THIS STUDY.. raison d’etre
Establishing correlation tools
between in vitro and in silico
studies for ligand receptor affinity
using oestrogen and androgen
receptors as case studies
• Drug discovery, by the traditional method of screening natural
and synthetic compounds is both expensive and laborious
• In drug design, potential compounds may be selected for
performance of required function based on essential
characteristics including idealized structural and physical
properties
• Molecular models of these new compounds may be built, and
virtual tests may be run to assess its suitability, before an
expensive synthesis attempt is made
• Virtual experiments are cheaper, faster, and safer than real
experiments, and the data elucidated from these experiments
helps in the elimination of compounds that would definitely not
perform the required function.
THIS STUDY.. raison d’etre
• Structure based drug design is a vital tool in this process
• The elucidation of the 3D structure of a binding site or active
site, of a target molecule such as a receptor protein, guides
researchers to subsets of compounds with desired features to
complement the 3D shape of the site
• Using geometric and functional features of the binding site,
specific moieties of a ligand are designed so as to have a high
binding affinity with the target molecule
VITAL TO THIS PROCESS….
• Is the understanding of binding modalities of molecules of
known affinity to the receptor sites of interest
• This is because molecule optimisation often represents another
important avenue that contributes significantly towards the
obtaining of novel therapeutic agents
THIS STUDY.. raison d’etre
• Also, since subtle modifications are made to a structure that is
known to bind well to its receptor, the predictive ability of
ligand enhancement software is enhanced
• This is because the effect of a small, as opposed to a drastic
change is being quantified
• What is actually being done is the determination of the rank
order of a list of derivative compounds
• This greatly increases the confidence that proposed structures
will bind in a more or less predictable manner
• Computer software facilitates this job by filtering off all weakly
binding compounds allowing synthesis and testing only of the
most promising ligand
• Thus, utilizing computer aided drug design software to aid in
the refinement of weak binding lead compounds has emerged
as an efficient tool in modern drug design.
THIS STUDY.. raison d’etre
•
•
•
•
•
Drug optimization is advantageous to de novo design in that with de
novo design, one cannot know with exact certainty at the outset how
the designed molecular structure will interact and bind within a target
receptor
In drug optimization, there is a far more reliable starting point namely,
a lead compound whose bound structure within the receptor has been
characterized, usually through X-ray crystallography
Subtle modifications can then be performed in order to generate
derivative compounds using structure based drug design to improve
binding affinity
The fact that only subtle modifications are made in order to generate
derivative compounds with a higher affinity than the original
pharmacophore is a greater guarantee of success
These “new” derivatives are then evaluated in order to determine
which modifications improve binding. This is an iterative process,
which continues until optimally binding ligands are obtained.
THIS STUDY.. raison d’etre
• This study aims to create reliable,
validated predictive tools such that
the binding affinities of ligands for
oestrogen and androgen receptors,
may be estimated with confidence
1
THIS STUDY.. raison d’etre
THIS STUDY.. raison d’etre
• The choice of oestrogen and androgen
receptors lends relevance to this study given
that, they are the subject of much research.
• Not only are both these nuclear receptors
closely linked with the development of breast
and prostate cancers respectively…
• Management of these conditions is generally
long term, and research into non steroidal
agents that can effectively treat these
conditions with a minimal side effect profile
continues to be a significant research
avenue
• Furthermore, both these receptors are capable of binding nonsteroidal molecules- generally classified as endocrine
disruptors
• Through this binding process, the endocrine disruptors, by
mimicking, blocking or otherwise disrupting the function of
endogenous hormones negatively impact the normal function
of the endocrine system
• These agents, are mainly pesticides & represent ideal lead
molecules owing to the fact that they are non-steroidal , have a
known binding affinity for these receptors, and may hence be
exploited from a design point of view for the development of
agents that may be used in the management of both breast and
prostate cancer
Methodology…..
Estimating the Ligand Binding Affinities of Unminimised
Ligands of the Oestrogen and Androgen Receptors
Preparing Parent Ligand and Allied Protein
Oestrogen Receptor
Androgen Receptor
• The Ligand-Protein
complexes to be worked with
were identified from the
Protein Data Bank (PDB)
• pdb ID 1A52 (Oestrogen
Receptorα Ligand Binding
Domain Complexed to
Oestradiol) was selected to
model the oestrogen bound
ligand
• The Ligand-Protein
complexes to be worked with
were identified from the
Protein Data Bank (PDB)
• pdb ID 1e3g (Human
Androgen Receptor in
Complex With the Ligand
Metribolone) was selected to
model the androgen bound
ligand
Methodology…..
Estimating Ligand Binding Affinities of Unminimised Ligands of Oestrogen &
Androgen Receptors- Molecular modelling software SYBYL molecular modelling
package (version 6.1, Tripos Associates, Inc., St. Louis, Missouri).
Oestrogen Receptor
Androgen Receptor
• Removal of one monomer, its
ligand, allied waters, the
heavy metal (Au)
• For the remaining monomer
the water molecules close to
the active site, and which
consequently could affect
binding were retained. All
others were deleted
• The Au atom was deleted
from the remaining monomer.
• The edited version of the pdb
file was saved for further use
• Water molecules close to the
active site, and which
consequently could affect
binding were retained
• All others were deleted
• The edited version of the pdb
file was saved for further use.
Methodology…..
Estimating the Ligand Binding Affinities of Unminimised
Ligands of the Oestrogen and Androgen Receptors
Preparing Parent Ligand and Allied Protein
OESTROGEN RECEPTOR
ANDROGEN RECEPTOR
Methodology…..
Estimating the Ligand Binding Affinities of Unminimised
Ligands of the Oestrogen and Androgen Receptors
• For both androgen and oestrogen ligand receptor complexes,
the same process was followed from this point onwards
• Edited files read into SYBYL preserving the original coordinates of the pdb files
• For each pdb file, the ligand was extracted, named and saved in
SYBYL mol2 format
• The natural ligand substructure was then totally deleted from
the protein molecule into which it was originally docked
• The protein molecule, now devoid of its allied ligand, was
saved in pdb format
• The result of this process therefore was, a ligand- oestradiol
and metribolone respectively, saved in mol2 format, and the
oestrogen and androgen receptors from which the ligands were
extracted and saved in pdb format.
2
Methodology…..
Estimating the Ligand Binding Affinity (LBA) between
the ligand and its receptor protein
• The generated pdb and mol2 files were exported to Score, a
software programme that uses an empirical scoring function to
describe the binding free energy, which includes terms to
account for van der Waals contact, metal-ligand bonding,
hydrogen bonding, desolvation effect, and deformation penalty
upon the binding process, thus giving a mathematical
estimation of the LBA between the ligand and its receptor
• This yields a value for pKd where Kd = [ligand] [protein]
[ligand:protein]
• Thus, if the receptors have a high affinity for the ligand, the Kd
will be low and the pKd will be high, as it will take a low
concentration of ligand to bind half the receptors
Methodology…
Preparing the Ligand Series for Scoring
• For each receptor type, oestrogen and androgen, a series of
ligand molecules was prepared for scoring
• The ligand series selected came from the paper of Gao et. al.
for the oestrogen receptor, and from the papers of Mekenyan &
Bradbury & Waller for the androgen receptor These papers
contained experimental Ligand Binding Affinity (LBA) data,
determined using laboratory assays. The experimental LBAs
were used to compare to those calculated in silico
• Each ligand series was built using Sybyl in mol2 format. The
individual ligands were fitted onto the co-ordinates of the
ligands in the original pdb file in a process that allowed saving
of the newly constructed ligand in an orientation identical to
that of the original ligand. This ensured that identical
coordinates would be maintained for eventual docking into the
active site when using Score.
Methodology…
The Androgen Receptor Ligand Series
Methodology…
The Oestrogen Receptor Ligand Series
Metribolone (parent from pdb)
Mibolerone
Metribolone
5α-Dihydrotestosterone
Oestradiol
5α-Androstane
Androstenedione
Progesterone
17α-hydroxyprogesterone
Corticosterone
Metribolone &
Androstenedione
Pregnenolone
Superimposed to
Fix Androstenedione Testosterone
to the Docked CoAndrostenedione
ordinates of
Metribolone
Methodology…
The Oestrogen Receptor Ligand Series
The 11 substituted ligands
CHMe2
(R isomer)
CHMe2
(S isomer)
CH2CH=CH2 (R isomer)
CH2CH=CH2 (S isomer)
CH=CH2
(R isomer)
CH=CH2
(S isomer)
Thienyl(2)
(R isomer)
Thienyl(2)
(S isomer)
CH2C6H5
(R isomer)
CH2C6H5
(S isomer)
The parent ligand
Oestradiol
The 16 substituted ligands
Br
(R isomer)
Br
(S isomer)
F
(R isomer)
F
(S isomer)
Cl
(R isomer)
Cl
(S isomer)
CH2Br
(R isomer)
CH2Br
(S isomer)
Methodology…
The Oestrogen Receptor Ligand Series
The 17 substituted ligands
CCH
(R isomer)
CCH
(S isomer)
Me
(R isomer)
Me
(S isomer)
CH2CCH
(R isomer)
CH2CCH
(S isomer)
CCMe
(R isomer)
CCMe
(S isomer)
CCI
(R isomer)
CCI
(S isomer)
CH2CCI
(R isomer)
CH2CCI
(S isomer)
CH2CH=CH2 (R isomer)
CH2CH=CH2 (S isomer)
C6H5
(R isomer)
C6H5
(S isomer)
CH=CHI(Z)
CH=CHI(E)
CH=CHBr(Z)
CH=CHBr(E)
CH=CHCl(Z)
3
Methodology…
Calculating Ligand Binding Affinity for the 2 Ligand
Series
• Each new ligand was then docked into the respective
receptor (oestrogen or androgen) pdb file
• The LBA for each superimposed ligand in both
series was calculated in Score
• Values obtained were retained for plotting against
the experimental LBAs obtained from the literature.
Methodology…
Minimising the Systems…
• There are several
different algorithms for
minimizing the energy of
the system
• They all involve
calculating the derivative
of the potential energy,
and possibly the second
derivative, and using that
information to adjust the
coordinates in order to
find a lower energy for
the system
Minimisation Algorithm
Types
• Steepest Descent
• Conjugate Gradient
• Conjugent Gradient Powell
• Newton Raphson
• Adopted basis Newton
Raphson
• Truncated Newton
Methodology
Minimising the Systems…Minimisation Protocols
Adopted in this Study
• Minimisation was carried out using CHARMM
(Chemistry at HARvard Macromolecular Mechanics)(a force field for molecular dynamics as well as the
name for the molecular dynamics simulation
package associated with this force field)
• Individual atom charges were generated in MOPAC
(Molecular Orbital PACkage)- a computer program
designed to implement semi-empirical quantum
chemistry
• The minimisation was carried out in a stepwise
fashion in order to allow for gentle bond relaxation
as well as to allow for more variation & control over
the protocol and its outcome
Methodology…
System Minimisation
• After the LBA was estimated for the
ligand- (oestrogen & androgen) protein
complexes, the systems were
minimised in order to evaluate whether
or not minimisation would affect the
LBA profiles
Methodology…
Minimising the Systems…The Algorithms used
Steepest Descent
The simplest minimization algorithm is
steepest descent (SD)
In each step of this iterative procedure, the
coordinates are adjusted in the negative
direction of the gradient
Steepest descents does not converge in
general (i.e. reach an absolute minimum), but
it will rapidly improve a very poor
conformation.
Adopted Basis Newton-Raphson
This routine performs energy minimization
using a Newton-Raphson algorithm applied
to a subspace of the coordinate vector
spanned by the displacement coordinates of
the last positions.
The second derivative matrix is constructed
numerically from the change in the gradient
vectors, and is inverted by an eigenvector
analysis allowing the routine to recognize
and avoid saddle points in the energy
surface.
At each step the residual gradient vector is
calculated and used to add a steepest
descent step onto the Newton-Raphson step,
incorporating new direction into the basis
set. This method is the best for most
circumstances..
Methodology…
Minimising the Systems…Minimisation Protocols
Adopted in this Study- PROTOCOLS 1&2
Ligand-Protein Complex
• STEP 1: Minimise only H atoms using 500 / 1000 steps
STEEPEST DESCENT
• STEP 2: Minimise protein amino acid side chains and H atoms
together using 2000 / 20 000 steps STEEPEST DESCENT
• STEP 3: Minimise protein amino acid side chains, H atoms &
ligand together using 2000 / 10 000 steps of the adopted basis
Newton-Raphson algorithm
• STEP4: Minimise entire system now including protein
backbone & water atoms using 25000 / 10 000 steps of the
adopted basis Newton-Raphson algorithm
4
Methodology…
Minimising the Systems…Minimisation Protocols
Adopted in this Study- PROTOCOL 3-Gentlest
Approach in 8 Stages
Methodology…
Minimising the Systems…Minimisation Protocols
Adopted in this Study- PROTOCOL 3
TRI-LAYER MODEL
•Protein : Ligand Complex
envisioned as trilayered
system:
•Layer 1: ligand
•Layer 2: protein amino acids &
side chains <15A from ligand.
Side chains that might crash
with ligand during the
minimisation could be present
in this layer
•Layer 3: protein amino acids &
side chains > 15A from ligand.
No side chain ligand clashes
envisaged in this area
1.
Fix ligand; Layer 2 strong harmonic constraints; Layer 3
strong harmonic constraints
Fix ligand; Layer 2 strong harmonic constraints; Layer 3
weak harmonic constraints
Ligand strong harmonic constraints; Layer 2 strong
harmonic constraints; Layer 3 weak harmonic constraints
Ligand strong harmonic constraints; Layer 2 weak
harmonic constraints; Layer 3 weak harmonic constraints
Ligand weak harmonic constraints; Layer 2 weak harmonic
constraints; Layer 3 weak harmonic constraints
Ligand weak harmonic constraints; Layer 2 weak harmonic
constraints; Layer 3 no constraints
Ligand weak harmonic constraints; Layer 2 no constraints;
Layer 3 no constraints
Ligand no constraints; Layer 2 no constraints; Layer 3 no
constraints
2.
3.
4.
5.
6.
7.
8.
Methodology…
Estimating LBA for the minimised systems
• The minimised ligand : protein
complexes were imported into
SYBYL
• Minimised ligand saved in mol2
format
• Minimised protein saved in pdb
format
• LBA estimated in SCORE
Methodology…
Oestrogen
Receptor Ligands
• Unminimised in
silico LBA vs
experimental LBA
(Gao et al.)
• Minimised in silico
LBA vs
experimental LBA
(Gao et al.)
Androgen Receptor
Ligands
• Unminimised in
silico LBA vs
experimental LBA
(Waller et al.)
• Minimised in
silico LBA vs
experimental LBA
(Waller et al.)
RESULTS
Androgen Receptor Ligands
RESULTS…
GRAPH OF pKd (EXPERIMENTAL)
vs pKd (in silico) UNMINIMISED
Predicted pKd
(Before minimisation)
Predicted pKd
(After charmm
minimisation)
Experimental pKd
(Mekenyan &
Bradbury)32, 5
Metribolone (parent from pdb)
7.41
6.22
3.00
Mibolerone
7.82
8.14
3.00
5a dihydrotestosterone
7.67
8.37
2.30
4
pKd (exptal)
ANDROGEN-RECEPTOR LIGANDS
2
0
6.5
-2
7
7.5
-4
7.15
7.42
0.96
7.08
7.96
0.70
Progesterone
6.65
7.82
-2.40
17a hydroxyprogesterone
6.80
7.53
-2.40
Corticosterone
6.58
7.28
-2.70
Pregnenolone
6.65
7.68
-2.70
Testosterone
7.53
7.36
1.82
Androstenedione
7.38
7.89
0.60
y = 5.0008x - 35.589
2
R = 0.8959
pKd (in silico)
GRAPH OF pKd (EXPERIMENTAL)
vs pKd (IN SILICO) (MINIMISED)
4
pKd (exptal)
Oestradiol
5a androstane
8
3
2
1
0
-1 6
6.5
7
7.5
8
8.5
-2
-3
pKd (in silico)
y = -0.2023x + 1.7373
2
R = 0.0025
5
SUMMARY RESULTS….
Oestrogen Receptor
Ligands
• No linear correlations were
observed when the
experimental binding affinity
for the ligand set was plotted
against its in silico
counterpart for either
unminimised or minimised
ligands, even when these
were grouped according to
position on which
substitution was made
Androgen Receptor
Ligands
• A linear correlation was
observed when the
experimental binding affinity
for the ligand set was plotted
against its in silico
counterpart for the
unminimised ligands
• Minimisation, irrespective of
the protocol utilised,
disrupted the linear
relationship
PART 2
NON STEROIDAL ANDROGEN RECEPTOR LIGANDS
• Literature based evidence
indicates that a substituted
phenyl ring, equivalent to the
A ring of the steroid structure,
is an essential feature that
acts as an anchor to the
molecular recognition site of
the androgen receptor
18
12
19 11
1
2
• Consequently
superimposition of the
substituted phenyl group on
the A ring of a docked steroid
molecule, would constitute an
ideal starting point for the
docking of non steroidal
molecules
CH3
10
3
4
5
9
CH3
17
13
8 14
16
15
7
6
PART 2
NON STEROIDAL ANDROGEN RECEPTOR LIGANDSMapping the Active Site of the Androgen Receptor
• a map of the
amino acid
perimeter of the
active site was
generated in
Sybyl, using the
ligand- protein
contacts in pdb
ID 1e3g
PART 2
NON STEROIDAL ANDROGEN RECEPTOR LIGANDS
Main issues to address
1. What binding modality that should be
adopted in the docking of the non
steroidal ligands to the active site?
2. No non steroidal co-ordinates
available from the pdb
3. Also there are significant structural
variations between the non-steroidal
ligands known to bind the androgen
receptor
PART 2
NON STEROIDAL ANDROGEN RECEPTOR LIGANDSMapping the Active Site of the Androgen Receptor
•a map of the active
site indicating polar
and hydrophobic
sites was generated
using Ligbuilder,
with testosterone
bound into the
androgen receptor
(pdb 1E3G)
PART 2
NON STEROIDAL ANDROGEN RECEPTOR LIGANDSMapping the Active Site of the Androgen Receptor
• all amino acids except Thr 877
Asn 705 Gln 711 and Arg 752
were deleted
• These amino acids were
indicated in the literature as
being of fundamental
importance for binding as
proved by site directed
mutagenesis
• the distances between the H
bond sites of the amino acids
and the corresponding H
bond sites on testosterone
were measured. This was
done in order to try to
maintain these distances
when the non steroidal
ligands were to be docked.
6
PART 2
Distance between the H bond Sites of the Critical
Amino Acids and the Corresponding H bond Sites on
Testosterone
Thr 877:
Gln 711:
OG1 of Thr and H44 of
testosterone: 1.927A
H39 of Thr and O43 of
testosterone: 3.552A
H17 of Gln and O49 of
testosterone: 2.660A
H18 of Gln and O49 of
testosterone: 4.581A
Arg 752:
H33 of Arg and O49 of
testosterone: 2.660A
H32 of Arg and O49 of
testosterone: 2.406A
Asn 705:
ND2 of Thr and O43 of
testosterone: 4.405A
PART 2
NON STEROIDAL ANDROGEN RECEPTOR LIGANDSMapping the Active Site of the Androgen Receptor
PART 2
Proposed Orientation for Hydroxyflutamide..
• The best representation of
hydroxyflutamide in its docked form
was achieved with the following
superimposition methodology:
• Superimposition of the nitro group of
hydroxyflutamide onto the 3 keto
group of testosterone to preserve the
H bonding between the 3 keto moiety
and Gln 711 and Arg 752.
• Superimposition of the CF3 group of
hydroxyflutamide onto C4 of
testosterone so that it would occupy
the hydrophobic region and also the
same plane as C4, 5 & 6 of
testosterone.
• Superimposition of the hydroxyl
group a to the carbonyl in
hydroxyflutamide onto the 17 OH of
testosterone in order to preserve H
bonding with Asn 705 and Thr 877.
PART 2
Proposed Orientation for Hydroxyflutamide..
• In this orientation for
hydroxyflutamide the substituted ring
occupies approximately the same
spatial orientation as does the A ring
of the steroid
• The distances between the anchored
regions of the hydroxyflutamide
ligand and its H bond partners on
amino acids Thr 877 Asn 705 Gln 711
and Arg 752 were comparable with
those of testosterone
• This exercise was applied to all
ligands forming part of the test set
TESTOSTERONE
HYDROXYFLUTAMIDE
• The training set ligands were then
superimposed directly onto the test
set ligand that was most similar to it
in structure
Finding the optimum docked spatial orientation for hydroxyflutamide
Based on the binding modality of testosterone
PART 2
The Training Set
pp’-DDT
op’-DDT
pp’-DDE
PART 2
The Training Set
Hydroxyflutamide
Vinclozolin
Procymidone
Linuron
Methoxychlor
Diethylstilboestrol
7
• (2-{[(3,5-dichlorophenyl)carbamoyl]oxy}-2-methyl-3butenoic acid
• 3’,5’-dichloro-2-hydroxy-2-methylbut-3-en anilide
• Hydroxylated analogue of PCB 153
• 2,2-bis(p-hydroxyphenyl)-1,1,1-trichloroethane
• 3,5-dichlorobenzanilide-2-cyclopropanecarboxylic
acid
• PCB 153
• Hydroxylinuron
Results Androgen Receptor
Ligands- Training & Test Sets..
Graph of pKd (exptal) vs pKd (in silico)
0.5
R2 = 0.7867
0
Hydroxyflutamide
-0.5 3
pKd (exptal)
PART 2
The Test Set
4
5
p,p-DDE
6
p,p-DDT
o,p-DDT
-1.5
M1
P1
Hydroxy PCB
-2
-2.5
Hpte
7
DES
-1
Linuron
Training Set
Test Set
Linear (Series1)
M2
Hydroxylinuron
M ethoxychlor
Vinclozolin
PCB
Procymidone
-3
-3.5
pKd in silico
CONCLUSIONS…
CONCLUSIONS…
• The experimental ligand binding affinity data
were obtained from literature. These were
essentially reports of in vitro competitive
assay studies
• Waller et al., carried out a competitive
binding assay study using [3H] R1881
radiolabelled synthetic androgen
• Waller’s study used a series of steroidal and
non-steroidal androgen receptor ligands.
• Waller’s study is valuable for its compilation
of binding data generated in one laboratory
under
consistent
conditions,
thereby
minimising the contribution of biological
variability to uncertainty in the modelling
results
• The results of the androgen receptor ligand
assays were obtained from the paper by
Waller, while those of the oestrogen receptor
ligand binding assays were obtained from
the paper of Gao et al.
CONCLUSIONS…
CONCLUSIONS…
• Gao et al’s study was a broad based
investigation of the interaction between
oestrogen ligands and their allied
receptor. For each substitution type
considered, a QSAR equation was
derived based on the interaction
between the specific ligand type and its
receptor.
• This paper contains experimental
ligand binding affinity data that was
obtained from various sources
• The data presented by Gao et al. was taken from different
sources, & placed on a common Relative Binding Affinity (RBA)
scale with oestradiol, by definition, was assigned a value of 100
(log RBA =2), with lower and higher affinity ligands than
oestradiol having lower and higher values than this benchmark
level
• The diverse sources could potentially detract from the
consistency that it would have had it all been generated in the
same laboratory
• The fact that different oestrogen receptor preparations (coming
from mouse, rat, lamb and calf, uterine cytosol) were used in
the estimation of the ligand binding affinities of the oestrogen
receptor ligands could also potentially detract from the
“relative consistency” of the data
8
CONCLUSIONS…
CONCLUSIONS…
•
The temperature at which the competitive binding affinity assays were carried
out is perhaps a far more important issue. Traditionally, competitive binding
affinity assays are sometimes run at 0oC and at 25oC. Purportedly, carrying out
the assays at 0oC preserves receptor stability, while carrying out assays at
25oC allows for more rapid equlibration of the binding process for both
competing ligand and labelled tracer
•
In fact, the RBA values presented by Gao et al at either temperature are
different, probably due to incomplete assay
equilibration at the lower
temperature
•
This problem was more evident with higher affinity ligands, for which
association and dissociation rates may be slower
•
This latter issue gains importance given that most of the RBA data presented
was derived from biological assays carried out at 0oC.
• In the case of the androgen receptor
ligands, linear relationships between
experimental and in silico pKd and
hence reliable correlation tools were
established for both unminimised
steroidal
and
unminimised
nonsteroidal ligands
• Two issues must still be pointed out
however:
•
All these reasons could account for the fact that no linear correlations between
experimental and in silico pKd could be established for the oestrogen receptor
ligands
CONCLUSIONS…
1.
2.
Minimisation did not enhance or even retain the
linear relationships for both the steroidal and the
non steroidal ligands. The implication is that the
binding conformation of these ligands is not their
lowest energy state
The traces for the steroidal and the non-steroidal
ligands were not super-imposable. This implies
that although both categories have an affinity for
the androgen receptor, the binding modality is
different- a fact that may be confirmed through
an investigation of the dynamics of the androgen
receptor bound to steroidal and non-steroidal
ligands respectively
REFERENCES
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Biological Activity: Structure-Activity Relationships for Aryl Hydrocarbon,
Estrogen and Androgen Receptor Binding Affinity. Environmental Toxicology
and Chemistry 1998 17(1): 15-25.
• Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M.
CHARMM: A Program for Macromolecular Energy, Minimization, and Dynamics
Calculations. J. Comp. Chem. 1983 4: 187-217.
• Matias PM, Donner P, Coehlo R, Thomaz M, Peixoto C, Macedo S Otto
N Joschko S, Scholz P, Wegg A, Basler S, Schafer M, Egner U, Carrondo MA.
Structural evidence for ligand specificity in the binding domain of the human
androgen receptor. Implications for pathogenic gene mutations. J.Biol.Chem.
2000 275: 26164-26171
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