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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5364_Библиотеки_им_академика_М_И_Перельмана.pdf
X
- •Foreword
- •Acknowledgments
- •Contents
- •1.1 Structure-Based Drug Discovery (SBDD)
- •1.2 Ligand-Based Drug Design (LBDD)
- •1.3 Echoes from the Past, Visions from the Future
- •References
- •1 Introduction
- •2.2 Second Step: Data Curation
- •2.4 Fourth Step: Updating and Maintenance
- •2 Databases and Curation
- •8 Perspectives
- •9 Conclusion
- •References
- •1 Introduction
- •2.1 Making and Matching Protein Models
- •2.2 Simulating Protein Movements
- •2.3 Analyzing Changes in Protein Shape
- •3 Pharmacogenomics in Drug Development
- •4 Case Studies of Genomics-Based Drug Design
- •References
- •1 Historical Background
- •1.1 Timeline
- •2 Methodology Overview
- •2.1 Neural Networks
- •2.1.1 Perceptron
- •2.1.2 Multilayer Neural Networks
- •2.1.3 Types of Neural Networks
- •Feedforward
- •Recurrent Neural Networks
- •LSTM
- •2.2 Deep Learning
- •3 Using Machine Learning
- •3.2 Data Collection
- •3.3 Data Preprocessing
- •3.4 Model Selection
- •3.5 Model Training
- •3.6 Validation
- •3.7 Tuning
- •3.8 Prediction
- •4 Limitations
- •4.1 Bias
- •4.3 Interpretability
- •4.4 Computational Cost
- •4.5 Data Dependency
- •4.6 Robustness
- •5 Applications in Drug Discovery
- •5.2 Lead Discovery
- •5.3 Preclinical and Clinical Development
- •6 Resources and Tools
- •7 Challenges and Perspectives
- •7.1 Future Trends
- •9 Conclusions
- •References
- •1 Historical Background
- •1.1 Applications in Drug Discovery
- •2 Validations and Controls
- •2.1 Internal Validation
- •2.2 External Validation
- •2.3 Relative Cluster Validation
- •3 Challenges and Perspectives
- •4 Conclusions
- •References
- •1 Historical Background
- •2 OECD Principles
- •2.1 A Defined Endpoint
- •2.2 An Unambiguous Algorithm
- •2.5 A Mechanistic Interpretation, if Possible
- •3 Software and Tools
- •4 Validations and Controls
- •4.1 Internal and External Validation
- •4.1.1 Regression Metrics
- •4.2 Applicability Domain
- •4.3 Randomization Tests
- •5 Interpretation
- •6 Practical Advice During QSAR Modeling
- •7 Application
- •8 Challenges and Perspectives
- •References
- •1 Molecular Docking
- •2 Advances in Scoring Functions and Search Algorithms
- •2.2 Critical Characteristics of Search Algorithms
- •2.3 Docking Programs and Scoring Functions
- •3 Calculations Performed During Docking Simulations
- •4 Essential Components for a Good Docking Program
- •5 Limitations of the Docking Technique
- •6 Validation of Docking Results
- •7 Inappropriate Use of Validation Methods in Docking
- •9 Use of Machine Learning in Molecular Docking
- •11 Challenges
- •12 Conclusions
- •References
- •3 System Preparation for MD Simulations
- •3.1 Solvation and Microensemble
- •3.2 Force Fields: General Concept and Relevant Choices
- •3.3 The Concept of Replicas and Timescale
- •4.1.2 Protein Root Mean Square Fluctuation (RMSF)
- •4.1.4 Protein Secondary Structure Analysis
- •4.1.5 Principal component Analysis (PCA)
- •4.1.6 Markov State Modelling
- •4.1.7 Distance Calculations
- •4.1.8 Angle and Plane Calculations
- •4.2.2 Distances and Ligand-Induced Geometry Rearrangements
- •4 Molecular Dynamics Analysis
- •4.1 Protein Perspective
- •4.1.1 Protein Root Mean Square Deviation (RMSD)
- •4.3 Ligand Perspective
- •4.3.1 Ligand Properties
- •4.3.2 Ligand Root Mean Square Deviation
- •4.3.3 Ligand Root Mean Square Fluctuation
- •4.3.4 Angles and Dihedrals
- •5.1 Protein Structure Prediction and Preparation
- •5.2 Molecular Docking
- •6 Concluding Remarks and Outlook
- •Glossary
- •References
- •1 Introduction
- •2.1 MDeNM
- •2.2 Collective Molecular Dynamics (coMD)
- •2.3 ClustENM and ClustENMD
- •3 Ensemble Docking
- •References
- •1 Introduction
- •1.1 Advantages, Disadvantages, Innovations, and Challenges
- •1.2 Recent Advances in Accessible FEP Software Tools
- •1.3 Applications of FEP in Industry and Consortiums
- •2 Expanding the Potential of FEP Calculations
- •2.1 Validating Binding Poses
- •2.2 Dealing with Solvent
- •2.3 FEP and Allostery
- •2.4 FEP and Covalent Ligands
- •2.5 Applications of FEP in Scaffold Hopping
- •2.6 Positional Analogue Scanning
- •2.7 Combinations and Alternative Approaches
- •3 Machine Learning for FEP
- •3.4 Implications for ML in FEP Calculations
- •4 Final Considerations
- •5 First Steps to FEP Simulations
- •References
- •1 Background
- •2 Ultra-Large Screening Libraries and Chemical Spaces
- •3.1 Implications of Dataset Size
- •4 Ligands on the Ultra-Large Scale
- •4.1 Ultra-Large 2D Similarity Searches
- •7 Challenges and Future Perspectives
- •7.1 Hit Triage: An Old Problem on a New Dimension
- •8 Conclusions
- •Appendix
- •References
- •1 Introduction
- •2 Enzymatic Activity Evaluations
- •3 Cytotoxicity Evaluation and Cell Viability
- •4 Antiviral Assays in Experimental Validation
- •6 In Vivo Evaluation of Compounds
- •7 Conclusions
- •References
- •1 Introduction
- •3.1 Data Collection
- •3.2 Data Preprocessing
- •3.4 Model Choice
- •3.5 Model Training
- •3.6 Model Assessment
- •3.7 External Validation
- •3.8 Implementation and Availability
- •3.9 Continuous Update
- •5 Conclusions and Perspectives
- •References
- •1 Experimental Approaches to Obtain Protein Structure
- •1.1 X-Ray Crystallography
- •1.2 Nuclear Magnetic Resonance
- •1.3 Cryo-EM
- •1.4 Hybrid Methods
- •2 Modeling Approaches to Obtain Protein Structure
- •2.1 Homology Modeling
- •2.2 Ab Initio Modeling
- •2.3 New Approaches
- •3 Conformational Diversity of Proteins
- •3.1 Characterization of Protein Conformational States
- •3.2 Experimental Methods to Study Protein Dynamics and Conformations
- •3.4 Molecular Dynamics Simulation
- •3.5 Sampling Strategies
- •4 Remarks and Perspectives
- •References
- •1 Introduction
- •2 Structure-Based Drug Design of HIV Protease Inhibitors
- •2.1 HIV-1 Protease as a Therapeutic Target
- •2.2.1 Saquinavir
- •2.2.2 Indinavir
- •2.3.1 Lopinavir
- •2.3.2 Darunavir
- •6 Conclusions
- •References
- •4 Experimental Methods to Analyze NR Activity
- •4.2 Coregulator-Recruitment
- •5 Concluding Remarks and Outlook
- •References

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Chapter 16
Computational Study of Conformational
Changes in Nuclear Receptors upon Ligand
Binding
Azam Rashidian, Dirk Pijnenburg, Rinie van Beuningen, Antti Poso,
and Thales Kronenberger
Abstract In this chapter, we introduce the nuclear receptor superfamily, highlight-
ing their different classes and structural features related to their functions, in order to
A. Rashidian (✉)
Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences,
Eberhard-Karls-Universität, Tübingen, Tübingen, Germany
Tübingen Center for Academic Drug Discovery & Development (TüCAD2), Tübingen,
Germany
Partner-Site Tübingen, German Center for Infection Research (DZIF), Tübingen, Germany
Excellence Cluster “Controlling Microbes to Fight Infections” (CMFI), Tübingen, Germany
e-mail: azam.rashidian@uni-tuebingen.de
D. Pijnenburg · R. van Beuningen
Pam Gene International, ‘s-Hertogenbosch, The Netherlands
A. Poso
Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences,
Eberhard-Karls-Universität, Tübingen, Tübingen, Germany
Tübingen Center for Academic Drug Discovery & Development (TüCAD2), Tübingen,
Germany
School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio,
Finland
T. Kronenberger (
Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences,
Eberhard-Karls-Universität, Tübingen, Tübingen, Germany
Tübingen Center for Academic Drug Discovery & Development (TüCAD2), Tübingen,
Germany
Partner-Site Tübingen, German Center for Infection Research (DZIF), Tübingen, Germany
Excellence Cluster “Controlling Microbes to Fight Infections” (CMFI), Tübingen, Germany
School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio,
Finland
e-mail: thales.kronenberger@uni-tuebingen.de
✉)
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
V. G. Maltarollo (ed.), Computer-Aided and Machine Learning-Driven Drug
Design, Computer-Aided Drug Discovery and Design 3,
https://doi.org/10.1007/978-3-031-76718-0_16
463

464 A. Rashidian et al.
focus on the different available computational techniques applied to these proteins.
Our focus lies on the use of classical molecular dynamics simulations and other
techniques explaining conformational changes in these receptors and novel
approaches to pursue them experimentally.
Keywords Nuclear receptors · Conformational changes · Reporter gene assay ·
Transcription factors · Agonists
Abbreviations
ADME Absorption, distribution, metabolism, and excretion
AF-1 Activation Function 1
AF-2 Activation Function 2
AR Androgen receptor
BPA Bisphenol A
CAR Constitutive Androstane Receptor
CoA Coactivator
CoR Corepressor
DDI drug-drug interactions
DDT Dichlorodiphenyltrichloroethane
DES Diethylstilbestrol
ED Endocrine Disruptor
EDCs Endocrine Disrupting Chemicals
EE2 17-α-ethinylestradiol
EGF epidermal growth factor receptor
ERα, β Estrogen receptor α, β
FDA Food and Drug Administration
FLIP Fluorescence Loss in Photobleaching
FRAP Fluorescence Recovery After Photobleaching
FXR Farnesoid X receptor
GPCR G-protein-coupled receptor
HTS High throughput screening
HREs Hormone response elements
LBD Ligand Binding Domain
LBP Ligand Binding Pocket
LXR α, β Liver X receptor
MD Molecular dynamics
MHC Major Histocompatibility Complex
MM Molecular Mechanics
MSMs Markov State model s
NCOR Nuclear Receptor Coregulator
NMR Nuclear magnetic resonance
NR Nuclear Receptor

16 Computational Study of Conformational Changes in Nuclear Receptors... 465
NR-CoA Nuclear Receptor Coactivator
PBC Periodic boundary conditions
PCA Principal component analysis
PCBs Polychlorinated biphenyls
PES Potential Energy Surface
PDB Protein Data Bank
PPARs peroxisome proliferator-activated receptors
PR Progesterone receptor
PXR Pregnane X receptor
RAR Retinoic acid receptor
RXR Retinoid X receptor
SAR Structure–activity relationship
SMRT Silencing mediator of retinoic acid and thyroid hormone
1 The Nuclear Receptor Super Family Shares a Conserved
Fold but a Diverse Activity and Role
Nuclear receptors represent, besides other drug targets such as G protein-coupled
receptors, ion channels, receptor tyrosine kinases, and immunoglobulin-like receptors, a major receptor target class for drug development. Human Nuclear Receptors
(NRs) are a superfamily of intracellular receptors consisting of 48 members. However, the number of functionally different NR proteins is by far larger, due to
alternative splicing processes and posttranslational modification, such as
ubiquitination and phosphorylation [1]. In 1974, the correlation between hormone
action and alterations in the gene expression status was reported [2]. Later studies
revealed the now-called classic model of the NR signaling pathway [3]. The first
NRs were cloned and investigated in 1985, and this represented the starting point of
modern NR research [4–6]. Subsequently, additional NRs were identified [7–9]
(Fig. 16.1) and by now the family is composed of over 500 members spread
among several metazoan species [ 10]. Based on their mechanism of action and
ability to bind to DNA, NRs are categorized into four groups [11]: types I, II, III,
and IV.
Type I, belongs to subfamily 3 [12], is steroid hormone receptors. Upon ligand
binding, these receptors translocate into the nucleus, where receptor homodimers
bind to specific DNA sequences, known as hormone response elements (HREs),
indirect repeat (Fig. 16.1). Estrogen receptor (ERα,ERβ), Androgen receptor
(AR), Progesterone receptor (PR), and Glucocorticoid receptor (GR) belong to
this type.
Type II belongs to subfamily 1 [13], which is found in the nucleus such as the thyroid
hormone receptor (TR), retinoic acid receptor (RAR), peroxisome proliferatoractivated receptors (PPARs), liver X receptor (LXRα, LXRβ), farnesoid X
receptor (FXR), vitamin D receptor (VDR), pregnane X receptor (PXR), and

e
A
on
e
d
d
e acids
enobiotics
C
e
y
e
V
X
COU
S
G
466 A. Rashidian et al.
Class I Class II
NR, Class I
R Testoster
PR Progestron
ERs Estrogen
R Mineralcorticoi
R Glucocorticoi
Class III Class IV
Orphan NR
Class III
NF-4 GCNF
TR2 TL
P
NA direct repeats
NA direct repeats
NA everted repeats
NA single Half-Sit
Optional
Dimer
NR, Class II
LXRs Oxysterols
FXR Bil
PPARs Faty acids
PXR X
AR Androstan
TRs Th
DR Vitamin D
AR Retinoic acid
roid hormon
Orphan NR
Class IV
F-1 REV-erb
ERR N
R
FI-B
Fig. 16.1 Schematic overview of the nuclear receptor superfamily. Four superfamilies of nuclear
receptors are represented based on dimerization, DNA binding (direct or everted repeat), and ligand
specificity (required or not required). Class I: steroid receptor (also known as hormone receptor);
class II: RXR heterodimers; class III: dimeric orphan receptor; class IV: often monomeric orphan
receptor
constitutive androstane receptor (CAR) heterodimerizing with retinoic X receptor
(RXR). Type II is usually complexed with corepressor proteins in the absence of
ligand and binding to DNA direct repeat. Upon binding the ligand, protein
conformation is changed, leading to dissociation of corepressor protein and
recruitment of coactivator. Subsequently, this complex, in addition to other
transcriptional machinery components, transcribes DNA. Type II receptors can
also bind everted repeats.
Type III, subfamily 2 [14], is like type I as being homodimers but in contrast, they
bind to DNA direct repeat. No ligand has been identified in this group. Receptors
such as HNF-4, TLX, and TR2 are examples of this subfamily.
Type IV nuclear receptors have the ability to bind to DNA in either a monomeric or
dimeric form [12]. One representative member of this group is Steroidogenesis
Factor-1 (SF-1) [10]. Like type III receptors, these receptors do not have any
known natural ligands, the reason that they are referred to as orphan nuclear
receptors. Both type III and type IV receptors are still not well understood in
terms of their function and structure.
Of interest, the space distance between repeats and, less often, their orientation
can vary within the same receptor, depending on its dimerization status [15], with
12 NRs unexpectedly bindi ng to a single monomeric half-site. Recently, it has been

16 Computational Study of Conformational Changes in Nuclear Receptors... 467
AB
ligand
coactivator
protein
LBD
LBD
NTD
DBD
DBD
class II
Fig. 16.2 Illustration of nuclear receptor structure (class II). (a) Schematic view of PPARγ-RXRα
complex. The N-terminal domain (shown as NTD) is ligand-independent; the DNA binding domain
(shown as DBD) is conserved with two zinc fingers; the Hinge region is shown as a black loop
connecting DBD and LBD; the Ligand-Binding Domain (shown as LBD) involved in the dimer,
ligand, and coactivator (in blue) binding. (b) A cartoon representation of image A. Gold color
denotes DNA; cyan spheres depict zinc; the green and grey cartoon illustrates the dimer structure of
PPARγ-RXRα complexed with a coactivator; Blue color refers to the coactivator. PDB ID: 3DZY
[25]
shown that different small-molecule ligands can alter the NR binding to distinct
DNA-binding sites [16], which has implications for their regulatory function, where
certain ligands would lead to different pathways and phenotypes.
Currently, we understand the nuclear receptor superfamily as a major group of
intracellular transcription factors, which regulate broad aspects of cell functions
including cell growth, differentiation, and metabolism in distinct organs. The activation of these receptors is regulated by endogenous or exogenous lipophilic
compounds and regulatory proteins. They share a highly similar structure, particularly, in ligand-binding domains (LBDs) and DNA-binding domains (DBDs). Having DBD reveals their genome transcriptional role as they are known as
transcriptional factors. These features all reveal NRs remarkable role in organism
survival (within the scope of managing metabolic rates, energy stores, salt homeostasis, responding to exogenous toxins, and inflammation to regulate growth, reproduction, and development) and highlight them as promising targets for therapeutic
development.
All NRs have a similar structural organization and typically contain five structural
domains (Fig. 16.2):

468 A. Rashidian et al.
1. N-terminal domain: which varies considerably among the receptors and is com-
monly unstructured; typically, it contains a transactivation domain known as
Activation Function 1 (AF-1) and is ligand-independent.
2. DNA-binding domain (DBD): which is highly conserved across various NR
receptors, this region has four cysteines that coordinate to two zinc atoms
which bind to DNA response elements (e.g., DBD functions in a post-
translational modification which happens at Thr38 in CAR [17, 18]).
3. Hinge: a highly flexible connecting region believed to regul ate the cellular
distribution of the NR. The hinge region conveys structural flexibility between
the LBD and DBD allowing different binding modes to the DNA and different
configurations for (hetero-) dimers [19].
4. Ligand-binding domain (LBD): comprised of a very conser ved bundle of eleven
α-helices, where the ligand binding pocket is located, However, the interior of the
ligand-binding pockets exhibits significant variation, enabling nuclear receptors
to bind a diverse array of endogenous and synthetic ligands [20, 21]. This binding
capacity extends to include the activation function-2 (also referred to as the αAF-
2 helix) and the three-stranded β-sheet, except PXR. The eleven α-helices can be
categorized into three distinct groups: H1/H3, H4/H5/H8/H9, and H7/H10/H11.
5. C-terminal domain: also varies considerably, in terms of sequence, among
nuclear receptors [11, 22–24].
Physiologically, NRs regulate genes involved in different physiological functions
such as cell growth, differentiation, homeostasis, and metabolism and were conserved through evolution. They are transcription factors and commonly function by
being activated by small lipophilic molecules (<1000 Da), able to cross the membrane. Initially, they were solely identified as endocrine receptors, however, it was
later discovered that NRs can also interact with xenobiotic compounds, such as
Endocrine Disruptor Chemicals (EDCs or EDs, [26–28]). EDCs can mimic the
behavior of endogenous ligands such as natural hormones and modify their metabolism and transport through NR-mediating signaling. This phenomenon causes a
wide range of developmental, reproductive, or metabolic diseases [26, 29, 30].
NRs’ transcriptional activation is typically facilitated through the LBD. This
complex domain consists of three distinct yet interconnected relevant regions,
namely:
1. Ligand-binding pocket (LBP): This pocket serves as a location for small mole-
cules to bind.
2. Activation function 2 domain (AF-2): Composed of the helices H3/H4/H5/H12
interface, AF-2 is responsible for ligand-dependent transactivation. It also func-
tions as the surface for binding coregulators.
3. Dimerization surface: This surface enables interaction with other LBDs in partner
molecules.
Starting from the ligand-free basal conditions, the protein NR, which might be
complexed with a corepressors protein, is located in the cytoplasm [
31]. Upon
binding to an activator/agonist ligand, the LBD undergoes an allosteric

16 Computational Study of Conformational Changes in Nuclear Receptors... 469
conformational change that results in the movement and stabilization of its H12. This
conformational change leads to the release of corepressor binding (referred to as
CoR, if present) [32, 33], as well as allowing dimerization and migration to the
nucleus.
The activated NRs bind to a conserved DNA region called response element
(RE) downstream in the promoter of target genes [34]. The canonical core motif has
the consensus sequence 5′-AGGTCA-3′ [35]. The specificity and affinity of NR
binding are dependent on the con figuration and number of the core motif
[36]. Another factor to affects the NR-specificity is the linker region between the
core motifs [37, 38].
Concomitantly with the DBD–DNA interaction, the NR complex has access to
different nuclear coregulatory proteins, specifically coactivators (CoAs). The aforementioned conformational change in the AF-2 region enables the coactivator recruitment, which is commonly referred to as the “coregulator switching” model
[32, 33]. The fully activated and DNA-bound complex then can regulate the
transcription of its target genes.
Of note, the transcriptional regulation, whether it involves activation or repression, occurs through a balance between the NR interaction with different
coactivators or corepressors, as well as other protein factors that interact with the
promoter of the target gene. Corepressor proteins interact with the NRs via the short
peptide motif LxxxIxxxL (where L is leucine, I is isoleucine, and x can be any amino
acid). Examples of corepressors are the silencing mediator of retinoid and thyroid
receptors (SMRT) or the nuclear receptor corepressor (NCoR) [39–42]. They contribute to gene silencing by recruiting histone deacetylases, chromatin modifiers, and
remodeling proteins. On the other hand, coactivator proteins, such as the steroid
receptor coactivator (SRC) family, can recruit histone acetyltransferases, histone
methyltransferases, and histone kinases, resulting in chromatin unpacking, promoter
opening, and activation of the target genes [32, 39, 43]. Coactivators bind via LxxLL
motifs (where L is leucine, and x can be any amino acid) to NRs [44].
Numerous studies have highlighted the pivotal role of H12 (also known as αAF-
2), which is part of the AF-2 region, in controlling the activation and deactivation
processes [45]. In this sense, NR agonists can stabilize the active AF-2 conformation, forming a surface that binds coactivator proteins. Then, different CoA proteins
can modulate the transcriptional activity. The CoA recruitment event together with
the DNA interaction marks the beg inning of the nuclear receptor activity. Depending
on the coactivators/corepressors binding and on the cellular context, alternative
transcriptional outcomes can take place [46–48]. Alternatively, antagonists can act
by destabilizing this relevant H12 conformation and partial-agonists can partially
trigger this molecular event. The nature of the ligand, occupancy in the binding
pocket and interactions, determines the position of H12 and subsequently the
coregulator interaction [20, 21].
The corepressor and coactivator motifs form amphipathic α-helices, of which the
hydrophobic residues interact with the AF-2 surfaces of the LBD [21]. However,
NR-LBD (in)-activation should probably not be seen as an “on/off
” switch model.

470 A. Rashidian et al.
Rather, NR-LBD acts as a regulator fine-tuning the interaction between NR domains
with the coregulators, which would allow a range of signaling outcomes [21].
In this context, the biological role of NR is not determined by each protein
individually but is rather a result of other protein-binding partner as well. The
molecular determinants dictating specificity/selectivity in NR–CoA interactions
remain understudied on a structural level. The pioneering work from Broekema
et al. (2014) [49] suggests that amino acid sequences in both the NR-LBD and
coregulator motif are relevant determinants in the NR-specific preferences for
particular coregulator binding motifs. However, most of the NR crystal structures
only offer a static vision of these individual components, lacking insights into the
conformational changes induced by the different ligands and protein-binding partners. The essence of the problem is the diffi culty of experimentally addressing
conformational change in complex structures; that is, how the effect of the ligandbinding propagates through the structure to affect other sites. In this chapter, we
focus on discussing the dynamic transitions of NRs using classical molecular
dynamics (MD) simulations.
2 Examples of Small Molecules Acting as Nuclear Receptor
Modulators
Many studies have shown the crosstalk of NRs that is followed by controlling the
homeostasis of glucose, bile acids, lipids, hormones, and in flammation [50]. This
ability stems from the flexibility and versatility of nuclear receptors, as their transcriptional activity can be regulated by ligands, partner proteins, coactivators,
corepressors, and promoter genes. This mechanism underscores their role in a
wide range of developmental, reprod uctive, or metabolic NR-related diseases.
Given these characteristics, NRs have emerged as prominent therapeutic targets.
ER is the most targeted NR due to its druggable nature. It belongs to the type I of
nuclear receptor found in cytoplasm connected with heat shock proteins. Upon
ligand binding, it forms a homodimer and translocates to the nucleus. This receptor
is found in two forms, ERα and ERβ, both of which bind to the native ligand
estradiol. Tamoxifen, approved in the 1970s [51], and raloxifene, approved in
1997 [52] are used for the treatment and prevention of ERα-dependent breast cancer
in women as antagonists. Both compounds have been co-crystallized with the
receptor, binding to the ligand binding pocket (LBP), They exert their effects by
dislocating H12 from an active conformation to an inactive state. However, Tamoxifen can lead to endometrial cancer as an agonist because of the variability of
coregulator proteins, whereas raloxifene, also acting as an agonist, is used for
osteoporosis treatment in women. Androgen receptor (AR) is another NR found in
the prostate and several other tissues with testosterone as a native ligand. Several
diseases, including prostate cancer, have been linked to this receptor. Although
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