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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5364_Библиотеки_им_академика_М_И_Перельмана.pdf
16 Computational Study of Conformational Changes in Nuclear Receptors... 481
ligand-binding pocket upon agonist binding, as well as the impact of CAR inducers
such as pregnenedione and CITCO on the helical structure of the H12.
Concomitantly, classical CAR studies [138, 147] could not precisely discriminate
between H12 conformation with agonist and antagonist. This observation was
attributed to either the short time of MD simulation or the lack of coregulator in
their simulations. In their follow-up study [138], the inclusion of corepressor
(SMRT) in their simulations allowed the observation of specific movements.
Among those, the movement of H12 toward H10, is favorable for SMRT binding
due to stabilizing van der Waals interaction between these helices. The interpretation
of the simulations is complex as their data correlates with cell-based reporter gene
assays, which are prone to permeability and coregulator availability in the chosen
cell models. In this sense, they also reported that the same ligand may be able to
recruit either coactivators or corepressors, and the CAR activity depends on the pool
of coregulators available in the cell [148].
These studies together with the limited availability of crystal structures depicting
agonist-bound CAR pursued us to employ in silico methods to model CAR isoforms
and comprehensively study their structure and confor mational changes when
interacting with various chemical compounds. These compounds range from
CITCO analogies to endocrine disruptors (EDs). We investigate specific human
CAR agonists with no activation of PXR or other NRs while maintaining favorite
ADME characteristics in human hepatocyte cellular models or the context of
humanized mouse models [149]. The exploration was carried out within the collection of kinase inhibitors. We discovered several derivatives of 3-(1H-1,2,3-triazol-4yl) imidazo [1,2-a] pyridine numbered 37, 39, 40 and 48 that directly activate human
CAR in nanomolar concentrations. We utilized docking studies with these compounds within CAR-LBD followed by MD simulations. The simulation revealed
that the most extensive movement occurs in H2 (Fig. 16.7a and see MD simulation
protocol chapter). In terms of protein–ligand interaction, MD revealed that the
ligands are stabilized by polar interaction via His203, Thr225 and Thr228
(Fig. 16.7b) with a U-shaped conformation similar to that of CITCO configuration
when they are accommodated in hydrophobic subpacket [149]. MD trajectories
analysis also revealed that compound 39 displays a notable interaction with residue
Ile164 and exhibits a higher frequency of interaction with Y326 compared to
CITCO. The result agreed with the earlier finding that geometrical stabilization
happens by H-bond interaction between Tyr326 and Asn165 (Fig. 16.7c)[150]
leading to stabilization of H12 near H3. These findings underscore the pivotal role
of H3 and H10/H11 in protein stabilization. Further in-depth analysis, spanning from
docking data to molecular dynamic (MD) simulations, has revealed that both CITCO
and compound 39 effectively engage with CAR1-LBD, primarily through hydrophobic contacts. Notably, compound 39 establishes more robust polar contacts with
CAR1-LBD than CITCO, attributed to the formation of hydrogen bonds between the
amide moiety of compound 39 and the backbone oxygen of Thr225 and Asp228
[149]. We have also considered the evaluations of metabolic stability and activity
towards PXR. The in vitro analysis revealed the selectivity of compound 39 towards
other nuclear receptors. Furthermore, a preliminary single-dose pharmacokinetic
482 A. Rashidian et al.
A
α2
βsheets
α1
CAR-LBD
α6
αX
α7
α5
α8
α3
α10/
αAF-2
α9
Hx
α11
F243
T225
D228
F217
α4
L239
Y224
CAR-LBP
F234
H203
LN
F161
Y326
N165
C202
B
Hydrophobic subpocket
F217
βsheets
Y224
Fig. 16.7 CAR1-LBD structure and the key elements. (a) Overview of the CAR1-LBD structure.
The regions of interest are highlighted as follows: H2′-H3 loop (residues 140–153), dark Gray; H3
loop (residues 157–178), green; H5 (residues196–209), violet; β sheets (residues 217–223), pink;
H10/H11 (residues 308–333), light brown; H
341–348), dark brown. The rectangular area denotes the location of the ligand-binding pocket
(LBP) and the residues forming the LBP. The dashed circle AF-2 surface area. The main residues
participating in ligand binding are depicted in the stick model with a transparent molecular surface.
Residues are colored according to their respective regions (see cartoon structure on the left). (b)
Hydrophobic subpocket consists of aromatic residues. (c) Hydrogen bonds between the Tyr326
oxygen atom and Asn165 polar group are shown as the black dashed line. (d) Close view of H4 and
H12 zooming in Lys195 (on H4) and Ser348 (on H12). The black dashed line represents the
hydrogen bond between Lys195 and Ser348
F161
α3
C
Y326-N165 H-bond
α3
S348-K195 H-bond
D
α11
α10/
Y326
AF-2
α
N165
(residues 336–339), light orange; H12 (residues
X
α4
S348
K195
study highlighted the efficacy of compound 39 as a novel human CAR agonist in
animal experiments, deman ding deeper exploration through repeated-dose, longterm proof-of-concept studies. Notably, the chemical tool employed in our investigations demonstrated no observable toxicity or genotoxic potential. This serves as
additional evidence for the substantial activation of human CAR by compound 39.
Meanwhile, having the conformational changes of CAR1 in the presence of
agonists (CITCO Imidazo[1,2-a]pyridine) we were interested in speculating the
effects of EDs on CAR1 and CAR3. In this investigation, we explore the binding
16 Computational Study of Conformational Changes in Nuclear Receptors... 483
mechanisms between ED compounds and CAR1, CAR2, and CAR3. We compared
their conformational changes with those elicited by the well-established CAR
agonist, CITCO. Additionally, we studied the effects on conformational alterations
caused by CAR1 reverse agonists PK11195 and S07662, clotrimazole, and the
CAR3 reverse agonist TO901317 [136]. Utilizing a combination of molecular
docking, MD simulations, and MM-GBSA calculations (see chapter discussing
MD simulations for more details), we predicted the binding affinities of the EDs.
This study includes the development of a robust model for CAR2 and CAR3,
accounting for the influence of the APYLT and SPTV insertions (L:H8–H9 and L:
H6–H7) (Fig. 16.6a–c ), respectively. This model is then compared with CAR1 in our
monomeric simulations.
The initial docking experiments with EDs yielded a singular notable conformation for interactions with CAR1, CAR2, and CAR3, whereas CAR1-S07662 binding
exhibited two distinct poses. Consequently, both docking poses were considered in
the subsequent analysis. As previous studies documented, the occurrence of multiple
binding modes for ligands is not uncommon in the realm of nuclear receptors. This
phenomenon is attributed to the expansive ligand binding pocket of CAR, affording
considerable flexibility for ligand movement, particularly in the case of PXR
and CAR.
MM-GBSA analysis was utilized to gain insights into the EDs impact on CARs’
binding behaviors. Notably, the significant involvement of Phe161 (from helix H3)
and Tyr224 (in the β-sheet) in stabilizing the complexes highlight their crucial role in
binding affinity (Fig. 16.7b). This emphasizes the critical role played by the hydrophobic pocket formed by helix H3 and the β-sheet for CAR potency and selectivity.
In another project, we examined interactions of individual branched
4-nonylphenols (22NP, 33NP, and 353NP) and linear 4-nonylphenol (4-NP) with
CAR1 and its variant CAR3 using MD simulations, and cellular experiments. Our
study demonstrates the enhanced stability of branched 4-nonylphenols (4-NPs) in
binding to activate both CAR1 and the CAR3 variant LBDs over the course of MD
simulations. Notably, the branched 4-NP exhibited superior efficacy in activating
both CAR3 and CAR1 LBDs compared to the linear 4-NP. To delve deeper into the
cellular effects of these compounds, we conducted experiments using HepaRG cells.
Remarkably, all tested NP compounds led to a significant upregulation of CYP2B6,
a pertinent indicator of CAR activation. Our simulation analysis highl ights the
critical roles of Helices H3, H5 and β-sheets in the interaction wi th CAR1 and
CAR3 (Fig. 16.7). Additionally, it is noteworthy that none of the examined ligands
directly interact with αAF-2, the region associated with receptor activation and
agonism. Nevertheless, the protein–ligand interaction data strongly suggest favorable interactions in both CAR3-LBD and CAR1-LBD, consistent with assembly
assay data [151]. Extended simulations provide insights into the dynamic behavior
of the AF-2 region, emphasizing the significant potential for conformational changes
and dynamic shifts within this helix, irrespective of the specific ligand. These
observations accentuate the importance of considering longer timescales in our
analyses to comprehensively capture NP binding dynamics. Relying solely on
short conformational changes may lead to an incomplete understanding of the
484 A. Rashidian et al.
process. Furthermore, the assessment of free binding energy substantiates the high
binding affinity to NP, comparable to CITCO.
3.4 Farnesoid X Receptor and the Effect
of Heterodimerization on the Coactivator Recruitment
The Farnesoid X receptor (FXR) belongs to the nuclear receptor superfamily
(NR1H4) and exhibits prominent expression in the liver and intestine. While its
presence is less pronounced in the kidney, adipose tissue, and adrenal glands, FXR
plays a pivotal role in regulating various physiological processes, including bile acid
homeostasis, lipid and glucose metabolism, and inflammation [152].
The endogenous ligands for FXR include farnesol derivatives, which are metabolic intermediates of the mevalonate pathway [153]. Additionally,
chenodeoxycholic acid (CDCA) and cholic acid (CA) serve as endogenous ligands
for FXR [154]. Two well-known genes associated with FXR are FXRα and FXRβ
[155, 156]. The FXRα gene is evolutionarily conserved from fish to humans [157]
and encodes four transcript isoforms: FXRα, FXRα2, FXRα3, and FXRα4in
humans and mice. In contrast, FXRβ is a pseudogene in humans and primates
[152, 158, 159]. Similar to other members of the nuclear receptor family, FXR
possesses a highly conserved domain. Ligand binding to FXR, while in complex
with the retinoid X receptor (RXR), induces conformational changes. These changes
lead to the recruitment of either coactivators or corepr essors, thereby modulating the
transcription of target genes by promoting or silencing their expression, respectively.
The activation of FXR ligands through agonists follows a well-established
classical αAF-2-trapping mechanism. However, the mecha nisms underlying antagonism appear to be more varied. Our prior short simulations indicated the potential
significance of destabilizing the L:H11–H12 interaction on monomers in the context
of FXR antagonists [160]. We conducted a project started from an agonist/
apostructure fully folded FXR-LBD in order to model the initial conformational
changes that would happen upon antagonist binding [161]. Leveraging the
microsecond-long all-atom molecular dynamics (MD) simulations of our recently
reported FXR antagonists 2a and 2h [160], we investigated the dynamic behavior
and conformational rearrangeme nt induced by ligand binding when compared to the
synthetic (GW4064) or steroidal (CDCA) FXR agonists, as a monomer or
heterodimer, and in the presence and absence of the coactivator [161]. We focused
on both the heterodimerization interface and the αAF-2 conformation (Figs. 16.8 and
16.9). Such investigations hold the promise of unveiling novel avenues for targeting
FXR-related pathways, thereby presenting potential therapeutic implications.
Our investigation revealed discrete conformational changes across various
regions. Notably, modifications were observed in the FXR/RXR interface, H9,
H10, and H11 (Fig. 16.8a, b). Extensive research on nuclear receptors, employing
RXR as a heterodimeric partner from a structural standpoint, has categorized them
16 Computational Study of Conformational Changes in Nuclear Receptors... 485
Fig. 16.8 FXR/RXR
interface. (a) The H11 from
FXR in light brown and the
H11 from RXR in
transparent brown
surrounded by brown
dashed line and dark dashed
line. Interacting residues are
labeled as His446 and
His447 (on H11) from FXR,
Lys431 and Glu343
(on H11) from RXR (b)A
top view of panel A. The H9
from FXR in grey and the
H10 from RXR in
transparent brown.
Interacting residues are
Glu405 (H9) from FXR and
Lys417 (H10) from RXR.
(Modified from [161])
A
CDCA
W454
heterodimer interface
FXR
α11
H445
H446
L:
α11-αAF-2
RXR
K431
E434
α11
90°
B
FXR
α9
E405
K417
RXR
α10
α10
L:α9-α10
α9
into permissive and nonpermissive heterodimers [153, 162, 163]. Structural alignment, utilizing RXR’s H11 as the benchmark, reveals distinct orientations of α11
based on their permissiveness. Permissive partners, such as PPARs and FXR itself,
exhibit a more pronounced bending, suggesting that this configuration enables
permissive heterodimers to effectively sense both receptor ligands. In contrast,
nonpermissive heterodimers exclusively respond to partner ligands, operating independently of the influence of RXR .
Alterations in the H11 region, exemplified by the pronounced unfolding evident
in the FXR-ivermectin structure, can result in the absence of detectable H12 and L:
H11–H12 [164]. Likewise, in our simulations with antagonists, we observe a
%
0
52
2
6
8
50
52
0
2
6
8
50
52
0
A
61
ces
6-OC
486 A. Rashidian et al.
FXR
α11
W454
CDCA
αAF-2 - α3distan
H447
K321
10.3 10.4 10.2
10.1 9.9 10.2
12.2 12.4
12.1 11.9
α5
W469
4
αAF-2
α3
CoA
- monomer /
DC
D
CDCA GW4064 2h 2a
- heterodimer /
DC
D D D
- heterodimer+CoA
MMMM
A
NCoA-2
E
74
L744
K74
N74
L74
4
α=50.3%
D7
7
A
LRYLLD
K74
N74
L744
D
7
α=31.6%
74
7
L74
D7
Fig. 16.9 αAF-2 helix displays moderate conformational changes relevant for corecruitment. (a)
αAF-2 helix and surrounding region in the FXR-LBD. (b) Distance between the centers of mass of
αAF-2 helix (residues number: 463–472, H12) and H3 (res. 281–304) shows that the antagonists
promote an open configuration of the αAF-2 helix in both monomeric (M) and dimeric simulations
(D and DC, for dimer and dimer with coactivator, respectively). (c) MARCoNI assay heatmap, the
red color shows positive interactions/increased FXR binding to coregulatory peptides, and the blue
color shows negative interactions/decreased FXR binding modulated by different ligands (6-OCA:
obeticholic acid). Fold Change (FC) is the log
–transformed relative binding value, calculated as
10
the compound’s binding value relative to the DMSO control. (d) FXR-NCoA-2/RXR-CoA complex cannot retain the coactivator’s fold upon Antagonist binding. Observed changes in the
secondary structure element (SSE%) of the NCoA-2 peptide throughout the simulation.
(Figure was modified from [161])
destabilization of H11, particularly emphasized in monomeric simulations, leading
to the displacement of L:H11–H12/αAF-2 (Fig. 16.9a, b ). Furthermore, our antagonist appears to destabilize L:α11–α12, subsequently affecting the active conformation of the αAF-2 helix [161]. This, in turn, hinders the recruitment of both
coactivators and corepressors. We propose a dynamic mechanistic interpretation
that links heterodimerization with the recruitment of co-regulatory proteins, providing a complementary perspective to previous crystallographic data
16 Computational Study of Conformational Changes in Nuclear Receptors... 487
[162]. Furthermore, research by Merk et al. (2019) elucidates that FXR activation
results from an equilibrium among conformational populations [165]. Specifically,
FXR agonists are identified to stabilize both the folded and extended helix H11
(H11, constituting the heterodimerization interface) and the H11–H12 loop (forming
the αAF-2 region) upon binding.
This stabilized αAF-2 region facilitates the recruitment of coactivators, thereby
promoting FXR activation. Further, ligands with partial agonistic properties can
induce alterations in the H11 conformation, subsequently destabilizing both the
H11–H12 loop and the orientation of H12, however still allowing the recruitment
of coactivators. Consistent with this, the CDCA agonist crystal structure exhibits
ample electron density, allowing for precise representation of both the H12 helix and
the entire loop connecting H11 and H12. Interestingly, the DM175 (partial agonist)
structure lacks visibility of L:H11–H12 due to destabilization, with H12 shifted to a
novel position [165]. Our simulations reveal the dynamic nature of the H12 position,
oscillating around the active site, as observed by fluctuating H12–H3 distances in
agonist modes (Fig. 16.9a, b). Conversely, simulated antagonists displace the αAF-2
from its active conformation to a point where stable binding of coregulatory proteins
becomes impossible. Even in artificially generated Antagonist + CoA systems, a
consistent displacement of the coactivator regulatory motif and further unfolding of
the peptide and αAF-2 are observed, albeit to a lower extent. This evidence was
along with shifts in the ligand-binding pocket (LBP) geometry and interaction
pattern. The assessment of available FXR crystal structures corroborated these
findings, unveiling diverse orientations in loops L: H1–H2, L: H5–H6, and L:
H11–H12 depending upon the bound ligand. Notably, the crystal structure with
CDCA showcased an unfolded L: H5–H6 loop, while the binding of the partial
agonist DM175 induced destabilization in both L:H5–H6 and L: H11–H12 loops.
The simulations further illuminated a comprehensive reorientation of the ligandbinding pocket (LBP), driven by interactions involving L: H1–H2 and L: H5–H6.
These structural properties emerged as a pivotal distinguishing factor between
agonists and antagonists. Specifically, CDCA and GW4064 exhibited smaller average H5 – H6 distances, driven by interactions with α5 residues.
Lastly, compounds 2a and 2h relied exclusively on interactions with H1 and H2.
Moreover, our Free Energy Perturbation (FEP) calculations yielded results in agreement with prior experimental binding studies, affirming that 2h functioned as a more
potent antagonist compared to 2a, albeit still demonstrating weaker binding in
comparison to CDCA. Additionally, a noteworthy observation emerged regarding
the FXR/RXR geometrical arrangement, using alterations in dihedral planes and the
interaction pattern among residues at the heterodimerization interface. The influence
exerted by antagonists on the heterodimerization interface seems to be tied to the
destabilization of αAF-2, effectively leading to the prevention of both coactivators
and corepressors recruitment. Our discoveries contribute valuable perspe ctives to the
understanding of the conformational dynamics of FXR, challenging convent ional
notions derived primarily from crystal structures. This underscores the necessity for
a more thorough evaluation of FXR antagonism/agonism dynamics. To achieve a
further precise depict ion, the exploration of extended timescales or alternative
488 A. Rashidian et al.
sampling approaches should be considered. It is important to highlight that longer
timescales or alternative sampling approaches could be used to generate a larger
picture of FXR-LBD conformational landscape, particularly when considering its
high flexibility.
Our hypothesized ligand binding mode appears to align more closely with the
partial agonist DM175 than with larger ligands like ivermectin. Interestingly, both
ivermectin and DM175 are recognized for recruiting corepressor proteins upon
binding. However, our MARCoNI assay (see description below) did not replicate
this phenomenon, hinting that our antagonists might employ an alternative binding
mechanism (Fig. 16.9c, d). While it is challenging to definitively assert that the
antagonist binds to FXR differently than the agonist (CDCA/GW4064), it seems to
lack the ability to stabilize the structural motifs responsible for coactivator recruitment. Therefore, we propose a passive binding mechanism for antagonism. Other
FXR-antagonist models, such as FXR-F6 [166], based on the oleanane-type
triterpenoid agonist-bound structure (PDB ID: 5WZX), propose a similar binding
mode, emphasizing the stable interaction of the Histidine and Tryptophane trap of
H12. Interestingly, the in vivo modulation of FXR introduces additional layers of
complexity, such as the expression of distinct FXR isoforms [167] and variations in
the availability of different coregulatory proteins within tissues. Notably, FXR exists
in four isoforms (named α1–4) with variants in the DBDs and hinge regions, while
sharing identical LBDs. Moreover, exploring the relationship between the dynamics
of the LBD and DBD could serve as another determinant revealing the dynamic
behavior of FXR. Lastly, a more in-depth analysis of these allosteric effects on gene
transcription profiles, especially in relation to the varied effects of ligands and
coactivators, would provide valuable insights from an agonist perspective and
could contribute to the development of more rational drug design strategies.
4 Experimental Methods to Analyze NR Activity
A variety of biochemical and biological assays is available, that help to explore and
understand the structure, function, and genetic changes related to nuclear receptors
and also elucidate NRs’ ligands [168]. Examples are (Fluorescence Recovery After
Photobleaching (FRAP) [165, 169] and Fluorescence Loss in Photobleaching (FLIP)
which provides insight into the dynamics and interaction of the molecules in various
cell processes [170], and chip-on-chip methodology to identify receptor-regulated
genes [171]. Study of NR-coregulator which is a major field of NR research [32, 39,
43, 172, 173], methods for screening for binding partners and for quantifying
specific receptor-target protein interactions [174], understanding the role of phosphorylation on receptor function [175, 176], advances in tissue-selective gene
targeting and knock-out strategies for generating mouse models of receptor function
in vivo [177], and studying genetic alterations in hormone-dependent cancers [178]
are some examples.
16 Computational Study of Conformational Changes in Nuclear Receptors... 489
In the following, the most relevant biochemical and cell-based approaches frequently used in the NR research field are briefly explained.
4.1 Luciferase Reporter Gene Transactivation Assay
(or Reporter Gene Assay, RGA)
Luciferase-based reporter assays measure the activity of the nuclear receptors on the
target gene expression upon ligand binding, within a relev ant cell system. The assay
is very sensitive and reproducible. To determine if a protein (or Protein–ligand
combination) can activate (or suppress) the transcription of a gene of interest,
recombinant DNA technology is used to produce a construct in which the gene’s
promoter is placed adjacent to a luciferase reporter gene. The cultured cells are
transfected with this construct, as well as a construct coding for the protein, in our
examples the NRs. If the NR can activate transcription, the cell will translate and
produce the luciferase reporter (often a firefly’s luciferase). The amount of produced
luciferase can be quantified using a luminometer. Protocols involve transient transfection of the receptor and a response element report er gene construct
[97, 179]. Other protocols also have an additional luciferase (Renilla), which is
constitutively and constantly being produced, to normalize by the number of
transfected cells and also act as a transfection control. Many cell lines have been
introduced as candidate recipients of these vectors, including CHO, HuH7, MCF-7,
HEK293, HepG2, and Caco-2 cells. This method can identify NR activators and
upon pretreatment with known agonists and unknown ligands in concentrationresponse, also identify antagonists [178]. On one hand, RGA is a staple in NR
research, being one of the most widely used approaches, as it provides on-target
specific activity in a cellular context. On the other hand, the cellular nature of this
assay makes it sensible to problems such as co-activator availability, cellular permeability, and the presence of transporter/exporters, which hinders the direct correlation between RGA data with modeling results.
4.2 Coregulator-Recruitment
An alternative transactivation assay system is the mammalian two-hybrid system
which is a technique to detect protein-protein interaction in cells. The result is
interpreted by expression or repression of reporter genes. Cells are treated with
NR ligands. In this method, the DBD of the yeast transcription factor GAL4 binds to
specific upstream activation sequence (or UAS) response elements. When the LBD
of the desired gene is fused to this DBD, it promotes co-activator binding. The
interaction between the NR and its co-activator is detected based on a reporter gene
containing multiple copies of the GAL4 upstream activating system. A set of
490 A. Rashidian et al.
agonists and inverse agonists were identified to bind to the human CAR using a
similar system [180].
In terms of purely in vitro coregulator recruitment tools, one can highlight the
Microarray Assay for real-time Coregulator-Nuclear Receptor Interaction (MARCoNI) technology [49, 181, 182]. The MARCoNI platform provides an assay which
allows for the characterization of nuclear receptor function, i.e., coregulator binding
(as seen in Fig. 16.9c) using a peptide array platform, each array containing up to
154 immobilized nuclear receptor coregulator peptides harboring either LXXLL
(coactivator) or LXXXIXXXL (corepressor) motifs. These peptides are immobilized
in a porous microarray membrane. Peptide binding of the nuclear receptor as a
function of the chemical ligands tested in the assay is visualized by fluorescently
labeled antibodies binding to the nuclear receptor of interest.
Lastly, time-resolved fluorescence resonance energy transfer (TR-FRET) is a
commonly used experimental technique to investigate the binding between nuclear
receptor and ligand or protein–protein interaction (e.g., nuclear receptor–coregulator
interaction). In this method, two fluorescent molecules are utilized: a donor
fluorophore and an acceptor fluorophore. When the two molecules interact, donor
and acceptor fluorophores are brought together. When the donor is excited, it transfers its emission energy to the acceptor. This event leads to the emission of
fluorescence at a specific wavelength. And the fluorescence emitted by the acceptor
fluorophore is measured at a delayed time after the excitation pulse. This delay
reduces the background noise and autofluorescence, resulting in a higher signal-tonoise ratio with improved sensitivity [178]. FRET is the transferring of energy from
a donor fluorophore in an excited state to a nearby acceptor fluorophore. The output
is proportional to the amount of binding [183]. Interestingly, also TR-FRET suffers
from nonobvious limitations, recently published CAR work [149, 151]. We
observed that TR-FRET data showing the interaction of CAR1-LBD with a fragment
of PGC1α would be a good surrogate for CAR activation, and it is true for CITCO or
other CITCO-like novel ligands. However, we observed that nonylphenol (mixture
and individual isomers), in fact, to not promote PGC1α recruitment, but competed
with CITCO in an antagonistic mode in a dose-dependent manner for the CAR
LBD–PGC1α interaction. Interestingly, MARCoNI data on those EDs demonstrate
that NPs can recruit different coactivators to exert their phenotype, but not PGC1α,
highlighting the relevance of a more holistic approach when interpreting assay data.
5 Concluding Remarks and Outlook
NRs are important transcriptional factors that regulate several genes involved in
physiological processes including development, differentiation, metabolism, and
systemic homeostasis, making them interesting therapeutic targets for many
human diseases, therefore, the discovery of ligands modulating their activity is
significant. They are ligand-induced activated and play their role in interaction
with multiple coregulatory proteins. Thus, a deeper insight into the NR–ligand