# Receptor Guided 3D-QSAR: A Useful Approach for  Designing of IGF-1R Inhibitors

Authors: M. Muddassar; F. A. Pasha; H. W. Chung; K. H. Yoo; C. H. Oh; S. J. Cho
Journal: Journal of Biomedicine and Biotechnology
 (2008)

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## Abstract

Research by other investigators has established that insulin-like growth factor‐1 receptor (IGF-1R) is a key oncological target, and that derivatives of 1, 3-disubstituted-imidazo[1,5-α] pyrazine are potent IGF-1R inhibitors. In this paper, we report on our three-dimensional quantitative structure activity relationship (3D-QSAR) studies for this series of compounds.  We validated the 3D-QSAR models by the comparison of two major alignment schemes, namely, ligand-based (LB) and receptor-guided (RG) alignment schemes. The latter scheme yielded better 3D-QSAR models for both comparative molecular field analysis (CoMFA) (q2=0.53, r2=0.95) and comparative molecular similarity indices analysis (CoMSIA) (q2=0.51, r2=0.86). We submit that this might arise from the more accurate inhibitor alignment that results from using the structural information of the active site.  We conclude that the receptor-guided 3D-QSAR may be helpful to design more potent IGF-1R inhibitors, as well as to understand their binding affinity with the receptor.


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## Body

## 1. Introduction

The insulin-like  growth factor-1 receptor is a membrane-associated receptor that belongs to subclass I of the
receptor tyrosine kinase (RTK) superfamily [1]. IGF-1R has been shown to
have significant roles in the regulation of normal cell growth. It has
mitogenic and survival effects on human cancer cells [2]. The Binding of IGF-1 to
IGF-1R activates the RTK, and later, in turn, activates a cascade of downstream
signals, which are postulated to stimulate cell proliferation and enhance
resistance to apoptosis [3]. Understandably, the abnormal
expression of the IGF-1R has been implicated to cancer. Epidemiological studies
have also shown a link between serum concentrations of IGF-1 and IGFBP-3 with
increased risks of breast cancer [4]. A number of anticancer
agents which inhibit the IGF-1R activity and proliferation [5] have been extracted from
plants [6] as well as synthesized, such
as BMS-554417 (2-(4-substituted-2-oxo-1,2-dihydropyridin-3-yl)-benzimidazole) [7] and NVP-AEW541 (pyrrolo[2,3-d]
pyrimidine derivative) molecules. Both of these compounds are orally
administered and have proved antitumor activity. Various QSAR techniques are being
used to explore more potent ligands [8–11]; but in this study, we
performed comparative three-dimensional quantitative structure activity relationship
(3D-QSAR) [12–14] analyses on IFG-1R inhibitors
[15] of imidazo [1, 5-a]
pyrazine derivatives. In 3D-QSAR [14], determination of the
bioactive conformer [16] and molecular alignment of
the compounds is key factor to get meaningful results. The biologically active
conformations of the structures should be aligned in a way that represents a
similar binding mode [17]. Here we first applied the ligand-based
(LB) strategy using the systematic search-based minimum energy conformer approach [18]. Second, receptor-based 3D-QSAR [19] using molecular docking of inhibitors
in the available X-ray crystal structure [20]  of the receptor protein. The qualities of these
3D-QSAR models were compared and discussed with respect to the IGF-1R target.


## 2. Material and Methods

A
series of 54 potent 1, 3-disubstituted imidazole [1, 5-a] pyrazine
derivatives with their inhibitory
activities to IGF-1R were taken from the literature [15]. The dataset was randomly divided
into 43 and 11 molecules, the training and test datasets, respectively. The
observed IC50 values were converted into pIC50 values and are reported in Table 1.Table 1
The structures and observed IGF inhibitory activities [15].
No.StructureRIC50
(M)pIC501A4-OBn1.97×10−65.7062A3-OH0.518×10−66.2863A3-OBn–4-OMe1.35×10−65.8704A3-OBn–4-OH3.31×10−65.4805*BCyclopentyl3.5×10−65.4566BCyclohexyl1.05×10−65.9797*B–CH2–cyclopropyl2.27×10−65.6448*B–CH2–cyclohexyl1.11×10−65.9559B–CH2CH2OMe6.28×10−65.20210B–CH2–2-pyridyl1.09×10−65.96311CH0.606×10−66.21812C2-F0.224×10−66.65013C3-F0.51×10−66.29214C4-F1.23×10−65.91015C2-Cl0.343×10−66.46516C3-Cl2.12×10−65.67417*C4-Cl0.980×10−66.00918C2-OCF2H3.28×10−65.48419C3-OCF2H5.78×10−65.23820C4-OCF2H2.82×10−65.55021C2,3-Difluoro0.898×10−66.04722C3,4-Difluoro4.48×10−65.34923*C2,5-Difluoro0.329×10−66.48324C2,6-Difluoro0.215×10−66.66825C3,5-Difluoro1.35×10−65.87026C2,6-Dichloro1.67×10−65.77727C2-Cl,6-F0.248×10−66.60628DCyclopentyl1.05×10−65.97929DCyclohexyl3.51×10−65.45530DCycloheptyl3.79×10−65.42131*DPhenyl1.68×10−65.77532Etrans-NH20.221×10−66.65633Ecis-NH20.775×10−66.11134*Etrans-NHMe0.105×10−66.97935Etrans-Pyrrolodinyl1.82×10−65.74036*Etrans-Piperidinyl3.40×10−65.46937Etrans-NHPh1.30×10−65.88638Etrans-NHBn1.39×10−65.85739Ftrans-NH20.119×10−66.92440Fcis-NH20.228×10−66.64241Ftrans-N(Et)20.115×10−66.93942Ftrans-Azetidinyl0.081×10−67.09243Ftrans-Pyrrolidinyl0.103×10−66.98744*Ftrans-Morpholino0.091×10−67.04145*Gtrans-Pyrrolidinyl0.116×10−66.93646Gcis-Pyrrolidinyl0.089×10−67.05147Gcis-NH20.060×10−67.22248Gcis-NMe20.166×10−66.78049*Gcis-Piperidinyl0.237×10−66.62550Gcis-Morpholino0.148×10−66.83051Gcis-NH-iPr0.220×10−66.65852Gcis-N(Me)-Piperizinyl0.265×10−66.57753Htrans-NH20.526×10−66.27954Hcis-NH20.554×10−66.256* Compounds used in the test set.


### 2.1. Computational Details

The molecular
modeling studies were carried out using SYBYL 7.3. The initial structures were minimized
at Tripos force field [21] with MMFF94 charge by using
conjugate gradient method, and convergence criterion was 0.005 kcal/mol.The comparative molecular field
analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA)
studies  require aligned structures [16].
The ligand-based (LB) and receptor-guided (RG) alignment techniques were used
in two geometrical schemes respectively.


### 2.2. CoMFA and CoMSIA

Lennard-Jones and Coulomb potentials-based CoMFA analysis has
been performed and the steric as well as electrostatic energies were calculated
by using sp3 carbon probe atom with Van der Waal radius of 1.52 Å
and +1 charge. The energies were truncated to ±30 kcal/mol and the electrostatic
contributions were ignored at lattice interactions with maximum steric
interactions. The CoMFA were generated by standard method in SYBYL. The CoMSIA
models were also derived with the  same lattice box used as in CoMFA
calculations. All five CoMSIA similarity indices (steric, electrostatic, hydrophobic, H-bond
donor, and H-bond acceptor) were evaluated using the probe atom. The CoMSIA
models from hydrophobic and H-bonds were calculated between the grid point and
each atom of the molecule by a Gaussian function [14]. An attenuation factor’s default value of
0.30 was used, which is the standard distance dependence of molecular
similarity.


### 2.3. PLS Analysis and Validation of QSAR Models

In order to derive 3D-QSAR models, the CoMFA and CoMSIA
descriptors were used as independent variables and the pIC50 values as the dependent variable. Partial
least-square (PLS) method [22] was used to linearly
correlate these CoMFA  and CoMSIA descriptors to the inhibitory activity values.
The CoMFA cutoff values were set to 30 kcal/mol for both steric and
electrostatic fields, and also all fields were scaled by the default options in
SYBYL. The cross-validation analysis was performed using the leave one out
(LOO) method in which one compound is removed from the dataset and its activity
is predicted using the model derived from the rest of the dataset. The
cross-validated correlation  coefficient (q2) that resulted in
optimum number of components and lowest standard error of prediction were
calculated using the
following formulae,(1)q2=1−∑y(ypred−yobserved)2∑y(yobserved−ymean)2,PRESS=∑y(ypredicted−yobserved)2,where
ypred, yactual, and ymean are predicted,
actual, and mean values of the target property (pIC50), respectively.
The non-cross-validated PLS analyses were performed with column filtering value
of 2.0, to reduce analysis time with small effect on the q2 values. To further assess the robustness and statistical confidence of the
derived models, bootstrapping analysis for 100 runs were performed.The predictive power of 3D-QSAR models, derived by using the training
set were examined by an external test set of eleven molecules. The predictive
ability of the models is expressed by the predictiver2 value, which is analogous to cross-validated r2 (q2)
and is calculated using the following formula:
(2)rpred2=SD−PRESSSD,
where
SD is the sum of the squared deviations between the biological activities of
the test set and mean activities of the training molecules and PRESS is the sum
of squared deviation between predicted and actual activities of the test set
molecules.


## 2.1. Computational Details

The molecular
modeling studies were carried out using SYBYL 7.3. The initial structures were minimized
at Tripos force field [21] with MMFF94 charge by using
conjugate gradient method, and convergence criterion was 0.005 kcal/mol.The comparative molecular field
analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA)
studies  require aligned structures [16].
The ligand-based (LB) and receptor-guided (RG) alignment techniques were used
in two geometrical schemes respectively.


## 2.2. CoMFA and CoMSIA

Lennard-Jones and Coulomb potentials-based CoMFA analysis has
been performed and the steric as well as electrostatic energies were calculated
by using sp3 carbon probe atom with Van der Waal radius of 1.52 Å
and +1 charge. The energies were truncated to ±30 kcal/mol and the electrostatic
contributions were ignored at lattice interactions with maximum steric
interactions. The CoMFA were generated by standard method in SYBYL. The CoMSIA
models were also derived with the  same lattice box used as in CoMFA
calculations. All five CoMSIA similarity indices (steric, electrostatic, hydrophobic, H-bond
donor, and H-bond acceptor) were evaluated using the probe atom. The CoMSIA
models from hydrophobic and H-bonds were calculated between the grid point and
each atom of the molecule by a Gaussian function [14]. An attenuation factor’s default value of
0.30 was used, which is the standard distance dependence of molecular
similarity.


## 2.3. PLS Analysis and Validation of QSAR Models

In order to derive 3D-QSAR models, the CoMFA and CoMSIA
descriptors were used as independent variables and the pIC50 values as the dependent variable. Partial
least-square (PLS) method [22] was used to linearly
correlate these CoMFA  and CoMSIA descriptors to the inhibitory activity values.
The CoMFA cutoff values were set to 30 kcal/mol for both steric and
electrostatic fields, and also all fields were scaled by the default options in
SYBYL. The cross-validation analysis was performed using the leave one out
(LOO) method in which one compound is removed from the dataset and its activity
is predicted using the model derived from the rest of the dataset. The
cross-validated correlation  coefficient (q2) that resulted in
optimum number of components and lowest standard error of prediction were
calculated using the
following formulae,(1)q2=1−∑y(ypred−yobserved)2∑y(yobserved−ymean)2,PRESS=∑y(ypredicted−yobserved)2,where
ypred, yactual, and ymean are predicted,
actual, and mean values of the target property (pIC50), respectively.
The non-cross-validated PLS analyses were performed with column filtering value
of 2.0, to reduce analysis time with small effect on the q2 values. To further assess the robustness and statistical confidence of the
derived models, bootstrapping analysis for 100 runs were performed.The predictive power of 3D-QSAR models, derived by using the training
set were examined by an external test set of eleven molecules. The predictive
ability of the models is expressed by the predictiver2 value, which is analogous to cross-validated r2 (q2)
and is calculated using the following formula:
(2)rpred2=SD−PRESSSD,
where
SD is the sum of the squared deviations between the biological activities of
the test set and mean activities of the training molecules and PRESS is the sum
of squared deviation between predicted and actual activities of the test set
molecules.


## 3. Results and Discussion

### 3.1. Ligand-Based Alignment

In this scheme, the most active molecule was used as a template. Systematic
search routine was used in the conformational analysis and all rotatable bonds
were searched in10° increments from 0° to 350°. Conformational energies were
computed with electrostatic term, and the lowest energy conformer was selected. The template was modified for other ligands
of the series. All ligands were minimized by Tripos force field but the common
moiety was constrained during minimization. The molecules were aligned by
superimposing common substructures using SYBYL database alignment option. These
aligned structures were subsequently used for ligand-based CoMFA/CoMSIA probe
interaction energy calculations.


### 3.2. Receptor-Guided Alignment

This  geometrical
scheme is based on docked geometry. The best docked mode of the smallest compound
was taken as template and modified for the other compounds. The compounds were minimized
by Tripos force field (Powell method, 2000 iterations, and 0.05 kcal·mol-1·Å-1 energy gradient convergence criteria). All minimized structures at this binding
mode were superimposed to get the molecular alignment for CoMFA and CoMSIA. The
superimposed structures inside the receptor site were further used for CoMFA
and CoMSIA analysis.


### 3.3. Molecular Docking

The structure
coordinates of IGF-1R were obtained from protein databank (1JQH) [20]. Recently, Mulvihill et al. [15] presented a possible binding
mode of compound-2 by using FlexX-based docking. Here we have also performed
molecular docking of same compound.  The PDB file obtained from protein data
bank was used as receptor site. All water molecules were removed and the protein
was modified to dock inhibitor. The active site was defined with a distance of
6.5 Å of ATP binding site. The ligand-2 was docked into the monomer unit (A) of
IGF-1R and out of 100 conformers the best mode was selected as template. This
binding mode seems prominent as the hydrophobic zone of inhibitor corresponds
to hydrophobic pocket of IGFR. The residue E-1080, M-1082, K-1033, D-1086, G-1006,
and L-1005 makes hinge contact and might have significant role in the
inhibition of IGF-1R. It is also clear from all the figures that the depicted
mode holds 3 H-bonds in this region. The –OH group of benzene ring makes H-bond
with –NH of K-1033,
nitrogen of pyrimidine ring makes contact with –NH of M-1082 and both act as H-bond
acceptor. The NH2 group of pyrimidine ring acts as H-bond donor and
makes contact with oxygen of E1080.


### 3.4. CoMFA and CoMSIA Results

The CoMFA and
CoMSIA studies were carried out by using both geometrical schemes with different
descriptors fields independently and in combination. The ligand-based alignment
gave better results for CoMFA model using both field descriptors with cross-validatedr2(q2)=0.52  and non-cross-validated r2=0.88,
while for CoMSIA model, combination of steric, electrostatic, and H-bond
acceptor yielded the best statistical values with q2=0.42 and r2=0.80. The internal predictivity of these CoMFA and CoMSIA models was also
good with boot-strapped correlation coefficient rbs2=0.91
and 0.85, respectively. These models were also validated on a  test set of 11
molecules with predictive r2=0.67 for CoMFA model and 0.57 for
CoMSIA model. In comparison to LB, receptor-guided alignment  yielded more significant models with better
understanding of these inhibitors and receptor interactions. Best CoMFA models
were obtained by combination of steric and electrostatic field descriptors with
q2=0.53 and r2=0.95. Whereas steric, electrostatic, and H-bond
acceptor filed descriptors gave the best CoMSIA model with q2=0.51 and r2=0.86. To further asses the robustness and statistical confidence,
the boot strapping analysis were performed for 100 runs. The rbs2 for CoMFA =0.97 and CoMSIA =0.90 models suggest that a good internal consistency
exists within the underlying dataset. The high r2 predictive values
for CoMFA and CoMSIA (0.67 and 0.64, resp.) also prove models validity. In
our efforts to obtain the more pronounced model, region focusing was performed.
It only yielded high q2 value which is not sufficient condition for
the model to have high predictive power [23]. The regression summary of
different 3D-QSAR models obtained at default parameters and after region
focusing are presented in Tables 2 and 3, respectively.  The predicted pIC50 values for training and
test set from CoMFA and CoMSIA models are given in Tables 4 and 5,
respectively.Table 2
Statistical summary of different PLS analysis. (GS: geometrical scheme; SE: standard error of estimate; n.:
number of components;F: Fischer’s F value for test of significance; rbs2: coefficient of determination after 100 bootstrapping runs; SD: standard deviation; Field contribution: (S) steric field, (E) electrostatic field, (H) hydrophobic field, (D) H-bond donor field, and  (A) H-bond acceptor field.).
AnalysisGSFieldq2n.r2FSErbs2SDrpred2CoMFALBS0.5240.8450.120.23———CoMFALBE0.3830.7334.70.30———CoMFALB0.49S/0.51E0.5240.88700.200.910.10.67CoMFARGS0.3840.7224.50.31———CoMFARGE0.4270.8633.120.22———CoMFARG0.45/0.550.5360.95113.60.130.970.000.67CoMSIALBS0.273——————CoMSIALBE0.361——————CoMSIALBH0.324——————CoMSIALBD0.001——————CoMSIALBA0.332——————CoMSIALBE/S0.372——————CoMSIALB0.60E/0.40A0.412——————CoMSIALB0.73E/0.27D0.413——————CoMSIALB0.51E/.49H0.392——————CoMSIALB0.41E/0.27S/0.31A0.4240.8039.20.260.850.040.57CoMSIALBE/A/D0.412——————CoMSIARGS0.371——————CoMSIARGE0.464——————CoMSIARGH0.353——————CoMSIARGD0.155——————CoMSIARGA0.393——————CoMSIARG0.71E/0.29S0.465——————CoMSIARG0.66E/0.34A0.5250.8541.20.230.890.040.57CoMSIARGE/D0.486——————CoMSIARG0.57E/0.43H0.485——————CoMSIARG0.54E/0.21S/0.25A0.5150.8645.40.220.90.030.64CoMSIARGE/A/D0.476——————Table 3
Statistics of different PLS analysis after region focusing. (GS: geometrical scheme; SE: standard error of estimate; n.: number of components;F: Fischer’s F value for test of significance; rbs2: coefficient of determination after 100 bootstrapping runs; SD: standard deviation; Field contribution: (S) steric field, (E) electrostatic field, (H) hydrophobic field, (D) H-bond donor field, and  (A) H-bond acceptor field.).
AnalysisGSFieldGrid spacingq2n.r2FSErbs2SDrpred2CoMFALBS0.5 Å0.5950.8440.410.2340.890.110.65CoMFALBE0.5 Å0.132——————CoMFALB0.57S/0.43E0.5 Å0.5640.8449.620.2350760.130.68CoMFALBS1.5 Å0.2510.3623.400.450———CoMFALBE1.5 Å−0.031——————CoMFALB0.35S/0.65E1.5 Å0.3820.5121.200.40———CoMFARGS0.5 Å0.4140.7123.470.315———CoMFARGE0.5 Å0.4270.8733.110.220———CoMFARG0.47S/0.53E0.5 Å0.5560.9497.780.1450.960.020.67CoMFARGS1.5 Å0.4440.6517.930.345———CoMFARGE1.5 Å0.1140.263.320.505———CoMFARG0.45S/0.50E1.5 Å0.2930.6018.990.370———Table 4
Experimental and predicted activities with their residuals by CoMFA and CoMSIA analyses of the  training set.
n.CoMFACoMSIAExperimentalPredictedPredictedpIC50pIC50ResidualpIC50Residual15.7065.5530.1535.6460.06026.2866.296−0.0106.1720.11435.8705.948−0.0785.7810.08945.4805.888−0.4085.814−0.33465.9795.3650.6145.4210.55895.2025.275−0.0735.0740.128105.9636.200−0.2376.199−0.236116.2186.1910.0276.1350.083126.6506.2760.3746.3300.320136.2926.0650.2275.8490.443145.9105.8700.0405.8420.068156.4656.487−0.0226.3630.102165.6745.927−0.2535.787−0.113185.4845.4730.0115.589−0.105195.2385.1020.1365.579−0.341205.5505.615−0.0655.625−0.075216.0476.0150.0326.226−0.179225.3495.544−0.1955.754−0.405246.6686.5710.0976.4740.194255.8706.034−0.1645.8320.038265.7775.929−0.1526.416−0.639276.6066.5860.026.4980.108285.9795.981−0.0025.6760.303295.4555.471−0.0165.562−0.107305.4215.473−0.0525.604−0.183326.6566.3660.2906.2970.359336.1116.366−0.2556.297−0.186355.7405.7060.0346.068−0.328375.8865.934−0.0485.8640.022385.8575.8280.0295.7630.094396.9246.8260.0986.7850.139406.6426.826−0.1846.785−0.143416.9397.009−0.0706.951−0.012427.0927.0320.0606.9160.176436.9877.012−0.0257.008−0.021467.0516.9500.1017.130−0.079477.2227.325−0.1037.1260.096486.7806.7630.0176.881−0.101506.8306.7760.0546.842−0.012516.6586.6480.0106.806−0.148526.5776.5470.0306.4290.148536.2796.328−0.0496.298−0.019546.2566.328−0.0726.298−0.042Table 5
Experimental and predicted activities with their residuals by CoMFA and CoMSIA analyses of the  test set.
n.CoMFACoMSIAExperimentalPredictedPredictedpIC50pIC50ResidualpIC50Residual55.4565.4040.0525.536−0.08075.6445.779−0.1355.811−0.16785.9556.015−0.0605.7870.168176.0095.9660.0435.8190.190236.4836.2740.2096.3040.179315.7755.7030.0725.7280.047346.9796.2690.7106.2050.774365.4695.696−0.2275.924−0.455447.0417.080−0.0397.227−0.186456.9366.950−0.0147.130−0.194496.6256.902−0.2776.834−0.209In 3D-QSAR, the determination of the
bioactive conformer and molecular alignment of the compounds is an important
step. In ligand-based techniques, the minimum energy conformers are often used
as bioactive conformer. In contrast, the binding poses obtained from cocrystal
structure are used in receptor-guided techniques. Here, both techniques were
used. The statistical results indicate that conformation obtained from
molecular docking is more reliable. In Figure1, the yellow conformer displays systematic search-based
minimum energy conformer while the red structure shows docked conformer. The
findings are reasonable as the oxygen attached with benzyl group of docked
conformer is more closed to amino acid (Asp1086) that facilitates an H-bonding
between –NH of Asp-1086
and this oxygen atom of the inhibitor; but in case of minimum energy conformer
(yellow), the benzyl moiety is quite far and disfavors such interactions.Figure 1
Comparison of minimum energy (yellow) and docking based (red) conformers.


### 3.5. The CoMFA Contour Maps

Figures2 and 3 show the electrostatic and steric contour
maps of the best models based on receptor-guided alignment scheme. The electrostatic
interactions are represented by red- and blue-colored contours while steric
interactions are represented by green and yellow colored contours. In
electrostatic field, blue color contour
represents region where electropositive group enhances the activity, whereas red-color region likes electron-rich groups to
increase the biological activity. In case of steric interactions, the green
region demands bulky substituents to enhance the activity, while in yellow
contours, bulky substituents decrease the activity.Figure 2
CoMFA electrostatic maps with the most (red) and least (orange) active compound within the active site.Figure 3
CoMFA steric maps with the most (red) and least (orange) active compound within the active site.The most potent compound-47 (red color) and least-active
compound-9 (orange color) of the series with CoMFA contour maps have been
superimposed in the active site of the receptor protein. Figure2 shows that
red polyhedrons locate the region where electron-rich group will enhance the
inhibitory activity, and vice versa for blue polyhedron. Therefore, the phenyl
ring in compound-47 might be responsible for its higher activity than methoxy
group of compound-9 because
it might have the p-p interactions with the phenyl ring of phenyl alanine (Phe1010)
amino acid. The red contour around 1–3 carbon of cyclobutane also demands the electron-rich group for higher potency. Compound-47 has amino group at C-3 position which
might be responsible for its higher activity than least-active compound-9. It
is also clear in most of compounds from the dataset that electron-rich group at
this position have higher activity than compound-9. In Figure 3, green polyhedron
locates the region
where bulky substitutent would increase the inhibitory activity and yellow
polyhedron where the steric bulk is not required for high potency of the
compounds. The small green contour near the phenyl ring of compound-47 explains
its higher activity than compound-9. Similarly, the green contour around 2 and 3
carbon of cyclobutane requires the bulky substitutent to be highly active. Thus
the bulky substitutent at this position in dataset favors the higher inhibitory
activity of the compounds than compound-9. Yellow polyhedron below the plane of
phenyl ring and cyclopropane requires the small group to be more active.


### 3.6. CoMSIA Contour Maps

The CoMSIA contour
maps were also developed on the models based on the geometrical scheme 2. Figures4, 5, and 6 show the steric electrostatic and H-bond acceptor contour maps
superimposed in the active site of the IGF-1R. In CoMSIA method, steric and
electrostatic contours maps have the same meaning as that of CoMFA contour maps
whereas H-bond acceptor contours are represented by magenta and red colors.
Magenta favors H-bond acceptor group while red disfavors. The steric and
electrostatic maps are more or less similar to the corresponding CoMFA models (Figures
2 and 3, resp.) except that there is a small green contour near phenyl
ring of compound-47 in CoMFA model. In Figure 6, the magenta contour around C-2
and C-3 position of cyclobutane favors the H-bond accepting group to enhance the
inhibitory activity of the molecules. Thus the H-bond accepting substituent at
C-4 position might enhance inhibitory activity of the compounds through H-bonding
with Glycine (Gly1008) or Valine (Val1013).Figure 4
CoMSIA electrostatic maps with the most (red) and least (orange) active compound within the active site.Figure 5
CoMSIA steric maps with the most (red) and least (orange) active compound within the active site.Figure 6
CoMSIA H-bond acceptor map with the most (red) and least (orange) active compound within the active site.


## 3.1. Ligand-Based Alignment

In this scheme, the most active molecule was used as a template. Systematic
search routine was used in the conformational analysis and all rotatable bonds
were searched in10° increments from 0° to 350°. Conformational energies were
computed with electrostatic term, and the lowest energy conformer was selected. The template was modified for other ligands
of the series. All ligands were minimized by Tripos force field but the common
moiety was constrained during minimization. The molecules were aligned by
superimposing common substructures using SYBYL database alignment option. These
aligned structures were subsequently used for ligand-based CoMFA/CoMSIA probe
interaction energy calculations.


## 3.2. Receptor-Guided Alignment

This  geometrical
scheme is based on docked geometry. The best docked mode of the smallest compound
was taken as template and modified for the other compounds. The compounds were minimized
by Tripos force field (Powell method, 2000 iterations, and 0.05 kcal·mol-1·Å-1 energy gradient convergence criteria). All minimized structures at this binding
mode were superimposed to get the molecular alignment for CoMFA and CoMSIA. The
superimposed structures inside the receptor site were further used for CoMFA
and CoMSIA analysis.


## 3.3. Molecular Docking

The structure
coordinates of IGF-1R were obtained from protein databank (1JQH) [20]. Recently, Mulvihill et al. [15] presented a possible binding
mode of compound-2 by using FlexX-based docking. Here we have also performed
molecular docking of same compound.  The PDB file obtained from protein data
bank was used as receptor site. All water molecules were removed and the protein
was modified to dock inhibitor. The active site was defined with a distance of
6.5 Å of ATP binding site. The ligand-2 was docked into the monomer unit (A) of
IGF-1R and out of 100 conformers the best mode was selected as template. This
binding mode seems prominent as the hydrophobic zone of inhibitor corresponds
to hydrophobic pocket of IGFR. The residue E-1080, M-1082, K-1033, D-1086, G-1006,
and L-1005 makes hinge contact and might have significant role in the
inhibition of IGF-1R. It is also clear from all the figures that the depicted
mode holds 3 H-bonds in this region. The –OH group of benzene ring makes H-bond
with –NH of K-1033,
nitrogen of pyrimidine ring makes contact with –NH of M-1082 and both act as H-bond
acceptor. The NH2 group of pyrimidine ring acts as H-bond donor and
makes contact with oxygen of E1080.


## 3.4. CoMFA and CoMSIA Results

The CoMFA and
CoMSIA studies were carried out by using both geometrical schemes with different
descriptors fields independently and in combination. The ligand-based alignment
gave better results for CoMFA model using both field descriptors with cross-validatedr2(q2)=0.52  and non-cross-validated r2=0.88,
while for CoMSIA model, combination of steric, electrostatic, and H-bond
acceptor yielded the best statistical values with q2=0.42 and r2=0.80. The internal predictivity of these CoMFA and CoMSIA models was also
good with boot-strapped correlation coefficient rbs2=0.91
and 0.85, respectively. These models were also validated on a  test set of 11
molecules with predictive r2=0.67 for CoMFA model and 0.57 for
CoMSIA model. In comparison to LB, receptor-guided alignment  yielded more significant models with better
understanding of these inhibitors and receptor interactions. Best CoMFA models
were obtained by combination of steric and electrostatic field descriptors with
q2=0.53 and r2=0.95. Whereas steric, electrostatic, and H-bond
acceptor filed descriptors gave the best CoMSIA model with q2=0.51 and r2=0.86. To further asses the robustness and statistical confidence,
the boot strapping analysis were performed for 100 runs. The rbs2 for CoMFA =0.97 and CoMSIA =0.90 models suggest that a good internal consistency
exists within the underlying dataset. The high r2 predictive values
for CoMFA and CoMSIA (0.67 and 0.64, resp.) also prove models validity. In
our efforts to obtain the more pronounced model, region focusing was performed.
It only yielded high q2 value which is not sufficient condition for
the model to have high predictive power [23]. The regression summary of
different 3D-QSAR models obtained at default parameters and after region
focusing are presented in Tables 2 and 3, respectively.  The predicted pIC50 values for training and
test set from CoMFA and CoMSIA models are given in Tables 4 and 5,
respectively.Table 2
Statistical summary of different PLS analysis. (GS: geometrical scheme; SE: standard error of estimate; n.:
number of components;F: Fischer’s F value for test of significance; rbs2: coefficient of determination after 100 bootstrapping runs; SD: standard deviation; Field contribution: (S) steric field, (E) electrostatic field, (H) hydrophobic field, (D) H-bond donor field, and  (A) H-bond acceptor field.).
AnalysisGSFieldq2n.r2FSErbs2SDrpred2CoMFALBS0.5240.8450.120.23———CoMFALBE0.3830.7334.70.30———CoMFALB0.49S/0.51E0.5240.88700.200.910.10.67CoMFARGS0.3840.7224.50.31———CoMFARGE0.4270.8633.120.22———CoMFARG0.45/0.550.5360.95113.60.130.970.000.67CoMSIALBS0.273——————CoMSIALBE0.361——————CoMSIALBH0.324——————CoMSIALBD0.001——————CoMSIALBA0.332——————CoMSIALBE/S0.372——————CoMSIALB0.60E/0.40A0.412——————CoMSIALB0.73E/0.27D0.413——————CoMSIALB0.51E/.49H0.392——————CoMSIALB0.41E/0.27S/0.31A0.4240.8039.20.260.850.040.57CoMSIALBE/A/D0.412——————CoMSIARGS0.371——————CoMSIARGE0.464——————CoMSIARGH0.353——————CoMSIARGD0.155——————CoMSIARGA0.393——————CoMSIARG0.71E/0.29S0.465——————CoMSIARG0.66E/0.34A0.5250.8541.20.230.890.040.57CoMSIARGE/D0.486——————CoMSIARG0.57E/0.43H0.485——————CoMSIARG0.54E/0.21S/0.25A0.5150.8645.40.220.90.030.64CoMSIARGE/A/D0.476——————Table 3
Statistics of different PLS analysis after region focusing. (GS: geometrical scheme; SE: standard error of estimate; n.: number of components;F: Fischer’s F value for test of significance; rbs2: coefficient of determination after 100 bootstrapping runs; SD: standard deviation; Field contribution: (S) steric field, (E) electrostatic field, (H) hydrophobic field, (D) H-bond donor field, and  (A) H-bond acceptor field.).
AnalysisGSFieldGrid spacingq2n.r2FSErbs2SDrpred2CoMFALBS0.5 Å0.5950.8440.410.2340.890.110.65CoMFALBE0.5 Å0.132——————CoMFALB0.57S/0.43E0.5 Å0.5640.8449.620.2350760.130.68CoMFALBS1.5 Å0.2510.3623.400.450———CoMFALBE1.5 Å−0.031——————CoMFALB0.35S/0.65E1.5 Å0.3820.5121.200.40———CoMFARGS0.5 Å0.4140.7123.470.315———CoMFARGE0.5 Å0.4270.8733.110.220———CoMFARG0.47S/0.53E0.5 Å0.5560.9497.780.1450.960.020.67CoMFARGS1.5 Å0.4440.6517.930.345———CoMFARGE1.5 Å0.1140.263.320.505———CoMFARG0.45S/0.50E1.5 Å0.2930.6018.990.370———Table 4
Experimental and predicted activities with their residuals by CoMFA and CoMSIA analyses of the  training set.
n.CoMFACoMSIAExperimentalPredictedPredictedpIC50pIC50ResidualpIC50Residual15.7065.5530.1535.6460.06026.2866.296−0.0106.1720.11435.8705.948−0.0785.7810.08945.4805.888−0.4085.814−0.33465.9795.3650.6145.4210.55895.2025.275−0.0735.0740.128105.9636.200−0.2376.199−0.236116.2186.1910.0276.1350.083126.6506.2760.3746.3300.320136.2926.0650.2275.8490.443145.9105.8700.0405.8420.068156.4656.487−0.0226.3630.102165.6745.927−0.2535.787−0.113185.4845.4730.0115.589−0.105195.2385.1020.1365.579−0.341205.5505.615−0.0655.625−0.075216.0476.0150.0326.226−0.179225.3495.544−0.1955.754−0.405246.6686.5710.0976.4740.194255.8706.034−0.1645.8320.038265.7775.929−0.1526.416−0.639276.6066.5860.026.4980.108285.9795.981−0.0025.6760.303295.4555.471−0.0165.562−0.107305.4215.473−0.0525.604−0.183326.6566.3660.2906.2970.359336.1116.366−0.2556.297−0.186355.7405.7060.0346.068−0.328375.8865.934−0.0485.8640.022385.8575.8280.0295.7630.094396.9246.8260.0986.7850.139406.6426.826−0.1846.785−0.143416.9397.009−0.0706.951−0.012427.0927.0320.0606.9160.176436.9877.012−0.0257.008−0.021467.0516.9500.1017.130−0.079477.2227.325−0.1037.1260.096486.7806.7630.0176.881−0.101506.8306.7760.0546.842−0.012516.6586.6480.0106.806−0.148526.5776.5470.0306.4290.148536.2796.328−0.0496.298−0.019546.2566.328−0.0726.298−0.042Table 5
Experimental and predicted activities with their residuals by CoMFA and CoMSIA analyses of the  test set.
n.CoMFACoMSIAExperimentalPredictedPredictedpIC50pIC50ResidualpIC50Residual55.4565.4040.0525.536−0.08075.6445.779−0.1355.811−0.16785.9556.015−0.0605.7870.168176.0095.9660.0435.8190.190236.4836.2740.2096.3040.179315.7755.7030.0725.7280.047346.9796.2690.7106.2050.774365.4695.696−0.2275.924−0.455447.0417.080−0.0397.227−0.186456.9366.950−0.0147.130−0.194496.6256.902−0.2776.834−0.209In 3D-QSAR, the determination of the
bioactive conformer and molecular alignment of the compounds is an important
step. In ligand-based techniques, the minimum energy conformers are often used
as bioactive conformer. In contrast, the binding poses obtained from cocrystal
structure are used in receptor-guided techniques. Here, both techniques were
used. The statistical results indicate that conformation obtained from
molecular docking is more reliable. In Figure1, the yellow conformer displays systematic search-based
minimum energy conformer while the red structure shows docked conformer. The
findings are reasonable as the oxygen attached with benzyl group of docked
conformer is more closed to amino acid (Asp1086) that facilitates an H-bonding
between –NH of Asp-1086
and this oxygen atom of the inhibitor; but in case of minimum energy conformer
(yellow), the benzyl moiety is quite far and disfavors such interactions.Figure 1
Comparison of minimum energy (yellow) and docking based (red) conformers.


## 3.5. The CoMFA Contour Maps

Figures2 and 3 show the electrostatic and steric contour
maps of the best models based on receptor-guided alignment scheme. The electrostatic
interactions are represented by red- and blue-colored contours while steric
interactions are represented by green and yellow colored contours. In
electrostatic field, blue color contour
represents region where electropositive group enhances the activity, whereas red-color region likes electron-rich groups to
increase the biological activity. In case of steric interactions, the green
region demands bulky substituents to enhance the activity, while in yellow
contours, bulky substituents decrease the activity.Figure 2
CoMFA electrostatic maps with the most (red) and least (orange) active compound within the active site.Figure 3
CoMFA steric maps with the most (red) and least (orange) active compound within the active site.The most potent compound-47 (red color) and least-active
compound-9 (orange color) of the series with CoMFA contour maps have been
superimposed in the active site of the receptor protein. Figure2 shows that
red polyhedrons locate the region where electron-rich group will enhance the
inhibitory activity, and vice versa for blue polyhedron. Therefore, the phenyl
ring in compound-47 might be responsible for its higher activity than methoxy
group of compound-9 because
it might have the p-p interactions with the phenyl ring of phenyl alanine (Phe1010)
amino acid. The red contour around 1–3 carbon of cyclobutane also demands the electron-rich group for higher potency. Compound-47 has amino group at C-3 position which
might be responsible for its higher activity than least-active compound-9. It
is also clear in most of compounds from the dataset that electron-rich group at
this position have higher activity than compound-9. In Figure 3, green polyhedron
locates the region
where bulky substitutent would increase the inhibitory activity and yellow
polyhedron where the steric bulk is not required for high potency of the
compounds. The small green contour near the phenyl ring of compound-47 explains
its higher activity than compound-9. Similarly, the green contour around 2 and 3
carbon of cyclobutane requires the bulky substitutent to be highly active. Thus
the bulky substitutent at this position in dataset favors the higher inhibitory
activity of the compounds than compound-9. Yellow polyhedron below the plane of
phenyl ring and cyclopropane requires the small group to be more active.


## 3.6. CoMSIA Contour Maps

The CoMSIA contour
maps were also developed on the models based on the geometrical scheme 2. Figures4, 5, and 6 show the steric electrostatic and H-bond acceptor contour maps
superimposed in the active site of the IGF-1R. In CoMSIA method, steric and
electrostatic contours maps have the same meaning as that of CoMFA contour maps
whereas H-bond acceptor contours are represented by magenta and red colors.
Magenta favors H-bond acceptor group while red disfavors. The steric and
electrostatic maps are more or less similar to the corresponding CoMFA models (Figures
2 and 3, resp.) except that there is a small green contour near phenyl
ring of compound-47 in CoMFA model. In Figure 6, the magenta contour around C-2
and C-3 position of cyclobutane favors the H-bond accepting group to enhance the
inhibitory activity of the molecules. Thus the H-bond accepting substituent at
C-4 position might enhance inhibitory activity of the compounds through H-bonding
with Glycine (Gly1008) or Valine (Val1013).Figure 4
CoMSIA electrostatic maps with the most (red) and least (orange) active compound within the active site.Figure 5
CoMSIA steric maps with the most (red) and least (orange) active compound within the active site.Figure 6
CoMSIA H-bond acceptor map with the most (red) and least (orange) active compound within the active site.


## 4. Conclusion

A
comparative CoMFA and CoMSIA models were developed for the series of potent
IGF-1R inhibitors. Ligand-based and receptor-guided protocols were applied to
develop the models. Receptor-guided alignment gave models with better
statistics than the ones from the ligand-based approach, presumably because the
alignment using receptor information is more realistic. Moreover, the
interpretation of receptor-guided models are directly associated with the
receptor information. That is, in general, the superposition of a CoMFA or
CoMSIA contour map inside the receptor shows reasonable correspondence between the
contour map property and the physical property of surrounding active site
region. This provides more detailed understanding about the interaction between
the series of inhibitors and IGF-1R. The information drawn here can be used to
design new inhibitors of IGF-1R.



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*Source: 837653-2008-03-09.xml*