ABSTRACT The retinoic acid receptor-related orphan receptor (ROR) γt receptor is a member of nuclear receptors, which is indispensable for the expression of pro-inflammatory cytokine IL-17.

RORγt has been established as a drug target to design and discover novel treatments for multiple inflammatory and immunological diseases. It is important to elucidate the molecular

mechanisms of how RORγt is activated by an agonist, and how the transcription function of RORγt is interrupted by an inverse agonist. In this study we performed molecular dynamics

simulations on four different RORγt systems, i.e., the apo protein, protein bound with agonist, protein bound with inverse agonist in the orthosteric-binding pocket, and protein bound with

pocket, because the bound agonist helped to enhance the triplet His479–Tyr502–Phe506 interactions and stabilized H12 structure. The stabilized H12 helped RORγt to form the protein-binding

site, and therefore made the receptor ready to recruit a coactivator molecule. We also showed that transcription function of RORγt could be interrupted by the binding of inverse agonist at

the orthosteric-binding pocket or at the allosteric-binding site. After the inverse agonist was bound, H12 either structurally collapsed, or reorientated to a different position, at which

the presumed protein-binding site was not able to be formed. You have full access to this article via your institution. Download PDF SIMILAR CONTENT BEING VIEWED BY OTHERS

CRYSTALLOGRAPHY-GUIDED DISCOVERY OF CARBAZOLE-BASED RETINOIC ACID-RELATED ORPHAN RECEPTOR GAMMA-T (RORΓT) MODULATORS: INSIGHTS INTO DIFFERENT PROTEIN BEHAVIORS WITH “SHORT” AND “LONG”

INVERSE AGONISTS Article 25 November 2020 MECHANISM OF ANTAGONIST LIGAND BINDING TO REV-ERBΑ Article Open access 10 April 2024 LIGAND EFFICACY SHIFTS A NUCLEAR RECEPTOR CONFORMATIONAL

ENSEMBLE BETWEEN TRANSCRIPTIONALLY ACTIVE AND REPRESSIVE STATES Article Open access 28 February 2025 INTRODUCTION Nuclear receptors (NRs) are ligand-regulated transcription factors [1]. The

retinoic acid receptor-related orphan receptor (ROR) γt, as an NR, is only expressed in lymphoid organs, binds to the retinoid-related orphan receptor response element (RORE) within DNA to

activate gene transcription, and has a constitutive level of activity even without binding to a ligand [2]. RORγt is indispensable for the pro-inflammatory cytokine interleukin IL-17 to be

expressed from T-helper (Th)17 cells [3]. Th17 cells play a central role in the pathogenesis of autoimmune diseases such as rheumatoid arthritis, multiple sclerosis, psoriasis, and

inflammatory bowel diseases, and are also actively involved in immunology processes [4,5,6,7]. As it is so important in multiple inflammatory and immunological pathways, RORγt has been

established as a suitable drug target for the design and discovery of novel treatments for these diseases [1, 7,8,9,10,11,12]. Structurally, RORγt contains four different domains, namely, an

N-terminal activation domain (activation function 1, AF1), a DNA-binding domain that binds the RORE in DNA via two zinc-finger motifs, a hinge region, and a C-terminal ligand-binding domain

(LBD) [13,14,15,16]. The structure of the RORγt LBD features a three-layered fold of approximately 12 α-helices and 2–3 β-strands, making a highly conserved hydrophobic orthosteric-binding

pocket in its core structure and a protein-binding site near helix 12 (H12) for the binding of a cofactor [17,18,19,20,21,22,23]. The transcription function of RORγt can be easily modulated

by a small ligand called a modulator. Once the RORγt LBD binds a modulator at its orthosteric-binding pocket, it is enabled to recruit either a coactivator or a corepressor to be bound at

the protein-binding site. H12 (also called activation function 2, AF2) of the LBD is the essential structural component of the protein-binding site, and it can adopt distinct conformations

in response to the binding of different modulators. Recent structural studies [17, 18, 20] revealed a new allosteric-binding site at the LBD that is formed by helices 4, 5, 11, and 12 and is

far from the orthosteric-binding pocket (Fig. 1). After the binding of an agonist at the orthosteric-binding pocket, H12 at the LBD is stabilized in a conformation that is ready to interact

with a coactivator, such as steroid receptor activator 2 (SRC2), at the protein-binding site. H12 can also be destabilized or completely reorientated after an inverse agonist is bound at

the orthosteric-binding pocket or at the new allosteric site, thus repressing the gene transcription function of RORγt [15, 17, 19, 21]. Although this structural information has provided

profound insights into how RORγt binds with an agonist or inverse agonist and where H12 of the LBD could interact with or reorient relative to the neighboring structural components, the

mechanisms of these conformational changes resulting from ligand binding remain ambiguous. For example, how does the binding of an agonist at the orthosteric-binding pocket cause the

stabilization of H12 and therefore help the RORγt in recruiting the coactivator? How does an inverse agonist binding at the orthosteric-binding pocket or at the allosteric-binding site

perturb the protein-binding site? This event leads to H12 destabilization and the coactivator not being able to bind. It has been suggested that the binding of an agonist or inverse agonist

can switch on or off the transcriptional activity of RORγt and that the dynamic conformational equilibrium is a fundamental attribute of this receptor. We think it is important to understand

the molecular mechanisms underlying the conformational dynamics of RORγt and their relationship to its transcription function [24]. Such understanding of the dynamic conformational

equilibrium is also important and helpful for the future design of novel RORγt modulators (agonists or inverse agonists) with better pharmacokinetic and/or pharmacodynamic properties, such

as improved cellular activity, potent inhibition of IL-17 release, and better oral bioavailability. In the present study, we selected a system of RORγt without ligand binding (apo-form) and

three typical systems of RORγt bound with different modulators: agonist, inverse agonist bound at the orthosteric-binding pocket, and inverse agonist bound at the allosteric-binding site. We

conducted molecular dynamics (MD) simulations on all four RORγt systems to track the intramolecular and intermolecular interactions. The results from our MD simulations confirm that the

apo-form RORγt is constitutively active. We also demonstrated that the binding of agonist stabilizes H12 and that the binding of inverse agonist makes H12 completely collapse or reorient to

a different position. Once H12 of RORγt is not able to partly form the presumed protein-binding site, the transcription function of the receptor is disrupted. These insights into the

inherent conformational dynamics of RORγt will be very helpful for the design of ligands with greater potency and specificity (agonists and/or inverse agonists) that could be used as

potential treatments for RORγt-related health problems. MATERIALS AND METHODS MD SIMULATIONS The details of the MD simulations on the RORγt protein are listed in Table 1. Specifically, we

started from crystal structures [17, 20] by selecting four PDB systems (the linker GGGG and the co-crystallized coactivator were removed) and named them PDB IDs. These systems include an

apo-form of the RORγt protein (PDB entry 5VB3, resolution of 1.95 Å), the RORγt-agonist bound complex (PDB entry 5VB7, resolution of 2.34 Å), the RORγt bound with an orthosteric inverse

agonist (PDB entry 5VB5, resolution of 2.23 Å), and the RORγt bound with an allosteric inverse agonist (PDB entry 5C4O, resolution of 2.24 Å). Each of the selected protein structures was

visually checked and prepared by using the Protein Preparation Wizard encoded in the Schrodinger 3.5 software package [25]. Protonation states of ionizable residues and histidine residues

were predicted according to the microenvironment and _pK_a values calculated with the PROPKA algorithm (http://propka.org) at pH = 7.0. Missing side chains and hydrogen atoms for each

structure were complemented and minimized with restraints. To prepare the molecular topology files for each ligand, we directly extracted the ligand structure from the cocrystal structure

and then generated the topological files using the PRODRG program [26]. Then, each system of protein structures was solvated in a cubic SPC water box with at least 10 Å from the box boundary

to any residue. Na+ and Cl− ions were added to neutralize each solvated system and to maintain the ionic concentration at 0.15 M. All MD simulation courses were carried out by using the

Gromacs 5.1.4 package applied with the GROMOS96 43A1 force field [27]. Based on each of the prepared systems, energy minimization was performed first for the solvent molecules, including

ions, and then on the protein and ligand molecule with constraints on protein backbone atoms [28]. After these two rounds of energy minimization, the whole system was minimized again without

any constraints. During the energy minimization, we used the steepest descent algorithm and conjugate gradient algorithm successively [27]. Afterwards, each system was equilibrated at 50,

100, 200, and 310 K for 10 ps each, followed by NPT equilibration for 20 ps at 310 K. The Langevin Nosé–Hoover thermostat [29, 30] and the Parrinello–Rahman method [31, 32] were employed to

maintain each system at a constant temperature and a constant pressure of 1 atm. All bonds were constrained by the LINCS algorithm [33]. The van der Waals interaction cutoff was set to 12 Å,

while long-range electrostatic interactions were calculated using the particle mesh Ewald method [34, 35] with a grid size of 1.2 Å. The time step was set as 2 fs with coordinates saved

every 10 ps for the purpose of analysis. The periodic boundary conditions were implemented in all directions along the simulation box. After equilibration of the system, the production MD

simulations were run for 150 ns for each system. DATA ANALYSIS We performed all the analyses based on the trajectories of the MD production stage. The conformations of each trajectory were

clustered by the tools inside the Gromacs 5.1.4 package. The representative structure of each system was derived from the largest conformational cluster based on the MD trajectories [36].

The DSSP program [37] was applied to standardize the secondary structure assignment of two helices (H11’ and H12). To reveal the most important internal motion of each simulated system, we

performed principal component analysis (PCA) [38,39,40] on the MD trajectories. Briefly, we used the starting system as the reference structure and performed a least square fitting of the MD

trajectory to the reference structure [41, 42]. The covariance matrix was built and diagonalized, and the first principal component was plotted by the convenient tools implemented inside

the Gromacs 5.1.4 package. All the structural graphics were processed with the PyMOL software [43]. RESULTS AND DISCUSSION STABILITY OF THE SIMULATION SYSTEMS For the purpose of comparing

the simulated structures, we superimposed the starting structure of each system onto the apo-form structure, 5VB3. As shown in Fig. S1 of the Supporting Information, the X-ray structures of

the apo-form in 5VB3, agonist-bound form in 5VB7, and form bound with an inverse agonist at the orthosteric-binding pocket in 5VB5 could be superimposed very well (Fig. S1a) with small

overall positional root-mean square deviation (rmsd) values (rmsd for Cα atoms of 5VB7 vs. those of 5VB3 was 0.367 Å, 5VB5 vs. 5VB3 as 0.178 Å). The Cα rmsd between the 5C4O protein

structure bound with the inverse agonist at the allosteric-binding site and the 5VB3 structure is 0.66 Å (Fig. S1b), and an obvious difference exists at H11, H11‘, and H12 (Fig. S1c). Figure

 2 shows the time-dependent rmsd curve of Cα atoms for each simulated system based on the starting X-ray structure. As the simulations progressed, each of the systems fluctuated for a short

period of time, _ca_. 5–10 ns, and then the rmsd curve became very flat until the end of simulation at 150 ns. However, in the 5VB5 system, the rmsd curve flattened at approximately 60 ns,

which was attributed to collapsed H11‘ and H12 (the details will be discussed later). In general, each of the simulations proceeded smoothly, and the protein structure was quite stabilized

by the solvent molecules. CONFORMATIONAL DYNAMICS OF THE APO-FORM OF RORΓT In a recent report on the X-ray structure of RORγt [20], it was claimed that the apo-form, i.e., the ligand-free

structure, was able to maintain an active state based on two observations. The first observation was that H12 was in an active conformation similar to the conformation of H12 in the

coactivator-bound structure (PDB entry 3L0L). H12 was stabilized by intramolecular interactions of the triplet residues His479–Tyr502–Phe506. Another observation was from the solution NMR

studies, which showed distinctive chemical shift perturbations at the backbone atoms with titration of coactivator SRC2. To confirm that the apo-form RORγt is active and ready to bind

coactivator if provided, we performed PCA and DSSP [37] calculations to track the secondary structure of H11‘ and H12 based on the MD trajectory. As shown in Fig. 3a, the plot of PCA results

suggests that the apo-form RORγt does have some structural flexibility and that the helices H2 and H11‘ are the most flexible parts of the structure. As helix H2 is located at the entrance

of the orthosteric-binding pocket, its structural fluctuation indicates that the orthosteric-binding pocket is open and ready to accept a ligand. The secondary structure contents of H11‘ and

H12 (Fig. 3b) indicate that H11‘ uncoiled entirely, from helix to random coil, at approximately 60 ns into the MD trajectory. As a result of the structural change in H11‘, the helix H11

tilted toward the helix H12. During this process, H12 was maintained as a regular α-helix and packed well with H11 (Fig. 3b). Tracking of the positions of the triplet residues

His479–Tyr502–Phe506 from the MD trajectory reveals the important details of their intramolecular interactions. As shown in Fig. 4, residue His479 from H11 formed a constant hydrogen bond

with the hydroxyl group at the side chain of Tyr502 from H12 (Fig. 4b), being maintained for 85.2% of the MD trajectory (black curve of Fig. 4a). The side chain of His479 also formed a

typical edge-to-face and/or face-to-face π–π interaction with the aromatic side chain of Phe506 (blue curve of Fig. 4a) for 85.9% of the the MD simulations (the cutoff distance from the

center of the His479 side chain to the center of the Phe506 side chain was 6.5 Å). There was also an intermediate π–π stacking interaction between the aromatic side chain of Tyr502 and the

side chain of Phe506 that was present 95.6% of the whole MD trajectory (red curve of Fig. 4a, and cutoff distance from the center of the Tyr502 side chain to the center of the Phe506 side

chain was 6.5 Å). All of these data of tracked distances suggest that the triplet residues His479–Tyr502–Phe506 had strong intramolecular interactions. Such hydrogen-bonding and aromatic

packing interactions are the most important structural determinants allow H12 to maintain a regular α-helix structure. MODE OF ACTION FOR RORΓT BINDING AN AGONIST As reported in previous

structural studies [7, 13, 20,21,22,23], the sandwich-like RORγt LBD contains an orthosteric-binding pocket formed by helices H2, H3, H5, and H11 at its central layer. This pocket is mainly

hydrophobic and large enough (~940 Å3) to accommodate ligands of various sizes and shapes [13, 20, 22]. As shown in Fig. 5a, for the binding structure derived from the trajectory of the MD

product, agonist 921 sits very well inside this orthosteric-binding pocket, with its pyridine head group located near the entrance of the pocket and its phenyl tail reaching the bottom of

the binding site (Fig. 5a). The isobutylene group of agonist 921 packs tightly with hydrophobic side chains of residues Val376, Leu387, Leu391, and Leu396. The tracked dynamics from MD

simulations (Fig. 5b) show that most of these receptor-agonist interactions are persistent. For example, the pyridine head group formed a constant hydrogen-bonding interaction with the

backbone amide hydrogen atom of Gln286 for 93.5% of the simulation, and the phenyl ether group of agonist 921 formed π–π interactions with the aromatic side chain of Trp317 96.7% of the time

and with the His479 side chain 98.7% of the simulation time. The π–π interactions of the phenyl head of 921 with His479 and Trp317 form a hydrophobic cluster. His479 is in the middle of the

cluster and forms a face-to-face π–π interaction with both the side chain of Trp317 and the phenyl head of 921, indicating the important role of His479. Since agonist 921 is bound tightly

at the orthosteric-binding pocket, we wanted to know how the agonist binding affects H12 and the protein-binding site. For this purpose, we tracked the changes of the triplet

His479–Tyr502–Phe506 through the MD trajectory. As shown in Fig. 6, these typical intramolecular interactions are conserved in the presence of agonist 921. The His479–Tyr502 hydrogen-bonding

interaction survived well, retained for 74.8% of the simulation (based on the same criterion as in Fig. 4). The π–π interactions between the side chains of the His479–Tyr502 pair remained

for 98.2% of the simulation, and 100% for the Tyr502–Phe506 side chain interactions. In addition to the persistence of the triplet interactions, we tracked the secondary structures of H11‘

and H12, which are presented in Fig. 7a. H12 is able to orientate in a position very similar to that in the starting structure (left panel of Fig. 7a), and keeps its helical structure (right

panel of Fig. 7a). The PCA plot (Fig. 7b) shows that the agonist-bound RORγt LBD is much less flexible than the apo-form RORγt (Fig. 4a). Although H11‘ is flexible to some extent (Fig. 7b),

it is mostly helical throughout the MD simulations (right panel of Fig. 7a). As shown in Fig. 7c, the hydrophobic cluster at the protein-binding site is also well maintained, with two

hydrophobic residues (Leu501 and Leu505) from H12. This dynamic information of the RORγt 5VB7 system denotes the mode of action for agonism of RORγt. That is, after agonist 921 is bound

inside the orthosteric-binding pocket, H12 is stabilized by the triplet His479–Tyr502–Phe506 interactions and maintains its structure as a typical helix. The stabilized H12 helps to form the

protein-binding site so the receptor is ready to recruit a coactivator such as SRC2. INVERSE AGONISM FOR RORΓT BY THE BINDING AT THE ORTHOSTERIC POCKET RORγt is constitutively active [2].

The results of our MD simulations on the apo-form structure confirmed that the orthosteric-binding pocket is mostly open (Fig. 3a) and that the triplet His479–Tyr502–Phe506 residues interact

with each other through hydrogen-bonding and aromatic π–π packing (Fig. 4). A straightforward way to interrupt the transcription function of RORγt is to design organic molecules that are

able to more fully occupy the large orthosteric-binding pocket so that its unique triplet interactions are destabilized or lost. Following this strategy, many more efforts have been focused

on the discovery of RORγt inverse agonists in recent years [8,9,10, 12, 14, 18,19,20,21,22,23]. Compound 92A is a typical inverse agonist, and it sterically clashes with the side chain of

His479 [20]. As shown in Fig. 8, regarding the dynamics of RORγt-92A binding, the side chain of His479 was forced to turn away from the side chain of Tyr502 after 92A was bound. Instead, the

imidazole group of His479 formed one hydrogen bond with the backbone oxygen atom of Leu475 for 85.3% of the simulation time and formed one hydrogen bond with the amide linker of 92A

(-N⋯H–N- type, 71.8%). The _para_-chloride-substituted phenylethyl group of the inverse agonist 92A also protruded into the steric gap between the side chains of His479 and Trp317 and formed

π–π stacking interactions with them for 91.6% and 99% of the simulation, respectively. Due to such intercalation of the inverse agonist 92A into the triplet His479–Tyr502–Phe506 of the

RORγt LBD structure, it could be reasonably expected that the local structures around H11, H11‘, and H12 were seriously perturbed or distorted, and thus, H12 was destabilized. As shown in

Fig. 9 for the structural information along the MD simulations, the helix H11 moved outwards approximately 5.1 Å (left panel of Fig. 9a), which would very possibly push away the H11‘ and H12

from their original position. As a result of such a steric push, H11‘ and H12 uncoiled after ~60 ns of the MD simulations (as shown in the right panel of Fig. 9a, and the blue curve in Fig.

 2 shows the rmsd change at approximately 60 ns of the MD trajectory). Furthermore, a PCA plot (Fig. 9b) shows that the overall RORγt 5VB5 structure is much more flexible than the apo-form

RORγt 5VB3 structure (Fig. 3a), particularly at the local regions of H2, H4, H11‘, and H12. The destabilized and distorted H11‘ and H12 would definitely make the RORγt LBD not able to

recruit any cofactor molecule. INVERSE AGONISM FOR RORΓT FROM THE BINDING AT THE ALLOSTERIC SITE The structural dynamics of the apo-form RORγt 5VB3 system (Fig. 3a), the agonist-bound 5VB7

system (Fig. 7b), and the inverse agonist-bound 5VB5 system (Fig. 9b) show that the activation function loop H11‘ is always flexible and possibly acts as a hinge between helices H11 and H12.

It will be beneficial to design a small molecule to directly intercalate among the triplet His479–Tyr502–Phe506 residues and reorientate H12. This strategy to design novel inverse agonists

will be more advantageous than the design of inverse agonists binding at the orthosteric-binding pocket, at least avoiding the possible competitive binding with natural ligands such as

cholesterol [21]. Fortunately, and surprisingly, a recent study [17] identified an allosteric-binding site that was induced by the inverse agonist 4F1 and formed among the helices H4, H5,

and H11, loop H11‘ and helix H12. We performed MD simulations on the 5C4O system for the RORγt LBD bound to the inverse agonist 4F1 [17] to test the dynamics of the binding structure and

stability of the reorientated H12. As shown in Fig. 10, the inverse agonist 4F1 binding at this allosteric-binding site formed extensive interactions with residues Val480, Leu483, Gln484,

Ala497, Phe498, and Leu505 and Phe506 from H12. In particular, the 2,6-disubstituted benzene ring of compound 4F1 formed a good π-π stacking interaction with the aromatic side chain of

Phe506. The sulfonyl head of compound 4F1 also formed hydrogen-bonding interactions with the backbone amide hydrogen atom of Ala497 (54% of the simulation time), amide hydrogen atom of

Phe498 (26%), and side chain of Gln329 (1.5%). Due to the binding of inverse agonist 4F1 at the allosteric-binding site, H12 is reorientated to a different position from that in the apo-form

RORγt 5VB3 system (Fig. S1). We tracked the secondary structures of H11‘ and H12 and the molecular motion of the RORγt LBD in the 5C4O system. The results are shown in Fig. 11. As expected,

H12 survived as a regular α-helix throughout the MD simulation, while H11‘ existed as a loop (Fig. 11a). A PCA plot (Fig. 11b) shows that the structure of H2 and the neighboring β-sheet are

obviously flexible, which are similar to what was observed in the apo-form RORγt 5VB3 system (Fig. 3a), mostly because the orthosteric-binding pocket is empty in the 5C4O system. Since H12

is reorientated and not able to form the protein-binding site, as observed in the apo-form RORγt 5VB3 system, it is definitely impossible for RORγt to recruit a coactivator, and therefore,

its transcription function would be completely disrupted. CONCLUSION In summary, by performing MD simulations on the four different RORγt systems, we explored the molecular mechanisms of the

agonism and inverse agonism of this receptor. We found that the orthosteric-binding pocket of the apo-form RORγt structure 5VB3 is mostly open and ready to accept an agonist, making the

apo-form RORγt somewhat constitutively active. Once an agonist is bound at the orthosteric-binding pocket in the RORγt 5VB7 system, the helical structure of H12 is stabilized by strong

triplet His479–Tyr502–Phe506 intramolecular interactions. The stabilized H12 helps RORγt to form the protein-binding site and therefore makes the receptor ready to recruit a coactivator

molecule. The transcription function of RORγt can be interrupted by the binding of an inverse agonist at the orthosteric-binding pocket. The inverse agonist 92A, which is bound at the

orthosteric-binding pocket in the RORγt 5VB5 system, clashed sterically with the side chain of His479, destabilized the triplet His479–Tyr502–Phe506, and completely collapsed the structure

of H11‘ and H12. The transcription function of RORγt can also be interrupted by the inverse agonist 4F1 binding at the allosteric-binding site in the RORγt 5C4O system. The inverse agonist

4F1 directly intercalated into the triplet His479–Tyr502–Phe506, reorientated H12, and destroyed the presumed protein-binding site. The tracked intermolecular and intramolecular interactions

from the MD simulations explicitly demonstrate the conformational dynamics and molecular mechanisms of how the RORγt receptor is activated by binding an agonist at the orthosteric-binding

pocket and how the RORγt is inversely agonized by destabilizing H12 or reorientating H12 to a different position. REFERENCES * Burris T, Busby SA, Griffin PR. Targeting orphan nuclear

receptors for treatment of metabolic diseases and autoimmunity. Chem Biol. 2012;19:51–59. CAS  PubMed  PubMed Central  Google Scholar  * Lee JS, Cua DJ. The emerging landscape of RORγt

biology. Immunity . 2014;40:451–2. CAS  PubMed  Google Scholar  * Isono F, Fujita-Sato S, Ito S. Inhibiting RORγt/Th17 axis for autoimmune disorders. Drug Discov Today. 2014;19:1205–11. CAS

  Google Scholar  * Rutz S, Eidenschenk C, Kiefer JR, Ouyang W. Post-translational regulation of RORγt-A therapeutic target for the modulation of interleukin-17-mediated responses in

autoimmune disease. Cyto Grow Fact Rev. 2016;30:1–17. CAS  Google Scholar  * Matsuyama M, Ishii Y, Sakurai H, Ano S, Morishima Y, Yoh K, et al. Overexpression of RORγt enhances pulmonary

inflammation after infection with mycobacterium avium. PLoS ONE. 2016;11:e0147064. https://doi.org/10.1371/journal.pone.0147064. PubMed  PubMed Central  Google Scholar  * Jetten AM, Takeda

Y, Slominski A, Kang HS. Retinoic acid-related orphan receptor γ (RORγ): connecting sterol metabolism to regulation of the immune system and autoimmune disease. Curr Opin Toxicol.

2018;8:66–80. PubMed  PubMed Central  Google Scholar  * Qiu R, Wang Y. Retinoic acid receptor-related orphan receptor γt (RORγt) agonists as potential small molecule therapeutics for cancer

immunotherapy. J Med Chem. 2018;61:5794–804. CAS  PubMed  Google Scholar  * Cege C, Schluter T, Hoffmann T. Identification of the first inverse agonist of retinoid-related orphan receptor

(ROR) with dual selectivity for RORb and RORγt. Bioorg Med Chem. 2014;24:5265–7. Google Scholar  * Wang Y, Yang T, Liu Q, Ma Y, Yang L, Zhou L, et al. Discovery of

N-(4-aryl-5-aryloxy-thiazol-2-yl)-amides as potent RORγt inverse agonists. Bioorg Med Chem. 2015;23:5293–302. CAS  PubMed  Google Scholar  * Wang Y, Cai W, Cheng Y, Yang T, Liu Q, Zhang G,

et al. Discovery of biaryl amides as potent, orally bioavailable, and CNS penetrant RORγt inhibitors. ACS Med Chem Lett. 2015;6:787–92. CAS  PubMed  PubMed Central  Google Scholar  *

Abdel-Magid AF. RORc modulators for the treatment of autoimmune diseases. ACS Med Chem Lett. 2015;6:958–60. PubMed  PubMed Central  Google Scholar  * Huang Y, Yu M, Sun N, Tang T, Yu F, Song

X, et al. Discovery of carbozole carboxamides as novel RORγt inverse agonists. Eur J Med Chem. 2018;148:465–76. CAS  PubMed  Google Scholar  * Jin L, Martynowski D, Zheng S, Wada T, Xie W,

Li Y. Structural basis for hydroxycholesterols as natural ligands of orphan nuclear receptor RORγ. Mol Endocrinol. 2010;24:923–9. CAS  PubMed  PubMed Central  Google Scholar  * Yang T, Liu

Q, Cheng Y, Cai W, Ma Y, Yang L, et al. Discovery of tertiary amine and indole derivatives as potent RORγt inverse agonists. ACS Med Chem Lett. 2014;5:65–68. CAS  PubMed  Google Scholar  *

Fauber B, Magnuson S. Modulators of the nuclear receptor retinoic acid receptor-related orphan receptor-γ (RORγ or RORc). J Med Chem. 2014;57:5871–92. CAS  PubMed  Google Scholar  * Xu HE.

Family reunion of nuclear hormone receptors: structures, diseases, and drug discovery. Acta Pharmacol Sin. 2015;36:1–2. Google Scholar  * Scheepstra M, Leysen S, van Almen GC, Miller JR,

Piesvaux J, Kutilek V, et al. Identification of an allosteric binding site for RORγt inhibition. Nat Commun. 2015;6:8833. Google Scholar  * Marcotte DJ, Liu Y, Little K, Jones JH, Powell NA,

Wildes CP, et al. Structural determinant for inducing RORγ specific inverse agonism triggered by a synthetic benzoxazinone. BMC Struct Biol. 2016;16:1–9. Google Scholar  * Olsson RI, Xue Y,

von Berg S, Aagaard A, McPheat J, Hansson E, et al. Benzoxazepines achieve potent suppression of IL-17 release in human T-helper 17 (TH17) cells through an induced-fit binding mode to the

nuclear receptor RORγ. ChemMedChem. 2016;11:207–16. CAS  PubMed  Google Scholar  * Li X, Anderson M, Collin D, Muegge I, Wan J, Brennan D, et al. Structural studies unravel the active

conformation of apo RORgt nuclear receptor and a common inverse agonism of two diverse classes of RORγt inhibitors. J Biol Chem. 2017;292:11618–30. CAS  PubMed  PubMed Central  Google

Scholar  * Kallen J, Izaac A, Be C, Arista L, Orain D, Kaupmann K, et al. Structural states of RORγt: X-ray elucidation of molecular mechanisms and binding interactions for natural and

synthetic compounds. ChemMedChem. 2017;12:1014–21. CAS  PubMed  Google Scholar  * Wang Y, Cai W, Tang T, Liu Q, Yang T, Yang L, et al. From RORγt agonists to two types of RORγt inverse

agonists. ACS Med Chem Lett. 2018;9:120–4. CAS  PubMed  PubMed Central  Google Scholar  * Narjes F, Xue Y, von Berg S, Malmberg J, Lliinas A, Olsson RI, et al. Potent and orally bioavailable

inverse agonists for RORγt resulting from structure-based design. J Med Chem. 2018;61:7796–813. CAS  PubMed  Google Scholar  * Wu X, Wang R, Xing Y, Xue X, Zhang Y, Lu Y, et al. Discovery

and structural optimization of 4-(4-(benzyloxy)phenyl)-3,4-dihydropyrimidin-2(1H)-ones as RORc inverse agonists. Acta Pharmacol Sin. 2016;37:1516–24. CAS  Google Scholar  * Sastry GM,

Adzhigirey M, Day T, Annabhimoju R, Sherman W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aid Mol Des. 2013;27:221–34.

Google Scholar  * Schuttelkopf AW, van Aalten DMF. PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Cryst. 2004;D60:1355–63. Google Scholar  * Abraham MJ,

van der Spoel D, Lindahl E, Hess B, and the GROMACS development team. GROMACS User Manual version 5.1.4, Available from: www.gromacs.org, 2016. * Bian Y, He X, Jing Y, Wang L, Wang J, Xie

X. Computational systems pharmacology analysis of cannabidiol: a combination of chemogenomics- knowledgebase network analysis and integrated in silico modeling and simulation. Acta Pharmacol

Sin. 2019;40:374–86. CAS  Google Scholar  * Nose S. A unified formulation of the constant temperature molecular dynamics methods. J Chem Phys. 1984;81:511–9. CAS  Google Scholar  * Hoover

WG, holian BL. Kinetic moments method for the canonical ensemble distribution. Phys Lett. 1996;211:253–7. CAS  Google Scholar  * Parrinello M, Rahman A. Crystal structure and pair

potentials: a molecular dynamics study. Phys Rev Lett. 1980;45:1196–9. CAS  Google Scholar  * Parrinello M, Rahman A. Polymorphic transitions in single crystals: a new molecular dynamics

method. J Appl Phys. 1981;52:7182–90. CAS  Google Scholar  * Hess B, Bekker H, Berendsen HJC, Fraaije JGEM. LINCS: a linear constraint solver for molecular simulations. J Comput Chem.

1997;18:1463–72. CAS  Google Scholar  * Ewald PP. Evaluation of optical and electrostatic lattice potentials. Ann Phys. 1921;369:253–87. Google Scholar  * Essmann U, Perera L, Berkowitz ML,

Darden T, Lee H, Pedersen LG. A smooth particle mesh Ewald method. J Chem Phys. 1998;103:8577–93. Google Scholar  * Huang Z, Zhao J, Deng W, Chen Y, Shang J, Song K, et al. Identification of

a cellular active SIRT6 allosteric activator. Nat Chem Biol. 2018;14:1118–26. CAS  PubMed  Google Scholar  * Wolfgang K, Christian S. Dictionary of protein secondary structure: pattern

recognition of hydrogen-bonded and geometrical features. Biopolymers. 1983;22:2577–637. Google Scholar  * Hotelling H. Analysis of a complex of statistical variables into principal

components. J Educ Psychol. 1933;24:417–41. Google Scholar  * Amadei A, Linssen AB, Berendsen HJ. Essential dynamics of proteins. Proteins. 1993;17:412–25. Google Scholar  * David  CC,

Jacobs DJ. Principal component analysis: a method for determining the essential dynamics of proteins. Methods Mol Biol. 2014;1084:193–226. CAS  PubMed  PubMed Central  Google Scholar  *

Jiang H, Deng R, Yang X, Shang J, Lu S, Zhao Y, et al. Peptidomimetic inhibitors of APC-Asef interaction block colorectal cancer migration. Nat Chem Biol. 2017;13:994–1001. CAS  PubMed 

Google Scholar  * Shen Q, Cheng F, Song H, Lu W, Zhao J, An X, et al. Proteome-scale investigation of protein allosteric regulation perturbed by somatic mutations in 7,000 cancer genomes. Am

J Hum Genet. 2017;100:5–20. CAS  PubMed  Google Scholar  * The PyMOL Molecular Graphics System, Version 1.2r3pre, Schrödinger, LLC. Download references ACKNOWLEDGEMENTS This work is

supported by grants from the National Natural Science Foundation of China (Nos. 81473136 and 81773635). We thank Dr. Xiao-qin Huang from Rice University (Houston, TX 77005, USA) for great

help and discussion about the overall research design and final writing of this work. AUTHOR INFORMATION Author notes * These authors contributed equally: Cong-min Yuan, Hai-hong Chen,

Nan-nan Sun AUTHORS AND AFFILIATIONS * Minhang Hospital and Department of Medicinal Chemistry at School of Pharmacy, Fudan University, 201203, Shanghai, China Cong-min Yuan, Hai-hong Chen, 

Nan-nan Sun, Xiao-jun Ma, Jun Xu & Wei Fu Authors * Cong-min Yuan View author publications You can also search for this author inPubMed Google Scholar * Hai-hong Chen View author

publications You can also search for this author inPubMed Google Scholar * Nan-nan Sun View author publications You can also search for this author inPubMed Google Scholar * Xiao-jun Ma View

author publications You can also search for this author inPubMed Google Scholar * Jun Xu View author publications You can also search for this author inPubMed Google Scholar * Wei Fu View

author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS CMY and WF designed the work; HHC performed the docking studies; CMY and HHC performed the MD

simulations; CMY, NNS, and XJM analyzed the trajectories from MD simulations; CMY, JX, and WF organized, discussed, and wrote the work. CORRESPONDING AUTHORS Correspondence to Jun Xu or Wei

Fu. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. SUPPORTING INFORMATION SUPPLEMENTARY INFORMATION RIGHTS AND PERMISSIONS Reprints and permissions ABOUT

THIS ARTICLE CITE THIS ARTICLE Yuan, Cm., Chen, Hh., Sun, Nn. _et al._ Molecular dynamics simulations on RORγt: insights into its functional agonism and inverse agonism. _Acta Pharmacol

Sin_ 40, 1480–1489 (2019). https://doi.org/10.1038/s41401-019-0259-z Download citation * Received: 16 March 2019 * Accepted: 21 May 2019 * Published: 17 July 2019 * Issue Date: November 2019

* DOI: https://doi.org/10.1038/s41401-019-0259-z SHARE THIS ARTICLE Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable link is

not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative KEYWORDS * RORγt * molecular dynamics simulation * agonism *

inverse agonism * protein-binding site * orthosteric-binding pocket * allosteric-binding site