GHENT UNIVERSITY
Master thesis performed at:
FACULTY OF PHARMACEUTICAL SCIENCES
UNIVERSITY OF CAMERINO
Department of pharmaceutics
SCHOOL OF PHARMACY
Laboratory for Medicinal Chemistry
Department of Chemical Sciences
Academic Year 2011-2012
ANALYSIS AND USE OF ADENOSINE RECEPTOR STRUCTURES
WITH MOLECULAR MODELING TOOLS
Pieter CARDOEN
First Master of Pharmaceutical Care
Promoter
Prof. dr. S. Van Calenbergh
Co-promoter
Dr. D. Dal Ben
Commissioners
Prof. dr. S. Van Calenbergh
Prof. dr. B. De Spiegeleer
Dr. M. Risseeuw
GHENT UNIVERSITY
Master thesis performed at:
FACULTY OF PHARMACEUTICAL SCIENCES
UNIVERSITY OF CAMERINO
Department of pharmaceutics
SCHOOL OF PHARMACY
Laboratory for Medicinal Chemistry
Department of Chemical Sciences
Academic Year 2011-2012
ANALYSIS AND USE OF ADENOSINE RECEPTOR STRUCTURES
WITH MOLECULAR MODELING TOOLS
Pieter CARDOEN
First Master of Pharmaceutical Care
Promoter
Prof. dr. S. Van Calenbergh
Co-promoter
Dr. D. Dal Ben
Commissioners
Prof. dr. S. Van Calenbergh
Prof. dr. B. De Spiegeleer
Dr. M. Risseeuw
COPYRIGHT
"The author and the promoters give the authorization to consult and to copy parts of this
thesis for personal use only. Any other use is limited by the laws of copyright, especially
concerning the obligation to refer to the source whenever results from this thesis are cited."
May 25, 2012
Promoter
Author
Prof. dr. S. Van Calenbergh
P. Cardoen
SUMMARY
Up to date, eleven crystal structures of the A2AAR co-crystallized with an agonist or
antagonist have been reported in the literature. Firstly, we seek a better understanding of these 3D
structures, which is necessary to carry out the subsequent research that has a threefold objective.
On the basis of the position and the role of receptor residues, we aim at interpreting mutagenesis
data for the A2AAR. When we are able to explain these data, this may lead to more targeted
research on A2AAR ligands. A second objective envisions a limitation of the number of compounds
to be synthesized in drug development through the implication of docking and quantitativestructure activity relationship methods. In this way research can be made cheaper and less timeconsuming. The last objective is to create homology models all adenosine receptor subtypes.
Discovering differences in binding mode of adenosine, the physiological agonist of the A2AAR, may
lead to the development of subtype selective compounds.
Analysis of the mutagenesis data allowed to confirm earlier suspicions and knowledge on the
A2AAR and to develop new insights. Inserting mutations can cause two effects: destabilization of
the receptor conformation and influencing the ligand affinity. So understanding mutagenesis data
may be useful for the development of A2AAR ligands and for receptor thermostabilization.
The second objective of our research consisted of two parts. The antagonist ZM241385 was
docked on three A2AAR crystal structures that were co-crystallized with it. This allowed identifying
the best placing algorithm-scoring function combination that was subsequently applied on a
dataset of A2AAR antagonist compounds with known affinity. Re-scoring of the best poses carried
out with the quantitative-structure activity relationship method resulted in a model that was
validated using a test set. In this way, a distinction can be made between promising and less
promising compounds, so the synthesis stage in developing new ligands is simplified.
Finally, homology models of the four subtypes of the adenosine receptor were generated.
Differences seemed to be subtle, but the mutation of glutamate 169, an important residue in
ligand binding, into a valine in case of the A3AR, was remarkable. This could be used to create
compounds with a higher affinity for the A3AR with respect to the other subtypes by the
introduction of more hydrophobic substituents on the ligand scaffold.
SAMENVATTING
Tot op heden werden elf kristalstructuren van de A2AAR gerapporteerd in de literatuur. Deze
betreffen een complex van deze receptor met een agonist of een antagonist. Vooreerst trachten
we een beter inzicht te krijgen in deze 3D structuren, wat noodzakelijk is voor het uitvoeren van
het aansluitend onderzoek. Het objectief van dit onderzoek is drieërlei. Aan de hand van de plaats
en de functie van receptorresiduen trachten we de resultaten van mutagenese studies te
interpreteren. Indien we hiertoe in staat zijn, kan dit leiden tot een gerichter onderzoek naar
liganden voor de A2AAR. Een tweede objectief beoogt het beperken van het aantal verbindingen
dat dient te worden gesynthetiseerd tijdens de ontwikkeling van nieuwe geneesmiddelen door
toepassing van docking en QSAR methoden. Zo kan het onderzoek goedkoper en minder
tijdrovend worden gemaakt. Het laatste objectief is het bouwen van homologie modellen van de
vier adenosine receptor subtypes. Het ontdekken van verschillen in het binden van adenosine, de
fysiologische agonist van de A2AAR, kan leiden tot de ontwikkeling van verbindingen met
selectiviteit voor één van de subtypes.
Analyse van de mutagenese data liet toe eerdere kennis en vermoedens betreffende de
A2AAR te bevestigen en nieuwe inzichten te ontwikkelen. Het invoeren van mutaties kan leiden tot
twee effecten: destabilisatie van de receptorconformatie en verandering van de affiniteit voor
liganden. Een beter begrip van mutagenese data kan dus nuttig zijn voor de ontwikkeling van
liganden voor de A2AAR en de mutaties kunnen toegepast worden voor thermostabilisatie.
Het tweede objectief bestond uit twee delen. De antagonist ZM241385 werd gedocked in
drie A2AAR kristalstructuren die gecokristalliseerd werden met deze molecule. Dit liet toe de beste
combinatie placing algorithme-scoring functie te selecteren en vervolgens toe te passen op een
dataset van A2AAR antagonisten met gekende affiniteit. Het herscoren van de beste bindingswijzen
met de QSAR methode resulteerde in een model dat werd gevalideerd gebruikmakende van een
test set. Zo werd een onderscheid gemaakt tussen veelbelovende en minder interessante
verbindingen, waardoor de synthese-inspanningen kunnen gereduceerd worden.
Tenslotte werden homologie modellen van de adenosine receptor subtypes gebouwd.
Hoewel verschillen subtiel zijn, valt vooral de mutatie van glutamaat 169 door valine op in de A3AR,
wat suggereert dat hydrofobe modificatie van liganden kan resulteren in A3AR selectieve
verbindingen.
TABLE OF CONTENTS
1. INTRODUCTION .............................................................................................................1
1.1 G PROTEIN-COUPLED RECEPTORS AND THE ADENOSINE A2A RECEPTOR ................................. 1
1.2. A2AAR LIGANDS .................................................................................................................. 2
1.2.1. A2AAR agonists ...................................................................................................................................... 2
1.2.2. A2AAR antagonists ................................................................................................................................ 2
1.3. STRUCTURE OF THE A2AAR .................................................................................................. 4
1.3.1. Molecular modeling studies ................................................................................................................. 4
1.3.2. Adenosine receptor crystal structures ................................................................................................. 6
1.3.2.1. Receptor engineered with T4 Lysozyme ........................................................................................................ 6
1.3.2.2. Receptor engineered through thermostabilization ....................................................................................... 7
1.3.2.3. Receptor engineered with Fab fragment ....................................................................................................... 8
1.3.2.4. A2AAR crystal structures in complex with ZM241385 .................................................................................... 8
1.3.2.5. Adenosine receptor crystal structures in complex with an agonist ............................................................. 12
2. OBJECTIVES.................................................................................................................. 14
3. METHODS .................................................................................................................... 15
3.1. ANALYSIS OF MUTAGENESIS DATA OF THE A2AAR ............................................................... 15
3.2. MAIN PRINCIPLES OF MOLECULAR MECHANICS AND MOLECULAR DOCKING ..................... 15
3.2.1. Molecular mechanics ........................................................................................................................ 15
3.2.2. Molecular Docking ............................................................................................................................. 20
3.2.2.1 Retrieval and analysis of A2AAR crystal structures as docking targets .......................................................... 21
3.2.2.2. Docking analysis .......................................................................................................................................... 23
3.2.2.3. Post-docking analysis: Quantitative structure-activity relationship............................................................. 27
3.3. HOMOLOGY MODELING ................................................................................................... 30
4. RESULTS AND DISCUSSION ........................................................................................... 32
4.1. ANALYSIS OF MUTAGENESIS DATA OF THE A2AAR ............................................................... 32
4.2. DOCKING AND QSAR ANALYSIS OF DOCKING RESULTS ....................................................... 37
4.3. DEVELOPMENT AND STUDY OF HOMOLOGY MODELS........................................................ 41
5. CONCLUSION ............................................................................................................... 45
6. BIBLIOGRAPHY ............................................................................................................ 47
LIST OF ABBREVIATIONS
A1AR
Adenosine A1 Receptor
A2AAR
Adenosine A2A Receptor
A2BAR
Adenosine A2B Receptor
A3AR
Adenosine A3 Receptor
bacRho
Bacterial Rhodopsin
bRho
Bovine Rhodopsin
EL
Extracellular Loop
ERK
Extracellular signal-Regulated Kinase
FDA
Food and Drug Administration
GDP
Guanosine Diphosphate
GPCR
G Protein-Coupled Receptor
GTP
Guanosine Triphosphate
IL
Intracellular Loop
PDB
Protein Data Bank
RMSD
Rooth Mean Square Deviation
TM
Transmembrane
1. INTRODUCTION
1.1 G PROTEIN-COUPLED RECEPTORS AND THE ADENOSINE A2A RECEPTOR
The adenosine receptor is a member of the family of G protein-coupled receptors (GPCRs),
which forms the largest family of drug-targetable cell surface receptors. These are all membrane
spanning proteins consisting of seven transmembrane (TM) α-helices that are connected to each
other by three intracellular (IL) and three extracellular (EL) loops. The N-terminal domain of the
receptor is located in the extracellular environment, the C-terminal domain in the cytosol. The
family of GPCRs is clustered into five classes: the rhodopsin family or family A, the secretin family
or family B, the glutamate family or family C, the adhesion family or family D and the Frizzled/Taste
family or family E. Four subtypes of adenosine receptors, which belong to family A, are
distinguished, adenosine A1 (A1AR), A2A (A2AAR), A2B (A2BAR) and A3 (A3AR) receptors, each of them
showing different pharmacological properties.1’2
Activation of a G protein-coupled receptor induces the exchange of guanosine diphosphate
(GDP) for guanosine triphosphate (GTP) in the heterotrimeric G protein, which is bound to an
intracellular binding pocket. Subsequent dissociation of the G protein into separate Gα and Gβγ
subunits takes place. These subunits are now able to interact with intracellular targets. A2AARs are
coupled to Gs proteins. Activation of the receptor results in stimulation of adenylate cyclase and
subsequent activation of cAMP-dependent protein kinase A or C. This protein kinase
phosphorylates various intracellular targets, among others receptors and ion channels. Other
mechanisms are the stimulation of Golf proteins in brain tissue and the interaction with Ca2+
channels to modulate ERK activity. A variety of proteins, among others α-actinin, calmodulin and β
arrestins, can interact at the exceptionally long C-terminus of the A2AAR.1,3
Adenosine receptors are not only expressed in the monomeric state, they also form dimeric
structures through self-association and hetero-oligomerization. While two identical adenosine
receptor subtypes forms homomers, heteromers are formed through linking of an adenosine
receptor and another receptor. A2AARs are highly expressed in the striatum of the brain, where
they interact with dopamine D2 receptors to form such heterodimers. As the A2AAR antagonists can
simulate the activation of dopamine D2 receptors, they may have potential in the treatment of
Parkinson’s disease and drug addiction.4
1
Within the class of adenosine receptors, a high degree of sequence identity is found, with
the highest conservation between the A2AAR and A2BAR. In particular, the extracellular part of the
helical bundle shows the highest domain similarity, while the loops share less amino acid sequence
identity. Highest sequence similarity is found in the binding pocket region.5
1.2. A2AAR LIGANDS
For several decades, potent and selective adenosine receptor ligands have been the object of
medicinal chemistry research. In what follows, the focus is on the discovery of agonists and
antagonists for the A2AAR. The endogenous purine nucleoside adenosine is the physiological
agonist for the A2AAR. Because the A2AAR is activated by nanomolar concentrations of adenosine,
the receptor is called a high affinity AR subtype.
1.2.1. A2AAR agonists
Due to its limited stability, the physiological agonist adenosine was modified in order to
discover stable and more selective ligands for the ARs. The presence of the adenosine scaffold is of
major importance and has to be conserved to obtain efficient and selective agonists. The scaffold
can be modified in the 3’ and 4’ positions of the ribose and the 2 and N6 positions of the purine in
order to find metabolically stable agonists. Lexiscan, an Ado derivative that was substituted at the
2 position, was approved by the Food and Drug Administration (FDA) for use as a pharmacological
stress agent.6
1.2.2. A2AAR antagonists
The A2AAR antagonists exhibit a high structural variability. Three major groups can be
distinguished: xanthine analogs, adenine analogs and atypical heterocyclic compounds. Fifteen
years ago it was reported that caffeine (Fig. 1.1A) possesses a high affinity for the A2AAR and its
interaction with this receptor mediates the behavioral stimulation typically observed by caffeine
intake. Hence, the xanthine scaffold was modified at the positions 1, 3, 7 and 8 in order to yield
some potent and selective A2AAR antagonists. 3,7-dimethyl-1- propargylxanthine (DPMX, Fig. 1.1B)
was the first weakly potent xanthine analogue designed as A2AAR antagonist. Subsequent research
into 1,3,8-substituted xanthines resulted in the discovery of the first strongly active and selective
A2AAR antagonists, such as 3-chlorostyrylcaffeine (CSC). However, the use of these xanthine
2
analogs for studying the A2AAR was limited because of photoisomerization and their low water
solubility. To overcome the phenomenon of isomerization, the styryl moiety of styrylxanthines can
be replaced by another functional group or the compounds can be administered as solid
substances. The substitution of the styryl moiety, however, led to a decrease in affinity.
Modification of the styrylxanthines was suggested to improve the water solubility and for example
the compound MSX-3, the prodrug of MSX-2 (Fig. 1.1C), seemed to be more efficient than the drug
itself.6
Figure 1.1: Structure of some A2AAR agonists and antagonists.
Considering the problems of low water solubility and photoisomerization of the xanthine
derivatives, the focus has shifted to the development of non-xanthine analogues. Promising A2AAR
antagonists are the non-xanthine imidazopyrimidine (purine)-type structures, such as ANR-152
(Fig. 1.1E) and VER-6947 (Fig. 1.1F) that are endowed with enhanced binding affinities. Further
research resulted in the synthesis of adenine analogs bearing an alkynyl chain in the 2 or 8
position. The highest affinities at the A2AAR were observed for the 2-alkynyl derivatives. It was
decided to further explore the 2 position of 9-ethyladenine derivatives by introducing
phenylethylamino or phenylethoxy groups. These ligands bear a bromine atom in the 8 position,
3
which results in ligands showing nanomolar Ki values. Replacing the 8-bromine atom by a 2-furyl
group increased selectivity for the A2AAR. 9-propyladenines substituted with bulky chains at the N6
position show improved binding affinities at the A1AR and A3AR, and subsequently selectivity for
these receptors. However, introduction of a chlorine atom in the 2 position leads to compounds
with increased A2AAR selectivity.6
Another class of A2AAR antagonists is that of the heterocyclic compounds, like CGS 15943
(Fig. 1.1D) and ZM241385 (Fig. 4) that are very potent as A2AAR antagonist. Also these analogs did
not show favorable water solubility. Other derivatives, such as benzothiazole and 1,2,4-triazole
analogs, can be used for generating therapeutic A2AAR antagonists.6
1.3. STRUCTURE OF THE A2AAR
1.3.1. Molecular modeling studies
Early approaches to describe the structural features of adenosine receptors with
computational tools afforded a “negative image” of the binding pockets. The analyses were carried
out by superimposing and comparing various antagonists, so the key factors for the affinity could
be clarified and a 3D structure made up.
The second generation of adenosine receptor 3D models was obtained by using homology
modeling. This technique aims at obtaining the three-dimensional structure of the receptor using
the structure of a comparable receptor as template. The sequence of the homologue receptor, the
adenosine receptor, is aligned with that of the template and subsequently converted into a threedimensional model. The better the resolution of the template and the sequence similarity, the
higher the accuracy of the predicted structure will be. So, in order to obtain trustable GPCR
models, suitable templates need to be available.
Solving the three-dimensional crystal structure of membrane proteins is quite complex. Two
important factors contribute to this difficulty in crystallizing transmembranal receptors. Firstly,
membrane proteins are present in very low concentrations in tissues; secondly their surface is
amphipathic. The surface, in contact with the intracellular and extracellular aqueous phases, is
polar while the transmembranal region is hydrophobic.
4
Figure 1.2: Representation of the structure of the human A2AAR. Colours of the ribbon vary
from dark blue to dark red. Dark blue represents the extracellular N-terminal tail of the receptor,
dark red the intracellular C-terminal tail. The seven helical segments are shown. In the upper
part of the picture, the binding pocket (yellow) and co-crystallized ZM241385 (orange) are
depicted.
Despite the absence of any sequence homology with bacterial rhodopsin (bacRho), the
bacRho structure obtained by cryo-electron microscopy studies was used for the generation of the
early adenosine receptor homology models. This procedure consists of the replacement of the
bacRho amino acids by the corresponding residues of the adenosine receptor. The models
obtained by this method were used to perform mutagenesis studies, which aim at highlighting the
role for ligand interaction of the residues present in the binding pocket.
The 3D structure of bovine rhodopsin (bRho) was solved through X-ray diffraction in 2000.7
Adenosine receptor homology models could be built from this template, which also provided the
structure of the intracellular and extracellular loops. The structural models obtained by homology
5
modeling were more accurate than the first models and even results of previous mutagenesis
studies could be rationalized.
In late 2007 both the crystal structure of the human and the turkey β2 adrenergic receptor
were reported.8,9 The human receptor was engineered with the insertion of a T4 Lysozyme
segment instead of the IL3. A different way of transmembranal helix packing, EL2 fold and a
different number of disulfide links, is observed between the bRho and adrenergic β2 receptor.
These differences result in another orientation of the binding site amino acids and a particular
binding pose and orientation of the ligands.
1.3.2. Adenosine receptor crystal structures
Tremendous progress was made by the crystallization of the human A2AAR (Fig 1.2).10 To
date, 11 crystal structures of human A2AAR have been obtained and published.10-15 In contrast to
the bRho and adrenergic β receptor binding sites, the binding pocket of A2AAR is located differently
and the receptor-ligand contacts are provided by residues of different transmembranal helices and
extracellular loops. Major causes for this different receptor-ligand interaction pattern are the
dissimilar ligand pose and orientation, and the different EL2 fold. In bRho the binding site is
covered by EL2, which restricts the insertion of ligands in the binding cavity. So, in bRho-based
adenosine receptor models bulky ligands are not able to enter the binding pocket.5 A first
comparative analysis of the human A2AAR crystal structures can be made considering the methods
used to stabilize and crystallize the receptor-ligand complex. In all cases, engineered receptors
were used because of the thermolability of the wild-type adenosine receptor. So, the goal of
engineering the receptors is to create stable receptor variants, which can undergo solubilization,
purification and X-ray studies.
1.3.2.1. Receptor engineered with T4 Lysozyme
In order to obtain the crystal structure, the wild-type A2AAR was engineered by substituting
most of the third cytosolic loop, which is of major importance for G-protein binding, with a T4
Lysozyme fragment. Further stabilization of the receptor was achieved by replacing a part of the Cterminal domain with a histidine purification epitope. Both procedures result in increasing the
likelihood of crystal formation, because now some flexible regions of the wild-type receptor are
removed. In the purification step, the addition of modulators such as sodium chloride and
6
cholesterol, and a receptor-saturating concentration of theophylline is seen as vital. Additionally,
modification of this construct at the amino terminus was performed by addition of a
hemagglutinin signal sequence and a FLAG-M2 detection tag. This method was used to obtain two
crystal structures, one in complex with the high affinity antagonist ZM241385, the other one in
complex with the potent agonist UK432097.11 Given the significant modifications applied to the
wild-type receptor, it is appropriate to characterize the pharmacological properties of the
construct with respect to the wild-type receptor properties.10,16
1.3.2.2. Receptor engineered through thermostabilization
Further A2AAR crystal structures were obtained by using conformational thermostabilization,
a mutagenic strategy that consists of introducing some point mutations into the receptor sequence
(Fig 1.3). This method aims at obtaining both higher receptor stability and agonist/antagonist
selectivity. Each point mutation is inserted to maintain interaction with one ligand species and to
reduce the binding of the other one, by changing the equilibrium between agonist and antagonist
conformation. When the bound ligand is an antagonist, the equilibrium will be altered in the
antagonist conformation. If it concerns an agonist, the equilibrium will be driven towards the
agonist conformation. The thermostabilization allows the use of short-chain detergents during
purification, crystallization and structure determination This method was used to obtain 7 crystal
structures of human A2AAR, in complex with agonist (Adenosine, NECA) or antagonist (ZM241385,
Caffeine, XAC, and two triazine derivatives) compounds.12,13,15
Figure 1.3: Effects and location of mutations into the A2AAR receptor. A. Thermostability
plots B. Sequence with mutated residues (red cercles)
7
1.3.2.3. Receptor engineered with Fab fragment
Stabilization of the receptor was achieved also by generating a complex of the receptor with
a mouse monoclonal antibody Fab fragment. In fact, the complementarity-determining region of
the Fab fragment binds to an intracellular binding pocket formed by TM2, TM3, TM6, and TM7.
This pocket is the critical site for transferring signals between the activated adenosine receptor and
the G protein. In contrast to the above described crystal structures, this does not cause disruption
of IL3. Two crystal structures were obtained, in both cases in complex with ZM241385
compound.14 The raising of Fab2838 against the intracellular surface of the A2AAR locks the
receptor in an inactive state configuration. This results in inhibition of agonist binding, while
antagonist binding is not affected.14
1.3.2.4. A2AAR crystal structures in complex with ZM241385
ZM241385 (Fig 1.4), which is co-crystallized with the A2AAR in different crystal structures, has
a prototypical AR antagonist structure. The bicyclic triazolotriazine unit, which is positioned in the
center of the binding pocket, is the core of the molecule. This scaffold bears a furan ring and a 4hydroxyphenylethyl side chain. The furan ring is located in the lower part of the binding pocket,
while the 4-hydroxyphenylethyl side chain points to the extracellular environment.1
Figure 1.4: Structure of the selective A2AAR antagonist ZM241385
Residues from different transmembranal helices and loops are involved in binding the
antagonist. The residues involved in the most significant interactions are described below. First,
some residues of extracellular loop 2 are involved in high-affinity ligand binding. Phenylalanine 168
and glutamate 169 interact with ZM241385 through an aromatic π-stacking interaction with the
triazolotriazine core and hydrogen bonding respectively. The side chain of phenylalanine 168 is
8
essential in binding ZM241385. The bicyclic system and the NH group of ZM241385 are coplanar. In
the 3EML (Fig 1.5) and the 3VG9 (Fig 1.7) crystal structure, where the NH group points towards the
receptor core, ZM241385 is found in the extended conformation and the molecule is orientated
perpendicular to the plane of the membrane bilayer. Here, glutamate 169 forms an intermolecular
hydrogen bond with the NH2 group. When the NH group points towards the extracellular side, as
seen in the 3PWH crystal structure (Fig 1.6), the ZM241385 ligand is found in the folded
conformation and glutamate forms a hydrogen bond with the NH group itself. These are the two
main conformations possible, because the co-planarity allows only few conformations. As the
glutamate residue interacts in the three crystal structures with ZM241385 through a hydrogen
bond, the results of mutagenesis studies are easily understandable. Mutation of glutamate 169 by
an alanine results in a reduction of the antagonist binding.17
Figure 1.5: A. Representation of the A2AAR binding pocket of the 3EML crystal structure.
Critical residues in ligand binding are colored in yellow, ZM241385 in orange; hydrogen bonds
are purple lines B. Schematic representation of ligand-receptor interactions.
Residues from TM6 are also involved in ligand binding. Hereby, asparagine 253 is of critical
importance. The NH2 group of ZM241385 forms a hydrogen bond with the carboxamide carbonyl
9
group of this asparagine. An additional hydrogen bond with the oxygen of the furyl group of
ZM241385 is made, even if it is not clearly observed in all A2AAR-ZM241385 crystal structures. The
furan ring and the triazolotriazine are positioned similarly in all of the three crystal structures. The
furyl moiety points to TM5 and TM7 and makes a hydrophobic interaction with tryptophane 246 at
the bottom of the binding pocket. The 4-hydroxyphenylethyl side chain, which is positioned
towards the extracellular environment, shows higher structural flexibility with respect to the furyl
and triazolotriazine moieties.
Figure 1.6: A. Representation of the A2AAR binding pocket of the 3PWH crystal structure.
Critical residues in ligand binding are colored in yellow, ZM241385 in orange; hydrogen bonds
are purple lines B. Schematic representation of ligand-receptor interactions.
The A2AAR binding cavity is composed of two hydrophobic walls between which the
ZM241385 scaffold is inserted in a well-defined way. The triazolotriazine two-ring π system is
orientated parallel to the one, consisting of phenylalanine 168, and the other, consisting of leucine
249, histidine 250, leucine 267 and methionine 270. A third wall is composed of some hydrophilic
residues, glutamate 169 and asparagine 253, which interact with the amine groups and nitrogen
atoms of the ligand through the formation of hydrogen bonds. Finally, a wall that is made up by
10
some water molecules fills up the cavity upwards to the TM5 helix. These water molecules can
interact with the ligand nitrogens. Also at the roof of the binding pocket, especially hydrophobic
interactions contribute to the binding of the ligand. At the bottom of the binding pocket, a
tryptophane is found. This tryptophane may be involved in receptor activation through a process
referred to as ‘toggle switch’.
Figure 1.7: A. Representation of the A2AAR binding pocket of the 3VG9 crystal structure.
Critical residues in ligand binding are colored in yellow, ZM241385 in orange; hydrogen bonds
are purple lines B. Schematic representation of ligand-receptor interactions.
A fourth crystal structure of the A2AAR in complex with ZM241385 was obtained through
raising a Fab fragment against the intracellular surface of the receptor, as for the 3VG9 crystal
structure. The only difference with the 3VG9 crystal structure is that the 3D structure is revealed
by X-ray diffraction with a lower resolution, 3,1 Å instead of 2,7 Å for the 3VG9 crystal structure.
So, the 3VGA structure is not discussed.
11
1.3.2.5. Adenosine receptor crystal structures in complex with an agonist
In comparison with the many crystal structures of the A2AAR solved in complex with an
antagonist, less crystal structures of the A2AAR in complex with an agonist are elucidated, i.e. three
to date. These are the pdb-deposited crystal structures 2YDO, 3QAK and 2YDV.
The way in which agonists and antagonists are positioned into the A2AAR binding pocket is
quite similar. The physiological agonist adenosine, co-crystallized with the receptor in the 2YDO
crystal structure, is composed of a heterocyclic purine core, the adenine, and a sugar, the ribose.
The hydrophobic adenine is found in almost every A2AAR agonist and is inserted into the binding
site in an analogous manner as the bicyclic core of ZM241385. The contribution of phenylalanine
168 to the hydrophobic interaction between this scaffold and the receptor is essential, as
demonstrated by mutagenesis data. In case the phenylalanine is mutated into an alanine, which is
a small apolar residue, the response observed by binding of the agonist CGS21680 was 63-fold
lower than for the wild-type receptor. Other hydrophobic interactions are provided by leucine 249,
a residue that is found opposite to phenylalanine 168.16,18
Figure 1.8: A. Binding pose of NECA in the 2YDV crystal structure B. Binding pose of
adenosine in the 2YDO crystal structure.
12
The exocyclic nitrogen of the adenine is comparable to that of ZM241385 and binds to
glutamate 169 and asparagine 253 through polar interactions. The hydrophilic ribose moiety,
found in nearly all A2AAR agonists, is orientated in a slightly different way in comparison with the
furan ring of ZM241385. This ribose interacts with threonine 88, serine 277 and histidine 278,
three residues that are located in the bottom of the binding pocket. Activation of the receptor
would be mediated by binding to these residues, since they are not involved in binding
antagonists. The 3’-OH group interacts with both serine 277 and histidine 278, while the 2’-OH
group only interacts with the histidine. In both the NECA and UK432097 co-crystallized A2AARs,
respectively 2YDV and 3QAK, the ligand interacts with threonine 88 through the N2 of the 5’-Nethyluronamide group. The bulky substituents on the adenine moiety of UK432097 interact with
residues such as tyrosine 9 and glutamate 169. This results in the high potency of this ligand. 11,12,16
13
2. OBJECTIVES
In this work, three objectives are defined, all of them departing from the crystal structure of
the adenosine A2A receptor, the only adenosine receptor of which the crystal structure is
elucidated up to date. The results are achieved using computational methods.
First, we aim at analysing mutagenesis data reported in the literature. The goal of this part is
to understand why mutation of a residue causes a decrease or increase of agonist or antagonist
affinity. In detail, we try to deduct which residues are important in ligand binding, their impact on
ligand binding, and in which way the mutation switches the binding affinity. Even more, these data
may highlight the contribution of certain residues to receptor stabilization. So, explaining
mutagenesis data could be of importance in the future development of A2AAR antagonists or in
refining stabilization methods of the other adenosine receptors to obtain new crystal structures.
Secondly, we try to develop a tool that allows us to distinguish between promising and less
promising A2AAR antagonists. Towards this end we compose a database of 77 compounds with
known affinity (training set). First, we determine which is the best placing algorithm-scoring
function combination. This is carried out by docking ZM241385 on the three crystal structures that
were co-crystallized with ZM241385. Now, the dataset is docked using the placing-scoring
combination that generated the best poses in docking ZM241385. Subsequently, re-scoring of the
dataset is performed with the quantitative structure-activity relationship method and the
developed model is validated using a test set of 10 A2AAR antagonists. The aim of re-scoring is to
obtain an approximation, and not a prediction, of the pKi values of a database with A2AAR
antagonists of interest. Only molecules assigned with a pKi that satisfies the predefined criteria, i.e.
a range in between which the pKi has to lie, are retained. So, the number of compounds that has to
be synthesized in the following stage of research could be restricted. This results in a cheaper and
more simplified development process of drug candidates for the treatment of Parkinson’s disease.
Finally, we use the crystal structure of the A2AAR in complex with adenosine, the
physiological agonist of the adenosine receptor, to generate homology models of the different
adenosine receptor subtypes. This allows us to compare the corresponding residues of the four
subtypes and to detect subtle differences in binding adenosine. The method permits to distinguish
between the binding manner of adenosine in the several subtypes and to synthesize ligands that
are more potent for the one subtype than for the other, so to synthesize selective ligands.
14
3. METHODS
3.1. ANALYSIS OF MUTAGENESIS DATA OF THE A2AAR
The first part of our computational research focuses on comparing the eleven crystal
structures of the A2AAR co-crystallized with a ligand, which is an agonist or antagonist. Therefore,
the receptors are superimposed. Subsequently, the eleven ligands are selected and all the receptor
residues which are situated within a distance of 4,5 Å are specified. When a residue is located in
the environment of one ligand, but not in that of another ligand, that residue also is selected in all
of the other crystal structures. Now, we determine residue per residue, for all the crystal
structures, what the function is of that residue in the receptor. Is the residue involved in ligand
binding? Does it influence the conformation or stability of the receptor through interaction with
other residues? After performing this analysis and making up a database of the results, we try to
interpret the results of previous mutagenesis studies carried out on the A2AAR. When for example
a decrease in agonist affinity is observed when a specific residue is substituted by another more
hydrophilic or lipophilic residue, we try to interpret mutagenesis data according to the position
and the role of the residue observed in the considered crystal structures.
3.2. MAIN PRINCIPLES OF MOLECULAR MECHANICS AND MOLECULAR DOCKING
3.2.1. Molecular mechanics
The molecular mechanics model is a way to describe molecules, ranging from very simple to
quite complex structures, in a more simple way. The method is used in conformational analysis,
molecular dynamics and docking, techniques for which energy calculations are needed. The key
principles of the molecular mechanics are some classical mechanics laws. First, nuclei and
electrons of an atom have to be treated as a unit. Secondly, atoms may be seen as spherical
objects and they have a well-defined charge. Both the sphere radius and the atomic charge are
calculated using quantum mechanics. Finally, classical mechanics laws describe the interactions
between the atoms and these interactions result in defining the molecules geometry and energy.
The interaction between the atoms of a compound are described using a molecular function based
on these previously listed classical mechanics principles. The empirical function of the potential
energy of a compound as function of its atoms coordinates is called the force field. Energy values
that are calculated using this function have no physical meaning, but when the energies are
15
calculated for different conformations, for example obtained by using the docking method, the
relative energies of these different conformations may be compared.
The formula used to calculate the potential energy consists of terms corresponding with
energies, which refer to bonding and nonbonding interaction. Each of the energy terms in formula
(3.1) is the sum of some particular kinds of atomic interactions.
𝐸𝑝𝑜𝑡 = 𝐸𝑠𝑡𝑟 + 𝐸𝑎𝑛𝑔 + 𝐸𝑠𝑡𝑏 + 𝐸𝑜𝑜𝑝 + 𝐸𝑡𝑜𝑟 + 𝐸𝑣𝑑𝑤 + 𝐸𝑒𝑙𝑒 + 𝐸𝑠𝑜𝑙
(3.1)
The bond stretch energy 𝐸𝑠𝑡𝑟 is the sum of the stretch energy of all the bonds i-j and is
calculated by the following equation:
𝐸𝑠𝑡𝑟 = 𝑤𝑠𝑡𝑟
𝑘𝑖𝑗 𝑟𝑖𝑗 − 𝐿𝑖𝑗
2
+ 𝑘𝑖𝑗′ 𝑟𝑖𝑗 − 𝐿𝑖𝑗
3
+ 𝑘𝑖𝑗′′ 𝑟𝑖𝑗 − 𝐿𝑖𝑗
4
(3.2)
𝑖−𝑗
Where:
𝑤𝑠𝑡𝑟 : a weight
𝑘𝑖𝑗 , 𝑘𝑖𝑗′ and 𝑘𝑖𝑗′′ : force constants
𝐿𝑖𝑗 : equilibrium bond length
When the Morse potential is expanded in a Taylor series about the equilibrium bond length
and truncated to the quartic term, it gives a good approximation of the bond stretch energy. The
linear term is zero at equilibrium and the constant term is negligible. The bond angle energy 𝐸𝑎𝑛𝑔
extends over all bond angles i-j-k and is given by the following equation:
3
2
′
′′
4
𝑘𝑖𝑗𝑘 𝑑𝑖𝑗𝑘
+ 𝑘𝑖𝑗𝑘
𝑑𝑖𝑗𝑘
+ 𝑘𝑖𝑗𝑘
𝑑𝑖𝑗𝑘
𝐸𝑎𝑛𝑔 = 𝑤𝑎𝑛𝑔
𝑖−𝑗 −𝑘
Where:
𝑤𝑎𝑛𝑔 : a weight
𝑘𝑖𝑗 , 𝑘𝑖𝑗′ and 𝑘𝑖𝑗′′ : force constants
16
(3.3)
Depending on the functional form chosen for the angle interactions, 𝑑𝑖𝑗𝑘 is either 𝑎𝑖𝑗𝑘 − 𝐴𝑖𝑗𝑘
(angle form) or cos 𝑎𝑖𝑗𝑘 − cos 𝐴𝑖𝑗𝑘 (cosine form). 𝐴𝑖𝑗𝑘 is the equilibrium bond angle (in radians)
and 𝑎𝑖𝑗𝑘 is given by the following equation:
𝑎𝑖𝑗𝑘
𝑥𝑗𝑖𝑇 𝑥𝑗𝑘
=
𝑟𝑗𝑖 𝑟𝑗𝑘
,
𝑥𝑗𝑖 = 𝑥𝑗 − 𝑥𝑖
(3.4)
For sp-hybridized compounds, the quartic expression is replaced by 𝑘𝑖𝑗𝑘 (1 + cos 𝑎𝑖𝑗𝑘 ), for
octahedral and square-planar geometries 𝑘𝑖𝑗𝑘 (1 − cos(4𝑎𝑖𝑗𝑘 )) is used. The stretch-bend energy
extends over all bond angles i-j-k, except of (near-)linear equilibrium angles and is given by formula
(3.5):
𝐸𝑠𝑡𝑏 = 𝑤𝑠𝑡𝑏
𝑘𝑖𝑗𝑘 𝑟𝑖𝑗 − 𝐿𝑖𝑗 + 𝑘𝑘𝑗𝑖 𝑟𝑗𝑘 − 𝐿𝑗𝑘
𝑑𝑖𝑗𝑘
(3.5)
𝑖−𝑗 −𝑘
Where:
𝑤𝑠𝑡𝑏 : a weight
𝑘𝑖𝑗𝑘 and 𝑘𝑘𝑗𝑖 : stretch-bend force constants
Other terms are similar to those in equation (2.2) and (2.3)
The out-of-plane energy is a sum which extends over all atoms I with three neighbours j, k
and l and is given by equation (3.6):
2
𝑘𝑖;𝑗𝑘𝑙 𝑋𝑖;𝑗𝑘𝑙
𝐸𝑜𝑜𝑝 = 𝑤𝑜𝑜𝑝
(3.6)
𝑖;𝑗𝑘𝑙
Where
𝑤𝑜𝑜𝑝 : a weight
𝑘𝑖;𝑗𝑘𝑙 : the out-of-plane force constant
𝑋𝑖;𝑗𝑘𝑙 : the Wilson angle (the angle between the bond il and the plane ijk)
17
Depending of the force field used to calculate the 𝐸𝑜𝑜𝑝 , the 𝐸𝑜𝑜𝑝 equation can be the same
as that of the torsion energy. Then, the only difference is that instead of using four consecutive
bonded atoms i-j-k-l, the sum extends over atoms i with three bonded neighbours j, k and l. The
torsion energy Etor is a sum that extends over all sets of i-j-k-l atoms bonded in sequence:
6
𝐸𝑡𝑜𝑟 = 𝑤𝑡𝑜𝑟
𝑘𝑛 ;𝑖𝑗𝑘𝑙 cos 𝑛𝑇𝑖𝑗𝑘𝑙 − 𝑃𝑛 ;𝑖𝑗𝑘𝑙
(3.7)
𝑖−𝑗 −𝑘−𝑙 𝑛=0
Where:
𝑤𝑡𝑜𝑟 : a weight
𝑘𝑛 ;𝑖𝑗𝑘𝑙 : force constants
𝑃𝑛 ;𝑖𝑗𝑘𝑙 : phase angle parameters
𝑇𝑖𝑗𝑘𝑙 =
(𝑥 𝑗𝑖 𝑥 𝑗𝑘 )𝑇 (𝑥 𝑗𝑘 𝑥 𝑙𝑘 )
𝑥 𝑗𝑖 𝑥 𝑗𝑘 𝑥 𝑗𝑘 𝑥 𝑙𝑘
, 𝑥𝑖𝑗 = 𝑥𝑗 − 𝑥𝑖
The van der Waals energy 𝐸𝑣𝑑𝑤 is given by the following equation:
𝐸𝑣𝑑𝑤 = 𝑤𝑣𝑑𝑤
𝑒𝑖𝑗
𝑖<𝑗
1 + 𝑎 𝑅𝑖𝑗
𝑟𝑖𝑗 + 𝑎𝑅𝑖𝑗
𝑛 𝑖𝑗
𝑚
𝑖𝑗
𝑛𝑖𝑗 1 + 𝑏 𝑅𝑖𝑗
𝑚𝑖𝑗 + 𝑛𝑖𝑗
𝑠 𝑟𝑖𝑗 𝑇𝑖𝑗 𝑙𝑖𝑗𝑣𝑑𝑤
𝑚 𝑖𝑗
𝑚 𝑖𝑗 −
𝑚𝑖𝑗 𝑅 + 𝑏𝑅
𝑚𝑖𝑗
𝑖𝑗
𝑖𝑗
(3.8)
Where: 𝑤𝑣𝑑𝑤 : a weight
𝑒𝑖𝑗 , 𝑅𝑖𝑗 , 𝑚𝑖𝑗 and 𝑛𝑖𝑗 : force field parameters
𝑎, 𝑏: buffering constants
1 𝑖𝑓 𝑟 < 𝑟0
𝑟 − 𝑟0
𝑖𝑓 𝑟 ∈ 𝑟0 , 𝑟1
𝑠 𝑟 = 1−𝑝
𝑟1 − 𝑟0
0 𝑖𝑓 𝑟 > 𝑟1
Where:
(3.9)
𝑝 𝑥 = 𝑥 3 (6𝑥 2 − 15𝑥 + 10)
By setting the cut-off parameters 𝑟0 and 𝑟1 , a variety of
smooth tapering functions that are continuous in both their
first and second derivatives can be created.
18
𝑙 𝑣𝑑𝑤 : interaction scale factor (0 for 1-2 and 1-3 interactions, a parameter
set-dependent scale value for 1-4 interactions and 1 for other interactions)
and 𝑇𝑖𝑗 : factor used to scale particular non-bonded interactions.
𝑇𝑙𝑖𝑘𝑒 𝑖𝑓 𝑇𝑖 = 𝑇𝑗
𝑇𝑖𝑗 𝑇𝑤𝑖𝑙𝑑 𝑖𝑓 𝑇𝑖 ≠ 𝑇𝑗 𝑎𝑛𝑑 𝑇𝑖 = 0 𝑜𝑟 𝑇𝑗 = 0
𝑇𝑢𝑛𝑙𝑖𝑘𝑒 𝑜𝑡𝑕𝑒𝑟𝑤𝑖𝑠𝑒
(3.10)
𝑇𝑙𝑖𝑘𝑒 , 𝑇𝑢𝑛𝑙𝑖𝑘𝑒 and 𝑇𝑤𝑖𝑙𝑑 : state parameters
Where:
The electrostatics energy Eele is given by the following equation:
𝑤 𝑒𝑙𝑒 𝑒 2
4𝜋𝜀 0 𝑑
𝐸𝑒𝑙𝑒 =
𝑤 𝑒𝑙𝑒 𝑒 2
4𝜋𝜀 0 𝑑
𝑤 𝑒𝑙𝑒 𝑒 2
4𝜋𝜀 0 𝑑
𝑖<𝑗
𝑞𝑖 𝑞𝑗
𝑖<𝑗
𝑞𝑖 𝑞𝑗
𝑖<𝑗
𝑞𝑖 𝑞𝑗
1
𝑠(𝑟𝑖𝑗 )𝑇𝑖𝑗 𝐼𝑖𝑗𝑒𝑙𝑒 𝐶𝑜𝑢𝑙𝑜𝑚𝑏
𝑟 𝑖𝑗 +𝑏 𝑒𝑙𝑒
1
(𝑟 𝑖𝑗 +𝑏 𝑒𝑙𝑒 )2
1
𝑟 𝑖𝑗 +𝑏 𝑒𝑙𝑒
−
(3.11)
𝑠(𝑟𝑖𝑗 )𝑇𝑖𝑗 𝐼𝑖𝑗𝑒𝑙𝑒 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡 𝑑𝑖𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐
𝛼 𝑟𝑖𝑗2
𝑅𝑐3
−
(1−𝛼 )
𝑅𝑐
𝑠(𝑟𝑖𝑗 )𝑇𝑖𝑗 𝐼𝑖𝑗𝑒𝑙𝑒 𝑅𝑒𝑎𝑐𝑡𝑖𝑜𝑛 𝑓𝑖𝑒𝑙𝑑, 𝛼 =
𝑑−𝑑 𝑥
𝑑+2𝑑 𝑥
Where: 𝑤𝑒𝑙𝑒 : a weight
𝑑: the dielectric constant in the interior of the solute
𝑑𝑥 : the dielectric constant of the solvent
𝑠 and 𝑇:
equations (2.9) and (2.10)
𝑞𝑖 : the partial charge on atom i
𝑏𝑒𝑙𝑒 : a buffering constant to prevent zero denominators
𝐼𝑒𝑙𝑒 : similar to 𝑙 𝑣𝑑𝑤
Esol is the implicit solvation energy calculated using the Generalized Born model (GB/VI):
𝐸𝑠𝑜𝑙 = −𝑤𝑠𝑜𝑙 𝑊 𝑑−1 − 𝑑𝑥−1
𝑒2 1
4𝜋𝜀 0 2
𝑛
𝑖=1
𝑛
𝑗 =1
𝑞 𝑖 𝑞 𝑗 𝐺𝑖 𝐺𝑗
exp −𝑦 𝑖𝑗
𝑦 𝑖𝑗 +
Where:
wsol and W: weights
19
4
𝑠 𝑟𝑖𝑗 𝑇𝑖𝑗 , 𝑦𝑖𝑗 = 𝑟𝑖𝑗2 𝐺𝑖 𝐺𝑗
(3.12)
d and dx: given in (3.11)
s and T: equations (3.9) and (3.10)
Gi is the self energy of atom i and is defined to be
1
3
1 1
𝐺𝑖 = −
−
𝑉
, 𝑉𝑖 ≈
𝑖
2 𝑅𝑖3
Where
𝑥 − 𝑥𝑖
−6
𝛿 𝑥 − 𝑥𝑖 > 𝑅𝑖 𝑑𝑥
(3.13)
𝑥∈𝑠𝑜𝑙𝑢𝑡𝑒
Ri: the solvent model radius of atom i
Vi is an approximation to the Born Integral
Another important concept of molecular mechanics is that of the atom types. The atoms are
considered as charged spheres and they are characterized by the chemical situation in which they
are found. For example, the AMBER force field defines five different atom types for oxygen: a
carbonyl oxygen (O), alcohol oxygen (OH), carboxylic acid or phosphate oxygen (O2), ester or ether
oxygen (OS) and water oxygen (OW). In order to make the energy calculations, different force fields
are used for both small organic molecules and biological macromolecules. We use the Merck
Molecular Force Field (MMFF) for the organic molecules and the Assisted Model Building with
Energy Refinement (AMBER) force field for the receptor.
3.2.2. Molecular Docking
The aim of docking is to find the binding geometry of two interacting molecules. We
distinguish two different types of docking, local and global docking. In local docking the binding
site in the receptor is known. The ligand has to find its position in the known binding pocket. In
global docking the binding site is unknown, so here we have to search for the binding site and the
position of the ligand in the binding pocket, which can be performed sequentially or
simultaneously. During docking simulation, the receptor is usually kept rigid, whereas for the
ligand flexibility is allowed. This docking strategy is called semi-flexible docking. The number of
degrees of freedom is 6 + Nfree. The goal of the testing docking method is to reproduce a known
complex, for example the A2AAR – ZM241385 complex as observed from the experimental crystal
structure data. The starting structures for both the receptor and the ligand are taken from the
minimized crystal structure.
20
In docking, 2 factors are of major importance. First, there is the algorithm that determines
the conformation of the ligand in the receptor. Second, there is the scoring function that scores the
different conformations that were generated by the algorithm. Once all possible docking
conformations are generated, a guide criterion or score is needed, which allows us to define which
of these conformations is the most energetically stable and can probably represent the real
binding situation. There are basically two major methods to assign a score during docking protocol,
energy score and grid score.
The energy score of a ligand within the binding site can be calculated by using molecular
mechanics force field. The calculated interaction energy values are usually negative to indicate a
stabilization of the complex with respect to the isolate ligand and target, and the absolute value
indicates the stabilization degree of the system. These values don’t have a thermodynamic
meaning as they are derived using a mechanic force field. The grid score method is based on the
calculation of electrostatic interaction energy between the ligand and the binding site. To calculate
such energy, the binding site is enclosed in a 3D grid with regular intervals.
3.2.2.1 Retrieval and analysis of A2AAR crystal structures as docking targets
3.2.2.1.1 Downloading the crystal structure
Crystal structures of biological macromolecules, such as proteins and nucleic acids, can be
found in the protein data bank (PDB). Searching for the A 2AAR results in a number of crystal
structures, from which we download only the files containing the crystal structure of the A2AAR in
complex with the antagonist ZM241385. The higher the resolution that the crystal structure is
obtained with, the more accurate the crystal structure will be. So, the resolution indicates the
quality of the crystallographic data. Subsequently, protein data obtained from the PDB have to be
corrected for errors and prepared for further computational research.
3.2.2.1.2. Correcting for mistakes
A major problem of solving the crystal structures of macromolecules is the presence of datarelated issues such as missing atoms or poorly resolved atomic data. Mistakes in the bonding
patterns of co-crystallized ligands are another issue. Bonding orders are predicted from the
interatomic distances and angles of heavy atoms, so flexible regions complicate inferring the
bonding pattern. Also knowledge of hydrogen atoms, which are difficult to be located, is of major
21
importance in deriving the bond orders of non-amino acids. So, the inspection of the correctness
of bond orders, charges and protonation states is an important step in preparing the crystal
structures for further analysis. Another characteristic that is of great importance is the geometric
quality of the protein. Bond lengths, bond angles and torsion angles are checked for the entire
receptor. For example, a Ramachandran phi-psi plot may highlight structural problems. In order to
solve these problems, the outliers in the Ramachandran dihedral plot are selected and an energy
minimization is carried out for these residues. So, strain can be maneuvered away and the
geometric quality is restored. Furthermore, hydrogen atoms have to be added to the crystal
structure, because performing X-ray crystallography leads to a crystal structure for which
information about hydrogens is not available. Macromolecular protonation can be obtained by
using the Protonate 3D application of MOE.
3.2.2.1.3. Replacing missing data
When this first step of preparing the protein-ligand complex for docking simulation is
completed, the primary sequence of the human adenosine A2A receptor is downloaded from the
Universal Protein Resource database (Uniprot database; http://www.uniprot.org). With the aid of
the primary sequence, missing structural data can be corrected. Sometimes, atoms cannot be
resolved, regions of the protein are to flexible or regions are invisible for the X-ray diffraction
technique. To fill in the missing residues, the Homology Model application of MOE is used. The
original human sequence, downloaded from the Uniprot database is used as target to make up this
self-homology model. The force field parameterized for proteins, AMBER99, is selected for scoring
the obtained models. Twenty-five different models are generated by the application and
subsequently scored by the AMBER99 force field. The top-scored model will be loaded in MOE.
After completing the crystal structure, a minimization of the restored residues is performed to
eliminate the strain that may be present because no refinement step was carried out.
3.2.2.1.4. Minimizing the energy
Errors in the ZM241385 and the receptor structure are corrected, missing residues inserted
and hydrogens added, so a next step in preparing the protein-ligand complex may be carried out.
The partial charges of the structure are calculated and a molecular mechanics refinement in the
form of a tethered energy minimization of the receptor in combination with the ligand is
performed.
22
3.2.2.2. Docking analysis
Once the receptor and the ligand are prepared for further computational analysis, the ligand
is docked into the binding pocket. Figure 2.1 provides a schematic overview of the different steps
of the docking methodology. Step 1 (Figure 3.1) is not carried out in our case. We use the
conformation of the ligand as it appears in the crystal structure. The algorithm or placing method
positions the ligand in various ways in the binding pocket (step 2, Fig 3.1). All the poses obtained
by the placing method in step 2 are scored by both the algorithm itself and other scoring functions.
The best poses are assigned the lowest (most negative) scores. Subsequently, a refinement step
(step 4, Fig 3.1) is performed using force field or the grid-based energetics method. A
pharmacophore constraint may be defined to filter the conformations. Only conformations that
satisfy the pharmacophore model are then retained. The refined poses are rescored using the
same scoring method as the one used in step 3. Finally the top scoring positions are output in a
database, subjected to optional duplicates removal. Are the placement methods of the software
(MOE) able to reproduce the binding position of the ligand as seen in the crystal structure? Are the
scores assigned by the scoring methods within our expectations, i.e. are the best poses assigned
the lowest values? We will evaluate the different placing methods and scoring functions in their
ability to generate good poses and to assign good scores. First, the ligand ZM241385 is removed
from the minimized crystal structure and the conformation of the 4-hydroxyphenylethyl side chain
is slightly changed. Also water molecules present in the receptor are removed. The partial charges
of both the receptor and the ligand are set. For the receptor, a macromolecule, the AMBER99 force
field is used, while the MMFF94 force field is used for the ligand. The poses of ZM241385 are
generated by different placement methods: Alpha Triangle, Alpha PMI, Proxy Triangle and Triangle
Matcher. Using the Alpha Triangle method, ligand atom triplets are superposed to triplets of
receptor site points, which are alpha sphere centers. Each pose is generated by selecting a random
conformation, a random triplet of ligand atoms and a random triplet of alpha sphere centers. The
Alpha PMI method is fast and appropriate for small binding pockets. Here, the principal moments
of inertia of the ligand conformation are aligned to a randomly generated subset of alpha spheres
in the receptor site to generate poses. The Proxy Triangle method is able to position larger ligands,
which may occur in a large number of conformations. Before placing the ligands in the binding
pocket, conformers are superposed to save computational time. The Triangle Matcher method
aligns ligand atom triplets on triplets of receptor site points, as in the Alpha Triangle method, but
in a more systematic way.
23
Figure 3.1: Stages in the Dock Algorithm
Once the ligand is positioned with different orientations into the binding pocket, poses are
scored. The lower is the score, the more favorable is the pose of the ligand. Four scoring functions
are used: ASE Scoring, Affinity dG Scoring, Alpha HB Scoring and London dG Scoring.
The score using the ASE Scoring method is obtained by calculating the sum of the Gaussians
(0,035kcal/mol)R1R2exp(-0,5d2) over all ligand atom-receptor atom pairs and ligand atom-alpha
sphere pairs. The radii, R1 and R2, are expressed in Å; for alpha spheres R is -1,85. The distance d
between the atoms or alpha spheres also is expressed in Å.
The Affinity dG Scoring function uses a linear function, which is given in (3.14), to estimate
the enthalpic contribution to the free energy of binding.
𝐺 = 𝐶𝑕𝑏 𝑓𝑕𝑏 + 𝐶𝑖𝑜𝑛 𝑓𝑖𝑜𝑛 + 𝐶𝑚𝑙𝑖𝑔 𝑓𝑚𝑙𝑖𝑔 + 𝐶𝑕𝑕 𝑓𝑕𝑕 + 𝐶𝑕𝑝 𝑓𝑕𝑝 + 𝐶𝑎𝑎 𝑓𝑎𝑎
Where:
(3.14)
Cx: weight coefficient
fx: value that represents the atomic contacts of a specific type
x: interaction type; hb = hydrogen bond; ion = ionic interaction; mlig = metal
ligation; hh = hydrophobic interaction; hp = interaction between hydrophobic
and polar atoms; aa = interaction between any two atoms
24
The score obtained by the Alpha HB Scoring function is a linear combination of two terms.
The first term is a measurement of the geometric fit of the ligand to the binding pocket, which is
composed of an attractive and a repulsive part. Each ligand atom that is within 3 Å of an alpha
sphere center contributes -0,6845exp(-0,5d2) to the attractive part, where d is the distance
between the ligand atom and the nearest sphere center. When atomic overlap between ligand and
receptor atoms is observed, this pair contributes to the repulsive part of the first term. The value
of this contribution varies between 0 and 1 depending on the severity of the overlap. Hydrogen
bonding effects are represented by the second term. If a non-sp3 donor or acceptor is present,
hydrogen bonding sites are projected from the atom. When the projection site contains a receptor
atom that is favorable for hydrogen bonding, a score of -2 is assigned. When the projection site
contains any other atom, a score of +1 is assigned. If a sp3 donor or acceptor is present, favorable
atoms within 3,5 Å contribute a score of -1 while all other atoms contribute +1. Metals are seen as
acceptors and their contribution is multiplied with three. Both parts are summed over all the
atoms in the ligand.
As the Affinity dG Scoring function, the London dG Scoring function uses equation (3.15) to
estimate the free energy of binding of the ligand.
∆𝐺 = 𝑐 + 𝐸𝑓𝑙𝑒𝑥 +
𝑐𝐻𝐵 𝑓𝐻𝐵 +
𝑕−𝑏𝑜𝑛𝑑𝑠
Where:
𝑐𝑀 𝑓𝑀 +
𝑚 −𝑙𝑖𝑔
∆𝐷𝑖
𝑎𝑡𝑜𝑚𝑠 𝑖
ΔG: free energy of binding
C: average gain/loss of rotational and translational entropy
Eflex: energy due to the loss of flexibility of the ligand
fHB: value that represents geometric imperfections of hydrogen bonds
CHB: energy of an ideal hydrogen bond
fM: value that represents geometric imperfections of metal ligations
CM: energy of an ideal metal ligation
Di: desolvation energy of atom i
The difference in desolvation energies is calculated according to formula (3.16).
25
(3.15)
∆𝐷𝑖 = 𝑐𝑖 𝑅𝑖3
𝑢
𝑢∉𝐴
Whereby:
−6
𝑑𝑢 −
𝐵
𝑢
−6
𝑑𝑢
(3.16)
𝑢∉𝐵
A: volume of the protein
B: volume of the ligand
i: atom belonging to volume B
Ri: solvation radius of atom i
Ci: desolvation coefficient of atom i
After positioning the ligand in the binding pocket and scoring the obtained poses with one of
the four scoring functions, a refinement step is performed. In fact, the refinement step is a method
to minimize the energy of the system. Two methods are available: force field refinement and
gridmin refinement. The force field refinement scheme is more time-consuming, but also more
accurate than the gridmin scheme.
An additional filtering tool is the insertion of a pharmacophore query during docking
analysis. The IUPAC definition of a pharmacophore is the following: "A pharmacophore is an
ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular
interactions with a specific biological target and to trigger (or block) its biological response".19 So, a
pharmacophore is a 3D model that describes both the types and locations of ligand-receptor
interactions. In MOE, a pharmacophore is represented by a pharmacophore query that is
composed of a number of query features each one assigned of a tolerance radius. When a
pharmacophore query is specified, matches are retained; poses that do not satisfy the
pharmacophore are eliminated. The restraints set are used during both the refinement stage and
the pharmacophore-based filtering stage.
Subsequently, duplication removal is performed. This method does not use the conventional
rooth mean square deviation (RMSD) cutoff approach to exclude duplicates, but it considers the
hydrogen bonds and hydrophobic interactions between ZM241385 and the A 2AAR. If the distance
between appropriate ligand-receptor atom types is less than 3,5 Å, it is supposed that a hydrogen
bond is present. Hydrophobic interactions between appropriate ligand atom types-receptor
residues are considered when the distance is less than 4,5 Å. If the set of ligand-receptor atoms
26
that are involved in hydrogen bonds and the set of ligand atoms-receptor residues that are
involved in hydrophobic interactions are the same for both the observed poses, the poses are
considered as duplicates.
Finally, the results are output in a database, which lists up only the top scoring poses. Firstly,
docking is performed for the 3EML, 3PWH and 3VG9 crystal structures. Sixteen different
combinations of algorithm-scoring function are tested on their ability to generate good poses and
to assign good scores. Secondarily, these sixteen combinations are tested again, but now with the
use of a pharmacophore query. After performing docking, the results of each algorithm-scoring
function combination with and without pharmacophore constraint are subjected to a dataanalysis. In addition to the RMSD of the total conformation, the RMSD of the scaffold, which is an
estimation of the distortion of the bicyclic scaffold in Å, is calculated. The ten best poses, which are
given in the database, are evaluated on both the RMSDtotal_conf and RMSDscaffold. The cut-off value
for the RMSDtotal_conf is set at 2 Å, while the value for the RMSDscaffold is set at 1 Å. Results are drawn
from this data-analysis. The two crystal structures, the three scoring functions and the placing
algorithm that lead to the best results are used to carry out the docking of a dataset of antagonists
in the next step of the research.
3.2.2.3. Post-docking analysis: Quantitative structure-activity relationship
The biological activity of a ligand is a function of the structure as described by its
physicochemical and structural properties. More than a century ago, Meyer and Overton observed
independently of each other that the depressant action of a group of organic compounds increases
linearly with their oil/water partition coefficients. The initial postulate of quantitative structureactivity relationship states “activity was a function of structure as described by electronic
attributes, hydrophobicity, and steric properties”.20
The objective of quantitative structure-activity relationship (QSAR) is to search for
relationships between the values of some descriptors and the known experimental data of a set of
chemical entities that is referred to as training set. This results in a numerical model, a linear
regression line, on the basis of which a dataset of compounds for which the binding data are not
known, the so-called test set, may be evaluated. The results of the numerical model may not be
seen as a predicted value for the biological activity, but the goal is rather to distinguish between
acceptable and unsatisfactory values. In this way one can decide which compounds have to be
27
synthesized and submitted to subsequent testing procedures, among others in vitro and in vivo
studies.
The experimental data may be biological parameters, such as the Ki or Kd, physical property
measurements or high throughput screening results. For each compound of the training set, a
numerical representation is constructed. This representation is provided by molecular descriptors,
quantities that depend on the molecule. Three classes of numerical descriptors can be
distinguished. 2D descriptors only contain information about the atoms and connections present in
the compound, while 3D coordinates and conformations are not taken into account. The i3D or
internal 3D descriptors contain information about the 3D coordinates of each compound, but
information about rotations and translations of the individual conformations is not considered. On
the other hand, x3D or external 3D descriptors consider the 3D coordinate information. For the
calculation of these descriptors, an absolute frame of reference is needed, for example the binding
pocket of the A2AAR.
The obtained model is a numerical formula of which the parameters are estimated using the
experimental data and the descriptors of the molecules of the training set. The experimental
results of linear regression models are expressed as a linear combination of the molecular
descriptors and a constant. Once a satisfactory model has been built, it can be used to predict
experimental data for new molecules. As previously mentioned, these predicted experimental
values are only estimations and are used only to distinguish between compounds which are useful
or not useful for further research.
𝑌𝑖 = 𝐴 + 𝐶1 𝑃11 + 𝐶2 𝑃21 + 𝐶3 𝑃31 + ⋯
Where:
(3.17)
𝑌𝑖 : predicted experimental result for compound i
𝐴: constant
𝐶𝑖 : parameterized coefficient of a descriptor Pj
𝑃𝑖𝑗 : value of a descriptor j for compound i
Because usually a linear correlation is observed between response and log dose in the mid
region of the log dose-response curve, the biological endpoints are transformed to a logarithmic
28
scale. The use of inverse logarithms leads to higher values for more effective compounds. It’s also
of great importance that the experimental data of the training set are spread over a wide range of
values in order to generate a model with a high predictive power. Of course, the interest of
researchers goes to the development of compounds with high activity, but also the compounds
which are of lower interest have to be included in the data set.20
3.2.2.3.1. QSAR study of A2AAR antagonists
After docking ZM241385 in the three crystal structures a database of seventy-seven A2AAR
antagonists is composed. The pKi values of these ligands are very different, ranging from 4.63 to
9.40. The pKi is the negative logarithm of the Ki, a value that designates the potential of the
antagonist to bind the receptor instead of the bound agonist. In fact, the K i is the concentration of
antagonist needed to remove half of the agonist concentration initially bound to the receptor. So,
the lower the Ki, the higher is the affinity of the antagonist for the receptor.
The database of antagonists is docked in the two crystal structures that led to the best
results in the previous docking study. The three best scoring functions and the best placing method
are used for docking. Some of the seventy-seven A2AAR antagonists can be protonated. For this
reason a second database is created in which structures are depicted like they should behave in an
environment of pH 7.4. So, the situation in the body is simulated. Docking is performed using both
the force field refinement and gridmin refinement. The placing method-scoring function
combination that leads to the best results is used for the QSAR study; both 3EML and 3VG9,
gridmin and force field, neutral and protonated are considered.
For each of the seventy-seven compounds that are docked, the best ten poses are retained in
a database. A selection of descriptors is calculated for all of these ligand poses. One of these
descriptors is the Eoop, a value that indicates the distortion of the scaffold. Only compounds with
an Eoop lower than three are retained. Five other descriptors and the known biological activities of
the seventy-seven A2AAR antagonists of the training set are used to estimate a QSAR model with
the partial least square (PLS) method. For each compound pred and xpred values are calculated
with the use of the obtained model. For each compound ten poses were obtained through
docking, these ten poses are assigned a pred and xpred value, only the one with the best xpred
value is selected for each compound. Now the xpred values are plotted against the pK i values for
all of the compounds left of the training set. The most significant outliers of the plot are removed
29
and the regression line is re-predicted by parameterizing using the molecules of the first prediction
but without the outliers.
Finally, a test set of ten A2AAR antagonists is composed. The obtained model is validated by
applying it to the test set. The ten compounds of the test set are first docked and pred values are
calculated for all ten poses of each compound. The best pose out of ten is selected for each
compound and pred values are plotted against the pKi. This will tell if the model is able to predict
which are good or bad compounds.
3.3. HOMOLOGY MODELING
When there is no three-dimensional structure available for a protein, its 3D structure can be
built using the homology modeling technique. As refolding of denatured proteins into their native
3D structure is possible in particular conditions, it means that the primary structure of a protein
contains information related to the secondary and tertiary structure. So, usually the less sequence
similarity between two proteins, the less similarity will be observed between their secondary and
tertiary structure. Homology modeling is based on this principle.
The homology modeling technique analyses structural similarity between proteins and
subsequently builds the 3D structure of a protein using the 3D structure of a template. In the
search for a suitable template it is of great importance to know the biological function of the target
protein. Since we wish to obtain a model for three subtypes of the adenosine receptor, the A 1AR,
A2BAR and A3AR, it is logical to use the homologue A2AAR, the only crystallized adenosine receptor,
as template. The primary structure of the three target proteins is set in space using the A 2AAR as
template. The obtained 3D structure is refined using molecular mechanics or molecular dynamics.
The first stage in our homology modeling procedure is aligning the primary structure of the
protein that has to be modeled and the template protein. Therefore, the relationship between
both sequences is determined with the aim at obtaining a maximum similarity degree. The global
and local alignment approaches are the two available methods to align sequences. Global
alignment considers each amino acid of both sequences; local alignment aims at finding the
subsequence of one strand that shows the highest similarity with a subsequence of the other
strand. In both global and local alignment, both sequence identity and similarity have to be
considered in the evaluation of the alignment. The sequence identity is the number of identical
residues found at the same relative position after alignment, while sequence similarity is the
30
number of residues with comparable physicochemical properties found at the same relative
position.
As previously mentioned, the template used is the A2AAR, more specifically the 2YDO crystal
structure. Here, adenosine, the physiological agonist of the adenosine receptor, is co-crystallized
with the receptor. The models are built in the presence of the ligand and co-crystallized water
molecules. The following computational steps are performed with the aim at obtaining a homology
model. The sequence of the receptor subtype that has to be modeled is aligned to the
corresponding template chain. Subsequently, it is checked for insertions and deletions. In the case
of insertions, there are more residues for the model sequence with respect to the template. In the
case of deletions, there are more residues in the template. Water molecules, which are located in
the neighborhood of intracellular loop 4, are deleted because they are not involved in ligand
binding and a homology model is made up by the MOE homology model function. Once the model
is built, it is refined through minimization and geometry check. Finally, we compare the binding
pose of adenosine in the four obtained models.
31
4. RESULTS AND DISCUSSION
The results are represented and discussed in different sections, according to the three aims
of this work: the analysis of mutagenesis data of the A2AAR, the QSAR analysis of docking results
and the study of self-made homology models.
4.1. ANALYSIS OF MUTAGENESIS DATA OF THE A2AAR
We compared the eleven A2AAR crystal structures solved to date. All the receptor residues
that are within a distance of 4,5 Å of one of the eleven ligands are listed in Table 3.1. Most of
them are similar for both agonist and antagonist bound crystal structures, but some are typically
found within a distance of 4,5 Å only in the agonist or in the antagonist bound crystal structures.
For each of the residues listed in Table 4.1, its involvement in ligand-receptor interactions and the
interaction energy values between residue and ligand are given. These values are calculated with a
force field, so they may be seen as relative and not absolute values. When mutagenesis data are
available for a residue that is listed in the table, we try to interpret these data according to the
position and the role of the residue observed in the considered crystal structures. When we go
through the A2AAR receptor sequence, a first residue for which mutagenesis data are known is
glutamate 13. Here both agonist and antagonist affinity are decreased when mutated into a
glutamine. When mutated, this residue cannot interact with histidine 278 at the expense of
stabilization. So, this could be an explanation for the decrease in affinity caused by the mutation
into glutamine. For threonine 88, a mutation into alanine, arginine or serine leads to a decrease of
affinity for both agonists and antagonists, probably due to the fact that the threonine doesn’t
stabilize the receptor conformation anymore. Normally, the threonine interacts with one of the
scaffold nitrogens of agonists, but when substituted by aspartic or glutamic acid, this interaction is
not present, so agonist affinity decreases. Mutation of glutamine 89 into alanine or aspartic acid
leads to an increased affinity. This is difficult to understand, certainly because also mutation into
alanine results in an increased affinity. Also the shift in affinity in case of the mutation of glutamate
151 and 161 is not easy to explain. Both these residues are located in EL2 and don’t interact with
the ligand. The loss of affinity that is observed when glutamate 169 is mutated into alanine is easy
to understand. The glutamate interacts with both agonists and antagonists through the formation
of hydrogen bonds. However, the alanine is not able anymore to undergo these interactions, so
loss of affinity is logically explainable. However, in case of mutation of the glutamate into a
glutamine, no variation in binding affinity is observed. The side chain of glutamine is similar to that
32
of glutamate, but it is not electrically charged. So, glutamine is still able to form the hydrogen
bonds seen in presence of a glutamate. Mutation of asparagine 181 in a serine results in a reduced
agonist affinity. A possible reason for this shift in affinity is that the presence of a hydrogen bond
network is important in agonist binding. Mutation of the asparagine breaks the network and
agonist affinity consequently is diminished. When phenylalanine 182 is mutated into alanine, loss
of affinity is observed for both agonists and antagonists. This could be explained in the following
way: the ring scaffold of the adenosine receptor ligands is inserted in a hydrophobic, aromatic
binding pocket. Among others phenylalanine is part of this hydrophobic cluster. When mutated by
an alanine, the hydrophobicity of this cluster decreases and so does the binding affinity. Even
when mutated in other hydrophobic residues with an aromatic side chain, affinity decreases.
Tryptophane and tyrosine possess more bulky side chains what possibly results in a distortion of
the other residues located in the hydrophobic binding pocket. The next residue for which
mutagenesis data are known is histidine 250. This residue interacts with asparagine 181 and 253
through hydrogen bond formation with a water molecule and keeps asparagine 253 in a
conformation needed for ligand binding. When mutated into a hydrophobic residue, the
asparagine loses its appropriate conformation and affinity for both agonists and antagonists
decreases. Mutation of asparagine 253 into alanine results in decrease of affinity, which is easy to
explain. Asparagine is directly involved in ligand binding, where alanine doesn’t give the possibility
to form a hydrogen bond. The reason of loss of affinity when phenylalanine 257 is substituted by
alanine is the same as for mutation of phenylalanine 182. Also isoleucine 274 makes part of the
hydrophobic binding pocket. When this residue is mutated, interaction of the hydrophobic scaffold
is more difficult and affinity for both agonists and antagonists is decreased. Serine 277 interacts
with agonists through the formation of hydrogen bonds with the ribose group of agonists. So,
mutation affects only the affinity for agonists. The positively charged side chain of histidine 278
normally interacts with the negatively charged glutamate 13, which stabilizes the receptor
conformation. When histidine is substituted by an alanine, interaction is impossible and ligand
affinity decreases. When mutated by an aspartate or a glutamate, affinity is not influenced. Here,
the possibility exists that the presence of positively charged ion, such as Na + or Mg2+, still allows
the interaction. A last residue for which mutagenesis data are available is serine 281, a residue that
interacts with histidine 278 in a hydrogen bond network. This histidine interacts with the hydroxyl
groups of agonists, but when the network is disturbed, the interaction may not take place
anymore.
33
Table 4.1: List of A2AAR residues. Each listed residue is located within a distance of 4,5 Å of at least one of the eleven co-crystallized ligands.
Mutagenesis data are represented if available. O: no interaction ligand-receptor; X: residue not in environment (4,5 Å) of ligand; NO: residue not
crystallized; H20: water molecule within 4,5 Å; L: ligand-receptor interaction; numerical value: relative interaction energy calculated with MOE
tool. Ballesteros and Weinstein numbering system is used, whereby for example 3.50 is the most conserved residue of TM3.
Res
Res
Nu
m
Mutagenesis data
3EML
int
3PWH
if
int
3REY
if
int
3RFM
if
int
3VG9
if
int
3VGA
if
int
3UZA
if
int
3UZC
if
int
3QAK
if
int
Gly 1.31
5
O
NO
NO
NO
NO
NO
NO
NO
O
Ser 1.32
6
O
NO
NO
NO
O
O
NO
NO
O
Val 1.34
8
O
O
O
O
O
O
O
O
O
Tyr 1.35
9
H2O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
H20
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
Glu 1.39
13
Ala 2.57
59
E13Q: reduced
agonists/antagonist
affinity
-7,0
Ile 2.58
60
O
O
Ala 2.61
63
H2O
O
-9,7
O
Ile 2.62
64
O
O
-8,3
O
-12,8
O
O
-5,2
O
-2,2
O
O
if
int
2YDV
if
NO
int
O
O
O
H20
O
O
O
-3,2
O
O
O
-4,5
H20
-11,4
O
O
O
-6,6
O
O
O
-4,7
O
H20
Ile 2.64
66
O
O
-9,0
O
-7,6
O
O
O
O
-16,1
O
67
O
L
-21,4
O
-9,6
O
O
O
O
O
O
-10,4
O
O
Thr 2.66
68
X
X
X
X
X
X
X
X
O
O
O
O
O
O
O
O
O
O
O
-3,6
O
-4,8
-3,7
O
-3,5
-3,1
O
Ile 3.28
80
H2O
O
O
-2,9
O
O
O
O
Ala 3.29
81
H2O
O
O
-4,9
O
O
O
O
Cys 3.30
82
O
O
O
-3,1
O
O
O
O
O
O
O
O
Phe 3.31
83
O
O
O
O
O
X
X
X
Val 3.32
84
Leu 3.33
O
V84L: marginal
variation
O
-2,7
-3,7
O
Ser 2.65
-4,9
if
H20
O
O
-3,7
2YDO
-2,4
H20
-3,9
O
-3,6
O
-6,5
O
-5,5
O
-3,5
O
-3,6
O
-9,1
O
-13,2
O
-15,0
O
-11,1
O
-14,1
85
O
-7,3
O
-4,9
O
-9,4
O
-2,6
O
-7,8
O
-5,0
O
-8,7
O
-10,7
O
-21,8
O
-17,0
O
-24,2
Val 3.34
86
X
X
X
X
X
X
X
X
O
O
O
Leu 3.35
87
X
X
X
X
X
X
X
X
O
O
O
34
Thr 3.36
88
Gln 3.37
89
T88A/R/S: reduced
affinity; T88D/E: loss
of agonists binding
Q89A/D: increased
affinity
O
-2,1
O
O
O
O
X
X
X
X
X
-2,4
O
-2,2
O
O
L
-25,9
O
X
X
X
O
-16,4
-3,3
-12,5
L
-26,7
O
O
-6,8
-3,3
Ile 3.40
92
X
X
X
X
X
X
X
X
O
O
O
Gly 4.57
136
X
X
X
X
X
X
X
X
O
O
O
Glu 151
151
X
X
X
X
X
X
X
X
X
X
X
Glu 161
161
X
X
X
X
X
X
X
X
X
X
X
Leu 167
167
O
O
O
O
O
O
O
O
-19,1
O
O
L
-20,1
H20/
L
-20,8
L
-19,2
L
111,
3
H20
-33,4
H20/
L
-33,2
Phe 168
168
E151A/D/Q: loss of
affinity
E161A: increased
antagonists affinity
O
F168A: loss of affinity
L
-3,0
-19,3
L
-16,7
L
Glu 169
169
E169A: loss of affinity;
E169Q: no variation
L
Asp 170
170
D170K: no variation
H20
Val 172
172
O
Met 174
174
O
O
-4,2
O
Met 5.38
177
H20
L
-5,3
O
Val 5.39
178
O
Asn 5.42
181
Phe 5.43
182
Cys 5.46
-45,2
-6,3
L
-31,3
-13,0
L
-7,9
-7,3
L
O
O
O
O
O
O
O
O
O
O
-3,0
O
O
-3,6
O
-3,2
L
O
-13,9
-40,1
-9,2
-4,4
L
L
-3,7
-17,1
-48,5
O
O
-13,0
-9,1
L
O
-16,9
-8,3
O
O
O
O
O
O
O
O
O
O
O
O
O
O
-5,9
O
-6,4
O
-4,2
O
-7,9
O
O
-3,3
O
-8,5
O
-8,0
O
-8,1
O
O
O
O
185
X
Val 5.47
186
X
Phe 6.44
242
X
X
X
Try 6.48
246
O
-6,3
O
-4,5
O
O
-2,5
O
-7,6
O
-6,0
O
-11,5
O
-12,7
O
-23,4
O
-16,4
O
-21,2
Leu 6.51
249
-6,5
O
-12,7
O
-13,1
O
-6,1
O
-12,6
O
-28,1
O
-22,7
O
-22,0
-5,3
O
-5,5
O
-12,6
O
-12,4
O
-21,3
H20
-10,1
H20
-17,8
O
-4,7
O
-13,5
O
O
-56,1
H20/
L
-41,3
H20/
L
O
O
O
O
O
O
O
O
O
X
X
X
X
X
X
X
O
X
X
X
X
X
X
X
O
X
-4,4
O
-15,4
L
-14,4
O
-11,1
O
H2O
-6,5
L
-7,1
O
-7,6
O
O
O
-4,8
O
O
O
N253A: loss of affinity
250
Ile 6.54
252
O
253
H20/
L
-42,2
L
-33,5
L
-26,5
L
-5,9
X
H250A: loss of affinity;
H250F/Y: decreased
agonists affinity;
H250N: increased
agonists affinity
His 6.52
Asn 6.55
-6,0
O
-14,8
L
35
O
O
-6,2
X
O
-6,4
X
O
-37,7
O
O
L
-6,1
X
O
-27,7
O
O
-48,5
L
O
O
-9,3
O
H20
O
O
O
N181S: reduced
agonists affinity
F182A: loss of affinity;
F182Y/W: reduced
agonists affinity
O
-9,1
O
-12,1
L
-12,5
O
-17,2
O
O
-2,1
O
O
-3,7
-5,8
O
O
-5,3
O
-44,7
O
O
O
O
-49,0
Thr 6.58
256
Phe 6.59
257
Cys 262
262
Ser 263
263
His 264
264
H2O
O
O
O
O
O
O
O
O
O
F257A: loss of affinity
X
X
X
X
X
X
X
X
C262G: no variation
X
X
X
X
X
X
X
X
X
X
X
X
X
X
-11,5
O
O
O
O
-8,3
O
-9,6
-7,3
O
O
O
O
-8,0
O
-7,5
O
O
O
O
-4,7
O
-10,3
Ala 265
265
O
Pro 266
266
O
Leu 267
267
O
-15,8
O
-10,6
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
-8,2
O
O
O
O
X
X
O
O
O
O
-9,7
O
-3,9
O
Tyr 7.36
271
O
-17,3
O
-12,1
O
-14,9
Ala 7.38
273
O
Ile 7.39
274
Val 7.40
275
281
O
O
0,0
Ser 7.46
O
X
O
278
O
O
270
His 7.43
O
O
X
Met 7.35
277
O
O
O
X
Ser 7.42
O
X
X
O
X
S277A/C: reduced
agonists affinity;
S277N/E/T: no
variation
H278A: loss of affinity;
H278D/E: no variation;
H278Y: decreased
affinity
S281A: loss of affinity;
S281T: increased
affinity; S281N:
increased agonists
affinity, decreased
antagonists affinity
X
O
O
X
O
-18,4
X
-6,6
O
L
X
O
269
-8,9
X
O
268
O
H20
-18,7
Try 7.33
I274A: loss of affinity
H2O
O
Leu 7.34
O
-20,2
O
-9,6
O
-10,3
O
-7,8
O
O
O
-16,5
O
-13,2
O
O
O
L
-3,9
-8,5
O
O
-8,4
O
-8,0
-6,8
O
-8,1
-19,3
O
-18,8
O
-8,6
O
O
L
-56,0
O
H20
O
-1,4
O
O
-2,0
O
O
O
-12,3
O
O
-25,6
O
-14,1
-18,6
O
O
O
O
O
O
-2,8
O
O
O
O
O
O
O
O
-1,8
O
-2,1
L
-23,0
L
-26,4
L
-24,4
O
O
O
O
O
O
O
-8,8
L
-24,9
L
-31,5
L
-26,0
L
-23,5
X
X
X
X
X
X
X
36
X
X
O
O
O
-3,4
O
-21,6
-8,9
X
O
X
4.2. DOCKING AND QSAR ANALYSIS OF DOCKING RESULTS
Data-analysis of ZM241385 docking results of the sixteen possible combinations of placing
algorithm and scoring function revealed that Trianglematcher is the most appropriate algorithm.
The use of Trianglematcher in combination with the Alpha HB Scoring function delivered the best
results, even without applying pharmacophore restrain. Hence, for subsequent docking of both the
training and test set, the combination Trianglematcher/Alpha HB was used. The results obtained by
using the combination Trianglematcher/Affinity dg and trianglematcher/London DG were also
acceptable, so these combinations were considered to support the docking results of the
Trianglematcher/Alpha HB docking combination. The docking of both training and test set were
carried out with the 3EML and the 3VG9 crystal structure because the best results were obtained
with these two structures. It appears in fact that in the case of the 3PWH crystal structure, the
Trianglematcher algorithm is apparently able to obtain some good conformations, but the scoring
functions rejected these results because of the low similarity with the folded conformation of the
4-hydroxyphenylethyl side chain of ZM241385 found in the crystal structure. Indeed, the docking
algorithm posed the ligand in the extended conformation as found in the other crystal structures.
Positioning ZM241385 in the extended conformation delivers good scores for the 3EML and 3VG9
10,00
10,00
9,00
9,00
8,00
8,00
PRED
XPRED
crystal structures, but unfavorable scores for the 3PWH crystal structure.
7,00
y = 0,5343x + 3,5311
R² = 0,49518
6,00
5,00
y = 0,2987x + 5,1961
R² = 0,4596
6,00
5,00
4,00
4,00
4,00
A
7,00
6,00
8,00
pKi
10,00
4,00
B
6,00
8,00
10,00
pKi
Figure 4.1: A. Plot of XPRED of the best-scored position of each compound (except of
outliers) of the training set versus the experimental pKi B. Plot of PRED of the best-scored
position of each compound (except of outliers) of the test set versus the experimental pK i.
Equation and cross-validated R2 are given in both A and B plots. Poses are obtained using 3EML
crystal structure and force field refinement.
37
Table 4.2: QSAR model obtained for training set using conformations generated by using
3EML crystal structure and force field refinement (left) or gridmin (right).
Activity Field: pKi
Weight Field:
Condition Limit: 1e+006
Component Limit: 0
Activity Field: pki
Weight Field:
Condition Limit: 1e+006
Component Limit: 0
Observations: 72
Descriptors: 5
Observations: 68
Descriptors: 5
ROOT MEAN SQUARE ERROR (RMSE): 0,77193
2
CORRELATION COEFFICIENT (R ): 0,57539
CROSS-VALIDATED RMSE: 0,84425
2
CROSS-VALIDATED R : 0,49518
ROOT MEAN SQUARE ERROR (RMSE): 0,97487
2
CORRELATION COEFFICIENT (R ): 0,38310
CROSS-VALIDATED RMSE: 1,06308
2
CROSS-VALIDATED R : 0,27514
ESTIMATED LINEAR MODEL
pKi =
5,00213
+0,14078 * dock_HB
+0,18596 * dock_HYD
-0,56233 * E_oop
-0,09373 * E_rele
ESTIMATED LINEAR MODEL
pKi =
5,07995
-1,34162 * dock_ENT
-0,07026 * dock_HB
+0,05255 * dock_HYD
-2,11463 * E_oop
-0,07815 * E_rele
ESTIMATED NORMALIZED LINEAR MODEL
pKi / SD(pKi) =
4,22254
-0,17742 *
dock_ENT / SD(dock_ENT)
+0,03189 *
dock_HB / SD(dock_HB)
+0,19069 *
dock_HYD / SD(dock_HYD)
-0,16392 *
E_oop / SD(E_oop)
-0,63651 *
E_rele / SD(E_rele)
ESTIMATED NORMALIZED LINEAR MODEL
pKi / SD(pKi) =
4,09279
-0,57520 * dock_ENT / SD(dock_ENT)
-0,01852 * dock_HB / SD(dock_HB)
+0,06209 * dock_HYD / SD(dock_HYD)
-0,34687 * E_oop / SD(E_oop)
-0,57818 * E_rele / SD(E_rele)
RELATIVE IMPORTANCE OF DESCRIPTORS
0,278741 dock_ENT
0,050098 dock_HB
0,299594 dock_HYD
0,257534 E_oop
1,000000 E_rele
RELATIVE IMPORTANCE OF DESCRIPTORS
0,994856 dock_ENT
0,032025 dock_HB
0,107388 dock_HYD
0,599931 E_oop
1,000000 E_rele
The training set of 77 A2AAR antagonists from literature (molecular structure reported in
ANNEX 1 Table)6,21-40 was docked twelve times, considering 3 algorithm/scoring function
combinations, 2 crystal structures, and 2 refinement methods. The pharmacophore created for
docking ZM241385 was used. It has to be remarked that the protonated database leads to better
results than the database with neutral compounds. Here, it seems that not protonation in se is
important, but rather the modified distribution of the partial charges when the compounds are
protonated. Hence, direct charge interactions seemed to be not of big importance in obtaining the
observed improvement of the results. For this reason, the protonated database was used for the
docking analysis of the 77 A2AAR antagonists. Subsequently, QSAR analysis of the docking result
38
was carried out. Only the results for Trianglematcher/Alpha HB are represented and discussed,
because the results obtained with Affinity DG and London DG were not good. Tables 4.2 and 4.3
show the QSAR models obtained from the training set. The number of observations, i.e. the
10,00
10,00
9,00
9,00
8,00
8,00
PRED
XPRED
number of compounds left from the original dataset, used for creating these models is given.
7,00
6,00
7,00
6,00
y = 0,3239x + 5,1467
R² = 0,2751
5,00
4,00
4,00
6,00
A
8,00
4,00
10,00
pKi
y = 0,2909x + 5,5088
R² = 0,2013
5,00
4,00
6,00
8,00
10,00
pKi
B
10,00
10,00
9,00
9,00
8,00
8,00
PRED
XPRED
Figure 4.2: A. Plot of XPRED of the best-scored position of each compound (except of
outliers) of the training set versus the experimental pK i B. Plot of PRED of the best-scored
position of each compound (except of outliers) of the test set versus the experimental pK i.
Equation and cross-validated R2 are given in both A and B plots. Poses are obtained using 3EML
crystal structure and gridmin refinement.
7,00
6,00
6,00
y = 0,3826x + 4,6516
R² = 0,3252
5,00
4,00
4,00
A
7,00
6,00
8,00
pKi
y = 0,2196x + 5,8478
R² = 0,0941
5,00
4,00
10,00
4,00
B
6,00
8,00
10,00
pKi
Figure 4.3: A. Plot of XPRED of the best-scored position of each compound (except of
outliers) of the training set versus the experimental pK i B. Plot of PRED of the best-scored
position of each compound (except of outliers) of the test set versus the experimental pK i.
Equation and cross-validated R2 are given in both A and B plots. Poses are obtained using 3VG9
crystal structure and force field refinement.
39
Some compounds were removed during post-docking QSAR analysis as outliers and their
removal resulted in a better QSAR model. The linear combination of five descriptors (Dock_ENT,
Dock_HYD, Dock_HB, Eoop and Erele) led to the model.
Table 4.3: QSAR model obtained for training set using conformations generated by using
3VG9 crystal structure and force field refinement (left) or gridmin (right).
Activity Field: pKi
Weight Field:
Condition Limit: 1e+006
Component Limit: 0
Activity Field: pKi
Weight Field:
Condition Limit: 1e+006
Component Limit: 0
Observations: 70
Descriptors: 5
Observations: 71
Descriptors: 5
ROOT MEAN SQUARE ERROR (RMSE): 0,92574
2
CORRELATION COEFFICIENT (R ): 0,44080
CROSS-VALIDATED RMSE: 1,02455
2
CROSS-VALIDATED R : 0,32521
ROOT MEAN SQUARE ERROR (RMSE): 0,86451
2
CORRELATION COEFFICIENT (R ): 0,50210
CROSS-VALIDATED RMSE: 0,94355
2
CROSS-VALIDATED R : 0,41161
ESTIMATED LINEAR MODEL
pKi =
5,08753
-0,42035 * dock_ENT
-0,79438 * dock_HB
+0,18342 * dock_HYD
-0,46403 * E_oop
-0,11690 * E_rele
ESTIMATED LINEAR MODEL
pki =
-0,35786 * dock_ENT
-0,15842 * dock_HB
+0,38960 * dock_HYD
-1,75549 * E_oop
-0,06437 * E_rele
ESTIMATED NORMALIZED LINEAR MODEL (SD = Standard
Deviation)
pKi / SD(pKi) =
4,10962
-0,18436 * dock_ENT / SD(dock_ENT)
-0,27994 * dock_HB / SD(dock_HB)
+0,21437 * dock_HYD / SD(dock_HYD)
-0,09831 * E_oop / SD(E_oop)
-0,72014 * E_rele / SD(E_rele)
ESTIMATED NORMALIZED LINEAR MODEL (SD = Standard
Deviation)
pKi / SD(pKi) =
3,96930
-0,17855 * dock_ENT / SD(dock_ENT)
-0,05556 * dock_HB / SD(dock_HB)
+0,45020 * dock_HYD / SD(dock_HYD)
-0,45910 * E_oop / SD(E_oop)
-0,62436 * E_rele / SD(E_rele)
RELATIVE IMPORTANCE OF DESCRIPTORS
0,256006 dock_ENT
0,388738 dock_HB
0,297685 dock_HYD
0,136511 E_oop
1,000000 E_rele
RELATIVE IMPORTANCE OF DESCRIPTORS
0,285978 dock_ENT
0,088986 dock_HB
0,721062 dock_HYD
0,735311 E_oop
1,000000 E_rele
The best cross-validated R2 for 3EML is 0,4952 (Fig 4.1, Table 4.2). It was obtained when using
the 3EML/Trianglematcher/AlphaHB/Forcefield combination. Also the number of compounds of
the original dataset that was taken into account is the highest for this combination. This QSAR
40
model also resulted performing the best prediction of pKi values for the test set of 10 literature
A2AAR antagonists (molecular structure reported in ANNEX 2 Table).6,21,23,41,42 A disadvantage of this
model is the relative low score assigned to the compound with the highest pK i (red data point in
Fig. 4.1). We also have to remark that the pharmacophore used in this case only shares one feature
with ZM241385 and the receptor was slightly modified.
In case of 3VG9, the best result was found with the 3VG9/Trianglematcher/AlphaHB/GridMin
combination. Here, the pharmacophore shares two features with ZM241385, the receptor did not
have to be modified, and the ligand of the test set with the highest pKi were also scored as the best
compounds. So, generally we can state that the obtained 3VG9/Trianglematcher/AlphaHB/GridMin
QSAR model was the best one among the developed models. Even the correlation coefficient for
this combination is relatively low. This is allowed because we do not aim at predicting the pK i of
the test set compounds, but only try to determine which compounds are lying within a certain
10,00
10,00
9,00
9,00
8,00
8,00
PRED
XPRED
range and are candidates to synthesize in a next stage.
7,00
6,00
6,00
y = 0,4556x + 4,0601
R² = 0,4116
5,00
4,00
4,00
A
7,00
6,00
8,00
pKi
5,00
y = 0,3762x + 4,702
R² = 0,2992
4,00
4,00
10,00
B
6,00
8,00
10,00
pKi
Figure 4.4: A. Plot of XPRED of the best-scored position of each compound (except of
outliers) of the training set versus the experimental pK i B. Plot of PRED of the best-scored
position of each compound (except of outliers) of the test set versus the experimental pKi.
Equation and cross-validated R2 are given in both A and B plots. Poses are obtained using 3VG9
crystal structure and gridmin refinement.
4.3. DEVELOPMENT AND STUDY OF HOMOLOGY MODELS
A third part of this work is the generation of homology models of the four adenosine
receptor subtypes of which the alignment is given in Table 4.4. We compared the binding of
adenosine, the physiological ligand, in all four models. Adenosine is bound in a similar way in the
four adenosine receptor subtype homology models. This is logically explainable, since the
41
adenosine used in the homology models has the same conformation as that of the pdb-deposited
2YDO crystal structure. The most noteworthy difference is the presence of a valine instead of
glutamate 169 in the A3AR homology model.
Table 4.4: Sequence alignment for the four human adenosine receptor subtypes (*
designates sequence identity in all the four subtypes,  and : indicate the presence of residues
with comparable physicochemical characteristics, residues marked in yellow are of importance in
ligand binding and are discussed in the text).
hA1
hA2A
hA2B
hA3
1
1
1
1
---MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFCFIVSLAVADVA
------MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVVSLAAADIA
-----MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFLVSLAAADVA
MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTTTFYFIVSLALADIA
. *: :*: *. :: ***** .*
*: * *:**** **:*
57
54
55
60
hA1
hA2A
hA2B
hA3
58
55
56
61
VGALVIPLAILINIGPQTYFHTCLMVACPVLILTQSSILALLAIAVDRYLRVKIPLRYKM
VGVLAIPFAITISTGFCAACHGCLFIACFVLVLTQSSIFSLLAIAIDRYIAIRIPLRYNG
VGLFAIPFAITISLGFCTDFYGCLFLACFVLVLTQSSIFSLLAVAVDRYLAICVPLRYKS
VGVLVMPLAIVVSLGITIHFYSCLFMTCLLLIFTHASIMSLLAIAVDRYLRVKLTVRYKR
** :.:*:** :. *
: **:::* :*::*::**::***:*:***: : : :**:
117
114
115
120
hA1
hA2A
hA2B
hA3
118
115
116
121
VVTPRRAAVAIAGCWILSFVVGLTPMFGWNNLSAVER----AWA---ANGSMGEPVIKCE
LVTGTRAKGIIAICWVLSFAIGLTPMLGWNN-------CGQPKEGKNHSQGCGEGQVACL
LVTGTRARGVIAVLWVLAFGIGLTPFLGWNSKDSATNNCTEPWDGTTNESCC---LVKCL
VTTHRRIWLALGLCWLVSFLVGLTPMFGWNMKLTSEYH-------------RNVTFLSCQ
:.* *
:. *:::* :****::***
: *
170
167
172
167
hA1
hA2A
hA2B
hA3
171
168
173
168
FEKVISMEYMVYFNFFVWVLPPLLLMVLIYLEVFYLIRKQLNKKVSAS--SGDPQKYYGK
FEDVVPMNYMVYFNFFACVLVPLLLMLGVYLRIFLAARRQLKQMESQPLPGERARSTLQK
FENVVPMSYMVYFNFFGCVLPPLLIMLVIYIKIFLVACRQLQRTEL----MDHSRTTLQR
FVSVMRMDYMVYFSFLTWIFIPLVVMCAIYLDIFYIIRNKLSLNLSN---SKETGAFYGR
* .*: *.*****.*: :: **::* :*: :*
.:*.
:
228
227
228
224
hA1
hA2A
hA2B
hA3
229
228
229
225
ELKIAKSLALILFLFALSWLPLHILNCITLFCPSC--HKPSILTYIAIFLTHGNSAMNPI
EVHAAKSLAIIVGLFALCWLPLHIINCFTFFCPDC-SHAPLWLMYLAIVLSHTNSVVNPF
EIHAAKSLAMIVGIFALCWLPVHAVNCVTLFQPAQGKNKPKWAMNMAILLSHANSVVNPI
EFKTAKSLFLVLFLFALSWLPLSIINCIIYFNG----EVPQLVLYMGILLSHANSMMNPI
*.: **** ::: :***.***: :**. *
. *
:.*.*:* ** :**:
286
286
288
280
hA1
hA2A
hA2B
hA3
287
287
289
281
VYAFRIQKFRVTFLKIWNDHFRCQPAPPIDEDLPEE-----------------------IYAYRIREFRQTFRKIIRSHVLRQQEPFKAAGTSARVLAAHGSDGEQVSLRLNGHPPGVW
VYAYRNRDFRYTFHKIISRYLLCQADVKSGNGQ----------AGVQPALGVGL-----VYAYKIKKFKETYLLILKACVVCHPSDSLDTSIEKNSE---------------------:**:: :.*: *: *
. :
322
346
332
318
hA1
hA2A
hA2B
hA3
323
347
333
319
----------RPDD---------------------------------------------ANGSAPHPERRPNGYALGLVSGGSAQESQGNTGLPDVELLSHELKGVCPEPPGLDDPLAQ
-----------------------------------------------------------------------------------------------------------------------
326
406
42
Figure 4.5: Schematic overview of interactions between adenosine and the A. A 1AR, B.
A2AAR, C. A2BAR and D. A3AR.
This residue substitution could explain why some A3AR selectivity of ligands is obtained
through introduction on the 6-NH2 group of the adenine moiety of a small alkyl group.43-46 This
group is more hydrophobic and may easily interact with the valine residue. In the A1AR homology
model, serine 277 seen in the three other subtypes is mutated by a threonine. This mutation has
no influence on binding the adenosine, because the characteristics of threonine are plus minus the
same as those of serine.
Also two residues of the hydrophobic pocket, where the scaffold is inserted, are mutated. In
the A2BAR model, we observe substitution of leucine 249 into a valine, which is also a hydrophobic
residue. So, this will not have a great influence on binding adenosine. Methionine 270 is replaced
by a threonine in the A1AR model and by a leucine in the A3AR, also this may not result in
significant consequences. Asparagine 181 is replaced by a serine in the A3AR. As discussed in the
study of mutagenesis data, this may reduce affinity for adenosine, because the hydrogen bond
network is not present anymore.
43
Figure 4.6: Representation of the binding pocket of the homology models in presence of
adenosine: A. A1AR, B. A2AAR, C. A2BAR and D. A3AR.
44
5. CONCLUSION
In the first part of this work, we analysed mutagenesis data of the A 2AAR reported in the
literature. All residues in proximity of the co-crystallized ligands were discussed. This analysis
makes it evident which are the key residues involved in agonist or antagonist interaction, like for
example phenylalanine 168, glutamate 169, and asparagine 253. In the case of the agonist-bound
crystal structures, also serine 277 and histidine 278 seem to contribute significantly in ligand
binding. So, in case of mutation of these residues, the loss of binding affinity is due to a direct
influence on ligand-receptor interactions. Also effects on hydrogen bonding or hydrophobic
contacts between residues may cause an increase or decrease of ligand affinity. This was observed
when residues such as asparagine 181 and phenylalanine 257 were substituted. Furthermore,
some residues may cause stabilization of the receptor conformation. This was seen for example for
glutamate 13 and histidine 278, two residues that interact with each other through hydrogen
bonding. In some cases the results of mutagenesis data did not find a clear explanation in our
analysis, for example the mutation of glutamate 89 into alanine or aspartate.
Our second objective was to search for a docking-scoring tool able to give an approximate
estimation of the biological activity of an arbitrary A2AAR antagonist. Therefore, we built a
database of 77 antagonists from literature. These compounds were docked with different
algorithms and the best-scored conformation of each compound was used to develop a scoring
model that was applied to a set of 10 test compounds (again from literature). The prediction values
obtained by the model were plotted against the experimental pKi affinity data. The results of this
analysis allowed us to define a placement-scoring algorithm combination of MOE software able to
provide a reasonable estimation of activity at A2AAR. It must be underlined that this tool is not
aimed at predicting the pKi values but rather at generally distinguishing the analysed compounds in
active vs. not active derivatives.
The third goal of this work was to build homology models of the four AR subtypes and to
check for eventual relevant differences comparing the binding sites of these models. These
variations could be considered to develop compounds that are selective for one of the subtypes.
The comparative analysis of the developed models highlights subtle discrepancies in the binding
region of adenosine. For example, a relevant difference among the AR subtypes is the presence of
a valine residue in EL2 of A3AR that is substituted by a glutamate in all the other three subtypes
(i.e. glutamate 169 in A2AAR). While glutamate is able to form a hydrogen bond with the exocyclic
45
amino group of adenosine, the side chain of valine is hydrophobic and hence not able to provide
an analogue interaction. This could explain the selectivity of N6-substituted adenosine derivatives
for A3AR with respect to the other AR subtypes.
46
6. BIBLIOGRAPHY
1.
Jaakola, V.-P. & IJzerman, A. P. The crystallographic structure of the human adenosine A 2A
receptor in a high-affinity antagonist-bound state: implications for GPCR drug screening and
design. Current Opinion in Structural Biology 20, 401-414 (2010).
2.
Rodriguez, D. et al. Molecular Dynamics Simulations Reveal Insights into Key Structural
Elements of Adenosine Receptors. Biochemistry 50, 4194-4208 (2011).
3.
Verzijl, D. & IJzerman, A. P. Functional selectivity of adenosine receptor ligands. Purinergic
Signalling 7, 171-192 (2011).
4.
Fredholm, B. B. et al. International Union of Basic and Clinical Pharmacology. LXXXI.
Nomenclature and Classification of Adenosine Receptors‚ An Update. Pharmacological
Reviews 63, 1-34 (2011).
5.
Dal Ben, D. et al. Adenosine Receptor Modeling: What Does the A2A Crystal Structure Tell
Us? Current Topics in Medicinal Chemistry 10, 993-1018 (2010).
6.
Cristalli, G. et al. Vol. 193 Handbook of Experimental Pharmacology (eds Constance N.
Wilson & S. Jamal Mustafa) 59-98 (Springer Berlin Heidelberg, 2009).
7.
Palczewski, K. et al. Crystal structure of rhodopsin: A G protein-coupled receptor. Science
289, 793-745 (2000).
8.
Rasmussen, S. G. et al. Crystal structure of the human beta2 adrenergic G-protein-coupled
receptor. Nature 450, 383-387 (2007).
9.
Warne, T. et al. Structure of a beta-adrenergic G-protein-coupled receptor. Nature 454, 486491 (2008).
10.
Jaakola, V.-P. et al. The 2.6 Angstrom Crystal Structure of a Human A2A Adenosine Receptor
Bound to an Antagonist. Science 322, 1211-1217 (2008).
11.
Xu, F. et al. Structure of an agonist-bound human A2A adenosine receptor. Science 332, 322327 (2011).
12.
Lebon, G. et al. Agonist-bound adenosine A2A receptor structures reveal common features
of GPCR activation. Nature 474, 521-525 (2011).
13.
Doré, A. S. et al. Structure of the Adenosine A2A Receptor in Complex with ZM241385 and
the Xanthines XAC and Caffeine. Structure 19, 1283-1293 (2011).
14.
Hino, T. et al. G-protein-coupled receptor inactivation by an allosteric inverse-agonist
47
antibody. Nature 482, 237-240 (2012).
15.
Congreve, M. et al. Discovery of 1,2,4-Triazine Derivatives as Adenosine A2A Antagonists
using Structure Based Drug Design. Journal of Medicinal Chemistry 55, 1898-1903 (2012).
16.
Lane, J. R. et al.. in Advances in Pharmacology Vol. 61 (eds A. Jacobson Kenneth & Linden
Joel) Ch. 1, 1-40 (Academic Press, 2011).
17.
Kim, J. et al. Site-directed mutagenesis identifies residues involved in ligand recognition in
the human A2A adenosine receptor. Journal of Biological Chemistry 270, 13987-13997
(1995).
18.
Jaakola, V.-P. et al. Ligand Binding and Subtype Selectivity of the Human A2A Adenosine
Receptor. Journal of Biological Chemistry 285, 13032-13044 (2010).
19.
Wermuth, C. G. et al. Glossary of terms used in medicinal chemistry (IUPAC
Recommendations 1998). Pure and Applied Chemistry 70, 1129-1143 (1998).
20.
Selassie, C. D. in Burger's Medicinal Chemistry and Drug Discovery Vol. 1 (ed D. J. Abraham)
(John Wiley and Sons, 2003).
21.
Stefanachi, A. et al. 1,3-Dialkyl-8-(hetero)aryl-9-OH-9-deazaxanthines as potent A2B
adenosine receptor antagonists: Design, synthesis, structure-affinity and structureselectivity relationships. Bioorganic & Medicinal Chemistry 16, 9780-9789 (2008).
22.
Bulicz, J. et al. Synthesis and pharmacology of pyrido[2,3-d]pyrimidinediones bearing polar
substituents as adenosine receptor antagonists. Bioorganic & Medicinal Chemistry 14,
2837-2849 (2006).
23.
Drabczynska, A. et al. Synthesis and biological activity of tricyclic cycloalkylimidazo-,
pyrimido- and diazepinopurinediones. European Journal of Medicinal Chemistry 46, 35903607 (2011).
24.
Moorjani, M. et al. 2,6-Diaryl-4-acylaminopyrimidines as potent and selective adenosine
A2A antagonists with improved solubility and metabolic stability. Bioorganic & Medicinal
Chemistry Letters 18, 5402-5405 (2008).
25.
Giovannoni, M. P. et al. Pyrazolo[1′,5′:1,6]pyrimido[4,5-d]pyridazin-4(3H)-ones as selective
human A1 adenosine receptor ligands. Bioorganic & Medicinal Chemistry 18, 7890-7899
(2010).
26.
Slee, D. H. et al. 2-Amino-N-pyrimidin-4-ylacetamides as A2A Receptor Antagonists: 1.
Structure-Activity Relationships and Optimization of Heterocyclic Substituents. Journal of
Medicinal Chemistry 51, 1719-1729 (2008).
48
27.
Neustadt, B. R. et al. Potent and selective adenosine A2A receptor antagonists: 1,2,4Triazolo[1,5-c]pyrimidines. Bioorganic & Medicinal Chemistry Letters 19, 967-971 (2009).
28.
Eastwood, P. et al. Discovery of potent and selective bicyclic A2B adenosine receptor
antagonists via bioisosteric amide replacement. Bioorganic & Medicinal Chemistry Letters
20, 1634-1637 (2010).
29.
Pastorin, G. et al. Synthesis and pharmacological characterization of a new series of 5,7disubstituted-[1,2,4]triazolo[1,5-a][1,3,5]triazine
derivatives
as
adenosine
receptor
antagonists: A preliminary inspection of ligand-receptor recognition process. Bioorganic &
Medicinal Chemistry 18, 2524-2536 (2010).
30.
Shah, U. et al. Design, synthesis, and evaluation of fused heterocyclic analogs of SCH 58261
as adenosine A2A receptor antagonists. Bioorganic & Medicinal Chemistry Letters 18, 42044209 (2008).
31.
Gillespie, R. J. et al. Antagonists of the human A2A receptor. Part 6: Further optimization of
pyrimidine-4-carboxamides. Bioorganic & Medicinal Chemistry 17, 6590-6605 (2009).
32.
Gillespie, R. J. et al. Antagonists of the Human A2A Adenosine Receptor. 4. Design, Synthesis,
and Preclinical Evaluation of 7-Aryltriazolo[4,5-d]pyrimidines. Journal of Medicinal
Chemistry 52, 33-47 (2008).
33.
Slee, D. H. et al. Identification of Novel, Water-Soluble, 2-Amino-N-pyrimidin-4-yl
Acetamides as A2A Receptor Antagonists with In Vivo Efficacy. Journal of Medicinal
Chemistry 51, 400-406 (2008).
34.
Cheong, S. L. et al. The Significance of 2-Furyl Ring Substitution with a 2-(para-substituted)
Aryl Group in a New Series of Pyrazolo-triazolo-pyrimidines as Potent and Highly Selective
hA3 Adenosine Receptors Antagonists: New Insights into Structure-Affinity Relationship and
Receptor-Antagonist Recognition. Journal of Medicinal Chemistry 53, 3361-3375 (2010).
35.
Eastwood, P. et al. Discovery of N-(5,6-diarylpyridin-2-yl)amide derivatives as potent and
selective A2B adenosine receptor antagonists. Bioorganic & Medicinal Chemistry Letters 20,
1697-1700 (2010).
36.
Mishra, C. B. et al. Novel 8-(furan-2-yl)-3-substituted thiazolo [5,4-e][1,2,4] triazolo[1,5-c]
pyrimidine-2(3H)-thione derivatives as potential adenosine A2A receptor antagonists.
Bioorganic & Medicinal Chemistry 18, 2491-2500 (2010).
37.
Borrmann, T. et al. 1-Alkyl-8-(piperazine-1-sulfonyl)phenylxanthines: Development and
Characterization of Adenosine A2B Receptor Antagonists and a New Radioligand with
49
Subnanomolar Affinity and Subtype Specificity. Journal of Medicinal Chemistry 52, 39944006 (2009).
38.
Baraldi, P. G. et al. Novel 1,3-Dipropyl-8-(3-benzimidazol-2-yl-methoxy-1-methylpyrazol-5yl)xanthines as Potent and Selective A2B Adenosine Receptor Antagonists. Journal of
Medicinal Chemistry 55, 797-811 (2011).
39.
Shao, Y. et al. Synthesis and SAR studies of trisubstituted purinones as potent and selective
adenosine A2A receptor antagonists. Bioorganic & Medicinal Chemistry Letters 19, 13991402 (2009).
40.
Kozma, E. et al. Novel fluorescent antagonist as a molecular probe in A3 adenosine receptor
binding assays using flow cytometry. Biochemical Pharmacology 83, 1552-1561 (2012).
41.
McGuinness, B. F. et al. Discovery of 2-aminoimidazopyridine adenosine A2A receptor
antagonists. Bioorganic & Medicinal Chemistry Letters 20, 6845-6849 (2010).
42.
Piersanti, G. et al. Synthesis of benzo[1,2-d;3,4-d’]diimidazole and 1H-pyrazolo[4,3b]pyridine as putative A2A receptor antagonists. Organic & Biomolecular Chemistry 5, 25672571 (2007).
43.
Volpini, R. et al. N6-methoxy-2-alkynyladenosine derivatives as highly potent and selective
ligands at the human A3 adenosine receptor. Journal of Medicinal Chemistry 50, 1222-1230
(2007).
44.
Volpini, R. et al. Synthesis and biological evaluation of 2-alkynyl-N6-methyl-5'-Nmethylcarboxamidoadenosine derivatives as potent and highly selective agonists for the
human adenosine A3 receptor. Journal of Medicinal Chemistry 52, 7897-7900 (2009).
45.
Volpini, R. et al. N6-alkyl-2-alkynyl derivatives of adenosine as potent and selective agonists
at the human adenosine A3 receptor and a starting point for searching A2B ligands Journal
of Medicinal Chemistry 45, 3271-3279 (2002).
46.
Dal Ben, D. et al. Molecular modeling study on potent and selective adenosine A3 receptor
agonists. Bioorganic & Medicinal Chemistry 18, 7923-7930 (2010).
50
ANNEX 1: TABLE WITH STRUCTURE OF ANTAGONISTS WITH A2AAR AFFINITY USED AS TRAINING SET IN DOCKING ANALYSIS cpd Structure ref pKi cpd Structure ref pKi 6
8,30 6
7,70 21
7,51 7,44 22
7,22 22
7,09 21
7,04 21
7,00 21
6,59 23
6,51 21
6,49 21
6,39 1 6
9,40 13 6
2 9,37 14 6
3 9,10 15 6
4 8,96 6
16 6
5 8,85 17 6
6 8,82 18 6
7 8,80 19 6
8 8,77 20 6
9 8,75 21 6
10 8,57 22 6
11 8,43 23 6 12 8,42 24 51 6
25 8,42 37 21
6,33 24
7,57 25
5,13 26
6,00 27
8,02 27
8,74 28
6,75 29
6,42 30
8,72 30
9,15 30
8,15 31
8,19 23
26 6,21 38 21
27 6,16 39 23
28 6,07 40 21
29 6,03 41 23
30 5,82 42 22
31 5,70 43 6
32 5,70 44 23
33 5,62 45 23
34 5,49 46 22
35 4,63 47 6
36 8,96 48 52 6
49 8,77 60 31
8,55 33
8,54 34
6,92 25
6,24 36
8,45 37
7,04 38
6,02 38
6,30 24
9,70 36
11,09 31
7,77 32
50 8,07 61 31
51 8,52 62 31
52 9,00 63 35
53 7,92 64 35
54 7,52 65 24
55 9,22 66 25
56 7,17 67 24
57 7,06 68 38
58 6,48 69 24
59 8,40 70 53 39
71 8,22 75 39
7,22 27
9,52 40
7,29 27
72 9,30 76 24
73 8,70 77 25
74 5,50 ANNEX 2: TABLE WITH STRUCTURE OF ANTAGONISTS WITH A2AAR AFFINITY USED AS TEST SET IN DOCKING ANALYSIS cpd Structure ref pKi cpd Structure ref pKi 41
6,36 23
5,80 21
5,61 42
5,47 23
4,66 1 6
8,92 6 6
2 8,18 7 6
3 7,72 8 6
4 7,34 9 21
5 6,98 10 54