Poria cocos compounds targeting neuropeptide Y1 receptor (Y1R) for weight management: A computational ligand- and structure-based study with molecular dynamics simulations identified beta-amyrin acetate as a putative Y1R inhibitor

Ann Rann Wong; Andrew Hung; Angela Wei Hong Yang; Harsharn Gill; George Binh Lenon

doi:10.1371/journal.pone.0277873

Abstract

Poria cocos (PC) is a medicinal herb frequently used in weight-loss clinical trials, however the mechanisms by which its compounds target orexigenic receptors including the neuropeptide Y₁ receptor (Y₁R) remain largely unknown. This study aimed to screen PC compounds for favourable pharmacokinetics profiles and examine their molecular mechanisms targeting Y₁R. Forty-three PC compounds were systematically sought from pharmacological databases and docked with Y₁R (PDB: 5ZBQ). By comparing the relative binding affinities, pharmacokinetics and toxicity profiles, we hypothesised that compounds designated PC1 3,4-Dihydroxybenzoic acid, PC8 Vanillic acid, PC40 1-(alpha-L-Ribofuranosyl)uracil, could be potential antagonists as they contact major residues Asn283 and Asp287, similar to various potent Y₁R antagonists. In addition, PC21 Poricoic acid B, PC22 Poricoic acid G and PC43 16alpha,25-Dihydroxy-24-methylene-3,4-secolanosta-4(28),7,9(11)-triene-3,21-dioic acid, contacting Asn299, Asp104 and Asp200 proximal to the extracellular surface could also interfere with agonist binding by stabilising the extracellular loop (ECL) 2 of Y₁R in a closed position. Owing to their selective interaction with Phe302, an important residue in binding of selective Y₁R antagonists, PC12 beta-Amyrin acetate, PC26 3-Epidehydrotumulosic acid and PC27 Cerevisterol were proposed as putative antagonists. Following the consensus approach, PC12 beta-Amyrin acetate, PC26 3-Epidehydrotumulosic acid and PC27 Cerevisterol were identified as candidate compounds due to their high affinities (-12.2, -11.0 and -10.8 kcal, respectively), high drug-likeness and low toxicity profiles. Trajectory analyses and energy contributions of PC12-Y₁R complex further confirmed their structural stability and favourable binding free energies, highlighting the feasibility and possible development of PC12 beta-Amyrin acetate as a future Y₁R inhibitor.

Citation: Wong AR, Hung A, Yang AWH, Gill H, Lenon GB (2023) Poria cocos compounds targeting neuropeptide Y1 receptor (Y₁R) for weight management: A computational ligand- and structure-based study with molecular dynamics simulations identified beta-amyrin acetate as a putative Y₁R inhibitor. PLoS ONE 18(6): e0277873. https://doi.org/10.1371/journal.pone.0277873

Editor: Chandrabose Selvaraj, Alagappa University, INDIA

Received: February 14, 2022; Accepted: October 9, 2022; Published: June 30, 2023

Copyright: © 2023 Wong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: No ethical or legal restrictions on data sharing is apparent in our study. Input parameters for molecular docking and dynamics simulations have been specified in the methods section and in the supplementary file as follows: • S1 Table. Autodock Vina configuration file inputs for blind and focused docking. Receptor and ligands were presented in the pdbqt format, while all co-ordinates and box size dimensions are presented in Angstroms. • S2 Table. Protein-ligand interaction profiler thresholds for each parameter. The mode of analysis was specified as “detection of macromolecule-ligand interactions.

Funding: We thank the National Computing Infrastructure (NCI), which is supported by the Australian Government, for providing resources and services to assist with computational work in this study. Molecular dynamics simulations were undertaken using the LIEF HPC-GPGPU facility hosted at the University of Melbourne. This Facility was established with the assistance of LIEF Grant LE170100200. AW acknowledges financial support through the Australian Government Research Training Program (RTP) Scholarship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Obesity, characterised by excessive fat accumulation that poses a risk to health, has been a growing health concern as it has been associated with serious and often life-threatening comorbidities. Globally, the prevalence of obesity in adults has increased significantly in the past 4–5 decades, with estimates of 281 and 309 million men and women identified with obesity in 2016 [1]. Compared to diabetes and other medications related to comorbidities of obesity, the number of anti-obesity pharmacotherapy is relatively limited. Five FDA approved medications for obesity are orlistat, phentermine-topiramate, naltrexone-bupropion, liraglutide and the recently approved semaglutide [2–6]. Except orlistat which works to reduce fat digestion and absorption, four of these medications target the central nervous system to control food intake and minimise binge-eating behaviour. With these approaches, it is not without a risk of adverse events associated with gastrointestinal discomfort, depressive behaviours, and surgical complications [7–9]. Considering the limitations of current approaches, many patients resort to complementary and alternative strategies for obesity management, including Chinese herbal medicine.

Poria cocos (PC) (Schw.) Wolf. also known as Fu ling, has been a widely used herb among Chinese herbal formulations for weight management and lipid-modulation. PC is the dried sclerotium of Wolfiporia cocos, a saprophytic fungus in the family of Polyporaceae. It is known to be enriched with triterpenes and polysaccharides, with anti-inflammatory, anti-tumour and anti-oxidant properties [10]. Its extensive therapeutic effects have attracted considerable research attention, particularly for the management of metabolic diseases. As observed in multiple systematic reviews and meta-analyses, PC was reported to be one of ten most highly prescribed herbs in obesity clinical trials [11, 12]. Furthermore, for dyslipidaemia studies on humans [13] and ob/ob mice [14, 15], PC has also been a common focus. Although various clinical and experimental studies of PC-containing herbs and formulations have indicated significant improvements in clinical and biochemical outcomes, the molecular mechanisms of PC compounds and their potential as druggable candidates remain largely unknown.

To elucidate the mechanistic action of PC compounds, protein targets were sought from the neuroactive ligand-receptor pathway, one of the most important targets to regulate feeding behaviour and energy homeostasis. Among the neuropeptide Y (NPY) receptors, recent studies have been focusing on the Y1 subtype (Y₁R) as a therapeutic target for the management of obesity and eating disorders [16, 17]. Belonging to class A (rhodopsin-like) G-protein coupled receptor (GPCR), Y₁R governs a range of signal transduction and is modulated by a broad range of stimuli included small peptides, lipid analogues, and endogenous and exogenous small molecules. The Y₁Rs are generally expressed in the basal ganglia and the limbic system, with specific co-localisation in the cerebral cortex, caudate putamen and central amygdala [18]. They have also been found in the subcutaneous and visceral adipose tissues by which they could alter metabolic parameters in visceral obesity [19]. The role of Y₁R in obesity pathophysiology is primarily induced by a polymorphism in the Y₁R gene with a Cystosine to Thymidine nucleotide substitution. Clinical studies on 306 obese participants with C to T polymorphism showed a lower tolerance for fasting [19]. Furthermore, the central administrations of antagonistic small molecules such as BIBP3226, LY357897 or 1229U91 have also resulted in a significant reduction in feeding behaviour in rats [20]. Thus, these studies support the role of Y₁R on obesity development and therefore Y₁R is selected to be the primary target of this research.

This study aimed to explore the mechanisms of actions of PC compounds targeting Y₁R and examine their pharmacokinetic profiles as druggable candidates. Computational molecular docking using blind and focused approaches [21] was conducted to predict possible binding sites and to examine the binding affinity and poses of PC compounds on the inactive Y₁R. The drug-likeness, absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles were examined using QSAR approaches. By comparing the binding affinities, ligand-residue interaction, pharmacokinetics profile, and molecular simulations of PC compounds against the native co-crystalised ligand, we proposed several putative inhibitors, which could be worthwhile for further in vivo and in vitro investigations as novel small molecules targeting Y₁R.

Materials and methods

Identification and preparation of PC compounds and Y₁R target

PC compounds were comprehensively searched on the Traditional Chinese Medicine Information Database (TCM-ID) [22] and the Traditional Chinese Medicine Systems Pharmacology platform (TCMSP) [23] using keywords including ‘Poria cocos’, ‘Fu ling’, and ‘茯苓’ up to October 2021. The criteria for screening of duplicated compounds were based on several molecular descriptors, including the PubChem CID, compound name, molecular formula, and molecular weight. The 3D conformer structures of included PC compounds were downloaded in sdf or mol2 format, and were subsequently converted into pdbqt format using Open babel (v 3.1.1) [24] following on the requirements of AutoDock Vina. Duplicates were removed based on similarities in molecular descriptors. Missing bonds and hydrogens were repaired, and all 3D structures were converted into pdbqt format for docking. No limit was set for the number of torsions.

The structure of Y₁R was obtained from RCSB PDB (PDB: 5ZBQ) [25] and processed in Visual Molecular Dynamics (VMD, v1.9.3) [26]. It adopts an inactive conformation co-crystallised with a known antagonist UR-MK299 at its canonical binding site. It has been reported that UR-MK299 has a PPARγ IC₅₀ of 80 nM and Ki of 28.67nM [27] using a LanthaScreen competitive binding assay and was therefore used as a positive control for this study. The antagonist contains three benzene rings and various carboxyl groups, forming seven hydrogen bonds with Gln219, Asn283 and Asp287 on helices V and VI, by which two of three residues (Asn283 and Asp287) were suggested to be crucial for Y₁R inhibition [25]. The native ligand (UR-MK299) was isolated from the original PDB structures for re-docking as controls in both blind and focused docking conditions. In the latter case, only the region near the known ligand binding site of Y₁R was probed. For the preparation of the original Y₁R PDB, missing loops and side chains were fixed through homology modelling with subsequent energy minimisation on the SWISS-MODEL [28] server based on target sequence obtained from UniProt [25]. The PDB structure was converted into the pdbqt format via Open babel (v 3.1.1) [24] for docking in AutoDock Vina (v1.1.2) [29].

Physicochemical space examination, drug-likeness prediction, and pharmacokinetics profiling

PC compounds were subjected to quantitative structural-activity analyses (QSAR) using Data Warrior, an open source cheminformatics tool [30] to examine their chemical space and drug-likeness potential. Known Y₁R ligands were obtained from ChEMBL database [31] and structural variables were used as comparison. Pharmacokinetics profiling including absorption, metabolism, distribution, excretion and toxicity (ADMET) indexes were obtained from running PC compounds through a graph-based structural signature modelling server, pkCSM [32]. These preliminary ligand-based QSAR predictions offer complementary roles to support structural screening such as the molecular docking and molecular dynamics (MD) simulations performed in the next sections.

Molecular docking and ligand-residue interaction analyses

Computational docking between Y₁R and PC compounds were first conducted by blind docking to predict available binding sites of Y₁R. Both allosteric and canonical binding sites were examined. The final binding site selection for focused docking was guided by (a) blind docking outcomes and (b) experimentally known sites [25] to provide more accurate binding poses and energy scores with less computational power [21]. Docking was performed in Autodock Vina [29] with all ligands set to allow full torsional flexibility while proteins were kept in a fixed position. By referencing the co-crystalised ligand coordinates, the grid size and grid center for blind docking were set to 46.69 x 48.12 x 78.56 Å (center x = -44.33, y = -21.39, z = 83.53) and that of focused docking were set to 36.14 x 28.99 x 32.20 Å (center x = -48.98, y = -20.01, z = 68.21). The Intel Xeon Cascade Lake processor nodes of the high-performance computing cluster (‘Gadi’) housed at the National Computational Infrastructure (Canberra, Australia) was used to perform AutoDock Vina calculations. Subsequently, hydrogen and hydrophobic interactions on docked protein-ligand complexes were assessed by the protein-ligand interaction profiler (PLIP) [33] according to pre-specified bond angles and distances cut-offs (S1 Table), and visualised as networks in Gephi (0.9.3) [34].

Molecular dynamics simulations and free binding energy calculations

To examine the stability of protein-ligand complexes, the candidate PC compounds with the most favourable drug-likeness, ADMET properties, docking energy scores and binding poses were selected for molecular dynamics simulation against the native ligand UR-MK299. Ligand topology and parameter files were generated using CGenFF [35, 36] while the membrane system was prepared using CHARMM36m force field [37]. The system was constructed from CHARMM-GUI membrane builder module using the replacement method [38]. Disulfide bonds between Cys113-Cys198 and Cys33-Cys296 were specified, and the protein orientation was set by outputs from the PPM 2.0 server along the Z-axis. The system consists of POPC components distributed in a 1:1 ratio in the upper (129 molecules) and lower (130 molecules) leaflet, which was solvated with TIP3P water model (25190 molecules) and neutralised with 0.15M of K⁺ and Cl^- counterions. Water thickness of at least 22.5 Å on the top and bottom ends was applied to the system box. Energy minimisation was carried out using the steepest descent algorithm following the suggested equilibration scheme to reduce force constraints [38] and periodic boundary conditions were applied to produce constant temperature at 310K with the Nose-Hoover thermostat [39] and constant pressure set at 1 atm using the Parrinello-Rahman barostat using semi-isotropic coupling [40]. Long-range electrostatic forces were calculated from the Particle Mesh Ewald (PME) method [41], and all covalent bonds involving hydrogen atoms were constrained using the LINCS algorithm [42]. The final production runs were performed on GROMACS 2020.4 for 100 ns in triplicates [43]. To examine the stability of ligand binding, the root-mean-squared deviation (RMSD), root-mean-squared fluctuation (RMSF), number of hydrogen bonds and number of contacts were examined. Additionally, binding free energies were calculated by Molecular Mechanics-Poisson Boltzmann Surface Area (MM-PBSA) using the g_mmpbsa tool [44] from the final 1ns of the stabilised trajectory. For these MM-PBSA calculations, the grid spacing was set to 0.05 nm, the solvent dielectric constant was set to 80, and the solute dielectric constant was set to 2. The non-polar energy contribution was approximated based on the solvent-accessible surface area (SASA) model, with a probe radius set to 0.14 nm. The linear Poisson−Boltzmann equation (LPBE) implemented in the Adaptive Poisson-Boltzmann Solver (APBS) package was used. The list of input parameters for MM-PBSA calculations applied in this study was determined following the specification in the original publication of g_mmpbsa tool [44].

Results and discussion

Virtual screening through ligand- and structure-based approaches

Ligand-based screening and pharmacokinetics profiling revealed a high number of promising drug-like PC compounds occupying similar chemical space as known Y₁R inhibitors.

The QSAR modelling of 43 PC compounds (S1 Fig) and 173 known Y₁R inhibitors suggest that they share similar chemical space characterised by molecular weight, cLogP and topological surface area, particularly for PC compounds with higher molecular weight (Fig 1a). This is not surprising as most therapeutic drug-like molecules range between 350 to 500 g/mol, whereas a molecular weight of < 350 g/mol appears to be more lead-like [45]. Fig 1b shows the drug-likeness scores of PC compounds, and 62.8% of compounds approached a positive score. Interestingly, compounds with a lower rotatable bond count, between 0 to 7, exhibited higher drug-likeness scores. This could be due to their relatively higher oral bioavailability, as previous studies have demonstrated a correlation between the number of rotatable bonds and oral bioavailability, irrespective of molecular weight [46, 47]. The absorption, metabolism, distribution, and excretion (ADMET) indexes were illustrated with detailed cut-off points in S2 Fig. Following pkCSM prediction, most PC compounds exhibit favourable ADMET ranges which suggest that they could be readily bioavailable. According to open molecules and pkCSM analyses, most PC compounds appear to be within safe limits of AMES toxicity, and are generally non mutagenic, carcinogenic or irritative (Fig 1c–1f). Although several compounds had low drug-likeness or unfavourable ADMET properties, possible compound re-design guided by these parameters may help synthesise better molecules for Y₁R. For example, compounds predicted to have low GI absorption rates could be alternatively administered, via nasal, pulmonary, subcutaneous, or transdermal routes or assisted with micro and nanotechnologies, similar to insulin delivery methods [48]. In addition, nanotechnology such as using nano gold particles have been reported to improve absorption rates of PC compounds [49]. Hence these sub-optimal pharmacokinetics profiles may not necessarily be a major concern for the present study. However, it is important to note that the variability and limitations of machine-learning based predictions exist [50]. Notably, non-important fragments may be more heavily weighted over relevant bioactive fragments involved in binding, and many factors including the quality of input molecules, the choice of descriptors and the selection of statistical modelling and validation methods could significantly alter the accuracy of predictive models. Considering the limitations from ligand-based screening, all PC compounds were subjected to structure-based molecular docking for binding affinity scoring, and a candidate compound is proposed for molecular dynamics simulation based on consensus from ligand-based and structure-based methods.

Download:

Fig 1. Ligand- and structure-based screening outcomes.

(a) Chemical space comparison of PC compounds (red) and known Y₁R inhibitors (blue). (b) Drug-likeness prediction of PC compounds based on substructure fragments. Toxicity indexes include AMES (c), mutagenicity (d), tumorgenicity (e), and irritant (f) following QSAR analysis from pkCSM and open molecules. 3D ligand-residue interactions in blind (g) and focused (h) docking, with an example compound PC12 at the orthosteric (i) and allosteric (j) sites.

https://doi.org/10.1371/journal.pone.0277873.g001

The docking protocol was validated with a known antagonist (UR-MK299) and PC compounds were observed to bind at the orthosteric binding site of Y₁R with favourable binding energies.

Docking protocol validations demonstrated that UR-MK299 bound favourably to Y₁R at the canonical binding site (blind docking affinity: -11.2 kcal/mol, focused docking affinity: -11.7 kcal/mol). As observed in S3 Fig, the hydrogen bonds (with Asn283 and Asp287), pi-stacking contacts (with Phe282 and Phe286), and disulphide bridges (with Cys121) were reflected in both the re-docked experiments, consistent with previously reported PPARγ co-crystal structures (S3 Fig). Applying the validated protocols, 44 compounds, identified from TCM-ID and TCMSP databases, were docked with Y₁R (PDB: 5ZBQ). A total of 396 docking poses, and binding energies were obtained, and an allosteric site within the transmembrane domain was identified through blind docking (Fig 1g–1j). Strikingly, all PC compounds in both blind and focused docking were scored with a negative (favourable) binding affinity, as shown in Table 1. Consistent with other docking studies [21] and the positive control UR-MK299, the median energy scores from focused docking (-10.1 kcal/mol) within the pre-defined box size were significantly more superior compared to blind docking (-7.95kcal/mol), and hence PC compounds in focused docking were selected for further analyses in the next few sections.

Download:

Table 1. Binding affinity results, calculated Ki values, and residue contacts from focused docking (kcal/mol) of PC ligands with Y₁R (PDB: 5ZBQ).

https://doi.org/10.1371/journal.pone.0277873.t001

Hydrogen bonds between PC compounds and key residues of Y₁R binding site within the orthosteric ligand-binding domain and the deep V-shaped sub-pocket

A high frequency (≥ 5) of hydrogen bonds between PC compounds and key polar or charged residues were observed (Fig 2a), including Asn299 (n = 31), Asp104 (n = 17), and Asp200 (n = 10), located near the extracellular domain, and Asn283 (n = 8), Asp287 (n = 8) found deep in the Y₁R sub-pocket. Distance information on hydrogen bond interactions is provided in S2 Table.

Download:

Fig 2. 3D ligand-residue interactions outlining hydrogen bonds (a) and hydrophobic interactions (b) in focused docking of Poria cocos compounds and Y₁R.

The node sizes of PC compounds (grey) and Y₁R residues are based on the number of degrees, i.e., the higher the frequency of connections between residues and PC compounds, the larger the node size.

https://doi.org/10.1371/journal.pone.0277873.g002

Orthosteric ligand-binding domain.

As a residue with a high propensity to donate and accept hydrogen due to its carboxylate and amide side chains, it is not surprising to observe a large majority (48.8%) of PC compounds form polar contacts with Asn299. Although the role of Asn299 in Y₁R is not yet well known, studies on Asn located in a similar location (near extracellular loop of helix VII) of other class A GPCRs, the M2 muscarinic acetylcholine receptor, suggest that a mutation from Asn to Cys (as well as Val to Cys) significantly reduced binding affinity of the orthosteric antagonist [51]. Similarly, Asp104 located in helix II could also play important roles in stabilising extracellular loop (ECL) 2 and affect ligand-binding activities, as the substitution of Asp104 to Ala completely abolished the binding of endogenous peptide, NPY [52]. In addition, the importance of Asp200 in endogenous ligand binding has been demonstrated in docking studies between peptide YY (PYY) and Y₁R, as a hydrogen bond was indicated with 1.8Å between the peptide and receptor residue Asp200 [53]. A study on Asp200 conducted by Walker et al. (1994) [52] also revealed that a mutation of Asp200 to Ala completely abolished ¹²⁵1-NPY binding activities at 50pM concentration. However, these findings contrasted with a later study conducted by Sautel et al. (1996) [54], which they found Asp200 to Ala mutation exhibited similar K_i values as the wild-type receptor for either the endogenous ligand (NPY) or non-peptide antagonist (BIBP 3226). Asp200 may therefore serve an indirect role to support binding events, pending confirmation with more recent investigations. Nevertheless, PC compounds (PC21, PC22 and PC43) interacting with Asn299, Asp104 and Asp200 by hydrogen bonds, could interfere with agonist binding and stabilising ECL2 in a closed position.

Deep V-shaped sub-pocket.

Within the deep sub-pocket, two highly important residues for maintaining inactive conformation of Y₁R are Asn283 on helix V and Asp287 on helix VI. These residues have been well studied for their ligand-recognition effects on Y₁Rs. PC compounds forming hydrogen bonds with both Asn283 and Asp287 were PC1, PC8 and PC40. Mutagenesis studies indicate that alanine mutations of Asn283 and Asp287 both showed no or significantly lower binding of both endogenous agonist (NPY) and non-peptide antagonist (BIBP3226) [52, 54]. This observation is consistent with recent structural studies [25], by which the binding affinity scores of the antagonist UR-MK299 (positive control in this study) were reduced by over 2,000-fold when Asn283 and Asp287 were mutated to Ala and Asp residues, respectively. Regardless of peptide or non-peptide ligands, these studies strongly support the roles of Asn283 and Asp287 in substrate binding. The individual and synergistic roles of these residues on receptor function and the selectivity of peptide and non-peptide molecules on Y₁R could be further explored.

Extensive contacts with conserved and non-conserved Phe residues within the hydrophobic cluster and may interfere with binding of endogenous ligands near the extracellular surface of Y₁R

Aside from hydrogen bonding residues, PC compounds were stabilised by hydrophobic contacts with Y₁R residues (Fig 2b), their distances were provided in S3 Table. The most targeted hydrophobic contacts within 4Å were Phe residues on extracellular loops (Phe28 and Phe199) and on transmembrane helices V and VI (Phe282, Phe286 and Phe302). As we aimed to identify possible inhibitors of Y₁R, only PC compounds contacting the hydrophobic cluster indicated for Y₁R antagonism were discussed. Among these hydrophobic clusters, it has been revealed that alanine mutation of the non-conserved Phe286 resulted in significantly lowered binding affinity for both agonist (NPY) and antagonist (BIBP3266) [54]. More specifically, Phe286 mutations to Ala resulted in a 3-fold increase in K_d values. Furthermore, Phe282 and Phe286 have also been implicated for G protein coupling, as substitutions of these residues aborted the activity of PYY without significant changes to the binding affinity of antagonists 1229U91 and J-104870 [55]. In contrast, alanine mutation of Phe302 has resulted in the complete abolishment of activities from known antagonists BMS-193885, BIBP3226, BIBO3304, UR-MK136 and UR-MK289 [25]. These findings suggest that Phe302 makes a profound impact on antagonistic activities of Y₁R, and those compounds that solely interact with Phe302 were PC12, PC26 and PC27 (S4 Fig).

A consensus approach identified PC12, PC26 and PC27 as putative Y₁R inhibitors

Following ligand- and structure-based screening, close examination of protein-ligand contacts at the important binding site residues identified several PC compounds for further analysis, as listed in Table 2. Considering the binding energies, drug-likeness profiles, and toxicity indexes, PC12, PC26 and PC27 could be proposed as candidate compounds targeting Y₁R and is worth further investigation. PC12, beta-amyrin acetate was selected as the compound for molecular dynamics simulations as it produced the highest binding score out of the three.

Download:

Table 2. Putative Y₁R inhibitors identified from ligand-based and structure-based screening approaches.

https://doi.org/10.1371/journal.pone.0277873.t002

Structural analyses from 100 ns of MDS trajectories suggest reasonable stability of PC12-Y₁R complex

The conformational dynamics on stability and fluctuations were examined with RMSD, RMSF, number of hydrogen bonds, number of contacts, minimum protein-ligand distance, and minimum residue-ligand distance. In all simulations, the RMSD increased steadily and reach a plateau by approximately 35ns. After reaching equilibrium, the average root mean-squared deviations (RMSD) of backbone atoms fitted to the starting structure (t = 0) for the apo-, PC12- and UR-MK299-Y₁R complexes were 0.331nm, 0.339nm and 0.319nm, respectively (Fig 3a). Interestingly, PC12-Y₁R appears to have marginally higher RMSD compared to UR-MK299- Y₁R, this is attributed to the drift in the intracellular loop 3 of the second and third MD runs. As expected, most movements occurred in the extracellular and intracellular loop regions, while the transmembrane helices were relatively stable. Furthermore, the magnitude of RMSD were comparable to other rhodopsin-like GPCRs suspended in a POPC membrane system, with an average of approximately 0.35nm [56]. Per-residue fluctuation across the trajectory (Fig 3b) revealed several major peaks which corresponds to the loop regions, consistent with the RMSD trends. The highest peaks with the greatest flexibility among all simulations were between Lys239 and Thr258, the longest intracellular loop connecting helices 5 and 6. No significant differences between the RMSF peaks of Y₁R were apparent, although a marginally lower average RMSF could be seen with the PC12-bound Y₁R (red) compared to the UR-MK299-bound Y₁R (blue), suggestive of a stronger loop-stabilising effect of PC12.

Download:

Fig 3. Structural analyses, snapshots of trajectories and binding free energy calculations from molecular dynamics simulation comparing PC12 (red) with its positive control UR-MK299 (blue) on Y₁R (green).

Structural analyses include RMSD (a), RMSF (b), number of hydrogen bonds (c), number of contacts (d), minimum protein-ligand distances (e), and minimum distance between residues (Pro117, Phe302) on either side of the ligand (PC12) (f). Snapshots of Y₁R (pale green cartoon) and PC12 (red CPK) visualised in VMD throughout 100 ns (g-k). Per-residue energy decomposition for PC12 and UR-MK-299 (l), with a 3D ligand-residue inset of PC12 (red line and sticks).

https://doi.org/10.1371/journal.pone.0277873.g003

For both UR-MK299 and PC12 complexes after equilibrium, the number of hydrogen bonds remained relatively constant throughout the trajectory (Fig 3c), despite a slight decline in numbers for PC12 shortly after system relaxation. As the number of hydrogen bonds for PC12 were comparatively low, further assessments on number of contacts and protein-ligand distances were performed to examine whether protein-ligand interactions were present throughout the 100 ns trajectory. Intriguingly, the differences in number of contacts (Fig 3d) and the minimum protein-ligand distances (Fig 3e) for PC12 (2988 and 0.202nm, respectively), appear comparable to that of UR-MK299 (3443 and 0.187nm, respectively). Similarly, the distances between Pro117 (on helix 3) and Phe302 (on helix 7), two residues situated at either side of PC12 in Fig 3f appeared consistent at approximately 0.23 to 0.24nm across the smoothed trajectory. As such, the proximity of PC12 with Y₁R residues indicates that the ligand remained bound and fitted well within the orthosteric ligand-binding domain. The stable and sustained interactions may be governed by non-polar contacts such as pi-stacking interactions, in stark contrast with the binding patterns of the positive control UR-MK299. Snapshots of the trajectories in Fig 3g–3k shows PC12-bound Y₁R at different time series (ns), aside from a small shift in ligand orientation, PC12 remains bound to the orthosteric binding domain.

Binding free energy estimated by MMPBSA indicate that PC12 could bind with relatively strong free energies to Y₁R

The stability of PC12 and its positive control (UR-MK299) was further assessed by the energy contributions from electrostatic, van der Waals, polar solvation and SASA terms using MMPBSA. Table 3 lists the components of binding energies (kcal/mol) averaged over three parallel simulations for binding of PC12 and UR-MK299 to the inactive Y₁R. Interestingly, PC12 revealed stronger binding energies (ΔG_binding = -36.1 kcal/mol) compared to the positive control UR-MK299 (ΔG_binding = -32.4 kcal/mol), where most interactions were driven by van der Waals forces in both systems. The major difference in binding energies was attributed to the lower positive polar solvation energy arising from binding with PC12 (14.8 kcal/mol), compared to UR-MK299 (56.3 kcal/mol). Consistently, using the same binding free energy calculation approach, the ΔG_binding of PC12 beta-amyrin acetate appears in agreement to its pentacyclic triterpenol variant, alpha-amyrin acetate, bound to cannabinoid type 1 receptor (-36.6 kcal/mol) [57]. Whether PC12 or UR-MK299 were able to compete with the endogenous ligands NPY or PYY to inactive Y₁R remains to be researched, as the ΔG_binding of PYY was found to be -50.6 kcal/mol, a significantly more favourable affinity compared to small molecules [53]. Nevertheless, the results suggest that PC12 could bind to the orthosteric ligand binding domain of Y₁R with relatively strong binding free energies.

Download:

Table 3. Components of binding energies (kcal/mol) averaged over three parallel simulations.

https://doi.org/10.1371/journal.pone.0277873.t003

To examine the relative importance of residues for ligand binding at the orthosteric site, the total energy contribution of each system was decomposed into each corresponding residue. The average per-residue binding free energies for the final 1ns of three trajectories is shown in Fig 3l. Trends in per-residue binding energies were mostly consistent between PC12 and UR-MK299. The most energetically favourable residues tended to be non-polar and aggregate around helix III (Pro117, Cys121), helix VI (Phe173), helix VI (Phe282, Phe286) and helix VII (Phe302). Interestingly, two charged asparagine residues on ECL2 and helix IV were involved in significantly stronger binding free energies for PC12 (Asp200: -1.98 kcal/mol, Asp287: -1.48 kcal/mol) compared to UR-MK299 (Asp200: -0.71 kcal/mol, Asp287: 0.05 kcal/mol). The contrasting contributions from Asp residues may be important for the selectivity of PC12 or PC compounds on Y₁R. Notably, the hydrophobic cluster governed by Phe282, Phe286 and Phe302 is also apparent in previous experimental studies on known Y₁R antagonists UR-MK299 and BMS-193885 [25, 58]. The most favourable contribution from Phe302 among the cluster (PC12: -1.67 kcal/mol, UR-MK299: -1.74 kcal/mol) is consistent with findings from the protein-ligand interaction analyses earlier, and warrants mutagenesis studies to confirm its role on receptor inactivation. As PC12 demonstrated strong hydrophobic interactions with residues in the hydrophobic cluster, this could serve as a pathway for stabilising the Y₁R conformation in the inactive state.

In contrast to the residues above, Arg208 and Gln219 were found to contribute to positive energies at the orthosteric binding site. The binding free energies of the basic residue, Arg208, appeared to be highly positive for UR-MK299 (3.40 kcal/mol) and to a slightly lesser extent for PC12 (1.39 kcal/mol). The presence of the basic residue may contribute to conformational changes at the apical surface of Y₁R, which could be supported by charged-assisted hydrogen bonds when interacting with endogenous peptides or small molecules. This mechanism has also been discussed in an early study by Sylte et al. [59], demonstrating the role of Arg208 on the stability of ECL2 through intramolecular electrostatic interactions with Asp182 and Asp205. In addition, the binding free energies of Gln219 were also positive when bound to UR-MK299 (0.63 kcal/mol) and PC12 (0.71 kcal/mol). Consistent with UR-MK299 and PC12 in our study, polar interactions with Gln219 were not found to interact with antagonist BIBP3226, but was found to be within the binding region with Tyr36 of NPY, suggesting that this residue could be important for receptor activation [60]. Although Arg208 (helix V) and Gln219 (ECL 2), were not found to be within close contact with the ligands at the stabilised conformations throughout the 100 ns simulation, these conserved residues among neuropeptide Y receptor subtypes (Y₄R, Y₅R) could be important for ligand recognition as they were located the extracellular surface, pending further confirmation with future mutagenesis studies.

Conclusion

In summary, this study was conducted to critically examined the pharmacokinetics profiles of PC compounds and to explore their molecular mechanisms targeting Y₁R. We discovered several putative Y₁R inhibitors using ligand- and structure-based approaches, which could be used as starting points for further development, optimisation, and refinement as anti-obesity agents. Compounds designated PC1 3,4-Dihydroxybenzoic acid, PC8 Vanillic acid, PC40 1-(alpha-L-Ribofuranosyl)uracil were proposed as potential antagonists as they contact major residues Asn283 and Asp287, similar to various potent Y₁R antagonists. PC21 Poricoic acid B, PC22 Poricoic acid G and PC43 16alpha,25-Dihydroxy-24-methylene-3,4-secolanosta-4(28),7,9(11)-triene-3,21-dioic acid, contacting Asn299, Asp104 and Asp200 proximal to the extracellular surface could also interfere with agonist binding by stabilising the extracellular loop (ECL) 2 of Y₁R in a closed position. Owing to their selective interaction with Phe302, an important residue in binding of selective Y₁R antagonists, PC12 beta-Amyrin acetate, PC26 3-Epidehydrotumulosic acid and PC27 Cerevisterol were also suggested to be putative antagonists. Following the consensus approach from ligand- and structure-based screening, candidate compounds PC12, PC26 and PC27 were identified due to their high affinities (-12.2, -11.0 and -10.8 kcal/mol, respectively), high drug-likeness and low toxicity profiles. In combination with molecular dynamics simulations and binding free energy calculations, we achieved successful validation of the binding feasibility of PC12, as shown by its structural stability and favourable binding free energies at the orthosteric Y₁R transmembrane domain.

As relatively few studies adopted multiple virtual screening strategies, the consensus framework from ligand- and structure-based approaches with subsequent validation of feasibility in molecular dynamics simulations makes this combinatorial approach a strength of this study. Aside from binding affinity values, binding poses and interactions, the pharmacokinetics properties were also examined, offering room for lead optimisation and refinement to assist with a more successful design of PC compounds as more potent Y₁R inhibitors. Furthermore, the application of high-throughput screening and analyses methods not only enables more efficient identification of candidate PC compounds, but it also provides a comprehensive understanding of ligand-residue interactions, binding characteristics and pharmacological regulation globally, at the herb level, in addition to many studies solely reporting actions of selected compounds from Poria cocos. Although findings from molecular dynamics simulations corroborate the accuracy of results from docking analyses, they were only performed for a relatively short duration and in triplicate. More computationally intensive simulations up to and beyond microsecond timescales with higher number of parallel runs may reveal additional behaviours of ligand-protein complexes, including ligand-entry and possible concerted motions between other functionally relevant regions and the allosteric sites, not sampled by the 100 ns simulations in this study. Finally, as a limitation among computational studies, the predictive binding affinities, inhibitory constants, and pharmacokinetic properties may not be reflective of experimental values. Therefore, further investigations from in vitro and in vivo studies are desired to validate, characterise, and expand on the actions induced by PC compounds on Y₁R for future obesity management.

Supporting information

S1 Table. Protein-ligand interaction profiler thresholds for each parameter.

The mode of analysis was specified as “detection of macromolecule-ligand interactions”.

https://doi.org/10.1371/journal.pone.0277873.s001

(PDF)

S2 Table. Distance of hydrogen contacts between PC compounds and Y₁R residues.

H-A, Distance between H-Bond hydrogen and acceptor atom (Å); D-A, Distance between H-Bond donor and acceptor atoms (Å).

https://doi.org/10.1371/journal.pone.0277873.s002

(PDF)

S3 Table. Distance of hydrophobic contacts between PC compounds and Y₁R residues.

Distance provided in Angstrom (Å).

https://doi.org/10.1371/journal.pone.0277873.s003

(PDF)

S1 Fig. 2D conformer structures of all 43 PC compounds.

Within each compound box, the following information were provided: PC index (top left), chemical formula (top right), PubChem CID (bottom center).

https://doi.org/10.1371/journal.pone.0277873.s004

(PDF)

S2 Fig. Overview of ADMET properties of PC compounds based on pkCSM outputs.

For each continuous parameter, molecular weight was plotted in the x-axis against the parameter. Red line shows suggested thresholds for continuous parameters, where applicable.

https://doi.org/10.1371/journal.pone.0277873.s005

(PDF)

S3 Fig. Best binding poses of three molecules of co-crystalised known antagonist UR-MK299 on Y₁R (PDB:5ZBQ).

Ligand residue interactions in 3D and 2D views. (a) 3D diagram of 9AO superimposed in Y₁R (green ribbons). (b) 2D diagram of 9AO (grey sticks) in its original co-crystallised form. (c) 2D diagram of 9AO (orange sticks) in blind docking. (d) 2D diagram of 9AO (yellow sticks) in focused docking.

https://doi.org/10.1371/journal.pone.0277873.s006

(PDF)

S4 Fig. Interaction distance between Poria cocos compounds and residues of the hydrophobic phenylalanine (Phe) cluster in focused docking.

The colour of tiles represents an interaction detected within 4Å (green), and more than 4Å (white). The colour gradient represents the distance between the Phe residues and Poria cocos compounds, the closer the distance the darker the colour.

https://doi.org/10.1371/journal.pone.0277873.s007

(PDF)

References

1. Bentham J, Di Cesare M, Bilano V, Bixby H, Zhou B, Stevens GA, et al. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet. 2017;390: 2627–2642. pmid:29029897
- View Article
- PubMed/NCBI
- Google Scholar
2. Wilding JPH, Batterham RL, Calanna S, Davies M, Van Gaal LF, Lingvay I, et al. Once-Weekly Semaglutide in Adults with Overweight or Obesity. N Engl J Med. 2021;384: 989–1002. pmid:33567185
- View Article
- PubMed/NCBI
- Google Scholar
3. Pi-Sunyer X, Astrup A, Fujioka K, Greenway F, Halpern A, Krempf M, et al. A Randomized, Controlled Trial of 3.0 mg of Liraglutide in Weight Management. N Engl J Med. 2015;373: 11–22. pmid:26132939
- View Article
- PubMed/NCBI
- Google Scholar
4. Wadden TA, Foreyt JP, Foster GD, Hill JO, Klein S, O’Neil PM, et al. Weight loss with naltrexone SR/bupropion SR combination therapy as an adjunct to behavior modification: The COR-BMOD trial. Obesity. 2011;19: 110–120. pmid:20559296
- View Article
- PubMed/NCBI
- Google Scholar
5. Allison DB, Gadde KM, Garvey WT, Peterson CA, Schwiers ML, Najarian T, et al. Controlled-release phentermine/topiramate in severely obese adults: A randomized controlled trial (EQUIP). Obesity. 2012;20: 330–342. pmid:22051941
- View Article
- PubMed/NCBI
- Google Scholar
6. Sjöström L, Rissanen A, Andersen T, Boldrin M, Golay A, Koppeschaar HPF, et al. Randomised placebo-controlled trial of orlistat for weight loss and prevention of weight regain in obese patients. Lancet. 1998;352: 167–172. pmid:9683204
- View Article
- PubMed/NCBI
- Google Scholar
7. Saunders KH, Umashanker D, Igel LI, Kumar RB, Aronne LJ. Obesity Pharmacotherapy. Med Clin North Am. 2018;102: 135–148. pmid:29156182
- View Article
- PubMed/NCBI
- Google Scholar
8. Dorman RB, Miller CJ, Leslie DB, Serrot FJ, Slusarek B, Buchwald H, et al. Risk for hospital readmission following bariatric surgery. PLoS One. 2012;7. pmid:22412881
- View Article
- PubMed/NCBI
- Google Scholar
9. Srivastava G, Apovian CM. Current pharmacotherapy for obesity. Nat Rev Endocrinol. 2018;14: 12–24. pmid:29027993
- View Article
- PubMed/NCBI
- Google Scholar
10. Nie A, Chao Y, Zhang X, Jia W, Zhou Z, Zhu C. Phytochemistry and Pharmacological Activities of Wolfiporia cocos (F.A. Wolf) Ryvarden & Gilb. Front Pharmacol. 2020;11: 1–17. pmid:33071776
- View Article
- PubMed/NCBI
- Google Scholar
11. Wong AR, Yang AWH, Li K, Gill H, Li M, Lenon GB. Chinese herbal medicine for weight management: a systematic review and meta-analyses of randomised controlled trials. J Obes. 2021. pmid:33763238
- View Article
- PubMed/NCBI
- Google Scholar
12. Sui Y, Zhao HL, Wong VCW, Brown N, Li XL, Kwan AKL, et al. A systematic review on use of chinese medicine and acupuncture for treatment of obesity. Obes Rev. 2012;13: 409–430. pmid:22292480
- View Article
- PubMed/NCBI
- Google Scholar
13. Wong AR, Yang AWH, Li M, Hung A, Gill H, Lenon GB. The Effects and Safety of Chinese Herbal Medicine on Blood Lipid Profiles in Placebo-Controlled Weight-Loss Trials: A Systematic Review and Meta-Analysis. Chin K-Y, editor. Evidence-Based Complement Altern Med. 2022;2022: 1368576. pmid:35082903
- View Article
- PubMed/NCBI
- Google Scholar
14. SUN S-S, WANG K, MA K, BAO L, LIU H-W. An insoluble polysaccharide from the sclerotium of Poria cocos improves hyperglycemia, hyperlipidemia and hepatic steatosis in ob/ob mice via modulation of gut microbiota. Chin J Nat Med. 2019;17: 3–14. pmid:30704621
- View Article
- PubMed/NCBI
- Google Scholar
15. Kim J-H, Sim HA, Jung DY, Lim EY, Kim YT, Kim BJ, et al. Poria cocus wolf extract ameliorates hepatic steatosis through regulation of lipid metabolism, inhibition of ER stress, and activation of autophagy via AMPK activation. Int J Mol Sci. 2019;20: 4801. pmid:31569635
- View Article
- PubMed/NCBI
- Google Scholar
16. Yi M, Li H, Wu Z, Yan J, Liu Q, Ou C, et al. A Promising Therapeutic Target for Metabolic Diseases: Neuropeptide y Receptors in Humans. Cell Physiol Biochem. 2018;45: 88–107. pmid:29310113
- View Article
- PubMed/NCBI
- Google Scholar
17. Yan C, Zeng T, Lee K, Nobis M, Loh K, Gou L, et al. Peripheral-specific Y1 receptor antagonism increases thermogenesis and protects against diet-induced obesity. Nat Commun. 2021;12: 1–20. pmid:33976180
- View Article
- PubMed/NCBI
- Google Scholar
18. Eva C, Serra M, Mele P, Panzica G, Oberto A. Physiology and gene regulation of the brain NPY Y1 receptor. Front Neuroendocrinol. 2006;27: 308–339. pmid:16989896
- View Article
- PubMed/NCBI
- Google Scholar
19. Hohmann S, Buchmann AF, Witt SH, Rietschel M, Jennen-Steinmetz C, Schmidt MH, et al. Increasing association between a neuropeptide Y promoter polymorphism and body mass index during the course of development. Pediatr Obes. 2012;7: 453–460. pmid:22941950
- View Article
- PubMed/NCBI
- Google Scholar
20. Yulyaningsih E, Zhang L, Herzog H, Sainsbury A. NPY receptors as potential targets for anti-obesity drug development. Br J Pharmacol. 2011;163: 1170–1202. pmid:21545413
- View Article
- PubMed/NCBI
- Google Scholar
21. Ghersi D, Sanchez R. Improving accuracy and efficiency of blind protein-ligand docking by focusing on predicted binding sites. Proteins Struct Funct Bioinforma. 2009;74: 417–424. pmid:18636505
- View Article
- PubMed/NCBI
- Google Scholar
22. Chen X, Zhou H, Liu YB, Wang JF, Li H, Ung CY, et al. Database of traditional Chinese medicine and its application to studies of mechanism and to prescription validation. Br J Pharmacol. 2006;149: 1092–1103. pmid:17088869
- View Article
- PubMed/NCBI
- Google Scholar
23. Ru J, Li P, Wang J, Zhou W, Li B, Huang C, et al. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6: 1–6. pmid:24735618
- View Article
- PubMed/NCBI
- Google Scholar
24. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. J Cheminform. 2011;3: 1–14.
- View Article
- Google Scholar
25. Yang Z, Han S, Keller M, Kaiser A, Bender BJ, Bosse M, et al. Structural basis of ligand binding modes at the neuropeptide y Y1 receptor. Nature. 2018;556: 520–524. pmid:29670288
- View Article
- PubMed/NCBI
- Google Scholar
26. Humphrey W, Dalke A, Schulten K. VMD: visual molecular dynamics. J Mol Graph. 1996;14: 33–38. pmid:8744570
- View Article
- PubMed/NCBI
- Google Scholar
27. Choi JH, Banks AS, Kamenecka TM, Busby SA, Chalmers MJ, Kumar N, et al. Antidiabetic actions of a non-agonist PPARγ ligand blocking Cdk5-mediated phosphorylation. Nature. 2011;477: 477–481. pmid:21892191
- View Article
- PubMed/NCBI
- Google Scholar
28. Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018;46: W296–W303. pmid:29788355
- View Article
- PubMed/NCBI
- Google Scholar
29. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31: 455–461. pmid:19499576
- View Article
- PubMed/NCBI
- Google Scholar
30. Sander T, Freyss J, Von Korff M, Rufener C. DataWarrior: An open-source program for chemistry aware data visualization and analysis. J Chem Inf Model. 2015;55: 460–473. pmid:25558886
- View Article
- PubMed/NCBI
- Google Scholar
31. Gaulton A, Hersey A, Nowotka M, Bento AP, Chambers J, Mendez D, et al. The ChEMBL database in 2017. Nucleic Acids Res. 2017;45: D945–D954. pmid:27899562
- View Article
- PubMed/NCBI
- Google Scholar
32. Pires DEV, Blundell TL, Ascher DB. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem. 2015;58: 4066–4072. pmid:25860834
- View Article
- PubMed/NCBI
- Google Scholar
33. Salentin S, Schreiber S, Haupt VJ, Adasme MF, Schroeder M. PLIP: fully automated protein–ligand interaction profiler. Nucleic Acids Res. 2015;43: W443–W447. pmid:25873628
- View Article
- PubMed/NCBI
- Google Scholar
34. Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks. Third international AAAI conference on weblogs and social media. 2009.
35. Vanommeslaeghe K, MacKerell AD Jr. Automation of the CHARMM General Force Field (CGenFF) I: bond perception and atom typing. J Chem Inf Model. 2012;52: 3144–3154. pmid:23146088
- View Article
- PubMed/NCBI
- Google Scholar
36. Vanommeslaeghe K, Raman EP, MacKerell AD Jr. Automation of the CHARMM General Force Field (CGenFF) II: assignment of bonded parameters and partial atomic charges. J Chem Inf Model. 2012;52: 3155–3168. pmid:23145473
- View Article
- PubMed/NCBI
- Google Scholar
37. Huang J, Rauscher S, Nawrocki G, Ran T, Feig M, De Groot BL, et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat Methods. 2017;14: 71–73. pmid:27819658
- View Article
- PubMed/NCBI
- Google Scholar
38. Wu EL, Cheng X, Jo S, Rui H, Song KC, Dávila-Contreras EM, et al. CHARMM-GUI membrane builder toward realistic biological membrane simulations. J Comput Chem. 2014;35: 1997–2004. pmid:25130509
- View Article
- PubMed/NCBI
- Google Scholar
39. Evans DJ, Holian BL. The nose–hoover thermostat. J Chem Phys. 1985;83: 4069–4074.
- View Article
- Google Scholar
40. Parrinello M, Rahman A. Crystal structure and pair potentials: A molecular-dynamics study. Phys Rev Lett. 1980;45: 1196.
- View Article
- Google Scholar
41. Darden T, York D, Pedersen L. Particle mesh Ewald: An N⋅ log (N) method for Ewald sums in large systems. J Chem Phys. 1993;98: 10089–10092.
- View Article
- Google Scholar
42. Hess B, Bekker H, Berendsen HJC, Fraaije JGEM. LINCS: a linear constraint solver for molecular simulations. J Comput Chem. 1997;18: 1463–1472.
- View Article
- Google Scholar
43. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, et al. Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015;1–2: 19–25.
- View Article
- Google Scholar
44. Kumari R, Kumar R, Consortium OSDD, Lynn A. g_mmpbsa—A GROMACS tool for high-throughput MM-PBSA calculations. J Chem Inf Model. 2014;54: 1951–1962. pmid:24850022
- View Article
- PubMed/NCBI
- Google Scholar
45. Teague SJ, Davis AM, Leeson PD, Oprea T. The design of leadlike combinatorial libraries. Angew Chemie Int Ed. 1999;38: 3743–3748. pmid:10649345
- View Article
- PubMed/NCBI
- Google Scholar
46. Veber DF, Johnson SR, Cheng H-Y, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002;45: 2615–2623. pmid:12036371
- View Article
- PubMed/NCBI
- Google Scholar
47. Hou T, Wang J, Zhang W, Xu X. ADME evaluation in drug discovery. 6. Can oral bioavailability in humans be effectively predicted by simple molecular property-based rules? J Chem Inf Model. 2007;47: 460–463. pmid:17381169
- View Article
- PubMed/NCBI
- Google Scholar
48. Mo R, Jiang T, Di J, Tai W, Gu Z. Emerging micro- and nanotechnology based synthetic approaches for insulin delivery. Chem Soc Rev. 2014;43: 3595–3629. pmid:24626293
- View Article
- PubMed/NCBI
- Google Scholar
49. Li W, Wan H, Yan S, Yan Z, Chen Y, Guo P, et al. Gold nanoparticles synthesized with Poria cocos modulates the anti-obesity parameters in high-fat diet and streptozotocin induced obese diabetes rat model. Arab J Chem. 2020;13: 5966–5977.
- View Article
- Google Scholar
50. Lo Y-C, Rensi SE, Torng W, Altman RB. Machine learning in chemoinformatics and drug discovery. Drug Discov Today. 2018;23: 1538–1546. pmid:29750902
- View Article
- PubMed/NCBI
- Google Scholar
51. Avlani VA, Gregory KJ, Morton CJ, Parker MW, Sexton PM, Christopoulos A. Critical role for the second extracellular loop in the binding of both orthosteric and allosteric G protein-coupled receptor ligands. J Biol Chem. 2007;282: 25677–25686. pmid:17591774
- View Article
- PubMed/NCBI
- Google Scholar
52. Walker P, Munoz M, Martinez R, Peitsch MC. Acidic residues in extracellular loops of the human Y1 neuropeptide Y receptor are essential for ligand binding. J Biol Chem. 1994;269: 2863–2869. pmid:8300621
- View Article
- PubMed/NCBI
- Google Scholar
53. Choong YS, Lim YY, Soong JX, Savoo N, Guida C, Rhyman L, et al. Theoretical study of the interactions between peptide tyrosine tyrosine [PYY (1–36)], a newly identified modulator in type 2 diabetes pathophysiology, with receptors NPY1R and NPY4R. Hormones. 2021;20: 557–569. pmid:33782920
- View Article
- PubMed/NCBI
- Google Scholar
54. Sautel M, Rudolf K, Wittneben H, Herzog H, Martinez R, Munoz M, et al. Neuropeptide Y and the nonpeptide antagonist BIBP 3226 share an overlapping binding site at the human Y1 receptor. Mol Pharmacol. 1996;50: 285–292. pmid:8700135
- View Article
- PubMed/NCBI
- Google Scholar
55. Kannoa T, Kanatani A, Keen SLC, Arai-Otsuki S, Haga Y, Iwama T, et al. Different binding sites for the neuropeptide Y Y1 antagonists 1229U91 and J-104870 on human Y1 receptors. Peptides. 2001;22: 405–413. pmid:11287095
- View Article
- PubMed/NCBI
- Google Scholar
56. Filizola M, Wang SX, Weinstein H. Dynamic models of G-protein coupled receptor dimers: indications of asymmetry in the rhodopsin dimer from molecular dynamics simulations in a POPC bilayer. J Comput Aided Mol Des. 2006;20: 405–416. pmid:17089205
- View Article
- PubMed/NCBI
- Google Scholar
57. Siraj MA, Rahman MS, Tan GT, Seidel V. Molecular docking and molecular dynamics simulation studies of triterpenes from Vernonia patula with the cannabinoid type 1 receptor. Int J Mol Sci. 2021;22: 3595. pmid:33808384
- View Article
- PubMed/NCBI
- Google Scholar
58. Park C, Kim J, Ko S-B, Choi YK, Jeong H, Woo H, et al. Structural basis of neuropeptide Y signaling through Y1 receptor. Nat Commun. 2022;13: 1–12.
- View Article
- Google Scholar
59. Sylte I, Andrianjara CR, Calvet A, Pascal Y, Dahl SG. Molecular dynamics of NPY Y1 receptor activation. Bioorg Med Chem. 1999;7: 2737–2748. pmid:10658578
- View Article
- PubMed/NCBI
- Google Scholar
60. Du P, Salon JA, Tamm JA, Hou C, Cui W, Walker MW, et al. Modeling the G-protein-coupled neuropeptide Y Y1 receptor agonist and antagonist binding sites. Protein Eng. 1997;10: 109–117. pmid:9089810
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Bentham J, Di Cesare M, Bilano V, Bixby H, Zhou B, Stevens GA, et al. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet. 2017;390: 2627–2642. pmid:29029897
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Wilding JPH, Batterham RL, Calanna S, Davies M, Van Gaal LF, Lingvay I, et al. Once-Weekly Semaglutide in Adults with Overweight or Obesity. N Engl J Med. 2021;384: 989–1002. pmid:33567185
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Pi-Sunyer X, Astrup A, Fujioka K, Greenway F, Halpern A, Krempf M, et al. A Randomized, Controlled Trial of 3.0 mg of Liraglutide in Weight Management. N Engl J Med. 2015;373: 11–22. pmid:26132939
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Wadden TA, Foreyt JP, Foster GD, Hill JO, Klein S, O’Neil PM, et al. Weight loss with naltrexone SR/bupropion SR combination therapy as an adjunct to behavior modification: The COR-BMOD trial. Obesity. 2011;19: 110–120. pmid:20559296
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Allison DB, Gadde KM, Garvey WT, Peterson CA, Schwiers ML, Najarian T, et al. Controlled-release phentermine/topiramate in severely obese adults: A randomized controlled trial (EQUIP). Obesity. 2012;20: 330–342. pmid:22051941
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. Sjöström L, Rissanen A, Andersen T, Boldrin M, Golay A, Koppeschaar HPF, et al. Randomised placebo-controlled trial of orlistat for weight loss and prevention of weight regain in obese patients. Lancet. 1998;352: 167–172. pmid:9683204
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref7] 7. Saunders KH, Umashanker D, Igel LI, Kumar RB, Aronne LJ. Obesity Pharmacotherapy. Med Clin North Am. 2018;102: 135–148. pmid:29156182
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref8] 8. Dorman RB, Miller CJ, Leslie DB, Serrot FJ, Slusarek B, Buchwald H, et al. Risk for hospital readmission following bariatric surgery. PLoS One. 2012;7. pmid:22412881
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref9] 9. Srivastava G, Apovian CM. Current pharmacotherapy for obesity. Nat Rev Endocrinol. 2018;14: 12–24. pmid:29027993
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref10] 10. Nie A, Chao Y, Zhang X, Jia W, Zhou Z, Zhu C. Phytochemistry and Pharmacological Activities of Wolfiporia cocos (F.A. Wolf) Ryvarden & Gilb. Front Pharmacol. 2020;11: 1–17. pmid:33071776
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref11] 11. Wong AR, Yang AWH, Li K, Gill H, Li M, Lenon GB. Chinese herbal medicine for weight management: a systematic review and meta-analyses of randomised controlled trials. J Obes. 2021. pmid:33763238
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref12] 12. Sui Y, Zhao HL, Wong VCW, Brown N, Li XL, Kwan AKL, et al. A systematic review on use of chinese medicine and acupuncture for treatment of obesity. Obes Rev. 2012;13: 409–430. pmid:22292480
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref13] 13. Wong AR, Yang AWH, Li M, Hung A, Gill H, Lenon GB. The Effects and Safety of Chinese Herbal Medicine on Blood Lipid Profiles in Placebo-Controlled Weight-Loss Trials: A Systematic Review and Meta-Analysis. Chin K-Y, editor. Evidence-Based Complement Altern Med. 2022;2022: 1368576. pmid:35082903
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref14] 14. SUN S-S, WANG K, MA K, BAO L, LIU H-W. An insoluble polysaccharide from the sclerotium of Poria cocos improves hyperglycemia, hyperlipidemia and hepatic steatosis in ob/ob mice via modulation of gut microbiota. Chin J Nat Med. 2019;17: 3–14. pmid:30704621
View Article
PubMed/NCBI
Google Scholar

[54] View Article

[55] PubMed/NCBI

[56] Google Scholar

[ref15] 15. Kim J-H, Sim HA, Jung DY, Lim EY, Kim YT, Kim BJ, et al. Poria cocus wolf extract ameliorates hepatic steatosis through regulation of lipid metabolism, inhibition of ER stress, and activation of autophagy via AMPK activation. Int J Mol Sci. 2019;20: 4801. pmid:31569635
View Article
PubMed/NCBI
Google Scholar

[58] View Article

[59] PubMed/NCBI

[60] Google Scholar

[ref16] 16. Yi M, Li H, Wu Z, Yan J, Liu Q, Ou C, et al. A Promising Therapeutic Target for Metabolic Diseases: Neuropeptide y Receptors in Humans. Cell Physiol Biochem. 2018;45: 88–107. pmid:29310113
View Article
PubMed/NCBI
Google Scholar

[62] View Article

[63] PubMed/NCBI

[64] Google Scholar

[ref17] 17. Yan C, Zeng T, Lee K, Nobis M, Loh K, Gou L, et al. Peripheral-specific Y1 receptor antagonism increases thermogenesis and protects against diet-induced obesity. Nat Commun. 2021;12: 1–20. pmid:33976180
View Article
PubMed/NCBI
Google Scholar

[66] View Article

[67] PubMed/NCBI

[68] Google Scholar

[ref18] 18. Eva C, Serra M, Mele P, Panzica G, Oberto A. Physiology and gene regulation of the brain NPY Y1 receptor. Front Neuroendocrinol. 2006;27: 308–339. pmid:16989896
View Article
PubMed/NCBI
Google Scholar

[70] View Article

[71] PubMed/NCBI

[72] Google Scholar

[ref19] 19. Hohmann S, Buchmann AF, Witt SH, Rietschel M, Jennen-Steinmetz C, Schmidt MH, et al. Increasing association between a neuropeptide Y promoter polymorphism and body mass index during the course of development. Pediatr Obes. 2012;7: 453–460. pmid:22941950
View Article
PubMed/NCBI
Google Scholar

[74] View Article

[75] PubMed/NCBI

[76] Google Scholar

[ref20] 20. Yulyaningsih E, Zhang L, Herzog H, Sainsbury A. NPY receptors as potential targets for anti-obesity drug development. Br J Pharmacol. 2011;163: 1170–1202. pmid:21545413
View Article
PubMed/NCBI
Google Scholar

[78] View Article

[79] PubMed/NCBI

[80] Google Scholar

[ref21] 21. Ghersi D, Sanchez R. Improving accuracy and efficiency of blind protein-ligand docking by focusing on predicted binding sites. Proteins Struct Funct Bioinforma. 2009;74: 417–424. pmid:18636505
View Article
PubMed/NCBI
Google Scholar

[82] View Article

[83] PubMed/NCBI

[84] Google Scholar

[ref22] 22. Chen X, Zhou H, Liu YB, Wang JF, Li H, Ung CY, et al. Database of traditional Chinese medicine and its application to studies of mechanism and to prescription validation. Br J Pharmacol. 2006;149: 1092–1103. pmid:17088869
View Article
PubMed/NCBI
Google Scholar

[86] View Article

[87] PubMed/NCBI

[88] Google Scholar

[ref23] 23. Ru J, Li P, Wang J, Zhou W, Li B, Huang C, et al. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6: 1–6. pmid:24735618
View Article
PubMed/NCBI
Google Scholar

[90] View Article

[91] PubMed/NCBI

[92] Google Scholar

[ref24] 24. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. J Cheminform. 2011;3: 1–14.
View Article
Google Scholar

[94] View Article

[95] Google Scholar

[ref25] 25. Yang Z, Han S, Keller M, Kaiser A, Bender BJ, Bosse M, et al. Structural basis of ligand binding modes at the neuropeptide y Y1 receptor. Nature. 2018;556: 520–524. pmid:29670288
View Article
PubMed/NCBI
Google Scholar

[97] View Article

[98] PubMed/NCBI

[99] Google Scholar

[ref26] 26. Humphrey W, Dalke A, Schulten K. VMD: visual molecular dynamics. J Mol Graph. 1996;14: 33–38. pmid:8744570
View Article
PubMed/NCBI
Google Scholar

[101] View Article

[102] PubMed/NCBI

[103] Google Scholar

[ref27] 27. Choi JH, Banks AS, Kamenecka TM, Busby SA, Chalmers MJ, Kumar N, et al. Antidiabetic actions of a non-agonist PPARγ ligand blocking Cdk5-mediated phosphorylation. Nature. 2011;477: 477–481. pmid:21892191
View Article
PubMed/NCBI
Google Scholar

[105] View Article

[106] PubMed/NCBI

[107] Google Scholar

[ref28] 28. Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018;46: W296–W303. pmid:29788355
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref29] 29. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31: 455–461. pmid:19499576
View Article
PubMed/NCBI
Google Scholar

[113] View Article

[114] PubMed/NCBI

[115] Google Scholar

[ref30] 30. Sander T, Freyss J, Von Korff M, Rufener C. DataWarrior: An open-source program for chemistry aware data visualization and analysis. J Chem Inf Model. 2015;55: 460–473. pmid:25558886
View Article
PubMed/NCBI
Google Scholar

[117] View Article

[118] PubMed/NCBI

[119] Google Scholar

[ref31] 31. Gaulton A, Hersey A, Nowotka M, Bento AP, Chambers J, Mendez D, et al. The ChEMBL database in 2017. Nucleic Acids Res. 2017;45: D945–D954. pmid:27899562
View Article
PubMed/NCBI
Google Scholar

[121] View Article

[122] PubMed/NCBI

[123] Google Scholar

[ref32] 32. Pires DEV, Blundell TL, Ascher DB. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem. 2015;58: 4066–4072. pmid:25860834
View Article
PubMed/NCBI
Google Scholar

[125] View Article

[126] PubMed/NCBI

[127] Google Scholar

[ref33] 33. Salentin S, Schreiber S, Haupt VJ, Adasme MF, Schroeder M. PLIP: fully automated protein–ligand interaction profiler. Nucleic Acids Res. 2015;43: W443–W447. pmid:25873628
View Article
PubMed/NCBI
Google Scholar

[129] View Article

[130] PubMed/NCBI

[131] Google Scholar

[ref34] 34. Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks. Third international AAAI conference on weblogs and social media. 2009.

[ref35] 35. Vanommeslaeghe K, MacKerell AD Jr. Automation of the CHARMM General Force Field (CGenFF) I: bond perception and atom typing. J Chem Inf Model. 2012;52: 3144–3154. pmid:23146088
View Article
PubMed/NCBI
Google Scholar

[134] View Article

[135] PubMed/NCBI

[136] Google Scholar

[ref36] 36. Vanommeslaeghe K, Raman EP, MacKerell AD Jr. Automation of the CHARMM General Force Field (CGenFF) II: assignment of bonded parameters and partial atomic charges. J Chem Inf Model. 2012;52: 3155–3168. pmid:23145473
View Article
PubMed/NCBI
Google Scholar

[138] View Article

[139] PubMed/NCBI

[140] Google Scholar

[ref37] 37. Huang J, Rauscher S, Nawrocki G, Ran T, Feig M, De Groot BL, et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat Methods. 2017;14: 71–73. pmid:27819658
View Article
PubMed/NCBI
Google Scholar

[142] View Article

[143] PubMed/NCBI

[144] Google Scholar

[ref38] 38. Wu EL, Cheng X, Jo S, Rui H, Song KC, Dávila-Contreras EM, et al. CHARMM-GUI membrane builder toward realistic biological membrane simulations. J Comput Chem. 2014;35: 1997–2004. pmid:25130509
View Article
PubMed/NCBI
Google Scholar

[146] View Article

[147] PubMed/NCBI

[148] Google Scholar

[ref39] 39. Evans DJ, Holian BL. The nose–hoover thermostat. J Chem Phys. 1985;83: 4069–4074.
View Article
Google Scholar

[150] View Article

[151] Google Scholar

[ref40] 40. Parrinello M, Rahman A. Crystal structure and pair potentials: A molecular-dynamics study. Phys Rev Lett. 1980;45: 1196.
View Article
Google Scholar

[153] View Article

[154] Google Scholar

[ref41] 41. Darden T, York D, Pedersen L. Particle mesh Ewald: An N⋅ log (N) method for Ewald sums in large systems. J Chem Phys. 1993;98: 10089–10092.
View Article
Google Scholar

[156] View Article

[157] Google Scholar

[ref42] 42. Hess B, Bekker H, Berendsen HJC, Fraaije JGEM. LINCS: a linear constraint solver for molecular simulations. J Comput Chem. 1997;18: 1463–1472.
View Article
Google Scholar

[159] View Article

[160] Google Scholar

[ref43] 43. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, et al. Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015;1–2: 19–25.
View Article
Google Scholar

[162] View Article

[163] Google Scholar

[ref44] 44. Kumari R, Kumar R, Consortium OSDD, Lynn A. g_mmpbsa—A GROMACS tool for high-throughput MM-PBSA calculations. J Chem Inf Model. 2014;54: 1951–1962. pmid:24850022
View Article
PubMed/NCBI
Google Scholar

[165] View Article

[166] PubMed/NCBI

[167] Google Scholar

[ref45] 45. Teague SJ, Davis AM, Leeson PD, Oprea T. The design of leadlike combinatorial libraries. Angew Chemie Int Ed. 1999;38: 3743–3748. pmid:10649345
View Article
PubMed/NCBI
Google Scholar

[169] View Article

[170] PubMed/NCBI

[171] Google Scholar

[ref46] 46. Veber DF, Johnson SR, Cheng H-Y, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002;45: 2615–2623. pmid:12036371
View Article
PubMed/NCBI
Google Scholar

[173] View Article

[174] PubMed/NCBI

[175] Google Scholar

[ref47] 47. Hou T, Wang J, Zhang W, Xu X. ADME evaluation in drug discovery. 6. Can oral bioavailability in humans be effectively predicted by simple molecular property-based rules? J Chem Inf Model. 2007;47: 460–463. pmid:17381169
View Article
PubMed/NCBI
Google Scholar

[177] View Article

[178] PubMed/NCBI

[179] Google Scholar

[ref48] 48. Mo R, Jiang T, Di J, Tai W, Gu Z. Emerging micro- and nanotechnology based synthetic approaches for insulin delivery. Chem Soc Rev. 2014;43: 3595–3629. pmid:24626293
View Article
PubMed/NCBI
Google Scholar

[181] View Article

[182] PubMed/NCBI

[183] Google Scholar

[ref49] 49. Li W, Wan H, Yan S, Yan Z, Chen Y, Guo P, et al. Gold nanoparticles synthesized with Poria cocos modulates the anti-obesity parameters in high-fat diet and streptozotocin induced obese diabetes rat model. Arab J Chem. 2020;13: 5966–5977.
View Article
Google Scholar

[185] View Article

[186] Google Scholar

[ref50] 50. Lo Y-C, Rensi SE, Torng W, Altman RB. Machine learning in chemoinformatics and drug discovery. Drug Discov Today. 2018;23: 1538–1546. pmid:29750902
View Article
PubMed/NCBI
Google Scholar

[188] View Article

[189] PubMed/NCBI

[190] Google Scholar

[ref51] 51. Avlani VA, Gregory KJ, Morton CJ, Parker MW, Sexton PM, Christopoulos A. Critical role for the second extracellular loop in the binding of both orthosteric and allosteric G protein-coupled receptor ligands. J Biol Chem. 2007;282: 25677–25686. pmid:17591774
View Article
PubMed/NCBI
Google Scholar

[192] View Article

[193] PubMed/NCBI

[194] Google Scholar

[ref52] 52. Walker P, Munoz M, Martinez R, Peitsch MC. Acidic residues in extracellular loops of the human Y1 neuropeptide Y receptor are essential for ligand binding. J Biol Chem. 1994;269: 2863–2869. pmid:8300621
View Article
PubMed/NCBI
Google Scholar

[196] View Article

[197] PubMed/NCBI

[198] Google Scholar

[ref53] 53. Choong YS, Lim YY, Soong JX, Savoo N, Guida C, Rhyman L, et al. Theoretical study of the interactions between peptide tyrosine tyrosine [PYY (1–36)], a newly identified modulator in type 2 diabetes pathophysiology, with receptors NPY1R and NPY4R. Hormones. 2021;20: 557–569. pmid:33782920
View Article
PubMed/NCBI
Google Scholar

[200] View Article

[201] PubMed/NCBI

[202] Google Scholar

[ref54] 54. Sautel M, Rudolf K, Wittneben H, Herzog H, Martinez R, Munoz M, et al. Neuropeptide Y and the nonpeptide antagonist BIBP 3226 share an overlapping binding site at the human Y1 receptor. Mol Pharmacol. 1996;50: 285–292. pmid:8700135
View Article
PubMed/NCBI
Google Scholar

[204] View Article

[205] PubMed/NCBI

[206] Google Scholar

[ref55] 55. Kannoa T, Kanatani A, Keen SLC, Arai-Otsuki S, Haga Y, Iwama T, et al. Different binding sites for the neuropeptide Y Y1 antagonists 1229U91 and J-104870 on human Y1 receptors. Peptides. 2001;22: 405–413. pmid:11287095
View Article
PubMed/NCBI
Google Scholar

[208] View Article

[209] PubMed/NCBI

[210] Google Scholar

[ref56] 56. Filizola M, Wang SX, Weinstein H. Dynamic models of G-protein coupled receptor dimers: indications of asymmetry in the rhodopsin dimer from molecular dynamics simulations in a POPC bilayer. J Comput Aided Mol Des. 2006;20: 405–416. pmid:17089205
View Article
PubMed/NCBI
Google Scholar

[212] View Article

[213] PubMed/NCBI

[214] Google Scholar

[ref57] 57. Siraj MA, Rahman MS, Tan GT, Seidel V. Molecular docking and molecular dynamics simulation studies of triterpenes from Vernonia patula with the cannabinoid type 1 receptor. Int J Mol Sci. 2021;22: 3595. pmid:33808384
View Article
PubMed/NCBI
Google Scholar

[216] View Article

[217] PubMed/NCBI

[218] Google Scholar

[ref58] 58. Park C, Kim J, Ko S-B, Choi YK, Jeong H, Woo H, et al. Structural basis of neuropeptide Y signaling through Y1 receptor. Nat Commun. 2022;13: 1–12.
View Article
Google Scholar

[220] View Article

[221] Google Scholar

[ref59] 59. Sylte I, Andrianjara CR, Calvet A, Pascal Y, Dahl SG. Molecular dynamics of NPY Y1 receptor activation. Bioorg Med Chem. 1999;7: 2737–2748. pmid:10658578
View Article
PubMed/NCBI
Google Scholar

[223] View Article

[224] PubMed/NCBI

[225] Google Scholar

[ref60] 60. Du P, Salon JA, Tamm JA, Hou C, Cui W, Walker MW, et al. Modeling the G-protein-coupled neuropeptide Y Y1 receptor agonist and antagonist binding sites. Protein Eng. 1997;10: 109–117. pmid:9089810
View Article
PubMed/NCBI
Google Scholar

[227] View Article

[228] PubMed/NCBI

[229] Google Scholar

Poria cocos compounds targeting neuropeptide Y1 receptor (Y₁R) for weight management: A computational ligand- and structure-based study with molecular dynamics simulations identified beta-amyrin acetate as a putative Y₁R inhibitor

Poria cocos compounds targeting neuropeptide Y1 receptor (Y₁R) for weight management: A computational ligand- and structure-based study with molecular dynamics simulations identified beta-amyrin acetate as a putative Y₁R inhibitor

Figures

Abstract

Introduction

Materials and methods

Identification and preparation of PC compounds and Y₁R target

Physicochemical space examination, drug-likeness prediction, and pharmacokinetics profiling

Molecular docking and ligand-residue interaction analyses

Molecular dynamics simulations and free binding energy calculations

Results and discussion

Virtual screening through ligand- and structure-based approaches

Ligand-based screening and pharmacokinetics profiling revealed a high number of promising drug-like PC compounds occupying similar chemical space as known Y₁R inhibitors.

The docking protocol was validated with a known antagonist (UR-MK299) and PC compounds were observed to bind at the orthosteric binding site of Y₁R with favourable binding energies.

Hydrogen bonds between PC compounds and key residues of Y₁R binding site within the orthosteric ligand-binding domain and the deep V-shaped sub-pocket

Orthosteric ligand-binding domain.

Deep V-shaped sub-pocket.

Extensive contacts with conserved and non-conserved Phe residues within the hydrophobic cluster and may interfere with binding of endogenous ligands near the extracellular surface of Y₁R

A consensus approach identified PC12, PC26 and PC27 as putative Y₁R inhibitors

Structural analyses from 100 ns of MDS trajectories suggest reasonable stability of PC12-Y₁R complex

Binding free energy estimated by MMPBSA indicate that PC12 could bind with relatively strong free energies to Y₁R

Conclusion

Supporting information

S1 Table. Protein-ligand interaction profiler thresholds for each parameter.

S2 Table. Distance of hydrogen contacts between PC compounds and Y₁R residues.

S3 Table. Distance of hydrophobic contacts between PC compounds and Y₁R residues.

S1 Fig. 2D conformer structures of all 43 PC compounds.

S2 Fig. Overview of ADMET properties of PC compounds based on pkCSM outputs.

S3 Fig. Best binding poses of three molecules of co-crystalised known antagonist UR-MK299 on Y₁R (PDB:5ZBQ).

S4 Fig. Interaction distance between Poria cocos compounds and residues of the hydrophobic phenylalanine (Phe) cluster in focused docking.

References

Figures

Abstract

Introduction

Materials and methods

Identification and preparation of PC compounds and Y1R target

Physicochemical space examination, drug-likeness prediction, and pharmacokinetics profiling

Molecular docking and ligand-residue interaction analyses

Molecular dynamics simulations and free binding energy calculations

Results and discussion

Virtual screening through ligand- and structure-based approaches

Ligand-based screening and pharmacokinetics profiling revealed a high number of promising drug-like PC compounds occupying similar chemical space as known Y1R inhibitors.

The docking protocol was validated with a known antagonist (UR-MK299) and PC compounds were observed to bind at the orthosteric binding site of Y1R with favourable binding energies.

Hydrogen bonds between PC compounds and key residues of Y1R binding site within the orthosteric ligand-binding domain and the deep V-shaped sub-pocket

Orthosteric ligand-binding domain.

Deep V-shaped sub-pocket.

Extensive contacts with conserved and non-conserved Phe residues within the hydrophobic cluster and may interfere with binding of endogenous ligands near the extracellular surface of Y1R

A consensus approach identified PC12, PC26 and PC27 as putative Y1R inhibitors

Structural analyses from 100 ns of MDS trajectories suggest reasonable stability of PC12-Y1R complex

Binding free energy estimated by MMPBSA indicate that PC12 could bind with relatively strong free energies to Y1R

Conclusion

Supporting information

S1 Table. Protein-ligand interaction profiler thresholds for each parameter.

S2 Table. Distance of hydrogen contacts between PC compounds and Y1R residues.

S3 Table. Distance of hydrophobic contacts between PC compounds and Y1R residues.

S1 Fig. 2D conformer structures of all 43 PC compounds.

S2 Fig. Overview of ADMET properties of PC compounds based on pkCSM outputs.

S3 Fig. Best binding poses of three molecules of co-crystalised known antagonist UR-MK299 on Y1R (PDB:5ZBQ).

S4 Fig. Interaction distance between Poria cocos compounds and residues of the hydrophobic phenylalanine (Phe) cluster in focused docking.

References

Identification and preparation of PC compounds and Y₁R target

Ligand-based screening and pharmacokinetics profiling revealed a high number of promising drug-like PC compounds occupying similar chemical space as known Y₁R inhibitors.

The docking protocol was validated with a known antagonist (UR-MK299) and PC compounds were observed to bind at the orthosteric binding site of Y₁R with favourable binding energies.

Hydrogen bonds between PC compounds and key residues of Y₁R binding site within the orthosteric ligand-binding domain and the deep V-shaped sub-pocket

Extensive contacts with conserved and non-conserved Phe residues within the hydrophobic cluster and may interfere with binding of endogenous ligands near the extracellular surface of Y₁R

A consensus approach identified PC12, PC26 and PC27 as putative Y₁R inhibitors

Structural analyses from 100 ns of MDS trajectories suggest reasonable stability of PC12-Y₁R complex

Binding free energy estimated by MMPBSA indicate that PC12 could bind with relatively strong free energies to Y₁R

S2 Table. Distance of hydrogen contacts between PC compounds and Y₁R residues.

S3 Table. Distance of hydrophobic contacts between PC compounds and Y₁R residues.

S3 Fig. Best binding poses of three molecules of co-crystalised known antagonist UR-MK299 on Y₁R (PDB:5ZBQ).