http://informahealthcare.com/rst ISSN: 1079-9893 (print), 1532-4281 (electronic) J Recept Signal Transduct Res, 2014; 34(5): 417–430 ! 2014 Informa Healthcare USA, Inc. DOI: 10.3109/10799893.2014.917323
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RESEARCH ARTICLE
Multiple receptor conformation docking, dock pose clustering and 3D QSAR studies on human poly(ADP-ribose) polymerase-1 (PARP-1) inhibitors Sabiha Fatima, Mohan Babu Jatavath, Raju Bathini, Sree Kanth Sivan, and Vijjulatha Manga Department of Chemistry, University College of Science, Osmania University, Hyderabad, Andhra Pradesh, India
Abstract
Keywords
Poly(ADP-ribose) polymerase-1 (PARP-1) functions as a DNA damage sensor and signaling molecule. It plays a vital role in the repair of DNA strand breaks induced by radiation and chemotherapeutic drugs; inhibitors of this enzyme have the potential to improve cancer chemotherapy or radiotherapy. Three-dimensional quantitative structure activity relationship (3D QSAR) models were developed using comparative molecular field analysis, comparative molecular similarity indices analysis and docking studies. A set of 88 molecules were docked into the active site of six X-ray crystal structures of poly(ADP-ribose)polymerase-1 (PARP-1), by a procedure called multiple receptor conformation docking (MRCD), in order to improve the 3D QSAR models through the analysis of binding conformations. The docked poses were clustered to obtain the best receptor binding conformation. These dock poses from clustering were used for 3D QSAR analysis. Based on MRCD and QSAR information, some key features have been identified that explain the observed variance in the activity. Two receptor-based QSAR models were generated; these models showed good internal and external statistical reliability that is evident from the q2loo , r2nev and r2pred . The identified key features enabled us to design new PARP-1 inhibitors.
Comparative molecular similarity indices analysis, multiple receptor conformation docking, partial least square analysis, poly(ADP-ribose) polymerase-1, three-dimensional quantitative structure activity relationship
Introduction Poly(ADP-ribose) polymerase-1 (PARP-1: EC 2.4.2.30) is a nuclear enzyme involved in the detection and repair of DNA damage (1). PARP-1 functions as a DNA damage sensor (2) and signaling molecule, binding to both single- and doublestranded DNA breaks. On binding to damaged DNA, PARP-1 forms homodimers and catalyzes the cleavage of NAD+ into nicotinamide and ADP-ribose to form long branches of ADP-ribose polymers on target proteins such as histones and PARP-1 itself. This process results in cellular energetic depletion, mitochondrial dysfunction and ultimately necrosis (3,4). As for the latter pathway, numerous transcription factors, DNA replication factors and signaling molecules have also been shown to become poly(ADP-ribosylated) by PARP1; but a PARP-mediated activation of the pluripotent transcription factor nuclear factor-kB appears to be of crucial importance (4,5). Importantly, numerous recent studies have suggested that PARP-1 activity can be modulated by several endogenous factors, and PARP-1 can also modulate important signaling pathways (6). Address for correspondence: Manga Vijjulatha, Department of Chemistry, University College of Science, Osmania University, Hyderabad, Andhra Pradesh 500 007, India. Tel: 09866845408. E-mail: [email protected]
History Received 18 December 2013 Revised 14 April 2014 Accepted 15 April 2014 Published online 21 July 2014
As PARP-1 plays a vital role in the repair of DNA strand breaks, including those induced by radiation and chemotherapeutic drugs, inhibitors of this enzyme have potential to improve cancer chemotherapy or radiotherapy (7,8). Drugs that inhibit PARP-1 cause multiple double-strand breaks to form in this way; and in tumors with BRCA1, BRCA2 or PALB2 mutations, these double-strand breaks cannot be efficiently repaired, leading to the death of the cells. Normal cells that do not replicate their DNA as often as cancer cells, and that lack any mutated BRCA1 or BRCA2 still have homologous repair operating, which allows them to survive the inhibition of PARP (9,10). PARP-1 inhibitors examined to date are competitive inhibitors of NAD+. Relatively low potency of these agents has led to the necessity for development of more potent and specific PARP-1 inhibitors. In this article, we report receptor-based three-dimensional quantitative structure activity relationship (3D-QSAR) studies using comparative molecular field analysis (CoMFA) (11,12) and comparative molecular similarity indices analysis (CoMSIA) (13) methodologies on benzimidazole carboxamide series, 2-phenyl substituted benzimidazole carboxamide, tricyclic indoles and tricyclic benzimidazole analogues. Receptor-based conformation for 88 molecules was obtained by multiple receptor conformation docking (MRCD) and dock poses clustering. Docking was performed on six different
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crystal structures to analyze each molecule dock pose and to confirm the presence of required hydrogen bond interactions with the active site amino acids. Partial least square (PLS)- (14) based statistical analysis was carried out on these molecules to identify the correlation between biological activity and structural parameters. The contour maps generated enabled us to explain the observed variation in activity and guided us to design new molecules.
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Methodology Six high-resolution X-ray crystal structure of PARP-1 in complex with inhibitors (pdb id: 2RD6, 2RCW, 3GJW, 3GN7, 3L3L and 3L3M) (15–20) were downloaded from the protein data bank; resolution of the crystal structures is listed in Table 1. GLIDE 5.6 (Schrodinger LLC, New York, NY) (21) was used for molecular docking. Proteins were prepared using protein preparation module applying the default parameters; grids were generated around the active site of PARP-1 with receptor van der Waals scaling for the non-polar atoms as 0.9 (21). A set of 88 known PARP-1 inhibitors (Table 2) with diverse structures and varied range of inhibition constants (Ki) were selected from literature (22–26), these were built using maestro build panel. Low energy conformers were obtained from LigPrep application in Schro¨dinger 2010 suite using the MMFF94s force field. These ligands were selected and docked into the grid generated from six protein structures using standard precision docking mode (27). The crystal structure ligands were also docked and its atomic root mean square deviation (RMSD) was calculated to validate docking process. The best dock pose (low binding energy conformer according to the glide dock score) of each ligand from six docking runs performed on six receptors grids were analyzed for their hydrogen bond interactions with the receptor. Poses with the required hydrogen bond interactions namely with Gly202 and Ser243 were selected for further clustering. Dock poses were clustered using clustering of conformer’s script in Schro¨dinger 2010 suite. Clustering was performed using atomic RMSD; in this, RMSD was calculated in place that does not alter the dock pose of each conformer. The lowest binding energy conformation from the most populated cluster was chosen for CoMFA and CoMSIA analysis without further alignment, i.e. super imposition of ligands based on the common substructure for a set of molecules was not done, instead the docked conformer pose obtained from clustering where taken as it is for all ligands. This imparts the flexible receptor binding to each ligand in data set. The resulting docked pose orientations are shown in Figure 1. Table 1. PDB ids of crystal structure of PARP, their resolution and RMSD of redocked co-crystallized ligand. PDB ID
RMSD
˚ X-ray resolution in A
2RD6 2RCW 3GJW 3GN7 3L3L 3L3M
0.101 0.2382 0.8409 0.5591 1.1984 0.7757
2.30 2.80 2.30 2.50 2.50 2.50
The molecules were imported into Sybyl-X 1.2 (St. Louis, MO) molecular modeling program package and Gasteiger– Hu¨ckel (28) charges were assigned. The standard Tripos force fields were employed for the CoMFA and CoMSIA analysis. ˚ in each direction with each A 3D cubic lattice of dimension 4 A ˚ was lattice intersection of regularly spaced grid of 2.0 A created. The steric and electrostatic parameters were calculated in case of the CoMFA fields, while hydrophobic, acceptor and donor parameters in addition to steric and electrostatic were calculated in case of the CoMSIA fields at each lattice. The sp3 carbon was used as a probe atom to generate steric (Lennard–Jones potential) field energies and a charge of +1 to generate electrostatic (Coulombic potential) field energies. A distance-dependent dielectric constant of 1.00 was used. The steric and electrostatic contributions were cut off at 30 kcalmol–1. A PLS regression was used to generate a linear relationship that correlates changes in the computed fields with changes in the corresponding experimental values of biological activity (pKi) for the data set of ligands. Eighty-eight PARP-1 inhibitors were divided into 64 training and 24 test set molecules in model-1, 46 and 42 in model-2, respectively, considering the set had a balanced distribution of more and less active compounds. Biological activity values of ligands were used as dependent variables in a PLS statistical analysis. The column filtering value(s) were set to 2.0 kcalmol1 to improve the signal-to-noise ratio by omitting those lattice points whose energy variations were below this threshold. Cross-validations were performed by the leave-one-out (LOO) procedure to determine the optimum number of components (ONC) and the coefficient r2loo . The ONC obtained is then used to derive the final QSAR model using all of the training set compounds with non-cross validation and to obtain the conventional regression coefficient (r2). Since the statistical parameters were found to be the best for the model from the LOO method, it was employed for further predictions of activity of test molecules for cross validation of the model. Multiple receptors conformation docking (29–32) is a method employed for incorporating the receptor flexibility while docking analysis. In this study, standard precision glide docking procedure was validated by removing crystal structure ligand from the binding site and redocking it to the binding site of PARP-1 for each of the receptor conformations.
Results and discussion The co-crystal ligands of all the crystal structures available in protein data bank were showing hydrogen bond interactions with Gly 202 and Ser 243. It clearly indicates that all inhibitor molecules should have key interaction with these amino acids. Hence, all crystal structures with co-crystallized ligands were used for molecular docking. A very good agreement between the localization of the inhibitor upon docking and from the crystal structure was observed, i.e. having similar hydrogen bonding interactions with Gly202 and Ser243. The RMSDs between the predicted conformation and the observed X-ray crystallographic ˚ to 1.19 A ˚, conformation for the ligands ranged from 0.1 A the values are provided in Table 1, these values suggests the
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Table 2. Structures of human PARP-1 inhibitors with their experimental pKi and predicted pKi. O
NH2 N R N H
Model 1 Mol.
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1
R
a,b
NH
2b
pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
8.222
8.358
8.327
8.266
8.150
7.523
7.692
7.595
7.780
7.605
8.222
7.744
8.329
7.851
8.244
7.824
7.686
7.743
7.834
7.733
8.397
8.273
8.269
8.045
8.099
8.397
8.403
8.388
8.420
8.464
8.222
8.277
8.225
8.432
8.013
7.602
7.600
7.515
7.597
7.482
7.721
7.842
7.727
7.734
7.753
6.89
6.774
6.837
7.756
7.730
8.523
8.545
8.564
8.525
8.340
8.301
8.333
8.465
8.324
8.491
7.886
7.598
7.601
7.641
7.940
N H
H
3a,b
Model 2
H3C
N CH3
4 N CH3
5b
H3C
NH
6 NH
7b
CH3
N H3C
CH3
8 R
9
N H H3C
H3C
N
10b H3C
11b
H3C
N
NH
12a,b NH
13a,b
H3C NH
(continued )
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Table 2. Continued
Model 1 Mol.
R
b
CH3
14
Model 2
pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
7.377
7.355
7.365
7.473
7.876
8.046
7.823
7.951
8.059
8.048
7.508
7.821
8.061
7.466
7.530
8.301
8.350
8.279
8.309
8.321
8.046
8.021
8.066
7.772
7.975
8.000
8.295
8.200
8.014
7.987
7.638
7.671
7.765
7.552
7.036
7.886
7.729
7.968
7.779
7.894
7.161
7.292
7.198
7.193
7.240
8.398
8.483
8.390
8.359
8.375
8.097
8.131
8.156
8.153
8.083
8.155
8.222
8.068
8.339
7.869
N CH3 CH3
15a
CH3
H3C
N
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CH3
16a
N H3C
17
H3C
N
18b
CH3
F N
H3C
19a
20b
H3C
N
H 3C
O
N H
H3C
21
N H CH3
22 N
CH3
H3C
23
24
H3 C
H3C
NH
N
25b H3C
CH3
CH3
N
O
NH2 N R N H
(continued )
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DOI: 10.3109/10799893.2014.917323
421
Table 2. Continued
Model 1 Mol. 26
R CH2NHCH3
b
27
Model 2
pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
8.398
8.320
8.416
8.371
8.476
8.699
8.669
8.685
8.258
8.117
9.000
8.912
8.960
9.057
8.984
8.699
8.775
9.023
8.614
8.784
8.523
8.551
8.486
8.532
8.410
8.523
8.493
8.493
8.549
8.552
8.301
8.454
8.477
8.389
8.360
8.301
8.312
8.345
8.527
8.389
7.886
7.949
7.954
7.830
7.904
8.398
8.424
8.414
8.466
8.473
8.523
8.324
8.542
8.561
8.515
8.699
8.588
8.664
8.596
8.436
8.398
8.386
8.395
8.089
8.098
8.097
8.123
8.075
8.623
8.399
9.119
8.883
8.863
9.094
9.057
8.921
8.687
9.058
8.860
8.924
N
28 N
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29a
N
30b N H
31 NH
32 N H3C
33b N
34
CH3 CH3
N
CH3
35 N H
36a N H
37b
NH
38b N CH3
39b
40
N
CH3
O N
41a
H N N
(continued )
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Table 2. Continued
Model 1 Mol.
R
pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
8.657
8.550
8.751
8.569
8.692
8.769
8.583
8.477
8.752
8.799
CH3
8.620
8.598
8.673
8.593
8.679
NH2
8.657
8.655
8.687
8.701
8.529
8.538
8.423
8.520
8.207
8.134
8.398
8.586
8.422
8.416
8.428
9.000
8.999
8.987
9.019
8.826
8.796
8.917
8.861
9.031
9.231
8.284
8.288
8.291
8.047
8.168
8.420
8.561
8.407
8.668
8.099
8.350
8.345
8.133
8.504
8.134
8.508
8.772
8.677
8.636
8.496
9.022
8.958
9.017
8.519
8.578
8.721
8.652
8.667
8.211
8.110
H N
42
Model 2
N
43a
N S
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44
N O
45b
N S
46a,b
N N
47b
N
CH3 N
O
48b
N N O
49b
H N
N
O
O
50b
H N S
N
51b
O
H N
S O
N
52b N
N
CH3
53
N
54b
55b
N
N
(continued)
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423
Table 2. Continued.
O
NH2
R2 N R N H
R1
Model 1 Mol.
R
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56
1
2
Model 2
R
pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
F
H
8.699
8.811
8.797
8.652
8.563
H
F
8.523
8.380
8.326
8.486
8.330
H
F
8.398
8.525
8.371
8.423
8.347
H
F
9.000
8.897
8.773
8.509
8.508
F
F
8.699
8.733
8.768
8.576
8.549
F
H
8.523
8.547
8.455
8.539
8.492
H
F
8.222
8.526
8.273
7.990
8.073
H
F
8.046
8.124
8.278
7.990
8.073
H
F
8.222
8.406
8.302
8.567
8.390
F
F
8.699
8.504
8.583
8.674
8.644
R
N H
57a,b N H
58a N H
59b N H
60a,b N H
61a N H
62a N H
63 N H
64b N H
65 N H
(continued)
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S. Fatima et al.
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Table 2. Continued.
H N
O
R
N R1
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Model 1
Model 2
Mol.
R1
R
pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
66 67a,b 68 69a,b 70 71a 72 73 74b 75a,b 76 77 78 79 80a,b 81b 82b 83a,b 84 85b
H H H H H H H H H H CH3 H H H H H H H H H
Ph 2-ClC6H5 2-OHC6H5 2-SMeC6H5 4-OMeC6H5 4-CF3C6H5 3-NH2C6H5 3-CF3C6H5 4-CH2NMe2C6H5 4-CH2(1-pyrrolidinyl) C6H5 C6H5 COOH CN CONH-iPr CONH C6H5 3-pyridinyl 1-naphthyl 2-biphenyl 2-thiophene 5-indolyl
8.221 8.096 7.638 7.494 8.221 8.301 8.154 8.221 8.301 8.221 8.15 6.432 6.959 7.490 7.000 8.150 8.300 7.640 8.301 8.050
8.213 7.888 7.632 8.044 8.316 7.965 8.102 8.257 8.134 7.928 8.146 6.423 7.139 7.519 7.871 7.939 8.312 7.714 8.122 7.909
8.364 7.898 7.617 8.047 8.270 8.468 8.171 8.258 8.118 8.039 8.134 6.397 7.045 7.448 7.635 8.088 8.335 7.925 8.227 8.056
8.142 7.805 7.652 7.794 8.320 8.249 8.268 8.245 8.100 8.428 8.184 6.361 7.112 7.546 7.734 7.874 8.161 7.769 8.165 8.193
8.273 7.957 7.640 8.082 8.168 8.363 8.235 8.251 8.030 8.034 8.013 6.472 7.002 7.466 7.515 7.835 8.246 7.948 8.213 7.963
8.304 8.270 8.262
8.223 8.086 8.326
8.270 8.068 8.295
O
H N
N N
86 87a 88 a
and
– – – b
4-Cl C6H5 2-Cl C6H5 1-naphthyl
8.240 8.110 8.310
8.183 8.127 8.371
R
represent molecules of test set in models 1 and 2, respectively.
Figure 1. Dock-based alignment of data set molecules.
reliability of Glide docking in reproducing the experimentally observed binding mode for PARP inhibitor, and the parameter set for the Glide docking is reasonable to reproduce the X-ray structure. To obtain the most preferred biologically active conformer of the ligand, a cluster analysis of the dock poses of each ligand was performed using clustering of conformer’s script. Pose from the most frequent cluster having lowest binding energy was selected. This pose of each molecule was taken as the basis for the CoMFA and CoMSIA analysis; these conformations provide the most vital information for the binding of ligand into the protein active site. 3D QSAR analysis was done by dividing the molecules into training and test set, two models were generated having 64 and 24 molecules in model-1, 46 and 42 in model-2, respectively. The CoMFA and CoMSIA statistical analysis is summarized in Table 3. Statistical data shows r2loo 0.613 and 0.515 for CoMFA models 1 and 2, 0.664 and 0.657 for the CoMSIA models 1
MRCD, dock pose clustering and 3D QSAR studies on human PARP-1 inhibitors
DOI: 10.3109/10799893.2014.917323
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and 2, respectively, which indicates a good internal predictive ability for both the models. The models developed also exhibited r2 of 0.956, 0.986 and 0.970, 0.988 for CoMFA and CoMSIA, respectively. To test the predictive ability of the models, a test set of 24 and 44 molecules for model 1 and model 2, respectively, excluded from the model derivation were used. The predictive correlation coefficient r2pred of 0.546 and 0.586 for CoMFA model 1 and 2, 0.650 and 0.530 for CoMSIA models 1 and 2, respectively, indicate good external predictive ability of the models. The experimental and predicted activity from CoMFA and CoMSIA models is
Table 3. Summary of PLS results. Model 1 Statistical parameters 2
Q loo Molecules in the training set Molecules in the test set ONC SEE R2 R2 pred
Model 2
CoMFA CoMSIA CoMFA CoMSIA 0.613 64 24 7 0.117 0.956 0.546
0.664 64 24 7 0.096 0.970 0.650
0.515 46 42 8 0.068 0.986 0.586
0.657 46 42 7 0.063 0.988 0.530
Q2loo ¼ cross-validated correlation coefficient by leave one out method. R2 ¼ conventional correlation coefficient; R2pred ¼ cross-validated correlation coefficient on test set; ONC ¼ optimum number of components; and SEE ¼ error of estimate.
425
given in Table 2. Scatter plot for actual and predicted pKi values for training and test set of CoMFA and CoMSIA studies of models 1 and 2 are shown in Figure 2. To visualize the content of the derived 3D QSAR models, CoMFA and CoMSIA contour maps were generated. The contour maps of CoMFA (electrostatic and steric) and CoMSIA (steric, electrostatic, hydrophobic, donor and acceptor) are represented by color codes. Contour plots are the representation of lattice points, and the difference in the molecular field values at lattice points strongly connected with difference in the receptor binding affinity. Molecular fields define the favorable or unfavorable interaction energies of aligned molecules with a probe atom traversing across the lattice plots suggesting the modification required to design new molecules. CoMFA contour maps denote the region in space where the molecules would favorably or unfavorably interact with the receptor, while CoMSIA contour maps denote areas within the specified region where the presence of the group with a particular physicochemical property binds to the receptor. CoMFA and CoMSIA results were graphically interpret by field contribution maps using the ‘‘STDEV COEFF’’ field type. CoMSIA contour maps rendered more information than CoMFA contour maps. Compound 40, the most potent inhibitor among the series, was embedded with the maps for visualization. All the contours represented the default 80 and 20% level contributions favored and disfavored, respectively.
Figure 2. (a and b) Scatter plot of experimental versus predicted pKi for model-1 and (c and d) model-2 (test set is represented in triangles).
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Figure 3. (a–d) CoMFA and CoMSIA steric standard deviation (SD coefficient) contour maps illustrating steric features in combination with molecule 40. (a and b) Contours for CoMFA – models 1 and 2. (c and d) contours for CoMSIA – models 1 and 2. Green [g] contours show favorable bulky group substitution at that point while yellow [y] regions are disfavorable for activity.
Steric contour maps To visualize the information content of the derived 3D QSAR models, CoMFA contours maps were generated to rationalize the regions of 3D space around the embedded molecule, where changes in the steric and electrostatic fields would influence the increase or decrease in inhibitory activity. CoMFA and CoMSIA steric contour map is shown in Figures 3. The green contour represents a steric favored region, where bulky group substitution increases the activity. Yellow contour represents a steric disfavored region, where presence of bulkier group depletes activity. Both CoMFA and CoMSIA steric contours showed a green patch near para position of the phenyl ring attached to benzimidazole, suggesting substitution with bulky groups at this region increases the activity. Substitution at ortho positions of phenyl ring, on NH2 of amide, NH of benzimidazole ring and on oxazole ring will decrease the activity. Hence, while designing the new molecules, this was taken into consideration.
a more electronegative group substitution will increase the activity. In CoMSIA model 1and 2, same blue contour is observed at the meta position of the aromatic ring. A red contour is observed near the nitrogen of the benzimidazole ring suggesting substitution with a more electronegative group at this position will increase the activity. Hydrophobic contour maps Hydrophobic fields are represented in Figure 5, yellow and white contours highlight areas where hydrophobic and hydrophilic groups are preferred, respectively. In both the models, hydrophilic contours are observed on the phenyl ortho position, oxazole and benzimidazole rings suggesting hydrophilic substitution at these regions. Based on the information rendered, new molecules have been designed where oxazole ring has been replaced by more hydrophilic groups and hydroxyl group has been substituted at the ortho position of the phenyl ring. Hydrogen bond donor–acceptor contour maps
Electrostatic contour maps Electrostatic contours for CoMFA and CoMSIA for models 1 and 2 are represented in Figure 4. Blue and red contours depict the position where positively charged groups and negatively charged groups would be beneficial to inhibitory activity. In CoMFA electrostatic, both the models 1 and 2 represent a blue patch at meta position of the aromatic ring attached at second position of benzimidazole ring suggesting less electronegative substitution will increase the activity. A red contour at the ortho position of the same ring suggests
In CoMSIA hydrogen bond donor–acceptor contours of both the models 1 and 2, shown in Figure 6, it is envisaged that there is a cyan contour on the nitrogen having the donor hydrogen of the benzimidazole ring. A purple contour (hydrogen bond donor disfavored) is observed near the meta position of the phenyl ring and oxazole ring is seen protruding into the hydrogen bond acceptor magenta contour self explaining the acceptor group substitutions in these regions. At the ortho position of the phenyl ring, purple (hydrogen bond donor disfavored) and magenta (hydrogen bond acceptor
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Figure 4. (a–d) CoMFA and CoMSIA electrostatic standard deviation (SD coefficient) contour maps illustrating electrostatic features in combination with molecule 40. (a and b) contours for CoMFA – models 1 and 2. (c and d) Contours for CoMSIA – models 1 and 2. Red [r] contours indicate negative charge favoring activity, whereas blue [b] contours indicate negative charge disfavoring activity.
Figure 5. CoMSIA hydrophobic standard deviation (S.D ) contour maps illustrating hydrophobic features in combination with molecule 40. (a and b) contours for models 1 and 2, respectively. Yellow [y] and white [w] represent favored and disfavored regions, respectively, for hydrophobic interaction.
Figure 6. CoMSIA H-bond donor and acceptor standard deviation (S.D coefficient) contour maps illustrating donor and acceptor features in combination with molecule 40. (a and b) contours for models 1 and 2, respectively. Magenta [m] and red [r] represent regions for favored and disfavored for H bond acceptor groups, respectively, cyan [c] and purple [p] represent regions for favored and disfavored for H bond donor groups respectively.
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Table 4. Structures of novel molecules with their predicted pKi. O
NH2 R3 N R1 N H
R2
. Model 1
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Mol. N1
R1 O
Model 2
R2
R3
CoMA Pred.pKi
CoMSIA Pred.pKi
CoMA Pred.pKi
CoMSIA Pred.pKi
H
H
9.115
8.722
9.140
8.327
H
H
8.801
8.391
9.132
8.338
OH
H
8.773
8.180
9.032
8.147
H
NO2
9.042
8.491
8.626
8.214
OH
NO2
8.846
8.325
8.694
8.231
OH O
N2
OH N
O
N3
OH N
O
N4
N N
N5
O
OH
Figure 7. (a) Dock pose of most active molecule 40 of the data set, (b) dock pose of newly designed molecule N5.
favored) contour are observed suggesting an acceptor group substitution at these positions. Detailed contour map analysis of both CoMFA and CoMSIA models empowered us to identify structural requirements for the observed inhibitory activity. The analogues were designed to improve the inhibitory activity. The best active molecule has been taken as a reference structure to design new molecules (Table 4) and to obtain new potent inhibitors. The newly designed analogues when docked into
the protein active site of six crystal structures showed similar binding interaction with comparable dock score and predicted activity with respect to the most active compound 40. Figure 7 shows dock poses of most potent molecule 40 and newly designed molecule N5, these molecules are embedded into the proteins (pdb ids: 2RCW and 3GN7) along with hydrogen bond interaction with active site amino acids where N5 shows better interactions compared to the existing active compound. The structures of new molecules and their
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Table 5. Pharmacokinetic properties of the newly designed molecules calculated from Qikprop. Mol. N1 N2 N3 N4 N5
CNS
M.W
H bond donor
H bond acceptor
Log P o/w
log S
log BB
Pmdck
2 2 2 2 2
337.3 360.3 350.3 365.3 370.0
3 2 3 4 5
7 7 6 5.250 7.750
1.569 1.476 2.598 2.703 0.542
3.942 4.484 5.064 5.137 3.692
2.423 2.251 1.738 2.021 3.338
2.673 11.205 9.973 6.143 0.393
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predicted pKi are listed in Table 4. The pharmacokinetic properties of these molecules were calculated using Qikprop (Table 5).
Conclusion In this study, MRCD and clustering was employed to obtain a flexible receptor-based alignment of molecules having diverse motifs; CoMFA and CoMSIA methodologies were used to generate highly accurate and predictive 3D QSAR models based on docking, and these models showed good internal and external statistical reliability that is evident from the q2loo , r2nev and r2pred . The 3D contour maps obtained from the CoMFA and CoMSIA models in combination with the detailed PARP1 inhibitor binding structures obtained from molecular docking help to better interpret the structure–activity relationship of these PARP-1 Inhibitors and provide valuable insights into rational drug design for further improvement in biological activity of PARP-1 inhibitors.
Acknowledgements We acknowledge Schro¨dinger Inc. for GLIDE software and Tripos Inc. for SYBYL-X1.2.
Declaration of interest We gratefully acknowledge support for this research from Department of Science and Technology (File no: SR/WOS-A/CS-40/2012), New Delhi, India, University Grants Commission, New Delhi, India and Department of chemistry, Nizam College, Hyderabad, India. We do not have any conflict of interest to declare.
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9. McCabe N, Nicholas CT, Lord CJ, et al. Deficiency in the repair of DNA damage by homologous recombination and sensitivity to poly(ADP-ribose) polymerase inhibition. Cancer Res 2006;66: 8109–15. 10. Gudmundsdottir K, Ashworth A. The roles of BRCA1 and BRCA2 and associated proteins in the maintenance of genomic stability. Oncogene 2006;25:5864–74. 11. Cramer RD, Patterson DE, Bunce JD. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids tocarrier proteins. J Am Chem Soc 1988;110:5959–67. 12. Cramer 3rd RD, Patterson DE, Bunce JD. Crossvalidation bootstrapping, and partial least squares compared with multiple regression in conventional QSAR studies. Quant Struct Act Relat 1998;7:18–25. 13. Klebe G, Abraham U, Mietzner T. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem 1994; 37:4130–46. 14. Wold S, Johansson A, Cochi M. 3D-QSAR in drug design: theory, methods and application. In: Kubinyi H, ed. PLS-partial least squares projection tolatent structures. Leiden: ESCOM; 1993:523–50. 15. Park CH. PARP complexed with A861695. doi:10.2210/pdb2rd6/ pdb. Available from: http://www.rcsb.org. pdbid: 2rd6. 16. Park CH. PARP complexed with A620223. doi:10.2210/pdb2rcw/ pdb. Available from: http://www.rcsb.org. pdbid: 2rcw. 17. Miyashiro J, Woods KW, Park CH, et al. Synthesis and SAR of novel tricyclic quinoxalinone inhibitors of poly (ADP-ribose) polymerase-1 (PARP-1). Bioorg Med Chem Lett 2009;19:4050–4. 18. Park CH. PARP complexed with A861696. doi:10.2210/pdb3gn7/ pdb. Available from: http://www.rcsb.org, pdbid: 3gn7. 19. Gandhi VB, Luo Y, Liu X, et al. Discovery and SAR of substituted 3-oxoisoindoline-4-carboxamides as potent inhibitors of poly(ADPribose) polymerase (PARP) for the treatment of cancer. Bioorg Med Chem Lett 2010;20:1023–6. 20. Penning TD, Zhu GD, Gong J, et al. Optimization of phenyl-substituted benzimidzole carboxamide poly(ADP-ribose) polymerase inhibitors: identification of (S)-2-(2-fluoro-4(pyrrolidin-2-yl)phenyl)-1H-benzimidazole-4-carboxamide (A966492), a highly potent and efficacious inhibitor. J Med Chem 2010;53:3142–53. 21. Friesner RA, Banks JL, Murphy RB, et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 2004;47:1739–49. 22. Penning TD, Zhu GD, Gandhi VB, et al. Discovery of the poly(ADP-ribose) polymerase (PARP) inhibitor2-[(R)-2methylpyrrolidin-2-yl]-1H-benzimidazole-4-carboxamide (ABT888) for the treatment of cancer. J Med Chem 2009;52:514–23. 23. Penning TD, Zhu GD, Gong J, et al. Optimization of phenylsubstituted benzimidazole carboxamide poly(ADP-ribose) polymerase inhibitors: identification of (S)-2-(2-fluoro-4-(pyrrolidin-2-yl)phenyl)-1H-benzimidazole-4-carboxamide (A-966492), a highly potent and efficacious inhibitor. J Med Chem 2010;53: 3142–53. 24. Tong Y, Bouska JJ, Ellis PA, et al. Synthesis and evaluation of a new generation of orally efficacious benzimidazole-based poly(ADP-ribose) polymerase-1 (PARP-1) inhibitors as anticancer agents. J Med Chem 2009;52:6803–13. 25. Skalitzky DJ, Marakovits JT, Maegley KA, et al. Tricyclic benzimidazoles as potent poly(ADP-ribose) polymerase-1 inhibitors. J Med Chem 2003;46:210–3. 26. Canan Koch SS, Thoresen LH, Tikhe JG, et al. Novel tricyclic poly(ADP-ribose) polymerase-1 inhibitors with potent anticancer
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analysis – a case study on HIV-1 protease inhibitors. J Mol Model 2012;18:569–82. 30. Carlson HA, Masukawa KM, McCammon JA. Method for including the dynamic fluctuations of a protein in computer-aided drug design. J Phys Chem 1999;103:10213–9. 31. Carlson HA, Masukawa KM, Rubins K, et al. Developing a dynamic pharmacophore model for HIV-1 integrase. J Med Chem 2000;43:2100–14. 32. Kanth SS, Vijjulatha M. Tetrahydroxy cyclic urea-potent inhibitor for HIV-1 protease wild type and mutant type – a computational design. E-J Chem 2008;5:584–92.
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RESEARCH ARTICLE
Multiple receptor conformation docking, dock pose clustering and 3D QSAR studies on human poly(ADP-ribose) polymerase-1 (PARP-1) inhibitors Sabiha Fatima, Mohan Babu Jatavath, Raju Bathini, Sree Kanth Sivan, and Vijjulatha Manga Department of Chemistry, University College of Science, Osmania University, Hyderabad, Andhra Pradesh, India
Abstract
Keywords
Poly(ADP-ribose) polymerase-1 (PARP-1) functions as a DNA damage sensor and signaling molecule. It plays a vital role in the repair of DNA strand breaks induced by radiation and chemotherapeutic drugs; inhibitors of this enzyme have the potential to improve cancer chemotherapy or radiotherapy. Three-dimensional quantitative structure activity relationship (3D QSAR) models were developed using comparative molecular field analysis, comparative molecular similarity indices analysis and docking studies. A set of 88 molecules were docked into the active site of six X-ray crystal structures of poly(ADP-ribose)polymerase-1 (PARP-1), by a procedure called multiple receptor conformation docking (MRCD), in order to improve the 3D QSAR models through the analysis of binding conformations. The docked poses were clustered to obtain the best receptor binding conformation. These dock poses from clustering were used for 3D QSAR analysis. Based on MRCD and QSAR information, some key features have been identified that explain the observed variance in the activity. Two receptor-based QSAR models were generated; these models showed good internal and external statistical reliability that is evident from the q2loo , r2nev and r2pred . The identified key features enabled us to design new PARP-1 inhibitors.
Comparative molecular similarity indices analysis, multiple receptor conformation docking, partial least square analysis, poly(ADP-ribose) polymerase-1, three-dimensional quantitative structure activity relationship
Introduction Poly(ADP-ribose) polymerase-1 (PARP-1: EC 2.4.2.30) is a nuclear enzyme involved in the detection and repair of DNA damage (1). PARP-1 functions as a DNA damage sensor (2) and signaling molecule, binding to both single- and doublestranded DNA breaks. On binding to damaged DNA, PARP-1 forms homodimers and catalyzes the cleavage of NAD+ into nicotinamide and ADP-ribose to form long branches of ADP-ribose polymers on target proteins such as histones and PARP-1 itself. This process results in cellular energetic depletion, mitochondrial dysfunction and ultimately necrosis (3,4). As for the latter pathway, numerous transcription factors, DNA replication factors and signaling molecules have also been shown to become poly(ADP-ribosylated) by PARP1; but a PARP-mediated activation of the pluripotent transcription factor nuclear factor-kB appears to be of crucial importance (4,5). Importantly, numerous recent studies have suggested that PARP-1 activity can be modulated by several endogenous factors, and PARP-1 can also modulate important signaling pathways (6). Address for correspondence: Manga Vijjulatha, Department of Chemistry, University College of Science, Osmania University, Hyderabad, Andhra Pradesh 500 007, India. Tel: 09866845408. E-mail: [email protected]
History Received 18 December 2013 Revised 14 April 2014 Accepted 15 April 2014 Published online 21 July 2014
As PARP-1 plays a vital role in the repair of DNA strand breaks, including those induced by radiation and chemotherapeutic drugs, inhibitors of this enzyme have potential to improve cancer chemotherapy or radiotherapy (7,8). Drugs that inhibit PARP-1 cause multiple double-strand breaks to form in this way; and in tumors with BRCA1, BRCA2 or PALB2 mutations, these double-strand breaks cannot be efficiently repaired, leading to the death of the cells. Normal cells that do not replicate their DNA as often as cancer cells, and that lack any mutated BRCA1 or BRCA2 still have homologous repair operating, which allows them to survive the inhibition of PARP (9,10). PARP-1 inhibitors examined to date are competitive inhibitors of NAD+. Relatively low potency of these agents has led to the necessity for development of more potent and specific PARP-1 inhibitors. In this article, we report receptor-based three-dimensional quantitative structure activity relationship (3D-QSAR) studies using comparative molecular field analysis (CoMFA) (11,12) and comparative molecular similarity indices analysis (CoMSIA) (13) methodologies on benzimidazole carboxamide series, 2-phenyl substituted benzimidazole carboxamide, tricyclic indoles and tricyclic benzimidazole analogues. Receptor-based conformation for 88 molecules was obtained by multiple receptor conformation docking (MRCD) and dock poses clustering. Docking was performed on six different
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crystal structures to analyze each molecule dock pose and to confirm the presence of required hydrogen bond interactions with the active site amino acids. Partial least square (PLS)- (14) based statistical analysis was carried out on these molecules to identify the correlation between biological activity and structural parameters. The contour maps generated enabled us to explain the observed variation in activity and guided us to design new molecules.
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Methodology Six high-resolution X-ray crystal structure of PARP-1 in complex with inhibitors (pdb id: 2RD6, 2RCW, 3GJW, 3GN7, 3L3L and 3L3M) (15–20) were downloaded from the protein data bank; resolution of the crystal structures is listed in Table 1. GLIDE 5.6 (Schrodinger LLC, New York, NY) (21) was used for molecular docking. Proteins were prepared using protein preparation module applying the default parameters; grids were generated around the active site of PARP-1 with receptor van der Waals scaling for the non-polar atoms as 0.9 (21). A set of 88 known PARP-1 inhibitors (Table 2) with diverse structures and varied range of inhibition constants (Ki) were selected from literature (22–26), these were built using maestro build panel. Low energy conformers were obtained from LigPrep application in Schro¨dinger 2010 suite using the MMFF94s force field. These ligands were selected and docked into the grid generated from six protein structures using standard precision docking mode (27). The crystal structure ligands were also docked and its atomic root mean square deviation (RMSD) was calculated to validate docking process. The best dock pose (low binding energy conformer according to the glide dock score) of each ligand from six docking runs performed on six receptors grids were analyzed for their hydrogen bond interactions with the receptor. Poses with the required hydrogen bond interactions namely with Gly202 and Ser243 were selected for further clustering. Dock poses were clustered using clustering of conformer’s script in Schro¨dinger 2010 suite. Clustering was performed using atomic RMSD; in this, RMSD was calculated in place that does not alter the dock pose of each conformer. The lowest binding energy conformation from the most populated cluster was chosen for CoMFA and CoMSIA analysis without further alignment, i.e. super imposition of ligands based on the common substructure for a set of molecules was not done, instead the docked conformer pose obtained from clustering where taken as it is for all ligands. This imparts the flexible receptor binding to each ligand in data set. The resulting docked pose orientations are shown in Figure 1. Table 1. PDB ids of crystal structure of PARP, their resolution and RMSD of redocked co-crystallized ligand. PDB ID
RMSD
˚ X-ray resolution in A
2RD6 2RCW 3GJW 3GN7 3L3L 3L3M
0.101 0.2382 0.8409 0.5591 1.1984 0.7757
2.30 2.80 2.30 2.50 2.50 2.50
The molecules were imported into Sybyl-X 1.2 (St. Louis, MO) molecular modeling program package and Gasteiger– Hu¨ckel (28) charges were assigned. The standard Tripos force fields were employed for the CoMFA and CoMSIA analysis. ˚ in each direction with each A 3D cubic lattice of dimension 4 A ˚ was lattice intersection of regularly spaced grid of 2.0 A created. The steric and electrostatic parameters were calculated in case of the CoMFA fields, while hydrophobic, acceptor and donor parameters in addition to steric and electrostatic were calculated in case of the CoMSIA fields at each lattice. The sp3 carbon was used as a probe atom to generate steric (Lennard–Jones potential) field energies and a charge of +1 to generate electrostatic (Coulombic potential) field energies. A distance-dependent dielectric constant of 1.00 was used. The steric and electrostatic contributions were cut off at 30 kcalmol–1. A PLS regression was used to generate a linear relationship that correlates changes in the computed fields with changes in the corresponding experimental values of biological activity (pKi) for the data set of ligands. Eighty-eight PARP-1 inhibitors were divided into 64 training and 24 test set molecules in model-1, 46 and 42 in model-2, respectively, considering the set had a balanced distribution of more and less active compounds. Biological activity values of ligands were used as dependent variables in a PLS statistical analysis. The column filtering value(s) were set to 2.0 kcalmol1 to improve the signal-to-noise ratio by omitting those lattice points whose energy variations were below this threshold. Cross-validations were performed by the leave-one-out (LOO) procedure to determine the optimum number of components (ONC) and the coefficient r2loo . The ONC obtained is then used to derive the final QSAR model using all of the training set compounds with non-cross validation and to obtain the conventional regression coefficient (r2). Since the statistical parameters were found to be the best for the model from the LOO method, it was employed for further predictions of activity of test molecules for cross validation of the model. Multiple receptors conformation docking (29–32) is a method employed for incorporating the receptor flexibility while docking analysis. In this study, standard precision glide docking procedure was validated by removing crystal structure ligand from the binding site and redocking it to the binding site of PARP-1 for each of the receptor conformations.
Results and discussion The co-crystal ligands of all the crystal structures available in protein data bank were showing hydrogen bond interactions with Gly 202 and Ser 243. It clearly indicates that all inhibitor molecules should have key interaction with these amino acids. Hence, all crystal structures with co-crystallized ligands were used for molecular docking. A very good agreement between the localization of the inhibitor upon docking and from the crystal structure was observed, i.e. having similar hydrogen bonding interactions with Gly202 and Ser243. The RMSDs between the predicted conformation and the observed X-ray crystallographic ˚ to 1.19 A ˚, conformation for the ligands ranged from 0.1 A the values are provided in Table 1, these values suggests the
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Table 2. Structures of human PARP-1 inhibitors with their experimental pKi and predicted pKi. O
NH2 N R N H
Model 1 Mol.
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1
R
a,b
NH
2b
pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
8.222
8.358
8.327
8.266
8.150
7.523
7.692
7.595
7.780
7.605
8.222
7.744
8.329
7.851
8.244
7.824
7.686
7.743
7.834
7.733
8.397
8.273
8.269
8.045
8.099
8.397
8.403
8.388
8.420
8.464
8.222
8.277
8.225
8.432
8.013
7.602
7.600
7.515
7.597
7.482
7.721
7.842
7.727
7.734
7.753
6.89
6.774
6.837
7.756
7.730
8.523
8.545
8.564
8.525
8.340
8.301
8.333
8.465
8.324
8.491
7.886
7.598
7.601
7.641
7.940
N H
H
3a,b
Model 2
H3C
N CH3
4 N CH3
5b
H3C
NH
6 NH
7b
CH3
N H3C
CH3
8 R
9
N H H3C
H3C
N
10b H3C
11b
H3C
N
NH
12a,b NH
13a,b
H3C NH
(continued )
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Table 2. Continued
Model 1 Mol.
R
b
CH3
14
Model 2
pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
7.377
7.355
7.365
7.473
7.876
8.046
7.823
7.951
8.059
8.048
7.508
7.821
8.061
7.466
7.530
8.301
8.350
8.279
8.309
8.321
8.046
8.021
8.066
7.772
7.975
8.000
8.295
8.200
8.014
7.987
7.638
7.671
7.765
7.552
7.036
7.886
7.729
7.968
7.779
7.894
7.161
7.292
7.198
7.193
7.240
8.398
8.483
8.390
8.359
8.375
8.097
8.131
8.156
8.153
8.083
8.155
8.222
8.068
8.339
7.869
N CH3 CH3
15a
CH3
H3C
N
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CH3
16a
N H3C
17
H3C
N
18b
CH3
F N
H3C
19a
20b
H3C
N
H 3C
O
N H
H3C
21
N H CH3
22 N
CH3
H3C
23
24
H3 C
H3C
NH
N
25b H3C
CH3
CH3
N
O
NH2 N R N H
(continued )
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Table 2. Continued
Model 1 Mol. 26
R CH2NHCH3
b
27
Model 2
pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
8.398
8.320
8.416
8.371
8.476
8.699
8.669
8.685
8.258
8.117
9.000
8.912
8.960
9.057
8.984
8.699
8.775
9.023
8.614
8.784
8.523
8.551
8.486
8.532
8.410
8.523
8.493
8.493
8.549
8.552
8.301
8.454
8.477
8.389
8.360
8.301
8.312
8.345
8.527
8.389
7.886
7.949
7.954
7.830
7.904
8.398
8.424
8.414
8.466
8.473
8.523
8.324
8.542
8.561
8.515
8.699
8.588
8.664
8.596
8.436
8.398
8.386
8.395
8.089
8.098
8.097
8.123
8.075
8.623
8.399
9.119
8.883
8.863
9.094
9.057
8.921
8.687
9.058
8.860
8.924
N
28 N
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29a
N
30b N H
31 NH
32 N H3C
33b N
34
CH3 CH3
N
CH3
35 N H
36a N H
37b
NH
38b N CH3
39b
40
N
CH3
O N
41a
H N N
(continued )
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Table 2. Continued
Model 1 Mol.
R
pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
8.657
8.550
8.751
8.569
8.692
8.769
8.583
8.477
8.752
8.799
CH3
8.620
8.598
8.673
8.593
8.679
NH2
8.657
8.655
8.687
8.701
8.529
8.538
8.423
8.520
8.207
8.134
8.398
8.586
8.422
8.416
8.428
9.000
8.999
8.987
9.019
8.826
8.796
8.917
8.861
9.031
9.231
8.284
8.288
8.291
8.047
8.168
8.420
8.561
8.407
8.668
8.099
8.350
8.345
8.133
8.504
8.134
8.508
8.772
8.677
8.636
8.496
9.022
8.958
9.017
8.519
8.578
8.721
8.652
8.667
8.211
8.110
H N
42
Model 2
N
43a
N S
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44
N O
45b
N S
46a,b
N N
47b
N
CH3 N
O
48b
N N O
49b
H N
N
O
O
50b
H N S
N
51b
O
H N
S O
N
52b N
N
CH3
53
N
54b
55b
N
N
(continued)
MRCD, dock pose clustering and 3D QSAR studies on human PARP-1 inhibitors
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Table 2. Continued.
O
NH2
R2 N R N H
R1
Model 1 Mol.
R
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56
1
2
Model 2
R
pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
F
H
8.699
8.811
8.797
8.652
8.563
H
F
8.523
8.380
8.326
8.486
8.330
H
F
8.398
8.525
8.371
8.423
8.347
H
F
9.000
8.897
8.773
8.509
8.508
F
F
8.699
8.733
8.768
8.576
8.549
F
H
8.523
8.547
8.455
8.539
8.492
H
F
8.222
8.526
8.273
7.990
8.073
H
F
8.046
8.124
8.278
7.990
8.073
H
F
8.222
8.406
8.302
8.567
8.390
F
F
8.699
8.504
8.583
8.674
8.644
R
N H
57a,b N H
58a N H
59b N H
60a,b N H
61a N H
62a N H
63 N H
64b N H
65 N H
(continued)
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Table 2. Continued.
H N
O
R
N R1
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Model 1
Model 2
Mol.
R1
R
pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
CoMFA Pred.pKi
CoMSIA Pred.pKi
66 67a,b 68 69a,b 70 71a 72 73 74b 75a,b 76 77 78 79 80a,b 81b 82b 83a,b 84 85b
H H H H H H H H H H CH3 H H H H H H H H H
Ph 2-ClC6H5 2-OHC6H5 2-SMeC6H5 4-OMeC6H5 4-CF3C6H5 3-NH2C6H5 3-CF3C6H5 4-CH2NMe2C6H5 4-CH2(1-pyrrolidinyl) C6H5 C6H5 COOH CN CONH-iPr CONH C6H5 3-pyridinyl 1-naphthyl 2-biphenyl 2-thiophene 5-indolyl
8.221 8.096 7.638 7.494 8.221 8.301 8.154 8.221 8.301 8.221 8.15 6.432 6.959 7.490 7.000 8.150 8.300 7.640 8.301 8.050
8.213 7.888 7.632 8.044 8.316 7.965 8.102 8.257 8.134 7.928 8.146 6.423 7.139 7.519 7.871 7.939 8.312 7.714 8.122 7.909
8.364 7.898 7.617 8.047 8.270 8.468 8.171 8.258 8.118 8.039 8.134 6.397 7.045 7.448 7.635 8.088 8.335 7.925 8.227 8.056
8.142 7.805 7.652 7.794 8.320 8.249 8.268 8.245 8.100 8.428 8.184 6.361 7.112 7.546 7.734 7.874 8.161 7.769 8.165 8.193
8.273 7.957 7.640 8.082 8.168 8.363 8.235 8.251 8.030 8.034 8.013 6.472 7.002 7.466 7.515 7.835 8.246 7.948 8.213 7.963
8.304 8.270 8.262
8.223 8.086 8.326
8.270 8.068 8.295
O
H N
N N
86 87a 88 a
and
– – – b
4-Cl C6H5 2-Cl C6H5 1-naphthyl
8.240 8.110 8.310
8.183 8.127 8.371
R
represent molecules of test set in models 1 and 2, respectively.
Figure 1. Dock-based alignment of data set molecules.
reliability of Glide docking in reproducing the experimentally observed binding mode for PARP inhibitor, and the parameter set for the Glide docking is reasonable to reproduce the X-ray structure. To obtain the most preferred biologically active conformer of the ligand, a cluster analysis of the dock poses of each ligand was performed using clustering of conformer’s script. Pose from the most frequent cluster having lowest binding energy was selected. This pose of each molecule was taken as the basis for the CoMFA and CoMSIA analysis; these conformations provide the most vital information for the binding of ligand into the protein active site. 3D QSAR analysis was done by dividing the molecules into training and test set, two models were generated having 64 and 24 molecules in model-1, 46 and 42 in model-2, respectively. The CoMFA and CoMSIA statistical analysis is summarized in Table 3. Statistical data shows r2loo 0.613 and 0.515 for CoMFA models 1 and 2, 0.664 and 0.657 for the CoMSIA models 1
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DOI: 10.3109/10799893.2014.917323
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and 2, respectively, which indicates a good internal predictive ability for both the models. The models developed also exhibited r2 of 0.956, 0.986 and 0.970, 0.988 for CoMFA and CoMSIA, respectively. To test the predictive ability of the models, a test set of 24 and 44 molecules for model 1 and model 2, respectively, excluded from the model derivation were used. The predictive correlation coefficient r2pred of 0.546 and 0.586 for CoMFA model 1 and 2, 0.650 and 0.530 for CoMSIA models 1 and 2, respectively, indicate good external predictive ability of the models. The experimental and predicted activity from CoMFA and CoMSIA models is
Table 3. Summary of PLS results. Model 1 Statistical parameters 2
Q loo Molecules in the training set Molecules in the test set ONC SEE R2 R2 pred
Model 2
CoMFA CoMSIA CoMFA CoMSIA 0.613 64 24 7 0.117 0.956 0.546
0.664 64 24 7 0.096 0.970 0.650
0.515 46 42 8 0.068 0.986 0.586
0.657 46 42 7 0.063 0.988 0.530
Q2loo ¼ cross-validated correlation coefficient by leave one out method. R2 ¼ conventional correlation coefficient; R2pred ¼ cross-validated correlation coefficient on test set; ONC ¼ optimum number of components; and SEE ¼ error of estimate.
425
given in Table 2. Scatter plot for actual and predicted pKi values for training and test set of CoMFA and CoMSIA studies of models 1 and 2 are shown in Figure 2. To visualize the content of the derived 3D QSAR models, CoMFA and CoMSIA contour maps were generated. The contour maps of CoMFA (electrostatic and steric) and CoMSIA (steric, electrostatic, hydrophobic, donor and acceptor) are represented by color codes. Contour plots are the representation of lattice points, and the difference in the molecular field values at lattice points strongly connected with difference in the receptor binding affinity. Molecular fields define the favorable or unfavorable interaction energies of aligned molecules with a probe atom traversing across the lattice plots suggesting the modification required to design new molecules. CoMFA contour maps denote the region in space where the molecules would favorably or unfavorably interact with the receptor, while CoMSIA contour maps denote areas within the specified region where the presence of the group with a particular physicochemical property binds to the receptor. CoMFA and CoMSIA results were graphically interpret by field contribution maps using the ‘‘STDEV COEFF’’ field type. CoMSIA contour maps rendered more information than CoMFA contour maps. Compound 40, the most potent inhibitor among the series, was embedded with the maps for visualization. All the contours represented the default 80 and 20% level contributions favored and disfavored, respectively.
Figure 2. (a and b) Scatter plot of experimental versus predicted pKi for model-1 and (c and d) model-2 (test set is represented in triangles).
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Figure 3. (a–d) CoMFA and CoMSIA steric standard deviation (SD coefficient) contour maps illustrating steric features in combination with molecule 40. (a and b) Contours for CoMFA – models 1 and 2. (c and d) contours for CoMSIA – models 1 and 2. Green [g] contours show favorable bulky group substitution at that point while yellow [y] regions are disfavorable for activity.
Steric contour maps To visualize the information content of the derived 3D QSAR models, CoMFA contours maps were generated to rationalize the regions of 3D space around the embedded molecule, where changes in the steric and electrostatic fields would influence the increase or decrease in inhibitory activity. CoMFA and CoMSIA steric contour map is shown in Figures 3. The green contour represents a steric favored region, where bulky group substitution increases the activity. Yellow contour represents a steric disfavored region, where presence of bulkier group depletes activity. Both CoMFA and CoMSIA steric contours showed a green patch near para position of the phenyl ring attached to benzimidazole, suggesting substitution with bulky groups at this region increases the activity. Substitution at ortho positions of phenyl ring, on NH2 of amide, NH of benzimidazole ring and on oxazole ring will decrease the activity. Hence, while designing the new molecules, this was taken into consideration.
a more electronegative group substitution will increase the activity. In CoMSIA model 1and 2, same blue contour is observed at the meta position of the aromatic ring. A red contour is observed near the nitrogen of the benzimidazole ring suggesting substitution with a more electronegative group at this position will increase the activity. Hydrophobic contour maps Hydrophobic fields are represented in Figure 5, yellow and white contours highlight areas where hydrophobic and hydrophilic groups are preferred, respectively. In both the models, hydrophilic contours are observed on the phenyl ortho position, oxazole and benzimidazole rings suggesting hydrophilic substitution at these regions. Based on the information rendered, new molecules have been designed where oxazole ring has been replaced by more hydrophilic groups and hydroxyl group has been substituted at the ortho position of the phenyl ring. Hydrogen bond donor–acceptor contour maps
Electrostatic contour maps Electrostatic contours for CoMFA and CoMSIA for models 1 and 2 are represented in Figure 4. Blue and red contours depict the position where positively charged groups and negatively charged groups would be beneficial to inhibitory activity. In CoMFA electrostatic, both the models 1 and 2 represent a blue patch at meta position of the aromatic ring attached at second position of benzimidazole ring suggesting less electronegative substitution will increase the activity. A red contour at the ortho position of the same ring suggests
In CoMSIA hydrogen bond donor–acceptor contours of both the models 1 and 2, shown in Figure 6, it is envisaged that there is a cyan contour on the nitrogen having the donor hydrogen of the benzimidazole ring. A purple contour (hydrogen bond donor disfavored) is observed near the meta position of the phenyl ring and oxazole ring is seen protruding into the hydrogen bond acceptor magenta contour self explaining the acceptor group substitutions in these regions. At the ortho position of the phenyl ring, purple (hydrogen bond donor disfavored) and magenta (hydrogen bond acceptor
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Figure 4. (a–d) CoMFA and CoMSIA electrostatic standard deviation (SD coefficient) contour maps illustrating electrostatic features in combination with molecule 40. (a and b) contours for CoMFA – models 1 and 2. (c and d) Contours for CoMSIA – models 1 and 2. Red [r] contours indicate negative charge favoring activity, whereas blue [b] contours indicate negative charge disfavoring activity.
Figure 5. CoMSIA hydrophobic standard deviation (S.D ) contour maps illustrating hydrophobic features in combination with molecule 40. (a and b) contours for models 1 and 2, respectively. Yellow [y] and white [w] represent favored and disfavored regions, respectively, for hydrophobic interaction.
Figure 6. CoMSIA H-bond donor and acceptor standard deviation (S.D coefficient) contour maps illustrating donor and acceptor features in combination with molecule 40. (a and b) contours for models 1 and 2, respectively. Magenta [m] and red [r] represent regions for favored and disfavored for H bond acceptor groups, respectively, cyan [c] and purple [p] represent regions for favored and disfavored for H bond donor groups respectively.
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Table 4. Structures of novel molecules with their predicted pKi. O
NH2 R3 N R1 N H
R2
. Model 1
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Mol. N1
R1 O
Model 2
R2
R3
CoMA Pred.pKi
CoMSIA Pred.pKi
CoMA Pred.pKi
CoMSIA Pred.pKi
H
H
9.115
8.722
9.140
8.327
H
H
8.801
8.391
9.132
8.338
OH
H
8.773
8.180
9.032
8.147
H
NO2
9.042
8.491
8.626
8.214
OH
NO2
8.846
8.325
8.694
8.231
OH O
N2
OH N
O
N3
OH N
O
N4
N N
N5
O
OH
Figure 7. (a) Dock pose of most active molecule 40 of the data set, (b) dock pose of newly designed molecule N5.
favored) contour are observed suggesting an acceptor group substitution at these positions. Detailed contour map analysis of both CoMFA and CoMSIA models empowered us to identify structural requirements for the observed inhibitory activity. The analogues were designed to improve the inhibitory activity. The best active molecule has been taken as a reference structure to design new molecules (Table 4) and to obtain new potent inhibitors. The newly designed analogues when docked into
the protein active site of six crystal structures showed similar binding interaction with comparable dock score and predicted activity with respect to the most active compound 40. Figure 7 shows dock poses of most potent molecule 40 and newly designed molecule N5, these molecules are embedded into the proteins (pdb ids: 2RCW and 3GN7) along with hydrogen bond interaction with active site amino acids where N5 shows better interactions compared to the existing active compound. The structures of new molecules and their
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Table 5. Pharmacokinetic properties of the newly designed molecules calculated from Qikprop. Mol. N1 N2 N3 N4 N5
CNS
M.W
H bond donor
H bond acceptor
Log P o/w
log S
log BB
Pmdck
2 2 2 2 2
337.3 360.3 350.3 365.3 370.0
3 2 3 4 5
7 7 6 5.250 7.750
1.569 1.476 2.598 2.703 0.542
3.942 4.484 5.064 5.137 3.692
2.423 2.251 1.738 2.021 3.338
2.673 11.205 9.973 6.143 0.393
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predicted pKi are listed in Table 4. The pharmacokinetic properties of these molecules were calculated using Qikprop (Table 5).
Conclusion In this study, MRCD and clustering was employed to obtain a flexible receptor-based alignment of molecules having diverse motifs; CoMFA and CoMSIA methodologies were used to generate highly accurate and predictive 3D QSAR models based on docking, and these models showed good internal and external statistical reliability that is evident from the q2loo , r2nev and r2pred . The 3D contour maps obtained from the CoMFA and CoMSIA models in combination with the detailed PARP1 inhibitor binding structures obtained from molecular docking help to better interpret the structure–activity relationship of these PARP-1 Inhibitors and provide valuable insights into rational drug design for further improvement in biological activity of PARP-1 inhibitors.
Acknowledgements We acknowledge Schro¨dinger Inc. for GLIDE software and Tripos Inc. for SYBYL-X1.2.
Declaration of interest We gratefully acknowledge support for this research from Department of Science and Technology (File no: SR/WOS-A/CS-40/2012), New Delhi, India, University Grants Commission, New Delhi, India and Department of chemistry, Nizam College, Hyderabad, India. We do not have any conflict of interest to declare.
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