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Introduction
Alzheimer's disease (AD) is a progressive and
neuro-degenerative disorder of the brain, with a loss of memory
and cognition, which and is a common form of dementia
among the elderly[1]. Acetylcholinesterase (AChE), one of
the most essential enzymes in the family of serine hydrolases,
catalyzes the hydrolysis of neurotransmitter acetylcholine,
which plays a key role in memory and cognition. It is clear
that the cholinergic deficiency is associated with
AD[2], therefore, one of the major therapeutic strategies for the
treatment of AD is to inhibit the biological activity of AChE, and
hence to increase the acetylcholine level in the brain.
Currently, most of the drugs used in clinics for the treatment
of AD are AChE inhibitors, such as donepezil and
rivastig-mine, which have been proven to improve the situation of
AD patients to some extent.
Four drugs have been approved by the Food and Drug
Administration (FDA) to treat AD in the US so far, namely
tacrine, rivastigmine, donepezil, and galanthamine. In
addition, huperzine A was marketed in China for the same
purpose, but only as a dietary supplement in the US. The
3-D structure of AChE from native Torpedo
californica (TcAChE) has been determined by X-ray crystallography (Protein Data
Bank, PDB code: 2ACE)[3]. There are also a lot of complex
structures of TcAChE with inhibitors determined
experi-mentally, such as donepezil (PDB code:
1EVE)[4], tacrine (PDB code:
1ACJ)[5], decamethonium ion (PDB code:
1ACL)[5],
m-(N,N,N-trimethylammonio) trifluoroacetophen-one (PDB
code: 1AMN)[6], huperzine A (PDB code:
1VOT)[3], galanthamine (PDB code:
1DX6)[7], and edrophonium (PDB code:
2ACK)[8]. All these structures significantly enhance
our understanding of the structural elements of AChE. The
binding pocket of AChE is a long and narrow region which
consists of 2 separated ligand binding sites: the catalytic
(central) site and the peripheral anionic
site[3,4,6]. The catalytic site is the binding site of classical AChE inhibitors,
such as tacrine and huperzine A, which has been studied
thoroughly. On the contrary, the function of the peripheral
site has not been elucidated clearly as yet. Recent studies
have demonstrated that the peripheral site might accelerate
the aggregation and deposition of beta-amyloid peptide,
which is considered as another cause of
AD[2,9,10]. Therefore, it is supposed that an ideal AChE inhibitor should bind to
the catalytic and peripheral sites simultaneously, which could
disrupt the interactions between the enzyme and the
beta-amyloid peptide, and hence slow down the progression of
the disease[11,12].
Among the AChE inhibitors, donepezil and rivastigmine
are usually used early to moderate the stages of AD patients
to treat cognitive loss. However, neither of them could cure
AD, and they often cause some adverse effects. For example,
donepezil can lead to diarrhea, and rivastigmine can cause
vomiting. Therefore, it is urgent to find more effective AChE
inhibitors to treat AD[11,12]. From the crystal structure, it is
clear that the 5,6-dimethoxy-1-indanone moiety and
benzyl-piperidine moiety of donepezil interact with the peripheral
and catalytic binding site of AChE,
respectively[4], whereas rivastigmine is the catalytic site-binding
inhibitor[13].
Therefore, combining the characteristics of the 2 inhibitors,
Sheng et al recently reported a new kind of AChE inhibitors
2-substituted 5,6-dimethoxy-1-indanone derivatives, which
have exhibited excellent inhibitory activities on
AChE in vitro[14,15]. These compounds consist of 3 functional
groups: a 5,6-dimethoxy-1-indanone, a benzene ring, and a
protonated nitrogen (Table 1). The first moiety, extracted
from donepezil, is supposed to interact with the peripheral
binding site, while the other 2 moieties, derived from
rivastigmine (Table 1), might be able to interact with the
catalytic binding site.
Recently, Akula et al performed molecular docking and
comparative molecular field analysis (CoMFA) on a series of
bis-tacrine compounds, which provides some new clues for
the rational design of AChE
inhibitors[16]. In order to further elucidate the mechanism of these 1-indanone derivatives on
the enzyme and provide hints for a new derivative design,
molecular docking and three-dimensional quantitative
structure-activity relationship (3D-QSAR) studies were carried
out in the present study. At first, 44 1-indanone derivatives,
together with donepezil, were docked into the active site of
the enzyme, and then the detailed interactions between the
inhibitors and the enzyme were investigated. Based on the
docked conformations of these compounds within the
active site of AChE, the 3D-QSAR analyses were performed
directly using both CoMFA and comparative molecular
similarity analysis (CoMSIA) methods. The CoMFA and CoMSIA
contour plots were subsequently mapped onto the active
site of the enzyme, which provided more information for the
structural modification of this kind of inhibitors.
Materials and methods
Preparation of ligands We modeled the structures of 44
1-indanone derivatives synthesized by Sheng et
al[14,15]. Their structures and bioactivities, together with donepezil, totally
45 compounds are listed in Table 1. Nine compounds
(com-pounds with asterisks in Table 1) were randomly selected as
an external test set for further model validation, and the rest
of the 36 compounds served as a training set to build the
3D-QSAR models. The 3-D structures of these compounds were
sketched with molecular modeling software package SYBYL
version 7.0 (Tripos Associates, St Louis, Missouri,
USA)[17] and energetically minimized using the Tripos force
field[18] with Gasteiger-Hückel
charges[19]. According to the inhibitors' pKa
values[20] (Table 1), the nitrogen atoms of ligands
1_21 and 26_45 were protonated. In the cases of ligands
22_25, only the nitrogen adjacent to the methyl group was
protonated since its pKa value was more than 7 and the other
nitrogen's pKa value was much less than 7. All the
calculations were performed on a Dell Precision 670 workstation
running Redhat Linux WS 3.0 (Red Hat, North Carolina, USA)
Preparation of the enzyme The crystal structure of
TcAChE in the complex with donepezil was used in the
study[4]. The ionization states of some residues in
TcAChE were
determined with the method of Gilson et
al[21]. The residues His440, Glu443, and Asp392 were neutralized as pointed out
by McCammon et al[22]. Because the pharmacological
activities of the inhibitors were determined in rat cortex
homo-genates, the enzyme should be rat AChE instead of
TcAChE. The only difference between rat AChE and
TcAChE in the binding site was that Phe330 of
TcAChE was replaced by Tyr330 of rat AChE, therefore Phe330 was mutated to Tyr330.
All the other ionizable residues were kept in their standard
protonation states. A few tightly bound water molecules in
the active site (WAT1158, WAT1159, WAT1160, WAT1161,
WAT1249, and WAT1254) were considered in the docking
of donepezil because they played a key role in ligand
binding[4], but they were removed when the other 44 compounds
were docked into the enzyme. After adding all the hydrogen
atoms, the donepezil_TcAChE complex was relaxed for 400
steps using the Tripos force field[18] with Kollman
all-atom charges[23] for the enzyme and Gasteiger_Hückel
charges[19] for the ligand in SYBYL.
Molecular docking GOLD version 3.0.1 (Cambridge
Crystallographic Data Centre, Cambridge, UK
)[24] was employed to investigate the binding mode between the inhibitors and
AChE. The binding site of TcAChE was defined as all the
residues within 17 Å from the CE1 atom of Phe331. The
default parameters of genetic algorithms (GA) were applied
to search the reasonable binding conformation of these
flexible ligands. In order to find more accurate geometries, the
option "allow early termination" was not selected. The
maximum number of GA runs was set to 10 for each compound.
The ChemScore[25] fitness function was used to evaluate the
docking conformations as proposed by Guo et
al[26]. Those docked conformations were saved in MOL2 format and then
imported into SYBYL for further analysis. The donepezil
pose derived from GOLD was used as the reference
compound for alignment. The final conformation for each ligand
was selected based on the following criteria: at first
considering the conformer with the highest
ChemScore[25], then considering the conformer forming a
good π-π interaction
between the indanone ring and Trp279, and lastly
considering hydrogen bonding between the carbonyl group and the
NH group of Phe288.
3D-QSAR analyses The docking geometries of all the
compounds were aligned together within the binding pocket
of TcAChE, which was directly used for further
CoMFA[27] and CoMSIA[28] analyses to discuss the specific
contributions of steric, electrostatic, hydrophobic, and hydrogen
bond effects on the bioactivities of the inhibitors.
CoMFA steric and electrostatic field energies were probed
using an sp3 carbon atom and a +1 net charge atom,
respec-tively. The Tripos force
field[18], with a distance-dependent dielectric constant at all intersections in a regularly spaced
(2 Å) grid, had been set for the calculation of steric and
electrostatic field interactions. The column-filtering
threshold value was set to 2.0 kcal/mol to improve the
signal-to-noise ratio. The default value of 30 kcal/mol was adopted as
the maximum steric and electrostatic energy cut-off. The
regression analysis was carried out using the full
cross-validated partial least-squares (PLS) method (leave-1-out) with
CoMFA standard options. The final conventional model was
developed with the optimum number of components equal
to that yielding the highest
q2LOO. Predictive
r2
(r2pred) values were calculated using the following equation:
r2pred=(SD-PRESS)/SD, where PRESS is the sum of the squared
deviation between the observed and predicted activities of the
test molecules, and SD is the sum of the squared deviation
between the biological activity of the test set and the mean
activity of the training set.
To further validate the model, the leave-4-out
cross-validation was applied to prove its robustness. In the
leave-4-out cross-validation, all the compounds were first randomly
divided into 9 groups with equal numbers of chemicals in
each group. Each group was systematically excluded once
from the data set when rebuilding the model. This procedure
was repeated 100 times due to the random nature of the
selection of chemicals in the process. The mean
q2LNO and the standard deviation of
q2LNO were reported to assess the
model's stability of prediction for diverse chemicals.
The 5 CoMSIA similarity index fields, namely steric,
electrostatic, hydrophobic, hydrogen bond donor, and
acceptor, respectively, were calculated at the grid points
using a probe atom (Wprobe,,k) with charge of +1. The
attenuation factor α was set to 0.3 for the Gaussian type distance.
The statistical evaluation for the CoMSIA analyses was
executed in the same way as described for CoMFA.
Mapping 3D-QSAR contours onto the AChE active site
The CoMFA and CoMSIA results were visualized by
coefficient contour maps. Compound 31 was displayed on the
map in aid of visualization. The contour maps obtained from
the CoMFA and CoMSIA models depicted actual versus
fitted activities of the training set. Key residues within 4.0 Å
around the ligand were visualized for further interpretations.
Results
Interactions of inhibitors with AChE To determine which
docking method was suitable for the AChE inhibitors, a
comparison of GOLD with FlexX was carried out on the enzyme
in our previous work[29]. The results demonstrated that GOLD
could reproduce the X-ray determined conformations of
known AChE inhibitors very well. Therefore, the GOLD
method was used in this study, and the resulting
enzyme_inhibitor interaction was reliable.
In order to consider the influence of cocrystallized water
molecules on docking, donepezil was docked into the
binding pocket of TcAChE with and without water molecules,
separately (Figure 1). The docking conformations of
donepezil superimposed with the X-ray crystal structure very
well, which indicated that the cocrystallized water had little
impact on docking.
Our result illustrated the docking conformations of all 45
compounds aligned in the binding pocket of AChE (Figure 2).
The 1-indanone structures superimposed with each other very
well (Figure 2). The CoMFA and CoMSIA analyses were
then performed based on the binding conformations and their
alignments.
As mentioned above, compound 31, the most potent
inhibitor among the 2-substituted 1-indanone derivatives, was
selected as an example to illustrate the detailed interactions
between inhibitors and the enzyme. As discussed later, all
descriptions referred to compound 31, unless otherwise
specified. Figure 3 shows the binding mode of compound 31
with AChE. In general, these inhibitors could be divided
into 3 parts: the 5,6-dimethoxy-1-indanone moiety, the
benzene ring moiety, and the protonated nitrogen
moiety[30]. All the inhibitors formed major interactions with the active site
of the enzyme through the 3 parts[31].
The 5,6-dimethoxy-1-indanone moiety of all the
compounds was approximately located at the same site. It was
obvious to see that the 5,6-dimethoxy-indanone moiety was
surrounded by residues Tyr70, Leu282, and Trp279 through
hydrophobic interactions at the entrance of the gorge (Figure
3). Among them, the indanone group formed a π-π stacking
interaction with the indole ring of Trp279, while the
5,6-dimethoxy group interacted with the side chains of Leu282
and Trp279 through the hydrophobic interaction. The
carbonyl group of the 5,6-dimethoxy-1-indanone moiety as an
acceptor formed a hydrogen bond with the NH group of Phe288.
In the middle of the gorge, the region was very narrow
and hydrophobic. The benzene ring of the inhibitor
interacted with the aromatic rings of Tyr330, Phe331, and Phe334.
Near the bottom of the gorge, the protonated nitrogen of the
diethylamine group as an anchor, formed cation-π
interactions with the aromatic planes of Trp84 and Tyr330. The
diethylamine group could also form interactions with the
side chain of His440.
3D-QSAR models
CoMFA The aim of the CoMFA analysis of these
donepezil analogues was to find the best predictive model
within the system. Thirty six of the 45 inhibitors were
randomly picked up as the training set to build the CoMFA
model. The remaining 9 were used as an external test set for
the model validation. The results of the CoMFA analysis
(leave-1-out) are summarized in Table 2 and Table 3. The PLS
analysis (leave-1-out) gave a correlation with a cross-validated q2LOO of 0.784 and an optimum component number
of 5. The non-cross-validated PLS analysis was repeated
with 5 components, as determined by the cross-validated
analysis, to give a conventional
r2 of 0.974. These values indicated a good conventional statistical correlation. The
predicted activities (PA) of the 45 inhibitors from the
3D-QSAR model versus their experimental activities (EA) are
shown in Table 4, and the correlation between PA and EA is
presented in Figure 4A.
One important issue in QSAR is cross-validation,
especially when the compounds in the test set are very similar to
those in the training test. It has been shown that a high
q2LOO is the necessary, but not the sufficient condition for
the model to have a high predictive
power[32]. Compared to the leave-1-out procedure, the leave-N-out procedure allows
N compounds to be omitted for the prediction of the model's
stability. Therefore,
q2LNO accounts for more extrapolation
of the model than q2LOO
does. Because of the random nature of the selection of chemicals in the leave-N-out process, the
q2LNO is varied for a selected N group in each run. In order to
obtain a valid statistical analysis, it is necessary to run the
leave-N-out procedure several times (100 times in the present
study) for each random N group. Consequently, the
standard deviation of
q2LNO can be used to assess the model's
stability for the bioactivity prediction of other
compounds[33]. As an alternative validation, the leave-4-out method has been
conducted for the CoMFA model. The leave-4-out
cross-validated q2 value and standard deviation were 0.753
and 0.017, respectively, which provided a good measure of the
statistical significance of the model again.
CoMSIA The CoMSIA analysis results (leave-1-out) are
also summarized in Table 2 and Table 3. A CoMSIA model
with a cross-validated
q2LOO of 0.736 for 4 components and a
conventional r2 of 0.947 was obtained. These data
demonstrated that the constructed CoMSIA model was reliable.
The predicted activities of the 45 inhibitors from the
3D-QSAR model versus their experimental activities are listed in
Table 4 and their correlation is shown in Figure 4B. The high
value of the conventional r2 relating to 5 different descriptor
variables (steric, electrostatic, hydrophobic, hydrogen bond
donor, and acceptor) illustrated that these variables were
necessary to describe the interaction mode of the inhibitors
with AChE, as well as the field properties around the
inhibitors.
The leave-4-out method was also applied to the CoMSIA
model and gave a cross-validated
q2 value and a standard deviation of 0.723 and 0.019, respectively.
External validation of the 3D-QSAR models
The external test set, composed of the 9 randomly selected inhibitors,
was used to further validate the established 3D-QSAR
models. The predicted activities of these compounds are
shown in Figure 4 (red dots). It is easy to see from Figure 4
that the predicted _logIC50 values of the test compounds
were in agreement with the corresponding experimental data,
leading to predictive r2 values of 0.968 for CoMFA and 0.927
for CoMSIA. The root mean square error of prediction
(RMSEP) in both models were 0.126 and 0.218, respectively.
The results indicated that the constructed CoMFA and
CoMSIA models could be used in the design of new
1-indanone derivatives.
Discussion
Comparison of binding mode of donepezil with those of
other inhibitors In the crystal structure of the
donepezil-TcAChE complex, the charged nitrogen of the piperidine ring
made a cation-π interaction with the phenyl ring of Tyr330,
while in the binding mode of compound 31 with AChE, the
phenyl ring might have formed π-π interactions with the side
chains of Tyr330, Phe331, and Phe334. The distance
between the OD2 atom of Asp72 and protonated nitrogen in
donepezil and compound 31 was 5.69 and 6.51 Å,
respec-tively, which explained why donepezil was more active than
compound 31.
The benzene ring in donepezil could form a π-π
interaction with the side chain of Trp84. After the benzene ring was
replaced with a positive-charged chain or cyclic amine in the
new compounds, the protonated nitrogen formed a
cation-π interaction with the side chains of Trp84 and Tyr330; the
carbon chain adjacent to the protonated nitrogen could form
hydrophobic interactions with the side chains of Trp84 and
Tyr330, which meant that the interaction modes of new
compounds were different from that observed in donepezil.
Mapping CoMFA and CoMSIA contours onto the AChE
binding site Based on the above 3D-QSAR models, the
CoMFA coefficient isocontour maps were made onto the
active site of the enzyme in Figure 5. Compound 31 was still
used as a reference molecule in the map. Colored polyhedra
in the map indicated these areas in the 3-D space where
changes in the field values for 1-indanone derivatives
correlated strongly with concomitant changes in inhibitory
activities.
Steric and electrostatic interactions from CoMFA and
CoMSIA In the CoMFA contour map, the green region
around the R1 group suggested that a bulky substituent in
the position might be favorable for biological activity, since
many functional groups, such as the diethylamine group,
pyrrolidine group, and piperidine group could interact with
the side chains of Trp84, Tyr330, and His440 through the
Van der Waals forces. However, if the
R1 group is too large, it will be detrimental to the biological activity of the
compound due to the possible clash between the compound and
the active site of AChE. The yellow polyhedron above the
side chain of Phe331 illustrated that increased steric bulk
nearby would decrease inhibitory activity. A small region of
red contours near the carbonyl group suggested that more
negative substituents would be favorable in these regions
for improving inhibitory potencies. This prediction is
consistent with the observations that the hydrogen bond can
be built between the carbonyl group and the NH group of
Phe288. The blue contour around the benzene ring of Tyr330
showed that the protonated nitrogen could interact with the
side chain of Tyr330 through the cation-π interaction.
Another blue contour near the phenol group of Tyr334
demonstrated that adding some positive groups around there would
be beneficial to biological activity. The positive group there
could interact with the carboxyl group of Asp72 through
electrostatic forces.
The steric and electrostatic fields of CoMSIA, as shown
in Figure 6A, were generally in accordance with the field
distribution of the CoMFA maps (Figure 5). Besides the
structural features already mentioned in the CoMFA steric
field analysis (Figure 6A), there was a small, yellow
polyhedron below the side chain of Tyr334, which illustrated that
the bulky substituent in this area would be unfavorable to
biological activity. At the midway of the deep gorge, the
aromatic side chains of Tyr330 and Tyr121 formed a
bottleneck that was large enough to only permit a water molecule
to pass through[3]. The residues Tyr121, Phe290, Tyr330,
Phe331, and Tyr334 in the binding pocket of AChE were in
the distance of less than 3.0 Å to the benzene moiety of the
inhibitor, as shown in Figure
6A[34]. Therefore, this area must
be very constricted and can not be allowed to add bulky
groups.
Hydrophobic interactions from CoMSIA As for the
hydrophobic interaction, CoMSIA could illustrate clearly the
hydrophobic interaction between the 1-indanone derivatives
and AChE (Figure 6B). A big, yellow contour near the
diethylamine group suggested that more hydrophobic groups
could increase biological activity dramatically (Figure 6B),
which is why the activity of the inhibitors had the order of
37<39<41<43<45. The diethylamine group of compound 31
was much more hydrophobic than the dimethylamine group
of compound 26, which could account for the biological
activity of compound 31 being much higher than that of
compound 26.
Hydrogen bond interactions from CoMSIA
As shown in Figure 6C, the hydrogen bond donor and acceptor maps of
CoMSIA were in agreement with the inhibitor-protein
binding model. The carbonyl group could form a strong
hydrogen bond with the NH group of Phe288. As donepezil
interacted with the AChE, the charged nitrogen might make an
in-line hydrogen bond with WAT1159, which in turn would
form H bonds with hydroxyl group of Tyr121, WAT 1158,
and WAT 1160, similarly; the benzyl group makes a classic
aromatic hydrogen bond (H-bond) with a water molecule
(WAT 1160)[4]. That explains why there are 2
H-bond-donor-favored areas around the protonated nitrogen. Adding a
hydrogen acceptor group to the 2-position substitutions on
the diethylamine group would be detrimental to biological
activity, as shown in Figure 6C.
Implication for new inhibitor design Combining all the
above mapping information, it was obvious that a more
hydrophobic and bulky group with a highly positive charge to
replace the small protonated nitrogen moiety could increase
the binding affinity through hydrophobic, Van der Waals,
and cation-π forces with Trp84, Tyr330, and His440. For
example, a phenyl ring linking to the protonated nitrogen
might increase the biological activity of 2-substituted
1-indanone derivatives.
In conclusion, molecule docking and 3D-QSAR studies
were carried out, not only to explore the interaction
mechanism between 1-indanone derivatives and AChE, but also to
construct highly accurate and predictive 3D-QSAR models
to design new AChE inhibitors for the treatment of AD. The
modeling results provided a satisfactory explanation for the
binding interaction of the 1-indanone compounds with AChE.
The reliability of the models was further verified by the
inhibitors in the external test set and the leave-4-out
cross-validation method. The 3D-QSAR results suggested that
some important Van der Waals, electrostatic, hydrophobic,
and H-bond forces contributed to raise the bioactivity of
compounds. Thus, useful clues could be obtained from the
models, which would be helpful for designing novel
inhibitors of AChE with high potency and specific activity.
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