Difference and Influence of Inactive and Active States of Cannabinoid

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Difference and Influence of Inactive and Active States of Cannabinoid Receptor Subtype CB2: From Conformation to Drug Discovery Jianping Hu, Zhiwei Feng, Shifan Ma, Yu Zhang, Qin Tong, Mohammed Hamed Alqarni, Xiaojun Gou, and Xiang-Qun (Sean) Xie J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.5b00739 • Publication Date (Web): 17 May 2016 Downloaded from http://pubs.acs.org on May 23, 2016

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Journal of Chemical Information and Modeling

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Difference and Influence of Inactive and Active States of Cannabinoid Receptor

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Subtype CB2: From Conformation to Drug Discovery Jianping Hu ‡1, 2, 3, Zhiwei Feng ‡1, Shifan Ma1, Yu Zhang1, Qin Tong1,

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Mohammed Hamed Alqarni1, Xiaojun Gou3, and Xiang-Qun Xie1, *

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1

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Screening Center, School of Pharmacy; NIH National Center of Excellence for

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Computational Drug Abuse Research; Drug Discovery Institute; Department of

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Computational Biology and Structural Biology, School of Medicine, University of

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Pittsburgh, Pittsburgh, Pennsylvania 15260, United States. E-mail: [email protected]

Department of Pharmaceutical Sciences and Computational Chemical Genomics

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2

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3

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Plants Resources Development, School of Bioengineering, Chengdu University,

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Chengdu, Sichuan 610106, China

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‡ These authors equally contributed to this work.

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*Corresponding Author: Xiang-Qun (Sean) Xie, MBA, Ph.D.

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Professor of Pharmaceutical Sciences/Drug Discovery Institute

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Director of CCGS and NIDA CDAR Centers

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335 Sutherland Drive, 206 Salk Pavilion

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University of Pittsburgh

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Pittsburgh, PA15261, USA

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412-383-5276 (Phone)

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412-383-7436 (Fax)

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Email: [email protected]

College of Life Science, Leshan Normal University, Leshan, Sichuan 614004,China School of Pharmacy and Bioengineering; Key Laboratory of Medicinal and Edible

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ACS Paragon Plus Environment


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ABSTRACT: Cannabinoid receptor 2 (CB2), a G protein-coupled receptor (GPCR), is a

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promising target for the treatment of neuropathic pain, osteoporosis, immune system,

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cancer, and drug abuse. The lack of experimental 3D CB2 structure hindered not only the

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development of conformational differences study between the inactive and active CB2

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but also the rationale discovery of novel functional compounds targeting CB2. In this

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work, we constructed both the inactive and active CB2 model by homology modeling.

30

Then we conducted two comparative 100 ns molecular dynamics (MD) simulations on

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the two systems -- the active CB2 bound with both the agonist and G-proteins, and the

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inactive CB2 bound with inverse agonist -- to analyze the conformational difference of

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CB2 proteins and the key residues involved in molecular recognition. Our results showed

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that the inactive CB2 and the inverse agonist kept stable during the MD simulation.

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However, during the MD simulations, we observed the dynamics details about the

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breakdown of the "ionic lock" between R1313.50 and D2406.30, as well as the

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outward/inward movements of trans-membrane domains (TM5, TM6 and TM7) at the

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active CB2 that bound with G proteins and agonist. All these results are congruent with

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the experimental data and recent reports. Moreover, our results indicated that W2586.48 in

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TM6 and residues in TM4 (V1644.56~L1694.61) contributed greatly to the binding of

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agonist based on the binding energy decomposition. While residues (S180-F183) in

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extracellular loop 2 (ECL2) may be importance of the recognition of inverse agonist.

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Furthermore, pharmacophore model and virtual screening were carried out for the

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inactive and active CB2 models in parallel. Among all twelve hits, two compounds

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exhibited the novel scaffolds and can be used as the novel chemical probes for future

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studies of CB2. Importantly, our studies showed that the hits obtained from the inactive

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CB2 model mainly acted as inverse agonist or neutral antagonist at low concentration.

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Moreover, the hits from the active CB2 model also behaved as a neutral antagonist at low

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concentration. Our studies provided new insight into the understanding of structural and

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conformational differences between two states of CB2 and illuminated the structural

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effects for virtual screening and drug design.

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2


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INTRODUCTION

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Seven trans-membrane domain G protein-coupled receptors (GPCRs), which are the

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important molecular sensors in various vital physiological processes throughout the body,

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comprise the largest family of protein targets engaged in drug discovery. Increasing

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number of solved co-crystal structures of GPCRs have emerged to illustrate the structural

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basis of biochemical functions of the super-family and assist the discovery of novel

59

therapeutic drugs. 1

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For each GPCR, there is a distinctive orthosteric binding site for its endogenous

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ligands.2 This binding site can be bound with orthosteric ligands (either native or

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synthetic ligands),3 including agonists (full/partial), neutral antagonists, and inverse

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agonists. An agonist can bind to and activate the receptor, producing a biological

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response. Inverse agonist also can bind to the same pocket as an agonist, but it can induce

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an opposite pharmacological response of an agonist. A neutral antagonist has no activity

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in the absence of an inverse agonist or agonist but can block the action of either an

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agonist or a inverse agonist.

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Two GPCRs, the principal cannabinoid (CB) receptors CB1 and CB2, are critical

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components of the endogenous CB (endocannabinoid) signaling system, which are

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involved in a variety of physiological processes including appetite, pain-sensation, mood,

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and memory.4 Both CB1 and CB2 receptors are coupled through Gi/o proteins, negatively

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to adenylate cyclase and positively to mitogen-active protein kinase. CB1 receptors,

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expressed most densely in some brain regions, are supposed to mediate several

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psychoactive effects of ligands, while CB2 receptors, mainly distributed in immune cells

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and neurons, play roles in cytokine release modulation.5 Recently, Xi's group reported

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that CB2 receptor can modulate midbrain dopamine neuronal activity and dopamine-

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related behavior in mice, indicating that CB2 is a promising target for the treatment of

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drug abuse.6

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There are few experimental data about CB1 and CB2 structures, which is mainly

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because of the inherent difficulties in isolating sufficient purified protein for the 3



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requirement of quality analysis by X-ray crystallography and nuclear magnetic resonance

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(NMR) spectroscopy.7 Absence of crystal structures of protein-ligand complexes makes

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computer-aided homology modeling together with site-directed mutagenesis studies

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increasingly important for facilitating the discovery and development of new ligands for

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cannabinoid receptors. Several three-dimensional crystal structures of GPCRs have been

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used to construct CB-receptor homology models by different groups, including rhodopsin,

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A2AAR, and β2AR.8,

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Reggio et al.10 for the inactive CB2 model, based on the CB2 sequence α-helical

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periodicity detected by Fourier Transform Analysis. Gouldson et al.11 generated a CB2

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model based on previous rat β2AR model, in which a docking position in their model

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resembled the results in the mutagenesis studies. Xie and his coworkers 12-14 constructed a

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comparative 3D CB2 model in 2003 based on the first class A GPCRs' crystal structure-

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bovine rhodopsin.15 In 2006, Other CB2 comparative models based on rhodopsin was

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clarified by Tuccinardi et al.16, Stern et al.17, and Raduner et al.18 Our previous work13

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showed that the constructed CB2 model built by β1AR/β2AR/D3R obtained the better

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predictions for prescreen than the models built by other seven GPCRs' crystal structures,

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involving SMO, bovine rhodopsin, CXCR4, M2MAR, A2AAR, H1R, and S1P. Therefore,

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in the present work, we used the inactive CB2 model that constructed according to β2AR.

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However, most of the currently reported CB2 models are actually the inactive CB2 model

100 101

9

The first 3D CB2 homology model was reported in 1999 by

as built on the reported inactive GPCR receptors. It is reported19,

20

that there are multiple states of GPCRs, including inactive,

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intermediate, and active states. The inactive and active states are more stable during the

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protein dynamics in GPCRs' function. However, the intermediate state of GPCR

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maintains a short time and account for a little proportion in the entire structural ensemble.

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In order to effectively compare with the previous abundant experimental and modeling

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data, here we mainly focused on two stable states, involving the active and inactive states

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of CB2. Switch between the two states results in a sequenced physiological or

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pharmacological process through binding with agonists and the G protein partners

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sequentially. Many groups tried to elucidate the differences of GPCRs in different states

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using computational methods combined with bioassays. Bhattacharya et al.21 studied the 4


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inactive state of GPCR and the ligand-stabilized β2-adrenergic receptor (β2AR)

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conformations in the presence of five different agonists with varied efficacy. They found

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that the binding sites in the inactive and the active model were very similar to each other,

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but the residues had some slight differences that triggered the outward movement of

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intracellular end of TM5 and TM6, inducing a large conformational change in the

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cytoplasmic interfaces, which was implicated with the G protein activation. Daga et al.22

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conducted a molecular dynamics (MD) simulation for the active-state nociceptin receptor

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(NOP) model and the inactive-state model in comparison. Their results indicated that

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NOP activation involved some activation micro switches in the intracellular end of TM3

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and TM6, which was also reported in Bhattacharya’s results. Recently, Mnpotra et al.23

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studied the structural basis of cannabinoid CB2 coupled with Gi protein. Their CB2-G

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proteins model was constructed according to the β2AR-G proteins template

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(PDB:3SN6).24 They illustrated that the second intracellular loop of CB2 played a key

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role in the formation of the Gi complex, which were supported by their mutated data. In

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the present work, we adapted the reported templates and protocols that were used by

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Mnpotra23 and co-workers to construct the active CB2 model.

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In the present work, we systematically analyzed the structural and conformational

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differences of CB2 in two states and illuminated the structural effects for virtual

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screening and drug design. Therefore, we built the inactive and active states CB2 models.

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MD simulations that carried out for these two models showed no significant change in the

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ligand binding sites but several conformational switches, including the outward

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movement of intracellular end of trans-membrane and loops, which was closed related to

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the G proteins activation. We also performed virtual screening by using these two CB2

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models. Our results showed that both these two CB2 models could yield the hits.

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However, the hits from the inactive CB2 acted as the inverse agonist or neutral

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antagonist, while the hit obtained from the active CB2 behaved as the neutral antagonist.

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MATERIALS AND METHODS

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Homology Modeling and Structure Validation of CB2. In the present work, we built

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two conformations of CB2, including the inactive and active states. The inactive

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conformation of CB213 was constructed according to the crystal structure of inactive

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β2AR (PDB entry: 2RH1; resolution, 2.40 Å)25 bound with partial inverse agonist

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carazolol. To represent the CB2-active state, we built the CB2 model based on the X-ray

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crystal structures of β2AR bound with both agonist and G proteins (PDB entry: 3SN6;

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resolution, 3.20 Å).24 Moreover, the crystal structure of Gαi1β1γ2 (PDB entry: 1GP2;

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resolution, 2.30 Å)26 was used to construct the complex of the active state CB2 and

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Gαi1β1γ2. All the crystal structures were downloaded from the Protein Data Bank

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(http://www.pdb.org/pdb/). SYBYL-X 1.327 was used to repair all the residues and

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minimize the energy.

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The full sequence of human CB2 receptor (CNR2_HUMAN or P34972, 360 residues)

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and the full sequence of guanine nucleotide-binding protein G (i) subunit alpha-1 (Gαi1 or

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P10824,

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(http://www.uniprot.org/uniprot/). In the present work, we truncated the residues of CB2

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before A351.34 at the N terminal as well as the residues after H3127.66 at the C terminal.

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The sequence alignments and homology modeling were based on this generated sequence

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of CB2 using our reported protocol.14 The disulfide bridge between C1744.66 and C1794.71

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in ECL211 was also patched for both the inactive and active model of CB2. For the crystal

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structure of Gαi1, both the extreme C-terminus (residue 1-4, "MGCT") and N-terminus

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(residue 349-354, "KDCGLF") were unresolved. However, we built the full sequence of

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Gαi1 in this work.

161 162 163

354

residues)

were

retrieved

from

the

UniProtKB

/

Swiss-Prot

Modeller 9.1228 was used to construct both the structure of CB2 and the whole one of Gαi1. The detailed parameters can be found in our previous publications.3, 12-14, 29 Once the 3D models were generated, SYBYL-X 1.3 was used to perform the energy

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minimizations.3,

12-14, 29

Then proSA-web Z-scores30 and PROCHECK Ramachandran

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plots31 were used for the validation of all models.

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Construction of CB2-G Proteins-GDP Complex. The relative orientations of active

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CB2 and Gαi1β1γ2 were based on the crystal structure of β2AR bound with Gαsβ1γ2.

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Superimposition and adjustment were applied to obtain an accurate complex. Briefly,

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CB2 were superimposed onto the α carbon (Cα) atoms of β2AR (specially for the N1.50,

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D2.50, R3.50, W4.50, and W6.48), while Gαi1β1γ2 was superimposed to the Gαsβ1γ2. In order to

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relieve steric clashes between CB2 and Gαi1β1γ2, we translated Gαi1β1γ2 along the z-

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direction.23 The detailed interactions of G-proteins and GDP was revealed in the work of

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Wall et al.26

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Data Set of Agonists and Inverse Agonists for CB2. 879 chemical structures and

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their bioactivities (IC50 or EC50 value) for CB2 were retrieved from ChEMBL

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(https://www.ebi.ac.uk/chembl/). We chose 833 CB2 agonists (EC50 for CB2 lower than

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2 µM) and 46 inverse agonists (IC50 values for CB2 lower than 8 µM) for our studies.

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Docking Study of Ligands into CB2 Receptor or CB2-G-Protein Complex. The

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docking program Surflex-Dock GeomX (SFXC) in SYBYL-X 1.3 was applied to

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construct3, 12-14, 29 the complex between receptor and ligand, in which the total score was

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expressed in -log10 (Kd).32 We used the MOLCAD module implemented in SYBYL-X

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1.3 to explore the potential binding pocket for CB2. The main protocols or parameters of

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docking were displayed in our previous publications.

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Molecular Dynamics Simulations in Lipid-Water Box. We carried out the molecular

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dynamics (MD) simulation for the active CB2 bound with both the agonist and G-

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proteins. For comparison, we also performed the MD simulation for the inactive

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conformation of CB2 bound with inverse agonist- SR144528.

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Special caution was applied to histidine (H) residues, which can be ionized at pH 7.40.

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We used VEGA ZZ 2.4.033 and PROPKA 3.134 software for the prediction of the pK

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values for the protein. For the CB2 models, all His were not protonated, since the

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calculated pK values were lower than 7.40 (from 4.0 to 7.0). Side chains of Asp, Glu,

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Arg, and Lys were charged (Asp-, Glu-, Arg+, and Lys+) in all simulations.

7



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VMD35 program was applied to embed the structure of receptor bound with ligands

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into a pre-equilibrated and periodic structure of 1-palmytoyl-2-oleoyl-sn-glycero-3-

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phosphatidylcholine (POPC). We eliminated the lipid molecules within 3 Å of the

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protein. Then we inserted it into a water box with TIP3P36 water moleculse and

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eliminated the water within 3 Å of the protein.

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The simulation boxes (CB2-SR144528 / CB2-WIN55212-2-G proteins-GDP,

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respectively) contained the CB2 model, 167 / 339 lipid molecules, 13441 / 47478 water

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molecules, 0 / 5 sodium ions and 11 / 0 chloride ions for a total of 67165/ 203973 atoms

201

per periodic cell. The box sizes were 94×94×88 Å3 / 117×112×148 Å3. The first

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minimization with 100,000 steps was performed with the fixed protein, while the second

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minimization with also 100,000 steps was carried out with flexible protein. Then 1.0 ns

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MD of heating and equilibration were performed from 0 to 300 K.

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Starting from the last frame of the equilibration, the molecular dynamics simulations

206

with 100 nano-second (ns) were carried out. The NAMD package37 (version 2.9b1) with

207

AMBER38-40 force field was applied to all the MD simulation. Particle Mesh Ewald41

208

(PME) method with a 12 Å non-bonded cutoff and a grid spacing of 1 Å per grid point in

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each dimension was used to calculate the Electrostatics. A smooth cutoff (switching

210

radius 10 Å, cutoff radius 12 Å) was used to calculate the van der Waals energies. The

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temperature and pressure were kept constant using a Langevin thermostat (300 K) and a

212

Langevin barostat (1 atm), respectively. The time step was set to 1 femtosecond (fs). The

213

data was saved every 10 pico-second (ps) for analysis. AMBER tools and VMD software

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were applied to analyze all the trajectories.

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Molecular Mechanics/ Generalized Born Surface Area (MM/GBSA) Calculation.

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MM/GBSA42 method was used to calculate the binding energies of CB2 receptors in

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inactive and active states under the treatment of inverse agonist SR144528 and agonist-

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WIN55,212-2, respectively. It was also applied to assess the G-protein coupling energies

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of activiated CB2 with G proteins, and the G proteins with GDP. 100 snapshots were

220

extracted from the trajectory every 400 ps from 60 to 100 ns in order to calculate the

221

mean binding energy. The formula was used as follows: 8


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△Ebind=△EMM+△ESOL=△EMM+△EGB+△ESA

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where △ Ebind is the binding energy, and △ EMM denotes the sum of molecular

224

mechanical energies in vacuo and can be further divided into the contributions from

225

electrostatic, van der Waals, and internal energies. This term could be computed through

226

molecular mechanics method. △ESOL is the solvation energy which includes the polar

227

solvation energy (△EGB) calculated with the Generalized Born (GB) approximation

228

model43, 44 and the non-polar part (△ESA) obtained by fitting solvent accessible surface

229

area (SASA)45 with the LCPO model.46, 47 Additionally, the energies of each residue were

230

decomposed into backbone and side-chain atoms. Through energy decomposition, we can

231

analyze the contributions of the key residues to the binding.

232

Pharmacophoric Filtering and Virtual Screening Using Inactive and Active CB2

233

Models. The GALAHAD program in SYBYL was used to construct a five-point

234

pharmacophore model for WIN55,212-2 (agonist) and SR144528 (inverse agonist),

235

respectively (So, we had two pharmacophore models). These two pharmacophores were

236

used to filter the National Cancer Institute chemical library (NCI2011, 210,000

237

compounds) to yield two compound libraries that satisfied the corresponding geometric

238

or physicochemical constraints. The parameters used for the generation of the

239

pharmacophore models and UNITY search in SYBYL were described in our previous

240

publication.3 After filtering the NCI database, we performed the virtual docking

241

screening against the corresponding optimized 3D chemical compounds library using

242

either the inactive- or active CB2 model, respectively (So, we had two sets of hits). The

243

detailed docking parameters can be found in our previous publications.3, 13, 14

244

Validation by Radioligand Competition Binding Assay. CB ligand competition

245

binding assay was carried out as described previously.48,

246

ligands were diluted in binding buffer, supplemented with 10% dimethyl sulfoxide and

247

0.4% methyl cellulose. Each assay plate well contained a total of 200 µL of reaction

248

mixture comprised of 5 µg of CB1 (or CB2) membrane protein, labeled [3H] CP-55,940

249

ligand at a final concentration of 3 nM and the unlabeled ligand at its varying dilutions as 9


49

Briefly, non-radioactive


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250

stated above. Plates were incubated at 30 oC for 1 h with gentle shaking. The reaction was

251

terminated by rapid filtration through Unifilter GF/B filter plates using a Unifilter Cell

252

Harvester (PerkinElmer). After the plate was allowed to dry overnight, 30 µL

253

MicroScint-0 cocktail (PerkinElmer) was added to each well and the radioactivity was

254

counted by using a PerkinElmer TopCounter. All assays were performed in duplicate and

255

data points represented as mean ± S.E.M. Bound radioactivity data was analyzed for Ki

256

values using non-linear regression analysis via GraphPad Prism 5.0 software. The

257

saturation binding of [3H] CP-55,940 to the membrane proteins was performed as

258

described previously.49,

259

incubated with increasing concentrations of [3H] CP-55,940 (0.05-4 nM) in 96-well

260

plates at 30 °C with slow shaking for 1 h. The incubation buffer was composed of 50 mM

261

Tris-HCl (pH 7.4), 5 mM MgCl2, 2.5 mM EGTA and 0.1% (w/v) fatty acid free BSA.

262

Ligand was diluted in incubation buffer supplemented with 10% dimethyl sulfoxide and

263

0.4% methylcellulose. Non-specific binding was determined in the presence of unlabeled

264

CP-55,940 (5,000 nM). The reaction was terminated and the radioactivity was counted as

265

stated above. Non-linear regression analysis revealed the receptor density (Bmax) and the

266

equilibrium dissociation constant (Kd) values of [3H] CP-55,940 for the CB2 receptor.

50

Briefly, the CB1 (or CB2) membrane fractions (5 µg) were

267

Validation by cAMP Assay. Cellular cAMP levels were measured according to

268

reported method with modifications using LANCE cAMP 384 kits (PerkinElmer).50, 51

269

The assay is based on competition between a Europium-labeled cAMP trace complex and

270

total cAMP for binding sites on cAMP-specific antibodies labeled with a fluorescent dye.

271

CB2 receptor wild type (WT) transfected CHO cells were seeded in a 384-well white

272

ProxiPlates with a density of 2000 cells per well in 5 µL of RPMI-1640 medium

273

containing 1% dialysed FBS, 25 mM HEPES, 100 µg/mL pennicilin, 100 U/ml

274

strepmicin and 200 µg/mL of G-418. After culture overnight, 2.5 µL of cAMP antibody

275

and RO20-1724 (final consentration50 µM) in stimulation buffer (DPBS 1x, containing

276

0.1% BSA) was added to each well, followed by addition of either 2.5 µL compound or

277

forskolin (final 5 µM) for agonist-inhibited adenylate cyclase (AC) activity assay. After

278

incubated at room temperature for 45 min, 10 µL of detection reagent was added into

279

each well. The plate was then incubated for 1 h at room temperature and measured in 10


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280

Synergy H1 hybrid reader (BioTek) with excitation at 340 nm and emission at 665 nm.

281

Each cAMP determination was made via at least two independent experiments, each in

282

triplicate. EC50 values were determined by nonlinear regression, dose–response curves

283

(GraphPad Prism 5).

284 285

RESULTS AND DISCUSSION

286

CB2 Ligand Dataset for Validating the 3D CB2 Models. 10 inactive and 10 active

287

CB2 models were obtained from homology modeling, respectively. Once the 3D models

288

were generated, energy minimizations were carried out by using SYBYL-X 1.3. The CB2

289

models were also validated by using proSA-web Z-scores30 and PROCHECK

290

Ramachandran plots.31 The best 3 inactive CB2 and the best 3 active CB2 conformations

291

were used to perform the prescreen against the data set of 879 CB2 compounds. These

292

compounds were retrieved from ChEMBL (https://www.ebi.ac.uk/chembl/). Among them,

293

833 were CB2 agonists (EC50 for CB2 lower than 2 µM) and 46 were inverse agonist

294

(IC50 values for CB2 lower than 8 µM) for the studies.

295

In order to test the rationality of the active and inactive CB2 models generated from

296

homology modeling, we carried out the prescreen of three inactive/active CB2 models

297

against a data set that included agonists (samples: 833, EC50 values: 0.14-1,580 nM) and

298

inverse agonists (samples: 46, IC50 values: 0.078-7,550 nM), respectively. Figure 1 shows

299

the best correlations between the docking scores and the corresponding pEC50 / pIC50

300

values of the agonists / inverse agonists within the inactive/active CB2 model,

301

respectively. The correlations of other two inactive / active CB2 models can be found in

302

Figure S1 in the Supporting Information. In term of the best inactive CB2 model, the

303

docking score ranges of agonists and inverse agonists were from 2.435 to 11.017 and

304

from 4.787 to 10.001. While for the best active CB2 model, the ranges of docking scores

305

for agonist/ inverse agonists were 0.398-10.541 / 1.904-8.188, respectively. We found

306

that the docking scores of ligands in Figure 1 showed the poor correlation with the

307

experimental data of pEC50 for agonists and pIC50 for inverse agonists. Part of this poor

308

correlation may be because the docking scores of ligands cannot accurately reflect their 11



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

309

activity data, and the docking score should not be regarded as the criteria for determining

310

agonist or inverse agonist. This finding was also supported by the poor correlation (0.27)

311

between the docking scores and Ki values of our ten hits discussed below. Moreover,

312

after examining the models, we found that few residues involved in the potential binding

313

pocket were in the unflavored region. For example, Leu182 in the inactive model and

314

Phe281 in the active one pointed to the binding pocket and lowered the docking scores

315

(steric restrain). These reasons may lead to the poor relationship between the docking

316

scores and their activity data.

317

Convergence Parameters of MD. In order to validate the homology model, the

318

binding pose of agonist/ inverse agonist, and the interactions as well as conformational

319

changes of CB2 with/without G proteins, we carried out two comparative MD

320

simulations with 100 for these two CB2 systems, respectively.

321

Figure S2a and S2b in the Supporting Information show the overall parameters of MD

322

for the inactive CB2 system. This system reached equilibrium after 5 ns with an average

323

root mean square deviation (RMSD) of 3.9 Å. Notably, the radius of gyration of inactive

324

CB2 kept stable at ~20.6 Å during the MD. For the active CB2 bound with G proteins,

325

the radius of gyration (see Figure S2c in the Supporting Information) maintained at

326

21.4±0.1 Å, which was slightly bigger than that in the inactive CB2. In addition, the

327

radius of gyration of G protein maintained at 30.9±0.2 Å. The RMSD value of the active

328

CB2 kept stable at 3.6 Å after 40 ns (Figure S2d in the Supporting Information).

329

Then we compared the root-mean-square fluctuation (RMSF) distribution of the

330

inactive and active CB2, as shown in Figure S3 in the Supporting Information. We found

331

that both the RMSD and RMSF of active CB2 were lower than those of the inactive one

332

due to the active CB2 coupling with G proteins (steric restrain). All the RMSD and

333

RMSF showed that our systems were very stable during the MD simulation.

334

Comparison of Inactive and Active CB2 Conformations after MD. We then aligned

335

the inactive CB2 (green) with the active one (cyan) after 100 ns MD, as shown in Figure

336

2a (from extracellular side) and 2b (from intracellular side). All the extracellular and 12


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337

intracellular loops were removed for clarification. Conformational change can be

338

observed in the active CB2 when binding with G proteins, especially for the contact

339

surface portions. We believed that the structural differences between the active and

340

inactive CB2 during the MD simulations, mainly caused by the significant difference in

341

the templates that we used for the homology modeling. However, the results from our

342

MD simulations showed more dynamic details. Our results showed that the intracellular

343

side of TM6 at the active CB2 moved outward the central axis of the receptor in

344

comparison to the inactive one, while the intracellular side of TM7 at the active CB2

345

moved toward the central axis. In addition, no significant movements were observed from

346

the extracellular side, but the residues endured conformational changes that may trigger

347

these outward movements (See the following discussion). The outermost edge residues

348

for each trans-membrane domain were given for reference. All these results were

349

congruent with the recent reports of GPCRs.52, 53

350

By comparing the intracellular side between the inactive CB2 and the active one, we

351

found that the potential binding pocket of the active CB2 for coupling with G proteins

352

was mainly formed by TM2, TM3, TM5, TM6, and TM7. A trumpet-shaped pocket

353

toward intracellular direction was defined using HOLE54 software, as shown in Figure S4

354

in the Supporting Information. The potential residues interacting with G proteins and the

355

distance between residual pairs were also shown in Figure S4 in the Supporting

356

Information. Our MD results showed that the radius of this binding pocket for G proteins

357

became larger and moved from the middle part to the intracellular side of TM due to the

358

movement of intracellular side of TM5-TM7.

359

“Ionic Lock”. Our previous study13 and other groups' work55-58 indicated that there

360

was a stable "ionic lock" between R1313.50 and D2406.30 in the inactive CB2. Analysis of

361

the distance change of “ionic lock”55-58 can partially reflect the structural transformation

362

for both the inactive and active CB2. The templates that used for the construction of CB2

363

model led to the significant differences between the starting point of “ionic lock”.

364

However, our results and discussions in this section showed more dynamic information

13



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Page 14 of 35

365

about the “ionic lock” during the MD simulations between the inactive CB2 and the

366

active one.

367

For the inactive CB2, an appropriate “ionic lock” distance (R1313.50-NH2 to D2406.30-

368

OD2) can be found in the starting stage (0~10 ns) during the MD simulation (~3 Å), as

369

shown in Figure 3a. Then this interaction endured a large change in lipid soluble

370

environment (from 10~60 ns with the distance from 3 to 9 Å). After 60 ns, these two

371

residues formed a stable "ionic lock" with a distance of 3.0±0.2 Å. The corresponding

372

“ionic lock” angle (R131-NH2-HH22--D240-OD2) versus MD simulation time was

373

shown in Figure 3b. Our results showed that the "ionic lock" angle was about 30 degree

374

at 0~10 ns, then fluctuated from 30 to 125 degree from 10 to 60 ns MD. After 60 ns MD,

375

the "ionic lock" angle kept stable at 157.42 degree, which shared the same trend with the

376

distance.

377

For the active CB2 system, our results showed that the "ionic lock" distance (Figure

378

3c) gradually reduced from 20 to 16 Å (0~10 ns), then slowly increased and reached a

379

peak at 26 Å (10~60 ns), and finally kept stable at 23 Å (80~100 ns). The distance variety

380

may have a good correlation with the outward tilting of TM6 and the dramatic outward

381

opening of loop between TM5 and TM6. The "ionic lock" angles for active CB2 can be

382

found in Figure 3d, where the angle gradually decreased and kept stable at 55 degree after

383

33 ns MD simulation.

384

Binding Energy Analysis. In this section, the MM/GBSA38,

59,

60

energy

385

decomposition approach was applied to provide information of key residues that were in

386

favor of molecular recognition in greatest extent from the energy point of view.

387

First, our results showed that the calculated binding energy between the inactive CB2

388

and inverse agonist SR144528 was -23.97 kcal/mol. For comparison, we also docked

389

SR144528 into the active CB2 model after MD as shown in Figure S5a in the Supporting

390

Information, with the binding energy of -15.75 kcal/mol. Figure 4a shows the

391

contributions of different residues to the binding energy and, Figure 4b shows the binding

392

pose of inverse agonist SR144528. The RMSD of SR144528 during the MD simulation 14


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393

was 0.7 Å. Residues in six major domains contributed to the binding with SR144528,

394

including C892.59~F942.64, L1103.29~F1173.36, S180-F183 in ECL2, Y1905.40~F1975.47,

395

V2616.51~L2626.52 and F2817.34~L2897.42. However, the binding energy of the active CB2

396

bound with agonist WIN55, 212-2 was -19.44 kcal/mol. For comparison, we also docked

397

WIN55, 212-2 to the inactive CB2 model after MD (Figure S5b in the Supporting

398

Information), with the binding energy of -7.63 kcal/mol. Figure 4c shows the

399

contributions of different residues to the binding energy and, Figure 4d shows the binding

400

pose of WIN55, 212-2. Our results showed that the agonist- WIN55, 212-2 kept stable

401

(~1.0 Å) during the MD simulation, indicating the binding pose predicted by our docking

402

was reasonable. Residues in six major domains contributed to the binding with ligand,

403

including C892.59~F942.64, L1103.29~F1173.36, V1644.56~L1694.61, W1945.44~F1975.47,

404

V2616.51~L2626.52, and F2817.34~L2897.42.

405

By analyzing the contributions of residues for both inverse agonist and agonist, we

406

found that most of the residues were the same, including V1133.32, W1945.44, and

407

F1975.47. Among them, V1133.32 was importance of the ligand recognition of CB2.13, 56

408

Moreover, W1945.44 in TM5 was crucial for the CB2 ligand binding and adenylyl cyclase

409

(AC) activity.61 Replacement of F197 with Valine resulted in a 14-fold decrease of

410

WIN55,212-2’s binding affinity to CB2, but this replacing showed no effect on the

411

association of hU-210, CP55940, or AEA.10 However, our results showed that W2586.48

412

contributed greater to the binding of agonist-WIN55, 212-2 (-2.18 kcal/mol) than that of

413

inverse agonist-SR144528 (-0.08 kcal/mol). W2586.48 was highly conserved in GPCRs,

414

supporting a proposal that its rotameric state had a role in CB2 activation.53 This result

415

indicated that W2586.48 may have the role for distinguishing of agonist from inverse

416

agonist, which was congruent with recent reports.52, 53 Importantly, our results showed

417

that residues in TM4 (V1644.56~L1694.61) contributed greatly to the binding energy of

418

agonist WIN55, 212-2 (Figure 4c), while extracellular loop 2 (ECL2, S180-F183) made

419

the contribution of binding to the inverse agonist SR144528 (Figure 4a). Our results

420

showed that many residues endured conformational changes that related to the

421

movements of trans-membrane domains. All these results helped us to understand the

422

differences of the recognition between agonist and inverse agonist. 15



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423

Moreover, we also analyzed the molecular recognition of GDP and G proteins (Figure

424

S6 in the Supporting Information). The results indicated that GDP mainly interacted with

425

Gα subunit in G proteins, with a total binding energy of -38.86 kcal/mol. The most

426

favorable areas of residues for binding were G42~K46, L175~R178, and N269~K270.

427

Among them, the most important residues included R178, K46, and K270, with the

428

binding energy of -14.21, -5.16, and -2.63 kcal/mol, respectively. Additionally, there

429

were seven relatively stable hydrogen bonds of GDP with D150 and R176 in Gα subunit,

430

especially R176 with a residue binding energy (-2.75 kcal/mol) in favor of binding. On

431

the other hand, both D150 and GDP were negative charged, and the binding energy

432

between them was merely -0.02 kcal/mol, which indicated that D150 was a residue that

433

can only contribute to the specific binding between G proteins and GDP/GTP. All these

434

residues were in accordance with the recent report of Louet et al.62

435

Finally, we looked into the binding energy between CB2 and G proteins. The

436

calculated binding energy between G proteins and the active CB2 was -50.01 kcal/mol.

437

During the long time MD simulation, CB2 moved towards and interacted with Gβ

438

subunit, forming more interactions with Gα/Gβ protein. The following residues and

439

regions played significant roles in binding: I58~R66, L213~H217, and R131~R147 in

440

active CB2, E25~A31 and T340~D354 in Gα subunit, H311~D312 in Gβ subunit (Figure

441

S7 in the Supporting Information). Importantly, two hydrogen bonds played key roles in

442

the recognition, including a hydrogen bond between K215 (CB2, -2.1 kcal/mol) and

443

D350 (Gα subunit, -1.59 kcal/mol) and a hydrogen bond between R147 (CB2, -7.89

444

kcal/mol) and E25 (Gα subunit, -2.72 kcal/mol). The similar results can be found in the

445

crystal structure of β2 receptor bound with G-proteins.25 Additionally, one non-polar

446

interaction between L135 (CB2, -3.06 kcal/mol) and I344 (Gα subunit, -1.05 kcal/mol)

447

was also important for the system. Last but not least, interactions between Gβ subunit and

448

active CB2 were mainly composed of long-range electrostatic interactions, in particular,

449

between S61 (CB2, -1.94 kcal/mol) and H311 (Gβ subunit, -4.43 kcal/mol), and between

450

Q63 (CB2, 0.11kcal/mol) and D312 (Gβ subunit, -0.97 kcal/mol). In the crystal structure

451

of β2 receptor (PDB ID: 3SN6),25 β2 also bound with D312 in Gβ subunit of Gs proteins,

16


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452

suggesting that the interaction between the active CB2 after MD simulation with multiple

453

β-sheet domain in Gβ subunit was reasonable.25

454

Pharmacophore Model and Virtual Screening. Based on the binding pose of

455

compounds and the contribution of residues at CB2, we used GALAHAD program in

456

SYBYL to construct the pharmacophore model of SR144528 (inverse agonist) and

457

WIN55,212-2 (agonist), respectively. As shown in Figure 5a and 5b, we defined one H-

458

bond acceptors (A) and four hydrophobic or hydrophobic aromatic centers (H) for these

459

two compounds. The distance restrictions of pharmacophore models were shown in Table

460

1 and Table 2. For filtering the database using the pharmacophore model of SR144528,

461

we required a four-point pharmacophore match allowing omission of either H1 or H2 or

462

H3 interactions, i.e., H1-H2-A1-H4; H1-H3-A1-H4; or H2-H3-A1-H4. For filtering the

463

database using the pharmacophore model of Win55212-2, we used a five-point

464

pharmacophore match allowing the H1-H2-H3-A1-H4. These two pharmacophore were

465

used to filter the National Cancer Institute chemical library (NCI2011, 210,000

466

compounds) with compounds that satisfied specific geometric or physicochemical

467

constraints of the defined pharmacophores, respectively. For the pharmacophore model of

468

the inverse agonist, a set of 210,000 NCI compounds was thereby narrowed down to

469

23,243 (deleted duplicate) compounds. For the pharmacophore model of the agonist, an

470

optimized 3D chemical compounds library with 10,337 compounds was obtained.

471

Virtual screening was carried out for both the inactive CB2 and active one against their

472

corresponding

473

conformations (inactive/active) of CB2 with the lowest energy during our MD

474

simulations were used to perform the virtual screening in parallel. We selected top 15

475

compounds for each screening for further bioassay test. For comparison, we also selected

476

one more representative conformation in the inactive form to perform the virtual

477

screening. Our results showed that the 36 out of top 50 compounds (72%) were the same

478

in two independent screenings of inactive CB2 models.

optimized

library

(23,243/10,337)

compounds.

Two

different

479

Novel Inhibitors from In-Silico Screening for Characterizing the Inactive and

480

Active CB2 Models. For an initial evaluation of the utility of the model for identifying 17



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481

novel structures with CB2 activity, we examined a panel of 30 compounds from NCI

482

database. Among the 30 compounds, 10 compounds yielded the CB2 binding affinity

483

with Ki values all lower than 3 µM. Eight compounds were the hits obtained from the

484

inactive CB2 model, including NSC93299, NSC162677, NSC273936, NSC273939,

485

NSC273940, NSC296950, NSC402300, and NSC745454, while other two compounds

486

(NSC618804 and NSC669611) were obtained from the active CB2 model.

487

Among these 12 compounds, two compounds, NSC27393951 and NSC273940

488

inhibited [3H]CP-55,940 binding to CB2 with Ki values of 0.192 and 0.195 µM,

489

respectively. NSC273936, NSC402300, and NSC618804 inhibited [3H]CP-55,940

490

binding to hCB2 with Ki values of 0.346, 0.398 and 0.656 µM, respectively. Other

491

compounds yielded Ki values from 0.923 to 2.360 µM. All the structures, docking scores

492

and the Ki values can be found in Table 3. Among all the active ligands, NSC273936,

493

NSC273939, and NSC273940 (obtained from H1-H2-A1-H4) shared the similar

494

structure, while the other nine compounds differed from each other. Some compounds

495

exhibited the novel scaffolds and can be used as the novel chemical probes for future

496

studies of CB2, including NSC93299 and NSC162677 (obtained from H1-H3-A1-H4). In

497

addition, we also predicted the correlation coefficient between docking scores and Ki

498

values of these ten hits, with the R-value of 0.27. This finding supported that the docking

499

scores of ligands cannot accurately reflect their activity data, and the docking score

500

should not be regarded as the criteria for determining agonist or inverse agonist.

501

Functional activities of compounds were investigated by using a cell-based LANCE

502

cAMP assay, providing a useful method to distinguish inverse agonists, agonists, and

503

neutral antagonists. Based on the raw counting data of the binding at CB2, two

504

compounds (NSC296950 and NSC402300) were selected from the inactive CB2 model,

505

one compound (NSC669611) from the active CB2 model, and two control compounds

506

(CP-55,940 and SR144528) to perform the cAMP assay.

507

As shown in Figure 6, reduction of the LANCE signal occurred with the concentrations

508

of NSC296950 (green) and NSC402300 (cyan) increasing. Increasing forskolin-induced

509

cAMP production suggested that NSC296950 acted as the inverse agonist. However, for 18


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510

NSC402300 (cyan), we suggested it behaved as a neutral antagonist at the concentration

511

below 10 µM while exhibiting an inverse agonist at concentration above 10 µM. It was

512

worth to note that these two compounds were obtained from the inactive CB2 model.

513

However, for NSC669611 that obtained from the active CB2 model (highlighted in

514

orange), our results showed that it behaved similar to NSC402300. These results were

515

congruent with the recent reports: inverse agonist could bind to and stabilize the inactive

516

GPCRs, while the neutral antagonist could bind to both the inactive and active GPCRs.63,

517

64

518

In summary, for the inactive CB2 model, we screened the inverse agonists. However,

519

for the active CB2 model, we only screened the neutral antagonist but not agonist. Here,

520

we performed the molecular docking of active/inactive CB2 models after MD simulations

521

against the same compounds library of 879 compounds (833 agonists and 46 inverse

522

agonists), as shown in Figure S8 in the Supporting Information. Our results showed that

523

even after the refinement by the MD simulations, the correlation between the docking

524

scores and pEC50/pIC50 values had some improvements but not significant. It can

525

therefore be speculated that the docking scores may be not good enough for the criteria of

526

selecting the potential hits. The binding free energy (generated by Glide or AutoDock)

527

should be introduced for the future work. Moreover, the known compounds should be

528

included in the compound library, so the binding energies (or the docking scores) of these

529

known compounds can be treated as the control to help us select the hits. Finally, we

530

think that it my not be accurate enough to generate a five-points pharmacophore model

531

only by the WIN55,212-2. We are in process of generating pharmacophore models by

532

incorporating additional cannabinoid agonists which will be reported elsewhere.In

533

addition, we continue to optimize our constructed homology models and pharmacophore

534

models and consider the above criteria for designing the agonists for CB2.

535 536

CONCLUSION

19



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

537

In the present work, we constructed both the inactive and active CB2 model by homology

538

modeling. MD simulations and binding energy decomposition were used to analyze the

539

conformational changes of CB2/G protein and the contributions of energy of residues.

540

Most of the results were congruent with our experimental data and recent reports. For

541

further comparisons, we used both the inactive and active CB2 models to perform the

542

virtual screening in parallel. Among all the hits, some compounds exhibited the novel

543

scaffolds and could be used as the novel chemical probes for future studies of CB2.

544

Importantly, our studies showed that the hits obtained from the inactive CB2 model

545

mainly acted as the inverse agonist or netural antagonist. Moreover, the hit predicted by

546

the active CB2 model behaved as the neutral antagonist below the concentration of 10

547

µM. Our studies provided a new insight into better understanding of structural and

548

conformational differences of inactive and active CB2 and illuminated the structural

549

effects for future virtual screening and drug design.

550 551

ASSOCIATE CONTENT

552

Supporting Information

553

Figure S1 shows prescreen validation of the other two inactive and active CB2 models.

554

Figure S2 shows the overall MD parameters of the inactive/active CB2 systems. Figure

555

S3 shows the RMSF distribution for both the inactive and active CB2 receptors. Figure

556

S4 shows the binding hole of the activated CB2 receptor with G proteins. Figure S5

557

shows the binding modes of SR144528 in the active CB2 model and WIN55,212-2 in the

558

inactive one. Figure S6 shows the binding energy contribution of GDP to the association

559

of Gα subunit. Figure S7 shows the optimized binding mode between the active CB2 and

560

G proteins after MD simulation. Figure S8 shows the validation of the active and inactive

561

CB2 models after MD simulations. This material is available free of charge via the

562

Internet at http://www.acs.org.

563

20


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564

AUTHOR INFORMATION

565

Corresponding Author

566

Author to whom correspondence should be addressed: Xiang-Qun (Sean) Xie, email:

567

[email protected]; Tel.: +1-412-383-5276; Fax: +1-412-383-7436.

568

Notes

569

The authors declare no competing financial interest.

570

Acknowledgement

571

The project is supported by funding to Xie laboratory from the NIH NIDA (P30

572

DA035778A1) and NIH (R01 DA025612), and in part by the National Natural Science

573

Foundation of China (11147175, 11247018), the Key Project of Sichuan Provincial

574

Education Bureau (12ZA066), the Project of Leshan Science and Technology

575

Administration (14GZD022).

576 577

REFERENCE

578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596

1. Salon, J. A.; Lodowski, D. T.; Palczewski, K., The Significance of G Protein-Coupled Receptor Crystallography for Drug Discovery. Pharmacol. Rev. 2011, 63, 901-937. 2. Wenthur, C. J.; Gentry, P. R.; Mathews, T. P.; Lindsley, C. W., Drugs for Allosteric Sites on Receptors. Annu. Rev. Pharmacol. Toxicol. 2014, 54, 165. 3. Feng, Z.; Kochanek, S.; Close, D.; Wang, L.; Srinivasan, A.; Almehizia, A. A.; Iyer, P.; Xie, X.Q.; Johnston, P. A.; Gold, B., Design and activity of AP endonuclease-1 inhibitors. J. Chem. Biol. 2015, 8, 79-93. 4. Vemuri, V. K.; Janero, D. R.; Makriyannis, A., Pharmacotherapeutic Targeting of the Endocannabinoid Signaling System: Drugs for Obesity and the Metabolic Syndrome. Physiol. Behav. 2008, 93, 671-686. 5. Bouaboula, M.; Poinot-Chazel, C.; Marchand, J.; Canat, X.; Bourrie, B.; Rinaldi-Carmona, M.; Calandra, B.; Le Fur, G.; Casellas, P., Signaling Pathway Associated with Stimulation of CB2 Peripheral Cannabinoid Receptor. Involvement of both Mitogen-Activated Protein Kinase and Induction of Krox-24 Expression. Eur. J. Biochem. 1996, 237, 704-711. 6. Zhang, H.-Y.; Gao, M.; Liu, Q.-R.; Bi, G.-H.; Li, X.; Yang, H.-J.; Gardner, E. L.; Wu, J.; Xi, Z.X., Cannabinoid CB2 Receptors Modulate Midbrain Dopamine Neuronal Activity and Dopamine-Related Behavior in Mice. P. Natl. Acad. Sci. 2014, 111, E5007-E5015. 7. Marriott, K. S.; Huffman, J. W., Recent Advances in the Development of Selective Ligands for the Cannabinoid CB(2) Receptor. Curr. Top. Med. Chem. 2008, 8, 187-204.

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704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746

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747 748 749 750 751 24


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752

FIGURE LEGEND

753

Figure 1. Validation of the active and inactive CB2 models. The first two figures show

754

the correlation between the calculated docking scores of inactive CB2 model binding

755

with (a) agonists/ (b) inverse agonists and experimental pEC50/ pIC50 values, respectively.

756

(c) and (d) show the same correlations for the active CB2 model.

757 758

Figure 2. The alignment of the inactive (highlighted in green) and active

759

(highlighted in cyan) CB2 after 100 ns MD simulation. (a) Viewing from the

760

extracellular side, (b) viewing from the intracellular side. Loops were removed and the

761

most outer edge residues for each trans-membrane domain were given for reference

762

shown by sticks.

763 764

Figure 3. Distance and angle of the "ionic lock" versus molecular dynamics

765

simulation time at the inactive and active CB2. (a) Distance of ionic lock at the

766

inactive CB2, (b) angle of ionic lock at the inactive CB2, (c) distance of ionic lock at the

767

active CB2, (d) angle of ionic lock at the active CB2.

768 769

Figure 4. Binding energy contribution of CB2 for the association with SR144528 and

770

WIN55,212-2. (a) Binding energy contribution of inactive CB2 bound with SR144528,

771

(b) binding pose of SR144528, (c) binding energy contribution of active CB2 bound with

772

WIN55,212-2, (d) binding pose of WIN55,212-2.

773 774

Figure 5. The two-dimensional pharmacophore model H4-A1 of SR144528 (inverse

775

agonist) and WIN55,212-2 (agonist). (a) The pharmacophore model H4-A1 of

776

SR144528 (inverse agonist), distance restrictions can be found in Table 1, (b) the

777

pharmacophore model H4-A1 of WIN55,212-2, distance restrictions can be found in 25



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

778

Table 2. For SR144528, we required at a four-point pharmacophore match allowing

779

omission of either H1 or H2 or H3 interactions, i.e., H1-H2-A1-H4; H1-H3-A1-H4; or

780

H2-H3-A1-H4. For Win55212-2, we used a five-point pharmacophore match allowing

781

the H1-H2-H3-A1-H4.

782 783

Figure 6. Comparisons of LANCE signal of different CB2 receptor ligands in stably

784

transfected CHO cells expressing human CB2 receptors in a concentration-

785

dependent fashion. Data are mean ± SEM of all experiments of two or more performed

786

in duplicate or triplicate. NSC296950 (green) and NSC402300 (cyan) acted as the inverse

787

agonists, while NSC669611 behaved as the neutral antagonist below the concentration of

788

10 µM.

789 790 791 792 793 794 795 796 797 798 799 800 26


Page 26 of 35

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


801

Table 1.

802

SR144528.

Distance restrictions of the pharmacophore model for inverse agonist-

H1-H2a

H2-H3

H3-A1

A1-H4

H4-H1

Distance 1b

Distance 2

Distance 3

Distance 4

Distance 5

3.7

4.6

3.6

5.0

11.4

803

a

804

b

A, hydrogen bond acceptor, H, hydrophobic or hydrophobic aromatic center. The distances were depicted in Å with an error range of ±1 Å.

805 806 807 808 809 810 811

Table 2. Distance restrictions of the pharmacophore model for agonist- WIN55,212-2. H1-H2a

H2-H3

H3-A1

A1-H4

H4-H1

Distance 1b

Distance 2

Distance 3

Distance 4

Distance 5

6.9

3.6

3.2

4.6

8.0

812

a

813

b

A, hydrogen bond acceptor, H, hydrophobic or hydrophobic aromatic center. The distances were depicted in Å with an error range of ±1 Å.

814 815 27



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

816

Page 28 of 35

Table 3. Novel structures of compounds for CB2 identified from in silico screen. Compound

Structure

Score

ID

Ki

Compound

(µM)

ID

Structure

Score

Ki (µM)

NSC93299

6.176

2.235

NSC273940

7.038

0.195

NSC745454

7.310

1.547

NSC296950

6.849

2.360

NSC162677

7.643

1.548

NSC402300

9.352

0.398

NSC273939

7.499

0.192

7.642 NSC618804

NSC273936

7.024

0.346

NSC669611

817 818 819 820 821 822 28


0.656

8.698

0.923

Page 29 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

823


Fig.1

824 825 826 827 828 829 830 831 832 833 29



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

834

Fig.2

835 836 837 838 839 840 841 842 843 844 845 846 847 30


Page 30 of 35

Page 31 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

848


Fig.3

849 850 851 852 853 854 855 856 857 858 859 31



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

860

Fig.4

861 862 863 864 865 866 867

32


Page 32 of 35

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

868


Fig.5 NH2

HO

Cl

O

O

F91

H2

H1

HO

NH2

O

N H3

H2N

N HO

HN

O

NH2

HO HO O

O

S285

A1

O

O

T114

HO

NH

NH

W258

H2N

O

H N

OH

Tyr190 HO

O

NH2

H2N

O

869

H4

OH NH

W258

W194

(a)

NH2 HO

O

W194

(b)

871 872 873 874 875 876 877 878 879 880 881 33


O OH

F281 NH2

OH

HO

H2N

H2 N

870

O

H2N

NH2

S285

OH

O

HO HO

O

V113

H4

F117

H2N

HO

O

O

H3

N H2N

A1

V113 HO

H2

O OH

F183

H2 N

H1

N

O

F94

OH V164


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

882

Fig.6

883 884 885 886 887 888 889 890 891 34


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892


TOC:

893

35


Difference and Influence of Inactive and Active States of Cannabinoid

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