ as this ligand shares the highest structural similarity with the ligands of interest ((R)-Ro65-6570 and MCOPPB). The chemical similarity was determined by calculating the Tanimoto coefficient based on MACCS Fingerprints³¹ (see Table S3†). The comparison between the pocket hosting N/OFQ Phe¹ residue in the cryo-EM and the selected X-ray structure is depicted in Fig. 4A and B. In particular, by inspecting the residues surrounding the pocket hosting N/OFQ Phe¹ aromatic ring in the receptor structure in complex with the endogenous peptide agonist and the selected non-peptide antagonist C-24, we observed a different conformation of Y131^3x33 and M134^3x36 side chains, leading to a larger pocket in the structure in complex with the non-peptide antagonist (Fig. 4B). This observation is consistent with the bulkier nature of the portion of the co-determined ligand (C-24) that occupies the pocket hosting N/OFQ Phe¹ as compared to N/OFQ Phe¹ itself (see structural onset in Fig. 4). From this analysis, we concluded that the pocket around the N/OFQ Phe¹ side chain in the cryo-EM structure could not accommodate the bulky acenaphthenyl and methyl-cyclo-octyl groups of (R)-Ro65-6570 and MCOPPB, respectively, due to the steric hindrance imposed by Y131^3x33 and M134^3x36 side chain conformations. To improve the likelihood of capturing a relevant BM, we adopted an alternative rotamer pair for these two residues and obtained a modified receptor structure called “8F7X_mod” (see Molecular docking, experimental part).

Fig. 4 Comparison between the lipophilic pockets located in the lower part of the TM bundle, delimited by Y131^3x33 and M134^3x36, in the cryo-EM active NOP (PDB ID: 8F7X, green ribbons and orange sticks, panel A) and X-ray inactive NOP (PDB ID: 4EA3, yellow ribbons and magenta sticks, panel B) structures. The 2D structure of the peptide/ligand moiety occupying the pocket is shown. Binding modes obtained by docking (R)-Ro65-6570 (green sticks, C) and MCOPPB (lime green sticks, D; pink sticks, E; orange sticks, F) in the “8F7X_mod” structure. The residues surrounding the ligands are depicted with pale cyan sticks. In panels C–F, TM5 is depicted on the left and TM1 on the right, while TM6 and TM7 are omitted to provide clearer visualization of the orthosteric binding pocket.

Molecular docking at modified cryo-EM structure (8F7X_mod). By repeating the docking analysis at the “8F7X_mod” structure, we obtained a new conformation for (R)-Ro65-6570 (RBM03, Fig. 4C) and the three new conformations, namely, MBM03, MBM04, and MBM05, for MCOPPB (Fig. 4D–F, respectively). These four new BMs were again compared with the available SAR data summarised above, and the following considerations were drawn:

i) RBM03 (Fig. 4C) agrees with the experimental data. Indeed, it shows a distance between the positively charged nitrogen atom of (R)-Ro65-6570 and the closest sidechain oxygen atom of D130^3x32 compatible with an ionic interaction (d_{N(Lig)---O(D130)} = 3.1 Å) and features the acenaphthenyl and phenyl groups in the pockets hosting N/OFQ Phe¹ and Phe⁴ side chains, respectively.

ii) MBM03 (Fig. 4D) features the methyl-cyclo-octyl group in the pocket surrounding N/OFQ Phe¹ residue. The phenyl group of MCOPPB is placed directly in front of the extracellular portion of TM7, while the distance between the ligand positively charged nitrogen atoms and the closest sidechain oxygen atom of D130^3x32 (d_{N(Lig)---O(D130)} = 6.5 Å for the 1,4-disubstituted piperidine; d_{N(Lig)---O(D130)} = 13.4 Å for the 3-monosubstituted piperidine) is incompatible with the formation of at least an ionic interaction.

iii) MBM04 (Fig. 4E) places the methyl-cyclo-octyl and piperidinyl groups in the pockets hosting N/OFQ Phe¹ and Phe⁴ side chains, respectively. The distance between the ligand positively charged nitrogen atoms and the closest sidechain oxygen atom of D130^3x32 (d_{N(Lig)---O(D130)} = 4.0 Å for the 1,4-disubstituted piperidine; d_{N(Lig)---O(D130)} = 7.7 Å for the 3-monosubstituted piperidine) is compatible with an ionic interaction.

iv) MBM05 (Fig. 4F) features the methyl-cyclo-octyl and the phenyl groups in the pocket hosting N/OFQ Phe¹ and Phe⁴ residues, respectively. The distance between the ligand positively charged nitrogen atoms and the closest sidechain oxygen atom of D130^3x32 (d_{N(Lig)---O(D130)} = 3.3 Å for the 1,4-disubstituted piperidine; d_{N(Lig)---O(D130)} = 10.3 Å for the 3-monosubstituted piperidine) is also compatible with an ionic interaction.

Of all the obtained BMs, those in better agreement with the available SAR data are RBM01, RBM03, MBM04, and MBM05, with RBM03 and MBM05 being the best-fitting conformations. To evaluate the stability of the resulting ligand–receptor complexes, we subjected the above-mentioned docking poses to three 50 ns MD simulations each, a technique that has been demonstrated to be capable of discerning the bioactive conformation of small-molecule ligands from alternative binding modes.^32,33 In particular, (R)-Ro65-6570 and MCOPPB were simulated in complex with the receptor conformation in which they were docked, i.e., the original cryo-EM structure (8F7X) for RBM01 and the modified structure (8F7X_mod) for RBM03, MBM04, and MBM05.

4) Molecular dynamics analysis of small molecule agonists

(R)-Ro65-6570. In both the RBM01 and RBM03 simulations, the average RMSD values (Table 2) for both the ligand and receptor are lower than the resolution of the starting cryo-EM structure¹⁶ (R = 3.28 Å) and indicate a stable system (see RMSD profiles in Fig. 5). As far as the average RMSD is concerned, RBM03 exhibits lower and more stable values for the ligand (see ligand RMSD profiles in Fig. 5A and B), while for the receptor, both systems show comparable values although slightly lower for RBM03. The average RMSF (Table 2) values for the two systems are also comparable. In particular, RBM03 shows slightly lower values for the ligand (i.e., minimum average RMSF value of 0.60 vs. 0.77 Å for RBM01 and RBM03, respectively), while the opposite is true for the receptor (i.e., minimum average RMSF value of 1.02 vs. 0.93 Å for RBM01 and RBM03, respectively). Concerning the total IE, the two systems have similar values (see Fig. 5E and F) with slightly lower average values in two of the three RBM01 runs (Fig. 5E and Table 2). This is also confirmed by the MM/PBSA analysis (Table S2†) computed for the two alternative binding modes, which show values in a narrow range from −34 to −27 kcal mol⁻¹. Nonetheless, RBM03 shows more stable and reproducible profiles across the replicas (Fig. 5), thus indicating a more stable system over time. Indeed, visual inspection of all trajectories for each system (Videos S2A and B†) and the computed median scores (Table S4†) highlights the better reproducibility of RBM03. Interestingly, in two of the three RBM01 runs (Video S2A†), the ligand changes its conformation (thus explaining the higher RMSD values) by assuming a position that is compatible with the functionalization vector discussed for the chimeric ligand mentioned in the docking analysis, while in run3, the ligand maintains a conformation similar to the starting docking pose. In both systems, the ligand constantly interacts through a salt bridge with D130^3x32, as confirmed by the highest median score associated with this residue in Table S4.† Another aspect considered when evaluating the systems is the network of polar interactions connecting D130^3x32, Y309^7x43, and Q107^2x60, which is crucial for maintaining the correct reciprocal positions of TM2, TM3, and TM7 (a behaviour observed in the MD simulation of N/OFQ(1–13)-NH₂, see Video S1†). A detailed analysis of these interactions (Table S4†) showed that the network is conserved in two out of three RBM01 runs, while for RBM03, it is constant in all runs and partially mediated by a water molecule bridging Q107^2x60 and D130^3x32 and, to some extent, T103^2x56 (Video S2B†). In particular, during the simulation, Q107^2x60 moves away from the initial position to form a hydrogen bond with the tertiary nitrogen of the imidazolinone moiety but maintains a stable network with D130^3x32 owing to the interplay of a water molecule. We also inspected the conformation of W276^6x48, a key residue in class A GPCR activation³⁴ in the trajectories. In the RBM03 system, W276^6x48 maintained the conformation captured in the NOP receptor active structure (χ₁ = gauche(−) and χ₂ = gauche(−); see Experimental part and ESI,† Fig. S3 for angle definition) more closely than in the RBM01 system. To better discern between the two binding modes, MM/PBSA values (see ESI,† Table S2) were calculated for each replica, highlighting slightly lower values for RBM03 compared to RBM01 (lowest RBM01 MM/PBSA value: −33.95 ± 4.72 kcal mol⁻¹; lowest RBM03 MM/PBSA value: −34.20 ± 7.63 kcal mol⁻¹). These data, together with the available SAR data discussed in the previous section, led us to consider RBM03 as the putative bioactive conformation of (R)-Ro65-6570.

Fig. 5 Root mean square deviation (RMSD), root mean square fluctuation (RSMF), and interaction energy (IE) profiles extracted from the triplicates of RBM01 (panels A, C, and E) and RBM03 (panels B, D, and F). RMSD and RMSF values were calculated for the receptor alpha carbon atoms (Cα, solid lines) and the ligand (dashed lines) during 50 ns of unrestrained MD using the first frame of the production run as a reference. Each trajectory was aligned on the receptor Cα atoms. The total IE was calculated as the sum of the electrostatic and van der Waals contributions as implemented in the namdenergy function.

MCOPPB. Similar to what was observed for (R)-Ro65-6570, the average RMSD values for both systems considered for MCOPPB, namely MBM04 and MBM05, are below the resolution of the original cryo-EM structure (average values shown in Table 2, profiles in Fig. 6). In this series of simulations, however, the MBM05 system consistently yielded lower average RMSD and RMSF values compared to MBM04 (e.g., minimum average ligand RMSD values of 0.87 and 1.81 Å; and minimum average ligand RMSF values of 0.61 and 0.83 Å for MBM05 and MBM04, respectively). This pattern is confirmed by both the average computed MM/PBSA values (Table S2†) and the median contact scores for the two systems (Table S5†), which clearly indicate more energetically favourable, persistent, and reproducible ligand–receptor interactions over time for the MBM05 system, also graphically indicated by the most stable RMSD and RMSF profiles over time (see Fig. 6A and Cvs. B and D). Considering the IE values (Table 2 and Fig. 6E and F), MBM05 showed lower average values and more persistent IE profiles compared to the (R)-Ro-65-6570 complex, thus indicating a stronger interaction between MCOPPB and the NOP receptor, which can be ascribed to the additional electrostatic interactions established by the second positively charged nitrogen atom in the ligand; this is possibly relevant for explaining the very high potency of this NOP agonist (pEC₅₀ = 9.73).²² The visual inspection of all replicas for each system reveals a much more reproducible behaviour for the ligand in the MBM05 system compared to MBM04 (see Videos S3A and B†). The average distances between the positively charged nitrogen atoms of MCOPPB and the closest negatively charged oxygen atom of D130^3x32 side chain were 4.89 Å and 5.40 Å for the 1,4-disubstituted and 3-monosubstituted piperidines, respectively, which are above the 4 Å value conventionally considered for a salt bridge interaction,²⁹ and 3.75–3.81 Å for MBM05, which is much more compatible with ionic interactions. Moreover, similar to the analysis conducted for the (R)-Ro65-6570 trajectories, we exploited PyContact²⁸ to evaluate the network of polar interactions connecting D130^3x32, Y309^7x43, and Q107^2x60 and observed greater inconsistency in the MBM04 replicas, while for MBM05, this important series of hydrogen bonds is consistent across all trajectories. Another noteworthy interaction observed exclusively in the MBM05 and not in the MBM04 trajectories is the salt bridge between the ammonium group located on the 3-monosubstituted piperidinyl ring of MCOPPB and E199^ECL2. In fact, during the equilibration, ECL2 shifts toward the orthosteric binding site, allowing the aforementioned salt bridge to form and mimic the interaction between Arg⁸ of N/OFQ(1–13)-NH₂ and E199^ECL2 (see ESI,† Table S5). Regarding the conformation of W276^6x48, from the MD simulations, it is possible to observe that MBM05 can maintain the conformation captured in the NOP receptor active structure (χ₁ = gauche(−), χ₂ = gauche(−); see Experimental part and ESI,† Fig. S3 for the angle definition) more consistently than MBM04. For the (R)-Ro65-6570 simulations and the MBM04 and MBM05 trajectories, we calculated MM/PBSA energies. The resulting values reported in Table S2† clearly indicate MBM05 as the conformation associated with the lowest binding energy values (lowest MBM04 MM/PBSA value: −41.68 ± 17.83 kcal mol⁻¹; lowest MBM05 MM/PBSA value: −57.72 ± 15.24 kcal mol⁻¹), exhibiting a higher difference with MBM04 at contrast with what we observed for the (R)-Ro65–6570 systems. These findings, together with the available SAR data discussed in the docking pose analysis, led us to consider MBM05 as the putative bioactive conformation of MCOPPB.

Fig. 6 Root mean square deviation (RMSD), root mean square fluctuation (RSMF), and interaction energy (IE) profiles extracted from the triplicates of MBM04 (panels A, C, and E) and MBM05 (panels B, D, and F). RMSD and RMSF values were calculated for the receptor alpha carbon atoms (Cα, solid lines) and the ligand (dashed lines) during 50 ns of unrestrained MD using the first frame of the production run as a reference. Each trajectory was aligned on the receptor Cα atoms. The total IE was calculated as the sum of the electrostatic and van der Waals contributions as implemented in the namdenergy function.

Overall, the proposed computational protocol consisting of the MD validation of alternative binding modes obtained at both the native and modified cryo-EM NOP receptor structures helped us identify the putative bioactive conformations (see Fig. S4 in the ESI†) of the two investigated small molecule agonists, namely (R)-Ro65-6570 and MCOPPB, for which an experimental three-dimensional structure in complex with the receptor is still lacking. The recognition of small molecule agonists by the NOP receptor has been the focus of previous molecular modelling studies exploiting either earlier homology models based on the Opsin template^35,36 or the NOP X-ray structure in complex with antagonists.¹⁴ The studies based on NOP homology models were aimed at rationalising Ro64-6198 analogues SAR data³⁶ and another small molecule selectivity at NOP compared with the mu opioid receptor,³⁵ while one of the studies exploiting the NOP inactive receptor structure aimed to shed light on the receptor activation mechanism by identifying activation switches, including a concerted M134^3x36 and W276^6x48 conformational change.³⁷ To the best of our knowledge, the only prior MD study of a small molecule agonist binding at the NOP receptor featured a single 100 ns long trajectory of SCH-221510 (ref. 38) characterised by a deviation from the initial binding mode and reported an RMSD graph with peaks as high as 4.50 Å, thus suggesting that the proposed binding mode was not stable over time. Thus, this study represents the first successful example of a thorough computational study of small molecule agonist binding at NOP, which exploits a reliable receptor structure, resulting in complexes featuring ligand–receptor interaction profiles that are highly reproducible and as stable as the experimental reference over time.

Conclusions

In this study, we carried out a thorough molecular modelling investigation of small molecule agonist binding at the NOP receptor. The NOP–N/OFQ system represents a strategic therapeutic target for the treatment of, among other conditions, chronic pain, sleep disorders, depression, and Parkinson's disease. Due to the known limitations of peptide drugs, the search for small molecule agonists selectively binding the NOP receptor has been the subject of intense and long-standing drug discovery efforts spanning the past three decades. In this study, we proposed a molecular modelling protocol consisting of the generation of diverse initial BMs and the use of short MD simulations to narrow down the selection and help identify the putative bioactive conformations. This same strategy, i.e., MD refinement of alternative initial conformation and inter-replica reproducibility evaluation, enabled us to anticipate the N/OFQ experimental binding mode before the cryo-EM structure was solved and released,³⁹ and we, therefore, applied it in the hope that it would be equally successful in anticipating the bioactive conformation of small molecule agonist binding. In particular, we focused our attention on predicting the BM of two prototype non-peptide NOP agonists (see ESI,† Fig. S4), namely (R)-Ro65-6570 and MCOPPB, using the simulation of the NOP complex with the peptide full agonist N/OFQ(1–13)-NH₂ as a reference system. Our data show that initial BMs that are in better agreement with available SAR data yield more stable and reproducible ligand–protein interaction profiles over time. Besides, the MD analysis of the proposed BMs highlighted key NOP residues involved in (R)-Ro65-6570 and MCOPPB binding, which have already been highlighted in site-directed mutagenesis experiments focused on N/OFQ.¹⁶ Although these studies have demonstrated the role of the highlighted residues in endogenous ligand binding,⁴⁰ further experiments are needed to confirm their involvement in the binding and activity of (R)-Ro65-6570 and MCOPPB, enabling us to corroborate our hypotheses. At the time being, we believe that the bioactive conformations proposed herein and their MD analysis help shed light on the mechanism of small molecule agonist recognition at the NOP receptor and provide useful information for the structure-based design of novel potent and selective non-peptide agonists.

Experimental part

In vitro pharmacology

Drugs and Reagents. N/OFQ and N/OFQ(1–13)-NH₂ were synthesized in house in line with solid phase peptide synthesis protocols previously reported.⁴¹ Ro65-6570 was synthesized in house, following the same procedure previously reported.⁴² The purity of newly synthetized compounds (≥95%) was checked by HPLC analysis (HPLC chromatograms are in the ESI†). MCOPPB was purchased from MedChemExpress. All tissue culture media and supplements were purchased from Invitrogen (Paisley, United Kingdom). The reagents used were from Sigma Chemical Co. (Poole, United Kingdom) and were of the highest purity available.

Calcium mobilization assay. Chinese hamster ovary (CHO) cells stably co-expressing the human NOP receptor and the Gα_qi5 protein were used in this assay. Cells were generated as described elsewhere.²¹ They were cultured in DMEM/F-12 (1 : 1) medium supplemented with 10% FBS, 2 mM L-glutamine, 200 mg ml⁻¹ G418, 100 IU ml⁻¹ penicillin, 100 IU ml⁻¹ streptomycin, and 1 μg ml⁻¹ fungizone. In the assays, the cells were maintained at 37 °C in a humidified atmosphere with 5% CO₂. After reaching confluence, the cells were detached by trypsinization, and 25 000 cells per well were seeded into 384-well black, clear-bottom plates 24 h before the test. At the assay time, cells were pre-incubated for 30 min at 37 °C protected from light with a loading solution consisting of HBSS supplemented with 2.5 mM probenecid, 3 μM Fluo-4 AM, and 0.01% pluronic acid. The loading solution was subsequently discarded, and 50 μL per well of assay buffer consisting of HBSS with 20 mM HEPES, 2.5 mM probenecid, and 500 μM Brilliant Black (Sigma-Aldrich, St. Louis, United States) was dispensed. Serial dilutions of ligands were prepared in HBSS buffer with 20 mM HEPES and 0.02% bovine serum albumin (BSA) to minimize ligand stickiness to plasticware. The automated microplate reader FlexStation III (Molecular Device, Union City, CA 94587, United States) was employed to detect changes in fluorescence intensity. Experiments were carried out at 37 °C. Automated additions were carried out in a volume of 12.5 μL per well. The effects of all compounds were expressed as the maximum change in percentage over the baseline fluorescence measured in samples treated with a vehicle.

Data analysis and terminology. The pharmacological terminology is consistent with the International Union of Basic and Clinical Pharmacology (IUPHAR) recommendations.⁴³ Concentration–response curves for agonists were fitted to the four-parameter logistic nonlinear regression model. Curve fitting was performed using PRISM 10.0 (GraphPad Software Inc., San Diego, CA). Data are expressed as mean ± SEM of 6 experiments and were analyzed statistically using one-way analysis of variance, followed by Dunnett's test for multiple comparisons. Potency values are expressed as the mean (CL_95%). p values <0.05 were considered statistically significant.

Molecular modelling

Molecular docking. The 3D conformations of the small-molecule NOP receptor agonists (R)-Ro65-6570 and MCOPPB used for the docking analysis were generated through OpenEye OMEGA 4.2.0.1 (ref. 44) using the default setting. The “Gi bound nociceptin receptor in complex with nociceptin peptide” structure¹⁶ (PDB ID: 8F7X) was retrieved from the Protein Data Bank⁴⁵ (PDB) database, and the co-determined Gi protein, single-chain antibody scFv16, ions, solvent and lipids were removed. The “8F7X_mod” structure was generated by selecting different rotamers for the Y131^3x33 and M134^3x36 side chains using the “Rotamers” function of the “Protein Builder” tool integrated in MOE 2022.02.⁴⁶ The rotamers with the best superimposition to their conformation in the inactive structure in complex with C-24 (ref. 14) (PDB ID: 4EA3) were selected and coincided with the lowest energy options. The preparation of the structures for the docking was conducted using the OpenEye Make Receptor tool⁴⁷ (v. 4.1.2.1) by selecting N/OFQ for the binding site definition, which returned a binding site of approximately 20 × 26 × 37 Å dimensions. The aforementioned small molecule conformational database was docked in the so-prepared receptor structures (i.e., “8F7X” and “8F7X_mod”) using FRED 4.1.2.1,⁴⁸ saving up to 10 poses and using the default Chemgauss4 scoring function. The conformations were visually inspected using PyMol,⁴⁹ and the best scoring pose for each different binding mode was retained for further examination of the ligand–receptor interactions through PLIP.⁵⁰

Molecular dynamics. Each small-molecule receptor complex was pre-oriented within the membrane based on the N/OFQ-NOP receptor complex structure (PDB ID: 8F7X) from the “orientations of proteins in membranes” (OPM) database.⁵¹ For the N/OFQ(1–13)-NH₂ complex, the receptor was oriented in the membrane using the “positioning of proteins in the membrane” (PPM2.0)⁵¹ functionality as implemented in the CHARMM-GUI⁵² web server and by selecting only the NOP receptor structure for the orientation step. System setup was carried out using CHARMM-GUI, employing a 1-palmitoyl-2-oleoyl-sn-glycero-3- phosphocholine (POPC) lipid bilayer (150 × 150 Å for N/OFQ(1–13)-NH₂, 80 × 80 Å for small molecules), with the receptor N-terminus acetylated (ACE) and C-terminus methyl-amidated (CT3). In the N/OFQ(1–13)-NH₂–NOP–Gi complex, N/OFQ(1–13) coordinates were obtained from the corresponding structure available in the PDB database (PDB ID: 8F7X) and the truncated peptide C-terminus was capped with an amide group (CT2). Regarding the Giα subunit, residue 1–326 was removed (thus preserving the C-terminal G.H5 α-helix), and the N-terminus was acetylated (ACE), while standard charged patches (NTER and CTER) were selected for the remaining terminal residues. A disulfide patch was applied to NOP C123 and C200 to maintain the disulfide bridge detected in the experimental structure. The membrane system was solvated with TIP3P water and Na⁺/Cl⁻ counter-ions at concentrations of 0.15 M were added to obtain charge neutrality. The systems were subjected to 50 ns of MD production run in triplicate (NPT Ensemble, 310.15 K, 2 fs integration time step), performed with OpenMM 8.0 (ref. 53) on a hybrid CPU/GPU cluster composed of two 12-core AMD Ryzen™ 97 900 CPU equipped with an NVIDIA RTX 4090 GPU each, an 8-core AMD Ryzen™ 75 800X CPU equipped with an NVIDIA RTX 3070Ti, and a 24-core AMD Ryzen™ Threadripper TR4 3960X CPU equipped with two NVIDIA RTX 3070, using CHARMM36m force field⁵⁴ with WYF parameters for cation–π interactions⁵⁵ and periodic boundary conditions. System equilibration followed a previously optimized protocol for membrane systems,⁵⁶ comprising two NVT steps, followed by four NPAT steps. Harmonic restraints on protein heavy atoms and ions, repulsive planar restraints to prevent water diffusion in the lipid bilayer and planar restraints applied to lipid head groups were gradually released. Temperature and pressure coupling were maintained using Langevin dynamics (friction coefficient = 1 ps⁻¹) and a semi-isotropic Monte Carlo barostat (frequency = 100), respectively. Bond lengths involving hydrogen atoms were constrained by applying the M-SHAKE algorithm, and long-range Coulomb interactions were handled using the particle mesh Ewald summation method (PME) with a non-bonded cutoff distance of 12 Å and a switching distance of 10 Å (see ESI,† Table S6 for further details).

Trajectory analysis. Trajectories were aligned on receptor Cα atoms, and global small molecule and receptor RMSD and RMSF values were calculated using an in-house tcl/bash script exploiting the RMSD Trajectory Tool implemented in VMD.⁵⁷ Per-residue RMSF of N/OFQ(1–13)-NH₂ (see ESI,† Fig. S1) was determined by exploiting the align and rms modules available in the MDAnalysis python toolkit.⁵⁸ Ligand–protein interaction energy was calculated using a tcl/bash script by exploiting the NAMD⁵⁹ 2.10 namdenergy function. RMSD, RMSF and Interaction Energy graphs were generated with Excel and Matplotlib.⁶⁰ Visual inspection of the trajectories and calculation of the persistence of hydrogen bond and salt-bridge interactions were performed using VMD and the integrated plug-ins. χ₁ and χ₂ dihedral angles for W276^6x48 were calculated using a tcl script based on VMD (see ESI,† Fig. S3 for conformation definition).

Hydrophobic interactions, hydrogen bonds and salt bridges established between ligands and receptors were analysed in depth by exploiting PyContact²⁸ (see ESI,† Tables S1, S4, and S5). For each trajectory, binding free energy calculations were performed using the MM/PBSA (molecular mechanics Poisson–Boltzmann surface area) method as implemented in the MMPBSA.py⁶¹ script within the AmberTools24.⁶² Molecular parameters and topology originally defined in the CHARMM36m force field were converted to Amber prmtop format using ParmEd.⁶³ For each molecular dynamics simulation, frames were extracted every 100 ps, resulting in 500 production phase frames per replicate.

Data availability

The coordinates of the modified receptor used for the docking studies and all docking poses generated in this study are provided as ESI† along with videos of the most relevant MD trajectories described. All MD input files, scripts, as well as all the trajectories described in the manuscript are freely available on Zenodo (https://doi.org/10.5281/zenodo.12806437).

Author contributions

AC conceptualised the study and oversaw the molecular modelling studies; MG carried out molecular docking and molecular dynamics under AC's supervision. RG and GC oversaw the experimental part of the study and acquired funding; VA, SP and DP provided NOP receptor agonists for the pharmacological studies. DM carried out in vitro pharmacological assays. AC and MG drafted the initial manuscript version, which was finalised through the contributions of all authors. All authors have given approval to the final version of the manuscript.

Abbreviations

ACE	N-terminal acetylation
AUD/IAAC	Insomnia associated with alcohol cessation/alcohol use disorder
BM	Binding mode
BSA	Bovine serum albumin
CL95%	Confidence level 95%
C-24	1-Benzyl-N-[3-(1′H,3H-spiro[2-benzofuran-1,4′-piperidin]-1′-yl)propyl]-D-prolinamide
C-35	1-Benzyl-N-{3-[4-(2,6-dichlorophenyl)piperidin-1-yl]propyl}-D-prolinamide
C-TER	Charged C-termini
Cα	Alpha carbon
Cha	Cyclohexyl-L-alanine
CHO	Chinese hamster ovary cells
CT2	C-terminal amidation
CT3	C-terminal methyl-amidation
Cryo-EM	Cryo-electron microscopy
DMEM/F-12	Dulbecco's modified Eagle medium/nutrient mixture F-12
ECL	Extracellular loop
FBS	Fetal bovine serum
Fluo-4 AM	Fluo-4 acetoxymethyl ester
G418	Geneticin
GPCR	G-protein coupled receptor
GPU	Graphics processing unit
HBSS	Hank's balanced salt solution
HEPES	(2-(4-(2-Hydroxyethyl)piperazin-1-yl)ethanesulfonic acid
IE	Interaction energy
IUPHAR	International Union of Pharmacology
MACCS	Molecular access system
MD	Molecular dynamics
MCOPPB	1-[1-(1-Methylcyclooctyl)piperidin-4-yl]-2-[(3R)-piperidin-3-yl]benzimidazole
N/OFQ	Nociceptin/orphanin FQ
NOP	N/OFQ receptor
NNC 63-0532	8-(1-Naphthalenylmethyl)-4-oxo-1-phenyl-1,3,8-triazaspiro[4.5]decane-3-acetic acid methyl ester
NPT	Constant number of particles, pressure, and temperature ensemble
NTER	Charged N-termini
NVT	Constant number of particles, volume, and temperature ensemble
NPAT	Constant number of particles, pressure, surface area and temperature
PDB	Protein Data Bank
PME	Particle mesh Ewald
POPC	1-Palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine
RMSD	Root mean square deviation
RMSF	Root mean square fluctuation
Ro64-6198	(8-[(1S,3aS)-2,3,3a,4,5,6-hexahydro-1H-phenalen-1-yl]-1-phenyl-1,3,8-triazaspiro[4.5]decan-4-one)
Ro65-6570	8-Acenaphthen-1-yl-1-phenyl-1,3,8-triaza-spiro[4.5]decan-4-one
SAR	Structure–activity relationship
SB-612111	(5S,7S)-7-{[4-(2,6-Dichlorophenyl)piperidin-1-yl]methyl}-1-methyl-6,7,8,9-tetrahydro-5H-benzo[7]annulen-5-ol
SCH-221510	3-endo-8-[Bis(2-methylphenyl)methyl]-3-phenyl-8-azabicyclo[3.2.1]octan-3-ol
SEM	Standard error of the mean
TM	Transmembrane helix

Conflicts of interest

The authors declare no competing financial interest.

Acknowledgements

This work was supported by the Italian Ministry of Universities and Research (MUR) “Progetti di Ricerca di Rilevante Interesse Nazionale (PRIN) - Bando 2022 PNRR” - Prot. P20225HP4C (awarded to RG and GC), by the University of Ferrara (FAR grant to RG, SP, DP, and AC) and the University of Padova (DOR grant to GC and DM). We are grateful to the Screening Facility of the Department of Pharmaceutical and Pharmacological Sciences of the University of Padova (SF@DSF) for the support with the in vitro assays. AC and MG are grateful to Dr. Giuseppe Deganutti (Coventry University) for his assistance with the MM/PBSA calculations.

References

1. J. R. Bunzow, C. Saez, M. Mortrud, C. Bouvier, J. T. Williams, M. Low and D. K. Grandy, Molecular Cloning and Tissue Distribution of a Putative Member of the Rat Opioid Receptor Gene Family That Is Not a Mu, Delta or Kappa Opioid Receptor Type, FEBS Lett., 1994, 347(2–3), 284–288,  DOI:10.1016/0014-5793(94)00561-3.
2. C. Mollereau, M. Parmentier, P. Mailleux, J. L. Butour, C. Moisand, P. Chalon, D. Caput, G. Vassart and J. C. Meunier, ORL1, a Novel Member of the Opioid Receptor Family. Cloning, Functional Expression and Localization, FEBS Lett., 1994, 341(1), 33–38,  DOI:10.1016/0014-5793(94)80235-1.
3. K. Fukuda, S. Kato, K. Mori, M. Nishi, H. Takeshima, N. Iwabe, T. Miyata, T. Houtani and T. Sugimoto, cDNA Cloning and Regional Distribution of a Novel Member of the Opioid Receptor Family, FEBS Lett., 1994, 343(1), 42–46,  DOI:10.1016/0014-5793(94)80603-9.
4. R. K. Reinscheid, H.-P. Nothacker, A. Bourson, A. Ardati, R. A. Henningsen, J. R. Bunzow, D. K. Grandy, H. Langen, F. J. Monsma Jr. and O. Civelli, Orphanin FQ: A Neuropeptide That Activates an Opioidlike G Protein-Coupled Receptor, Science, 1995, 270(5237), 792–794.
5. J.-C. Meunier, C. Mollereau, L. Toll, C. Suaudeau, C. Moisand, P. Alvinerie, J.-L. Butour, J.-C. Guillemot, P. Ferrara, B. Monsarrat, H. Mazarguil, G. Vassart, M. Parmentier and J. Costentin, Isolation and Structure of the Endogenous Agonist of Opioid Receptor-like ORL1 Receptor, Nature, 1995, 377(6549), 532–535,  DOI:10.1038/377532a0.
6. G. T. Whiteside, D. J. Kyle, R. P. Kapil, A. Cipriano, E. He, M. Zhou, M. S. Shet, M. Hummel, T. Knappenberger, K. Fukumura, Y. Matsuo, M. Uehira, S. Hiroyama, N. Takai, S. K. Willsie and S. C. Harris, The Nociceptin/Orphanin FQ Receptor Partial Agonist Sunobinop Promotes Non-REM Sleep in Rodents and Patients with Insomnia, J. Clin. Invest., 2024, 134(1), e171172,  DOI:10.1172/JCI171172.
7. S. R. Morairty, Y. Sun, L. Toll, M. R. Bruchas and T. S. Kilduff, Activation of the Nociceptin/Orphanin-FQ Receptor Promotes NREM Sleep and EEG Slow Wave Activity, Proc. Natl. Acad. Sci. U. S. A., 2023, 120(13), e2214171120,  DOI:10.1073/pnas.2214171120.
8. E. C. Gavioli and G. Calo', Nociceptin/Orphanin FQ Receptor Antagonists as Innovative Antidepressant Drugs, Pharmacol. Ther., 2013, 140(1), 10–25,  DOI:10.1016/j.pharmthera.2013.05.008.
9. M. Marti, F. Mela, M. Budri, M. Volta, D. Malfacini, S. Molinari, N. T. Zaveri, S. Ronzoni, P. Petrillo, G. Calò and M. Morari, Acute and Chronic Antiparkinsonian Effects of the Novel Nociceptin/Orphanin FQ Receptor Antagonist NiK-21273 in Comparison with SB-612111, Br. J. Pharmacol., 2013, 168(4), 863–879,  DOI:10.1111/j.1476-5381.2012.02219.x.
10. W. Schröder, D. G. Lambert, M. C. Ko and T. Koch, Functional Plasticity of the N/OFQ-NOP Receptor System Determines Analgesic Properties of NOP Receptor Agonists, Br. J. Pharmacol., 2014, 171(16), 3777–3800,  DOI:10.1111/bph.12744.
11. N. T. Zaveri and M. E. Meyer, NOP-Targeted Nonpeptide Ligands, in The Nociceptin/Orphanin FQ Peptide Receptor; Handbook of Experimental Pharmacology, ed. Ko M.-C. and Caló G., Springer International Publishing, Cham, 2019, vol. 254, pp. 37–67.  DOI:10.1007/164_2019_213.
12. S. Schunk, K. Linz, C. Hinze, S. Frormann, S. Oberbörsch, B. Sundermann, S. Zemolka, W. Englberger, T. Germann, T. Christoph, B.-Y. Kögel, W. Schröder, S. Harlfinger, D. Saunders, A. Kless, H. Schick and H. Sonnenschein, Discovery of a Potent Analgesic NOP and Opioid Receptor Agonist: Cebranopadol, ACS Med. Chem. Lett., 2014, 5(8), 857–862,  DOI:10.1021/ml500117c.
13. A. Rizzi, M. C. Cerlesi, C. Ruzza, D. Malfacini, F. Ferrari, S. Bianco, T. Costa, R. Guerrini, C. Trapella and G. Calo', Pharmacological Characterization of Cebranopadol a Novel Analgesic Acting as Mixed Nociceptin/Orphanin FQ and Opioid Receptor Agonist, Pharmacol. Res. Perspect., 2016, 4(4), e00247,  DOI:10.1002/prp2.247.
14. A. A. Thompson, W. Liu, E. Chun, V. Katritch, H. Wu, E. Vardy, X.-P. Huang, C. Trapella, R. Guerrini, G. Calo, B. L. Roth, V. Cherezov and R. C. Stevens, Structure of the Nociceptin/Orphanin FQ Receptor in Complex with a Peptide Mimetic, Nature, 2012, 485(7398), 395–399,  DOI:10.1038/nature11085.
15. R. L. Miller, A. A. Thompson, C. Trapella, R. Guerrini, D. Malfacini, N. Patel, G. W. Han, V. Cherezov, G. Caló, V. Katritch and R. C. Stevens, The Importance of Ligand-Receptor Conformational Pairs in Stabilization: Spotlight on the N/OFQ G Protein-Coupled Receptor, Structure, 2015, 23(12), 2291–2299,  DOI:10.1016/j.str.2015.07.024.
16. Y. Wang, Y. Zhuang, J. F. DiBerto, X. E. Zhou, G. P. Schmitz, Q. Yuan, M. K. Jain, W. Liu, K. Melcher, Y. Jiang, B. L. Roth and H. E. Xu, Structures of the Entire Human Opioid Receptor Family, Cell, 2023, 186(2), 413–427.e17,  DOI:10.1016/j.cell.2022.12.026.
17. D. Preti, G. Caló and R. Guerrini, NOP-Targeted Peptide Ligands, Handb. Exp. Pharmacol., 2019, 254, 17–36,  DOI:10.1007/164_2018_198.
18. J. Wichmann, G. Adam, S. Röver, A. M. Cesura, F. M. Dautzenberg and F. Jenck, 8-Acenaphthen-1-Yl-1-Phenyl-1,3,8-Triaza-Spiro[4.5]Decan-4-One Derivatives as Orphanin FQ Receptor Agonists, Bioorg. Med. Chem. Lett., 1999, 9(16), 2343–2348,  DOI:10.1016/s0960-894x(99)00385-6.
19. S. Hayashi, A. Hirao, A. Imai, H. Nakamura, Y. Murata, K. Ohashi and E. Nakata, Novel Non-Peptide Nociceptin/Orphanin FQ Receptor Agonist, 1-[1-(1-Methylcyclooctyl)-4-Piperidinyl]-2-[(3 R )-3-Piperidinyl]-1 H-Benzimidazole: Design, Synthesis, and Structure−Activity Relationship of Oral Receptor Occupancy in the Brain for Orally Potent Antianxiety Drug, J. Med. Chem., 2009, 52(3), 610–625,  DOI:10.1021/jm7012979.
20. P. Coward, H. G. Wada, M. S. Falk, S. D. Chan, F. Meng, H. Akil and B. R. Conklin, Controlling Signaling with a Specifically Designed Gi-Coupled Receptor, Proc. Natl. Acad. Sci. U. S. A., 1998, 95(1), 352–357,  DOI:10.1073/pnas.95.1.352.
21. V. Camarda, C. Fischetti, N. Anzellotti, P. Molinari, C. Ambrosio, E. Kostenis, D. Regoli, C. Trapella, R. Guerrini, S. Severo and G. Calo, Pharmacological Profile of NOP Receptors Coupled with Calcium Signaling via the Chimeric Protein G Alpha Qi5, Naunyn-Schmiedeberg's Arch. Pharmacol., 2009, 379(6), 599–607,  DOI:10.1007/s00210-009-0396-x.
22. F. Ferrari, D. Malfacini, B. V. Journigan, M. F. Bird, C. Trapella, R. Guerrini, D. G. Lambert, G. Calo' and N. T. Zaveri, In Vitro Pharmacological Characterization of a Novel Unbiased NOP Receptor-Selective Nonpeptide Agonist AT-403, Pharmacol. Res. Perspect., 2017, 5(4), e00333,  DOI:10.1002/prp2.333.
23. R. Guerrini, G. Calo, A. Rizzi, C. Bianchi, L. H. Lazarus, S. Salvadori, P. A. Temussi and D. Regoli, Address and Message Sequences for the Nociceptin Receptor: A Structure-Activity Study of Nociceptin-(1-13)-Peptide Amide, J. Med. Chem., 1997, 40(12), 1789–1793,  DOI:10.1021/jm970011b.
24. J. A. Ballesteros and H. Weinstein, [19] Integrated Methods for the Construction of Three-Dimensional Models and Computational Probing of Structure-Function Relations in G Protein-Coupled Receptors, in Methods in Neurosciences, Elsevier, 1995, vol. 25, pp. 366–428.  DOI:10.1016/S1043-9471(05)80049-7.
25. R. Guerrini, G. Caló, R. Bigoni, A. Rizzi, K. Varani, G. Toth, S. Gessi, E. Hashiba, Y. Hashimoto, D. G. Lambert, P. A. Borea, R. Tomatis, S. Salvadori and D. Regoli, Further Studies on Nociceptin-Related Peptides: Discovery of a New Chemical Template with Antagonist Activity on the Nociceptin Receptor, J. Med. Chem., 2000, 43(15), 2805–2813,  DOI:10.1021/jm990075h.
26. R. Guerrini, G. Caló, R. Bigoni, D. Rizzi, A. Rizzi, M. Zucchini, K. Varani, E. Hashiba, D. G. Lambert, G. Toth, P. A. Borea, S. Salvadori and D. Regoli, Structure-Activity Studies of the Phe(4) Residue of Nociceptin(1-13)-NH(2): Identification of Highly Potent Agonists of the Nociceptin/Orphanin FQ Receptor, J. Med. Chem., 2001, 44(23), 3956–3964,  DOI:10.1021/jm010221v.
27. G. Calo' and R. Guerrini, Medicinal Chemistry, Pharmacology, and Biological Actions of Peptide Ligands Selective for the Nociceptin/Orphanin FQ Receptor, in ACS Symposium Series, ed. Ko M.-C. and Husbands S. M., American Chemical Society, Washington, DC, 2013, vol. 1131, pp. 275–325,  DOI:10.1021/bk-2013-1131.ch015.
28. M. Scheurer, P. Rodenkirch, M. Siggel, R. C. Bernardi, K. Schulten, E. Tajkhorshid and T. Rudack, PyContact: Rapid, Customizable, and Visual Analysis of Noncovalent Interactions in MD Simulations, Biophys. J., 2018, 114(3), 577–583,  DOI:10.1016/j.bpj.2017.12.003.
29. D. J. Barlow and J. M. Thornton, Ion-Pairs in Proteins, J. Mol. Biol., 1983, 168(4), 867–885,  DOI:10.1016/S0022-2836(83)80079-5.
30. R. Guerrini, G. Carra', G. Calo', C. Trapella, E. Marzola, D. Rizzi, D. Regoli and S. Salvadori, Nonpeptide/Peptide Chimeric Ligands for the Nociceptin/Orphanin FQ Receptor: Design, Synthesis and in Vitro Pharmacological Activity, J. Pept. Res., 2004, 63(6), 477–484,  DOI:10.1111/j.1399-3011.2004.00157.x.
31. J. L. Durant, B. A. Leland, D. R. Henry and J. G. Nourse, Reoptimization of MDL Keys for Use in Drug Discovery, J. Chem. Inf. Comput. Sci., 2002, 42(6), 1273–1280,  DOI:10.1021/ci010132r.
32. B. Knapp, L. Ospina and C. M. Deane, Avoiding False Positive Conclusions in Molecular Simulation: The Importance of Replicas, J. Chem. Theory Comput., 2018, 14(12), 6127–6138,  DOI:10.1021/acs.jctc.8b00391.
33. H. Guterres and W. Im, Improving Protein-Ligand Docking Results with High-Throughput Molecular Dynamics Simulations, J. Chem. Inf. Model., 2020, 60(4), 2189–2198,  DOI:10.1021/acs.jcim.0c00057.
34. Q. Zhou, D. Yang, M. Wu, Y. Guo, W. Guo, L. Zhong, X. Cai, A. Dai, W. Jang, E. I. Shakhnovich, Z.-J. Liu, R. C. Stevens, N. A. Lambert, M. M. Babu, M.-W. Wang and S. Zhao, Common Activation Mechanism of Class A GPCRs, eLife, 2019, 8, e50279,  DOI:10.7554/eLife.50279.
35. M. E. Meyer, A. Doshi, D. Yasuda and N. T. Zaveri, Structure-Based SAR in the Design of Selective or Bifunctional Nociceptin(NOP) Receptor Agonists, AAPS J., 2021, 23(3), 68,  DOI:10.1208/s12248-021-00589-7.
36. P. R. Daga and N. T. Zaveri, Homology Modeling and Molecular Dynamics Simulations of the Active State of the Nociceptin Receptor Reveal New Insights into Agonist Binding and Activation, Proteins, 2012, 80(8), 1948–1961,  DOI:10.1002/prot.24077.
37. S. Della Longa and A. Arcovito, Microswitches for the Activation of the Nociceptin Receptor Induced by Cebranopadol: Hints from Microsecond Molecular Dynamics, J. Chem. Inf. Model., 2019, 59(2), 818–831,  DOI:10.1021/acs.jcim.8b00759.
38. G. Kothandan, C. G. Gadhe, A. Balupuri, J. Ganapathy and S. J. Cho, The Nociceptin Receptor(NOPR) and Its Interaction with Clinically Important Agonist Molecules: A Membrane Molecular Dynamics Simulation Study, Mol. BioSyst., 2014, 10(12), 3188–3198,  10.1039/C4MB00323C.
39. A. Ciancetta, D. Malfacini, M. Gozzi, E. Marzola, R. Camilotto, G. Calò and R. Guerrini, A Multi-Angle Approach to Predict Peptide-GPCR Complexes: The N/OFQ-NOP System as a Successful Alpha Fold Application Case Study, J. Chem. Inf. Model., 2024, 64(20), 8034–8051,  DOI:10.1021/acs.jcim.4c00499.
40. Y. Wang, Y. Zhuang, J. F. DiBerto, X. E. Zhou, G. P. Schmitz, Q. Yuan, M. K. Jain, W. Liu, K. Melcher, Y. Jiang, B. L. Roth and H. E. Xu, Structures of the Entire Human Opioid Receptor Family, Cell, 2023, 186(2), 413–427.e17,  DOI:10.1016/j.cell.2022.12.026.
41. S. Pacifico, F. Ferrari, V. Albanese, E. Marzola, J. A. Neto, C. Ruzza, G. Calò, D. Preti and R. Guerrini, Biased Agonism at Nociceptin/Orphanin FQ Receptors: A Structure Activity Study on N/OFQ(1–13)-NH ₂, J. Med. Chem., 2020, 63(19), 10782–10795,  DOI:10.1021/acs.jmedchem.9b02057.
42. S. Röver, G. Adam, A. M. Cesura, G. Galley, F. Jenck, F. J. Monsma, J. Wichmann and F. M. Dautzenberg, High-Affinity, Non-Peptide Agonists for the ORL1(Orphanin FQ/Nociceptin) Receptor, J. Med. Chem., 2000, 43(7), 1329–1338,  DOI:10.1021/jm991129q.
43. R. R. Neubig, M. Spedding, T. Kenakin and A. Christopoulos, International Union of Pharmacology Committee on Receptor Nomenclature and Drug Classification. International Union of Pharmacology Committee on Receptor Nomenclature and Drug Classification. XXXVIII. Update on Terms and Symbols in Quantitative Pharmacology, Pharmacol. Rev., 2003, 55(4), 597–606,  DOI:10.1124/pr.55.4.4.
44. P. C. D. Hawkins, A. G. Skillman, G. L. Warren, B. A. Ellingson and M. T. Stahl, Conformer Generation with OMEGA: Algorithm and Validation Using High Quality Structures from the Protein Databank and Cambridge Structural Database, J. Chem. Inf. Model., 2010, 50(4), 572–584,  DOI:10.1021/ci100031x.
45. H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov and P. E. Bourne, The Protein Data Bank, Nucleic Acids Res., 2000, 28(1), 235–242,  DOI:10.1093/nar/28.1.235.
46. Molecular Operating Environment(MOE), 2021.
47. OEDOCKING 4.3.1.0. OpenEye, Cadence Molecular Sciences, Inc., Santa Fe, NM, https://www.Eyesopen.Com.
48. M. McGann, FRED Pose Prediction and Virtual Screening Accuracy, J. Chem. Inf. Model., 2011, 51(3), 578–596,  DOI:10.1021/ci100436p.
49. The PyMOL Molecular Graphics System, Version 2.5.0 Schrödinger, LLC.
50. S. Salentin, S. Schreiber, V. J. Haupt, M. F. Adasme and M. Schroeder, PLIP: Fully Automated Protein–Ligand Interaction Profiler, Nucleic Acids Res., 2015, 43(W1), W443–W447,  DOI:10.1093/nar/gkv315.
51. M. A. Lomize, I. D. Pogozheva, H. Joo, H. I. Mosberg and A. L. Lomize, OPM Database and PPM Web Server: Resources for Positioning of Proteins in Membranes, Nucleic Acids Res., 2012, 40(Database issue), D370–D376,  DOI:10.1093/nar/gkr703.
52. J. Lee, X. Cheng, J. M. Swails, M. S. Yeom, P. K. Eastman, J. A. Lemkul, S. Wei, J. Buckner, J. C. Jeong, Y. Qi, S. Jo, V. S. Pande, D. A. Case, C. L. I. Brooks, A. D. MacKerell, Jr, J. B. Klauda and W. Im, CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Chem. Theory Comput., 2016, 12(1), 405–413,  DOI:10.1021/acs.jctc.5b00935.
53. P. Eastman, R. Galvelis, R. P. Peláez, C. R. A. Abreu, S. E. Farr, E. Gallicchio, A. Gorenko, M. M. Henry, F. Hu, J. Huang, A. Krämer, J. Michel, J. A. Mitchell, V. S. Pande, J. P. Rodrigues, J. Rodriguez-Guerra, A. C. Simmonett, S. Singh, J. Swails, P. Turner, Y. Wang, I. Zhang, J. D. Chodera, G. De Fabritiis and T. E. Markland, OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials, J. Phys. Chem. B, 2024, 128(1), 109–116,  DOI:10.1021/acs.jpcb.3c06662.
54. J. Huang, S. Rauscher, G. Nawrocki, T. Ran, M. Feig, B. L. De Groot, H. Grubmüller and A. D. MacKerell, CHARMM36m: An Improved Force Field for Folded and Intrinsically Disordered Proteins, Nat. Methods, 2017, 14(1), 71–73,  DOI:10.1038/nmeth.4067.
55. H. M. Khan, A. D. MacKerell and N. Reuter, Cation-π Interactions between Methylated Ammonium Groups and Tryptophan in the CHARMM36 Additive Force Field, J. Chem. Theory Comput., 2019, 15(1), 7–12,  DOI:10.1021/acs.jctc.8b00839.
56. S. Jo, T. Kim and W. Im, Automated Builder and Database of Protein/Membrane Complexes for Molecular Dynamics Simulations, PLoS One, 2007, 2(9), e880,  DOI:10.1371/journal.pone.0000880.
57. W. Humphrey, A. Dalke and K. Schulten, VMD: Visual Molecular Dynamics, J. Mol. Graphics, 1996, 14(1), 33–38,  DOI:10.1016/0263-7855(96)00018-5.
58. R. Gowers, M. Linke, J. Barnoud, T. Reddy, M. Melo, S. Seyler, J. Domański, D. Dotson, S. Buchoux, I. Kenney and O. Beckstein, MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations, Austin, Texas, 2016, pp. 98–105,  DOI:10.25080/Majora-629e541a-00e.
59. J. C. Phillips, R. Braun, W. Wang, J. Gumbart, E. Tajkhorshid, E. Villa, C. Chipot, R. D. Skeel, L. Kalé and K. Schulten, Scalable Molecular Dynamics with NAMD, J. Comput. Chem., 2005, 26(16), 1781–1802,  DOI:10.1002/jcc.20289.
60. J. D. Hunter, Matplotlib: A 2D Graphics Environment, Comput. Sci. Eng., 2007, 9(3), 90–95,  DOI:10.1109/MCSE.2007.55.
61. B. R. Miller, T. D. McGee, J. M. Swails, N. Homeyer, H. Gohlke and A. E. Roitberg, MMPBSA.Py: An Efficient Program for End-State Free Energy Calculations, J. Chem. Theory Comput., 2012, 8(9), 3314–3321,  DOI:10.1021/ct300418h.
62. D. A. Case, H. M. Aktulga, K. Belfon, D. S. Cerutti, G. A. Cisneros, V. W. D. Cruzeiro, N. Forouzesh, T. J. Giese, A. W. Götz, H. Gohlke, S. Izadi, K. Kasavajhala, M. C. Kaymak, E. King, T. Kurtzman, T.-S. Lee, P. Li, J. Liu, T. Luchko, R. Luo, M. Manathunga, M. R. Machado, H. M. Nguyen, K. A. O'Hearn, A. V. Onufriev, F. Pan, S. Pantano, R. Qi, A. Rahnamoun, A. Risheh, S. Schott-Verdugo, A. Shajan, J. Swails, J. Wang, H. Wei, X. Wu, Y. Wu, S. Zhang, S. Zhao, Q. Zhu, T. E. I. Cheatham, D. R. Roe, A. Roitberg, C. Simmerling, D. M. York, M. C. Nagan and K. M. Merz, Jr, AmberTools, J. Chem. Inf. Model., 2023, 63(20), 6183–6191,  DOI:10.1021/acs.jcim.3c01153.
63. https://Parmed.Github.Io/ParmEd/Html/Parmed.Html .

---

Footnote

† Electronic supplementary information (ESI) available: A file containing the following additional data is included as ESI: per residue N/OFQ(1–13)-NH₂ RMSF values (Fig. S1), N/OFQ(1–13)-NH₂ median interaction scores (Table S1), MM/PBSA trajectory analysis for N/OFQ(1–13)-NH₂, (R)-Ro65-6570, and MCOPPB (Table S2), poses obtained by docking the ligands in the native 8F7X receptor structure (Fig. S2), Tanimoto similarity coefficients for (R)-Ro65-6570 and MCOPPB (Table S3), computed RBM01 and RBM03 median interaction scores (Table S4), definitions of χ₁ and χ₂ W276^6x48 values and conformations (Fig. S3), computed MBM04 and MBM05 median interaction scores (Table S5), selected binding modes for (R)-Ro65-6570 and MCOPPB (Fig. S4), details of molecular dynamics systems (Table S6), and HPLC chromatograms of in house synthesized NOP agonists. See DOI: https://doi.org//10.1039/d4md00747f

---