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Molecular modeling studies of [6,6,5] Tricyclic Fused Oxazolidinones as FXa inhibitors using 3D-QSAR, Topomer CoMFA, molecular docking and molecular dynamics simulations
Accepted Manuscript Molecular modeling studies of [6,6,5] Tricyclic Fused Oxazolidinones as FXa inhibitors using 3D-QSAR, Topomer CoMFA, molecular docking and molecular dynamics simulations Cheng Xu, Yujie Ren PII: DOI: Reference:
S0960-894X(15)30021-4 http://dx.doi.org/10.1016/j.bmcl.2015.08.070 BMCL 23064
To appear in:
Bioorganic & Medicinal Chemistry Letters
Received Date: Revised Date: Accepted Date:
25 June 2015 22 August 2015 26 August 2015
Please cite this article as: Xu, C., Ren, Y., Molecular modeling studies of [6,6,5] Tricyclic Fused Oxazolidinones as FXa inhibitors using 3D-QSAR, Topomer CoMFA, molecular docking and molecular dynamics simulations, Bioorganic & Medicinal Chemistry Letters (2015), doi: http://dx.doi.org/10.1016/j.bmcl.2015.08.070
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Bioorganic & Medicinal Chemistry Letters
Molecular modeling studies of [6,6,5] Tricyclic Fused Oxazolidinones as FXa inhibitors using 3D-QSAR, Topomer CoMFA, molecular docking and molecular dynamics simulations Cheng Xua, Yujie Rena, a
School of Chemical and Environmental Engineering, Shanghai Institute of Technology, Shanghai 201418, China.
A RT I C L E I N F O
A BS T RA C T
Article history: Received Revised Accepted Available online
Coagulation factor Xa (Factor Xa, FXa) is a particularly promising target for novel anticoagulant therapy. The first oral factor Xa inhibitor has been approved in the EU and Canada in 2008. In this work, 38 [6,6,5] Tricyclic Fused Oxazolidinones were studied using a combination of molecular modeling techniques including three-dimensional quantitative structure–activity relationship (3D-QSAR), molecular docking, molecular dynamics and Topomer CoMFA(comparative molecular field analysis) were used to build 3D-QSAR models. The results show that the best CoMFA model has q2 = 0.511 and r2 = 0.984, the best CoMSIA (comparative molecular similarity indices analysis) model has q 2 = 0.700 and r2 = 0.993 and the Topomer CoMFA analysis has q2 = 0.377 and r2 = 0.886. The results indicated the steric, hydrophobic, H-acceptor and electrostatic fields play key roles in models. Molecular docking and molecular dynamics explored the binding relationship of the ligand and the receptor protein.
Keywords: FXa Tricyclic Fused Oxazolidinones Topomer CoMFA 3D-QSAR Molecular dynamics simulations
2015 Elsevier Ltd. All rights reserved .
——— Corresponding author. e-mail: [email protected]
1
Thromboembolic diseases, which include deep vein thrombosis, pulmonary embolism, and blood clots caused by stroke, have high incidence and mortality rates worldwide.1-3 Anticoagulation therapy is the first-line treatment for cardiovascular diseases. Anticoagulant drugs, such as heparin and warfarin, exert favorable therapeutic effects. However, these drugs cannot be administered orally and are limited by complications such as thrombocytopenia and bleeding.4,5 Thus, the development of new anticoagulant drugs with improved efficacy and safety is important.
Sankyo), have currently entered the late stages of clinical development. Rivaroxaban is a potent and direct FXa inhibitor. It has high bioavailability through oral medication and is excreted through the kidneys, bile, and feces.15 In addition, it has a half-life of 5–9 hours and a plasma drug concentration that is closely related to prothrombin time and activated partial thromboplastin time. Therefore, rivaroxaban can be administered orally without detecting INR.16 Recent studies have reported some progresses in FXa inhibitors in theoretical calculations. For example, Sumit Deswal et al.17 used a 2D quantitative structure–activity relationship to study 42 aryl heterocycle-based thrombin inhibitors. Bhoomendra et al.18 built a CoMFA model based on trypsin-like serine protease inhibitors. Anshuman Dixit et al.19 developed CoMFA, advance CoMFA and CoMSIA models based on 16 pyrroloquinazolines.
Factor Xa (FXa) is an activated product produced by coagulation factor X (FX) during clotting. It is a serine protease with a structure that is similar to that of thrombin. FXa is a complex formed by activated FX, Ca2+, and phospholipids. It is located at the junction of the intrinsic and extrinsic pathways of coagulation and catalyzes the conversion of prothrombin to thrombin. FXa inhibitors impede the formation of thrombin and fibrin. They also hinder the activation of FV, FVIII, FXIII, and PrC, thereby inhibiting the formation and expansion of blood clots. The physiological effect of FXa inhibition is equivalent to restraining 138 thrombin molecules. Thus, FXa inhibitors may be more effective than thrombin inhibitors. Animal experiments also show that FXa inhibitors have lower risk of bleeding compared with thrombin inhibitors.6 Recent research has focused on oral small-molecule FXa inhibitors.7,8
In the present work, a total of 38 new [6,6,5] Tricyclic Fused Oxazolidinones reported by Yushe Yang20 were selected as dataset, after discarding those compounds with unspecified activity. The IC50 values were converted to the pIC50 (-log IC50) values, which were used as dependent variables in the 3D-QSAR models. The whole data set was divided into two parts: a training set of 31 compounds for 3D-QSAR model generation and a test set containing 7 compounds for model checking. A series of studies were performed using some computational methods include 3D-QSAR, Topomer CoMFA, molecular docking and MD simulation. The results of CoMFA and CoMSIA studies will not only explain the conformation or spatial orientation of [6,6,5] Tricyclic Fused Oxazolidinones but also provide useful information for the design of potent and selective FXa inhibitor.
The clinical study results of fondaparinux sodium confirmed that FXa is a promising target for novel anticoagulant therapy. FXa inhibitors were previously mainly used to prevent thrombosis. However, results demonstrated that these inhibitors are also effective in treating thrombosis. Three oral FXa inhibitors, namely, rivaroxaban9−11 (Bayer), apixaban12,13 (Bristol–Myers Squibb/Pfizer), and edoxaban14 (Daiichi–
Table 1. Molecular structures of the compounds, their actual and predicted pIC50 values of CoMFA and CoMSIA.
R1
Actual pIC50
Predicted CoMFA
Residual
Predicted CoMSIA
Residual
1
oxazolidin-2-one-3-yl
6.137
6.22
0.083
6.202
0.065
2
1,4-dioxa-8-azaspiro[4,5]decan-7-one-8-yl
5.6
5.453
-0.147
5.589
-0.011
3
(4R)-4-methoxypiperidin-2-one-1-yl
6.229
6.399
0.17
6.232
0.003
4
(4S)-4-methoxypiperidin-2-one-1-yl
5.73
5.802
0.072
5.721
-0.009
5
(4R)-4-hydroxypiperidin-2-one-1-yl
6.886
6.919
0.033
6.865
-0.021
6
(4S)-4-hydroxypiperidin-2-one-1-yl
7.331
7.363
0.032
7.293
-0.038
7
(4R)-4-fluoropiperidin-2-one-1-yl
7.392
7.336
-0.056
7.387
-0.005
8
piperazin-2-one-1-yl
6.721
6.374
-0.347
6.785
0.064
9
4-methty-piperazin-2-one-1-yl
5.656
5.531
-0.125
5.317
-0.339
2
10
4-ethty-piperazin-2-one-1-yl
4.802
4.87
0.068
4.967
0.165
11
4-benzyl-piperazin-2-one-1-yl
4.945
4.833
-0.112
4.923
-0.022
12
4-acetyl-piperazin-2-one-1-yl
7.126
6.913
-0.213
7.148
0.022
13
4-mesyl-piperazin-2-one-1-yl
5.379
5.363
-0.016
5.423
0.044
14
4-phenylsulfonyl-piperazin-2-one-1-yl
5.257
5.478
0.221
5.268
0.011
15
5-chloropyridin-2-yl
6.149
5.974
-0.175
6.171
0.022
16
4-methoxyphenyl
5.321
5.48
0.159
5.324
0.003
17
2-chlorothiazole-2-yl
5.151
5.315
0.164
5.193
0.042
18
dichloromethyl
4.278
4.271
-0.007
4.256
-0.022
Test1
N-methyl-acetamido
7.06
6.144
-0.916
6.38
-0.68
Test2
(4S)-4-hydroxypiperidin-2-one-1-yl
5.914
6.895
0.981
6.576
0.662
Test3
4-chlorophenyl
6.796
6.076
-0.72
6.173
-0.623
Test4
6-chloro-benzo[b]thiophene-2-yl
5.757
6.353
0.596
6.396
0.639
Y
R1
Actual pIC50
Predicted CoMFA
Residual
Predicted CoMSIA
Residual
19
-CH2NHCONH-
5-chlorothiophene-2-yl
7.053
7.267
0.214
7.076
0.023
20
-CH2NHCOCONH-
4-chlorophenyl
7.494
7.521
0.027
7.487
-0.007
21
-CH2NHCOCONH-
5-chloropyridin-2-yl
5.917
6.052
0.135
5.910
-0.007
22
-CH2NHCOCONH-
4-chlorophenyl
6.509
6.363
-0.146
6.519
0.010
Test5
-CH2NHCOCONH-
5-chorothiophene-2-yl
6.252
6.392
0.140
6.540
0.288
23
X
R1
R2
Actual pIC50
Predicted CoMFA
Residual
Predicted CoMSIA
Residual
S
morpholin-3-one-4-yl
5-chlorothiophene-2-yl
8.600
8.427
-0.173
8.598
-0.002
3
24
S
piperidin-3-one-4-yl
5-chlorothiophene-2-yl
8.311
8.435
0.124
8.290
-0.021
25
SO
morpholin-3-one-4-yl
5-chlorothiophene-2-yl
7.195
7.343
0.148
7.212
0.017
26
SO2
piperidin-3-one-4-yl
5-chlorothiophene-2-yl
7.297
7.169
-0.128
7.305
0.008
27
C
morpholin-3-one-4-yl
5-chlorothiophene-2-yl
8.327
8.352
0.025
8.369
0.042
28
C
piperidin-3-one-4-yl
5-chlorothiophene-2-yl
8.181
8.313
0.132
8.095
-0.086
29
(Boc)N
piperidin-3-one-4-yl
5-chlorothiophene-2-yl
5.087
5.054
-0.033
5.109
0.022
30
N
morpholin-3-one-4-yl
5-chlorothiophene-2-yl
8.296
7.979
-0.317
8.214
-0.082
31
N
piperidin-3-one-4-yl
5-chlorothiophene-2-yl
7.818
8.009
0.191
7.924
0.106
Test6
S
morpholin-3-one-4-yl
4-chlorophenyl
7.345
8.293
0.948
8.484
1.139
Test7
SO2
morpholin-3-one-4-yl
5-chlorothiophene-2-yl
7.760
7.177
-0.583
7.607
-0.153
Fig. 1 The alignment of all molecules , compound 23 is used as the template. a The common substructure (shown in bold) for the alignment of all molecules. b The alignment of all the compounds. Molecules are colored in white for common C, blue for N, red for O, yellow for S, cyan for H atoms, respectively.
The three-dimensional structures of the molecules were drawn and all molecular modeling calculations were performed in Sybyl 2.0.21,22 All molecules charges were calculated by the GasteigerHuckel method,23 energy minimization and conformational search were performed using Tripos force field24 by Powell method.25 In order to obtain stable conformation, the energy gradient limit was set to 0.005 kcal/ (mol·Ǻ) and the maximum iterations were set to 10000. The highest bioactivity compound 23 was selected as the template molecule. The bold atoms in Fig. 1a was chosen as a common structural. The alignment of training set is shown in Fig. 1. Both ligand- and receptor- based alignment rules were adopted to build statistical model.26
Fig. 2 R1 fragment is represented by the blue color and R 2 fragment is represented by red color.
Docking and scoring technology is applied to drug discovery process for predicting the binding mode of a known active ligand.31,32 In this work, three molecules were selected as the ligand. The X-ray crystal structure of FXa inhibitor (PDB code: 2W26) was retrieved from the RCSB Protein Data Bank. It is necessary to do preparatory work before docking. We analyzed the protein and repaired the side chains and the terminal of the main chain firstly. Then, the protein was added hydrogen atoms and charges. Finally, we specified the type of the atoms of the protein. The docking area was defined by protomol, the molecular was flexibly docked into the active pocket.
Topomer CoMFA27-29 is the second generation of CoMFA. It is a 3D-QSAR technique that automates the creation of models for predicting the biological activity of compounds. The original CoMFA uses only one column. In this study, each molecule was divided into two sets of fragments shown as R1 (blue) and R2 (red) groups (Fig. 2). Steric and electrostatic interaction energies were calculated using the carbon sp3 probe.30 Partial least-squares (PLS) regression was used to generate a Topomer CoMFA model.
The MD simulations were performed using the dynamics module of SYBYL. Compound 23 was used as the template molecule to explain the MD simulations. Then, energy minimization was performed for the complex molecule with AMBER7 FF99 force field and Gasteiger-Huckel charge without
4
water using Boltzmann initial velocity. The simulations were executed using normal temperature and volume (NTV) ensemble 300 K with coupling 100 fs. Besides, we perform a 5 ns simulation with a time step of 1 fs and snapshot the conformation every 100 fs.22
Fraction
The structural alignment of compounds plays in the development of successful 3D-QSAR models. In this study, we used the ligand-based alignment rule. The alignment result is shown in Figure 1b. To obtain an effective 3D-QSAR model, a number of statistical parameters (including q2, r2, standard error of estimate (SEE) and F-statistic values) should be analyzed. The statistical results are listed in Table 2.
CoMSIA
q2
0.511
0.700
noc
6
9
r2
0.984
0.993
SEE
0.174
0.119
F
240.836
348.096
0.197
Electrostatic
0.404
0.151
Hydrophobic
-
0.296
H-donor
-
0.094
H-acceptor
-
0.263
（b）
（a） 9
9
training set test set
8.5 8
8
7.5
7.5
7 6.5 6 5.5
7 6.5 6 5.5
5
5
4.5
4.5
4 4
4.5
5
training set test set
8.5
The predictive pIC50
The predictive pIC50
0.596
The CoMFA model resulted in a cross-validated q2 of 0.511, a non-cross-validated correlation coefficient r2 of 0.984, a SEE value of 0.174, and an F value of 240.836. For the CoMSIA model, the q2, r2, SEE, and F values were 0.700, 0.993, 0.119, and 348.096, respectively. The CoMFA model showed that the contributions of the steric and electrostatic fields were 59.6% and 40.4%, respectively. The steric field was the main contribution in this model. To obtain the optimal CoMSIA model, the steric, electrostatic, hydrophobic, H-donor, and acceptor field contribution were 19.7%, 15.1%, 29.6%, 9.4%, and 26.3%, respectively. Steric, electrostatic, hydrophobic, and H-acceptor were important contributions in this model. The actual and predicted pIC50 values of the training and test set molecules are illustrated in Fig. 3.
Table 2. Statistical results for the CoMFA and CoMSIA models CoMFA
Steric
5.5
6
6.5
7
7.5
8
8.5
4
9
4
The actual pIC50
4.5
5
5.5
6
6.5
7
7.5
8
8.5
9
The actual pIC50
Fig. 3 Plot of actual activity against activity predicted by the best CoMFA (a) and CoMSIA (b) models.
In the Topomer CoMFA analysis, all of the models were investigated using the full cross validated (q2) PLS leave-one-out (LOO) method with CoMFA standard options for variable scaling. This model provided a cross-validated q2 value of 0.377, an optimized component of 4, and an r2 value of 0.886. These data indicate that the model has a predictive ability of (q2 > 0.2). The StDev*Coeff contour maps were built by the optimal CoMFA and CoMSIA and Topomer models. The compound 23 is used to illustrate all contour maps of the optimal CoMFA and CoMSIA models. From Figures 4–6, we can explain the key structural features required for inhibitory activity.
Fig. 4 Steric contour of the CoMFA (a) and CoMSIA (b) models. Topomer CoMFA contour maps around R1(c) and R2(d).The green color shows the favored steric area and the yellow color show steric area.
5
The steric fields of CoMFA, CoMSIA, and Topomer CoMFA are shown in Fig. 4, where the green (favorable) and yellow (unfavorable) contours represent 80% and 20% level contributions, respectively. Figs. 4(a) and 4(b) show that the steric contour map of CoMFA is similar to that of CoMSIA. Figs. 4(c) and 4(d) depict that the steric contour map of Topomer CoMFA is similar to those of the CoMFA and CoMSIA models. The steric contour maps confirmed the reliability of the models. All steric contour maps show that the yellow contour maps appear near R1 while the green maps appear near R2. This observation is consistent with the results. The order of inhibitory activity is as follows: morpholin > piperidin, chlorophenyl > chlorothiophene. For example, 23 > 24, test7 > 26, 27 > 28, 30 > 31, 20 > 19, 22 > test5.
Fig. 6 Hydrophobic (a), hydrogen bond donor field (b) and hydrogen bond acceptor field(c) contours of the CoMSIA models. Yellow represents the favored hydrophobic area, white represents the disfavored hydrophobic area, magenta represents the favored H-acceptor area, red represents the disfavored H-acceptor area, cyan represents the favored H-donor area, and purple represents the disfavored H-donor area.
The hydrophobic and hydrogen bonding contour map of CoMSIA is shown in Fig. 6, where the yellow (hydrophobic favorable) and white (hydrophobic unfavorable) contours represent 80% and 20% level contributions, respectively. Fig. 6a shows that a large white (hydrophilic favorable) contour is near R2. This result suggests that introducing hydrophilic substituents into the R2 group enhances the inhibitory activity. One small white contour is in R1, and one small white contour is near the peptide. For example, test5 < 19, 22 < 20, 31 < 30. Fig. 6(b) shows a cyan (H-bond donor favorable) map near R2. This result suggests that the electropositive H-bond donors in these regions improve the inhibitory activity. A purple (H-bond donor unfavorable) contour is near the peptide. As shown in Fig. 6(c), three large purple (H-bond receptor favorable) contour maps and one red (H-bond receptor unfavorable) contour map are distant from compound 23. This result indicates that the H-bond acceptor elicits minimal effect on inhibitory activity.
Fig. 5 Electrostatic contour of the CoMFA (a) and CoMSIA (b) models. Topomer CoMFA contour maps around R1(c) and R2(d). The red color shows the favored negative electrostatic area and the blue color shows the favored positive electrostatic area.
Fig. 5(a) shows the CoMFA electrostatic contours. A mediumsized red contour appears near O of the morpholin. A mediumsized blue contour is near R2 and a small blue contour is also near the S. As a result, the order of inhibitory activity is: morpholin > piperidin. The potency of the compounds will be decreased by adding electronegative groups into R2. For example, 16 < test3, 15 < test3. The CoMSIA and Topomer CoMFA electrostatic contour maps are basically consistent with those of CoMFA, and are thus not discussed here.
Molecular docking is the most widespread method for calculating protein-ligand interactions. This method is effective in predicting the potential ligand binding site(s) on the whole protein target. The crystal structure of the protein (PDB code: 2W26) with the cognate ligand was re-docked. The docking result is shown in Fig. 7. From Fig. 7, it can be seen that some
6
key amino acids (Gly219, Gln192, Lys96, Tyr99, Ala190, Cys191, Ser195, Val213, Ser214, Gly216, Trp215, Glu217, Cys220, Gly226, Ile227, Tyr228, Glu97, Thr98, Phe174, Met180 and Asp189) interacted with the inhibitor at the binding site. The hydrogen bond distances were observed to be 2.02 Å(Gly219=O…H-N).
binding site. The hydrogen bond distance was observed to be 1.98 Å(Gly219=O … H-N). In addition, some amino acids (Gly219, Gln192, Ala190, Cys220, Glu217, Gly216, Trp215, Lys96, Tyr99 and Glu97) interacted with the compound through electrostatic interaction.
Compounds 23, 27, and 18 were selected to dock into the binding site of 2W26. The results are shown in Fig. 8. As shown in Fig. 8a, some key amino acids (Gly219, Gln192, Arg143, Tyr99, Glu97, Ala190, Cys220, Glu217, Ser195, Lys96, Gly216, Met180, Asp189 and Trp215) interacted with compound 23 at the
Fig. 7 Re-docking result of the cognate ligand into the binding site of the protein (PDB code: 2W26). Hydrogen bonds are shown as yellow dashed lines, with distance unit of Å.
Fig. 8 Docking result of the compounds 23 (a), 27 (b) and 18(c) into the binding site of 2W26. Hydrogen bonds are shown as yellow li nes, with distance unit of Å. The inhibitor and the important residues are shown as stick model.
As shown in Fig. 8b, some key amino acids (Gly219, Gln192, Lys96, Tyr99, Ser195, Gly216, Cys220, Trp215, Ile227, Gly226, Ala190 and Cys191) interacted with compound 27 at the binding site. The hydrogen bond distances observed were observed to be 2.23Å(Gly219=O…H-N), 2.12Å(Gly219N-H…O-). In addition, some amino acids (Gln192, Ser195, Gly216, Cys220, Trp215, Ser214 and Ile227) interacted with the compound by electrostatic interaction. As shown in Fig. 8c, some key amino acids (Gln192,
7
Tyr99, Arg222, Gly219, Gly216, Glu217, Trp215, and Ser214) interacted with compound 18 at the binding site. The hydrogen bond distances were observed to be 2.02Å (Arg222-HN-H…O=), 2.62Å (Arg222-HN-H…O=), 1.94Å (Gln192--HN-H…O-), and 1.84Å (Tyr99-N-H … O-). However, significantly fewer amino acids electrostatically interacted with compound 18 compared with compounds 23 and 27. In addition, the key residue Gly219 was not observed in the binding pocket. To further confirm the reliability of docking results, MD simulations, were used to explore the probable binding modes between the ligand and the receptor protein. In this study, a 5 ns simulation of the ligand-receptor complex was run to obtain a stable conformation. The root mean square deviation (RMSD) of the receptors and the ligands is shown in Fig. 9. After 2 ns, the RMSD of the complex reaches about 4.3 Å and maintains similar value at last, which indicates that the docked complex can reach metastable conformation after 2 ns of simulation. The MD-simulated structure and the original docked structure is shown in. From Fig. 10, the green and red ribbon represents the original docked structure and the lowest energy structure, respectively. The compound 23 were docked into the same binding site of the original docked structure and the lowest energy structure. Besides, their structures are basically similar.
Fig. 9 The root-mean-square deviation (RMSD) of the docked complex versus the MD simulation structures.
Fig. 10 View of superimposed backbone atoms of the lowest energy structure of the MD simulation (red) and the initial structure (green) for the docked complex.
Fig. 11 Docking result of the MD-simulated structure. Hydrogen bonds are shown as yellow dashed lines, with distance unit of Å. Molecular surface is shown as cyan.
After 5 ns MD simulation studies, we found the residues are basically similar between the inhibitor and the protein. But, hydrogen bonds at the binding site increase. As shown in Fig. 11, there are four acid residues Gly219, Gln192, Tyr99 and Lys96 forming 4 hydrogen bonds with the compound 23. It can be seen that some residues in this binding are important H-bond donors, which is consistent with the CoMSIA model. Furthermore, the H-bonds between the ligand and Gly219 provide stability. The docking result and MD simulation both show that residue Gly219 in the protein is a key residue to confer the inhibitory activity.
3
The analysis described above suggests that there are no significant differences between the original docked structure and the MDsimulated structure. Therefore, the docking results is reliable and valid. In this study, a series of computer-aided drug design processes, such as 3D-QSAR studies, Topomer CoMFA, molecular docking, and molecular dynamics were used to build models and explore the probable binding modes between the ligand and the receptor protein. The built models show good internal and external predictive ability and can be extrapolated to predict novel and more potent molecules. The contour maps obtained from the CoMFA, CoMSIA and Topomer CoMFA analysis could be used to guide designing new compounds with high FXa inhibitory activity. To study the binding modes of inhibitors at the active site of the protein, molecular docking studies of representative compounds were conducted. To validate the docking results, MD simulation is performed. Some key residues (Gly219, Gln192, Lys96, Tyr99, Ala190, Cys220, Glu217, Gly216, Trp215 and Glu97) and four hydrogen bonds (Gly219, Gln192, Tyr99 and Lys96) were detected in the binding site. These results show that the model is useful to predict new FXa inhibitors and can offer guidelines for further research. References and notes 1.
2.
3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.
17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32.
Roger, V. L.; Go, A. S.; Lloyd-Jones, D. M.; Benjamin, E. J.; Berry, J. D.; Borden, W. B.; Bravata, D. M.; Dai, S.; Ford, E. S.; Fox, C.S.; Fullerton, H. J.; Gillespie, C.; Hailpern, S. M.; Heit, J. A.; Howard, V. J.; Kissela, B. M.; Kittner, S. J.; Lackland, D. T.; Lichtman, J. H.; Lisabeth, L. D.; Makuc, D. M.; Marcus, G. M.; Marelli, A.; Matchar, D. B.; Moy, C. S.; Mozaffarian, D.; Mussolino, M. E.; Nichol, G.; Paynter, N. P.; Soliman, E. Z.; Sorlie, P. D.; Sotoodehnia, N.; Turan, T. N.; Virani, S. S.; Wong, N. D.; Woo, D.; Turner, M. B. Circulation 2012, 125, e2. Furie, K. L.; Kasner, S. E.; Adams, R. J.; Albers, G. W.; Bush, R. L.; Fagan, S. C.; Halperin, J. L.; Johnston, S. C.; Katzan, I.; Kernan, W. N.; Mitchell, P. H.; Ovbiagele, B.; Palesch, Y. Y.; Sacco, R. L.; Schwamm, L. H. S.; Wassertheil-Smoller; Turan, T. N. D.; Wentworth Stroke 2011, 42, 227. Cohen, A. T.; Agnelli, G.; Anderson, F. A.; Arcelus, J. I.; Bergqvist, D.; Brecht, J. G.; Greer, I. A.; Heit, J. A.; Hutchinson, J. L.; Kakkar, A. K.; Mottier, D.; Oger, E.; Samama, M.-M. Thromb. Haemostasis 2007, 98, 756. Fareed, J.; Thethi, I.; Hoppensteadt, D. Annu. Rev. Pharmacol. Toxicol. 2012, 52, 79. Ahmed, S.; Levin, V.; Malacoff, R. Cardiovasc Hematol Agents Med. Chem. 2012, 10(2):116. Eriksson, B. I.; Quinla, D. J.; Weitz. J. I. Clinical Pharmacokinetics 2009, 48 (1): 1. Morris, T. Clin. Chest Med. 2010, 31 (4), 707. Mehta, R. Expert Rev. Hematol 2010, 3 (2), 227. Roehrig, S.; Straub, A.; Pohlmann, J.; Lampe, T.; Pernerstorfer, J.; Schlemmer, K.-H.; Reinemer, P.; Perzborn, E. J. Med. Chem. 2005, 48, 5900. Perzborn, E.; Roehrig, S.; Straub, A.; Kubitza, D.; Misselwitz, F.; Nat. Rev. Drug Discovery 2011, 10, 6. Perzborn, E.; Strassburger, J.; Wilmen, A.; Pohlmann, J.; Roehrig, S.; Schlemmer, K. H.; Straub, A.; Thromb, J. Haemost. 2005, 3, 514. Pinto, D. J. P.; Orwat, M. J.; Koch, S.; Rossi, K. A.; Alexander, R. S.; Smallwood, A.; Wong, P. C.; Rendina, A. R.; Luettgen, J. M.; Knabb, R. M.; He, K.; Xin, B.; Wexler, R. R.; Lam, P. Y. S. J. Med. Chem. 2007, 50, 5339. Wong, P. C.; Crain, E. J.; Xin, B.; Wexler, R. R.; Lam, P. Y.; Pinto, D. J.; Luettgen, J. M.; Knabb, R. M. J. Thromb. Haemost. 2008, 6, 820. Furugohri, T.; Isobe, K.; Honda, Y.; Kamisato-Matsumoto, C.; Sugiyama, N.; Nagahara, T.; Morishima, Y.; Shibano, T. J. Thromb. Haemost. 2008, 6, 1542. Wright, R. S.; Anderson, J. L.; Adams, C. D.; Bridge, C. R.; Casey, D. E.; Ettinger, S. M.; Fesmire, F. M.; Ganinats, T. G.; Jneid, H.; Lincoff, A. M.; Peterson, E. D.; Philippides, G. J.; Theroux, P.; Wenger, N. K.; Zidar, J. P. J. Am. Coll. Cardiol. 2011, 57(19), e215. Kushner, F. G.; Smith, S. C.; Hand, M.; King, S. B.; Anderson, J. L.; Antman, E. M.; Bailey, S. R.; Bates, E.R.; Blankenship, J. C.; Casey, D. E.; Green, L. A.; Hochman, J. S.; Jacobs, A. K.; Krumholz, H. M.; Morrison, D. A.; Ornato, J. P.; Pearle, D. L.; Peterson, E. D.; Sloan, M. A.; Whitlow, P. L.; Williams, D. O. J. Am. Coll. Cardiol. 2009, 54(23), 2205. Deswal, S.; Roy. N. Eur. J. Med. Chem. 2006, 41 (11), 1339. Bhongade, B. A.; Gouripur, V. V.; Gadad, A. K. Bioorgan. Med. Chem. 2005, 13 (8), 2773. Matter, H.; Will, D. W.; Nazare, M.; Schreuder, H.; Laux, V.; Wehner, V. J. Med. Chem. 2005, 48 (9), 3290. Xue, T.; Ding, S.; Guo, B.; Zhou, Y. R.; Sun, P.; Wang, H. Y.; Chu, W. J.; Gong, G. Q.; Wang, Y. Y.; Chen, X. Y.; Yang, Y. S. J. Med. Chem. 2014, 57, 7770. SYBYL-X 2.0 TRIPOS, Assoc. Inc., St. Louis, MO. 2012. Wang, Z.; Cheng , L. P.; Kai, Z. P.; Wu, F. H.; Liu, Z. Y.; Cai, M. F. Bioorg. Med. Chem. Lett. 2014, 24(16), 3869. Gasteiger, J.; Marsili, M. Tetrahedron. 1980, 36, 3219. Clark, M.; Cramer III, R. D.; Opdenbosch, N. V. J. Med. Chem. 1989, 10(8), 982. Powell M. J. D. Math. Program. 1977, 12(1), 241. AbdulHameed, M. D. M.; Hamza, A.; Liu, J. J.; Zhan, C. G. J. Chem. Inf. Model 2008, 48, 1760. Cramer, R. D.; Cruz, P.; Stahl, G.; Curtiss, W. C.; Campbell, B.; Masek, B. B.; Soltanshahi, F. J. Chem. Inf. Model, 2008, 48, 2180. Cramer, R. D. J. Med. Chem. 2003, 46, 374. Cramer, R. D.; Wendt, B. J. Comput. Aid. Mol. Des. 2007, 21, 23. Dong, M. H.; Yu, J. R. RSC Advances 2015, 5, 13754. Andrew, R. L.; Brian, K. S.; Catherine, E. P. J. Med. Chem. 2006, 49, 5851. Politi, A.; Durdagi, S.; Moutevelis-Minakakis, P.; Kokotos, G.; Mavromoustakos, T. J. Mol. Graph. Model. 2010, 29, 425.
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Molecular modeling studies of [6,6,5] Tricyclic Fused Oxazolidinones as FXa inhibitors using 3D-QSAR, Topomer CoMFA, molecular docking simulations and molecular dynamics simulations
Leave this area blank for abstract info.
Cheng Xua, Yujie Rena,*
Docking result of the MD-simulated structure. Hydrogen bonds are shown as yellow dashed lines, with distance unit of Å. Molecular surface is shown as cyan.
5
S0960-894X(15)30021-4 http://dx.doi.org/10.1016/j.bmcl.2015.08.070 BMCL 23064
To appear in:
Bioorganic & Medicinal Chemistry Letters
Received Date: Revised Date: Accepted Date:
25 June 2015 22 August 2015 26 August 2015
Please cite this article as: Xu, C., Ren, Y., Molecular modeling studies of [6,6,5] Tricyclic Fused Oxazolidinones as FXa inhibitors using 3D-QSAR, Topomer CoMFA, molecular docking and molecular dynamics simulations, Bioorganic & Medicinal Chemistry Letters (2015), doi: http://dx.doi.org/10.1016/j.bmcl.2015.08.070
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Bioorganic & Medicinal Chemistry Letters
Molecular modeling studies of [6,6,5] Tricyclic Fused Oxazolidinones as FXa inhibitors using 3D-QSAR, Topomer CoMFA, molecular docking and molecular dynamics simulations Cheng Xua, Yujie Rena, a
School of Chemical and Environmental Engineering, Shanghai Institute of Technology, Shanghai 201418, China.
A RT I C L E I N F O
A BS T RA C T
Article history: Received Revised Accepted Available online
Coagulation factor Xa (Factor Xa, FXa) is a particularly promising target for novel anticoagulant therapy. The first oral factor Xa inhibitor has been approved in the EU and Canada in 2008. In this work, 38 [6,6,5] Tricyclic Fused Oxazolidinones were studied using a combination of molecular modeling techniques including three-dimensional quantitative structure–activity relationship (3D-QSAR), molecular docking, molecular dynamics and Topomer CoMFA(comparative molecular field analysis) were used to build 3D-QSAR models. The results show that the best CoMFA model has q2 = 0.511 and r2 = 0.984, the best CoMSIA (comparative molecular similarity indices analysis) model has q 2 = 0.700 and r2 = 0.993 and the Topomer CoMFA analysis has q2 = 0.377 and r2 = 0.886. The results indicated the steric, hydrophobic, H-acceptor and electrostatic fields play key roles in models. Molecular docking and molecular dynamics explored the binding relationship of the ligand and the receptor protein.
Keywords: FXa Tricyclic Fused Oxazolidinones Topomer CoMFA 3D-QSAR Molecular dynamics simulations
2015 Elsevier Ltd. All rights reserved .
——— Corresponding author. e-mail: [email protected]
1
Thromboembolic diseases, which include deep vein thrombosis, pulmonary embolism, and blood clots caused by stroke, have high incidence and mortality rates worldwide.1-3 Anticoagulation therapy is the first-line treatment for cardiovascular diseases. Anticoagulant drugs, such as heparin and warfarin, exert favorable therapeutic effects. However, these drugs cannot be administered orally and are limited by complications such as thrombocytopenia and bleeding.4,5 Thus, the development of new anticoagulant drugs with improved efficacy and safety is important.
Sankyo), have currently entered the late stages of clinical development. Rivaroxaban is a potent and direct FXa inhibitor. It has high bioavailability through oral medication and is excreted through the kidneys, bile, and feces.15 In addition, it has a half-life of 5–9 hours and a plasma drug concentration that is closely related to prothrombin time and activated partial thromboplastin time. Therefore, rivaroxaban can be administered orally without detecting INR.16 Recent studies have reported some progresses in FXa inhibitors in theoretical calculations. For example, Sumit Deswal et al.17 used a 2D quantitative structure–activity relationship to study 42 aryl heterocycle-based thrombin inhibitors. Bhoomendra et al.18 built a CoMFA model based on trypsin-like serine protease inhibitors. Anshuman Dixit et al.19 developed CoMFA, advance CoMFA and CoMSIA models based on 16 pyrroloquinazolines.
Factor Xa (FXa) is an activated product produced by coagulation factor X (FX) during clotting. It is a serine protease with a structure that is similar to that of thrombin. FXa is a complex formed by activated FX, Ca2+, and phospholipids. It is located at the junction of the intrinsic and extrinsic pathways of coagulation and catalyzes the conversion of prothrombin to thrombin. FXa inhibitors impede the formation of thrombin and fibrin. They also hinder the activation of FV, FVIII, FXIII, and PrC, thereby inhibiting the formation and expansion of blood clots. The physiological effect of FXa inhibition is equivalent to restraining 138 thrombin molecules. Thus, FXa inhibitors may be more effective than thrombin inhibitors. Animal experiments also show that FXa inhibitors have lower risk of bleeding compared with thrombin inhibitors.6 Recent research has focused on oral small-molecule FXa inhibitors.7,8
In the present work, a total of 38 new [6,6,5] Tricyclic Fused Oxazolidinones reported by Yushe Yang20 were selected as dataset, after discarding those compounds with unspecified activity. The IC50 values were converted to the pIC50 (-log IC50) values, which were used as dependent variables in the 3D-QSAR models. The whole data set was divided into two parts: a training set of 31 compounds for 3D-QSAR model generation and a test set containing 7 compounds for model checking. A series of studies were performed using some computational methods include 3D-QSAR, Topomer CoMFA, molecular docking and MD simulation. The results of CoMFA and CoMSIA studies will not only explain the conformation or spatial orientation of [6,6,5] Tricyclic Fused Oxazolidinones but also provide useful information for the design of potent and selective FXa inhibitor.
The clinical study results of fondaparinux sodium confirmed that FXa is a promising target for novel anticoagulant therapy. FXa inhibitors were previously mainly used to prevent thrombosis. However, results demonstrated that these inhibitors are also effective in treating thrombosis. Three oral FXa inhibitors, namely, rivaroxaban9−11 (Bayer), apixaban12,13 (Bristol–Myers Squibb/Pfizer), and edoxaban14 (Daiichi–
Table 1. Molecular structures of the compounds, their actual and predicted pIC50 values of CoMFA and CoMSIA.
R1
Actual pIC50
Predicted CoMFA
Residual
Predicted CoMSIA
Residual
1
oxazolidin-2-one-3-yl
6.137
6.22
0.083
6.202
0.065
2
1,4-dioxa-8-azaspiro[4,5]decan-7-one-8-yl
5.6
5.453
-0.147
5.589
-0.011
3
(4R)-4-methoxypiperidin-2-one-1-yl
6.229
6.399
0.17
6.232
0.003
4
(4S)-4-methoxypiperidin-2-one-1-yl
5.73
5.802
0.072
5.721
-0.009
5
(4R)-4-hydroxypiperidin-2-one-1-yl
6.886
6.919
0.033
6.865
-0.021
6
(4S)-4-hydroxypiperidin-2-one-1-yl
7.331
7.363
0.032
7.293
-0.038
7
(4R)-4-fluoropiperidin-2-one-1-yl
7.392
7.336
-0.056
7.387
-0.005
8
piperazin-2-one-1-yl
6.721
6.374
-0.347
6.785
0.064
9
4-methty-piperazin-2-one-1-yl
5.656
5.531
-0.125
5.317
-0.339
2
10
4-ethty-piperazin-2-one-1-yl
4.802
4.87
0.068
4.967
0.165
11
4-benzyl-piperazin-2-one-1-yl
4.945
4.833
-0.112
4.923
-0.022
12
4-acetyl-piperazin-2-one-1-yl
7.126
6.913
-0.213
7.148
0.022
13
4-mesyl-piperazin-2-one-1-yl
5.379
5.363
-0.016
5.423
0.044
14
4-phenylsulfonyl-piperazin-2-one-1-yl
5.257
5.478
0.221
5.268
0.011
15
5-chloropyridin-2-yl
6.149
5.974
-0.175
6.171
0.022
16
4-methoxyphenyl
5.321
5.48
0.159
5.324
0.003
17
2-chlorothiazole-2-yl
5.151
5.315
0.164
5.193
0.042
18
dichloromethyl
4.278
4.271
-0.007
4.256
-0.022
Test1
N-methyl-acetamido
7.06
6.144
-0.916
6.38
-0.68
Test2
(4S)-4-hydroxypiperidin-2-one-1-yl
5.914
6.895
0.981
6.576
0.662
Test3
4-chlorophenyl
6.796
6.076
-0.72
6.173
-0.623
Test4
6-chloro-benzo[b]thiophene-2-yl
5.757
6.353
0.596
6.396
0.639
Y
R1
Actual pIC50
Predicted CoMFA
Residual
Predicted CoMSIA
Residual
19
-CH2NHCONH-
5-chlorothiophene-2-yl
7.053
7.267
0.214
7.076
0.023
20
-CH2NHCOCONH-
4-chlorophenyl
7.494
7.521
0.027
7.487
-0.007
21
-CH2NHCOCONH-
5-chloropyridin-2-yl
5.917
6.052
0.135
5.910
-0.007
22
-CH2NHCOCONH-
4-chlorophenyl
6.509
6.363
-0.146
6.519
0.010
Test5
-CH2NHCOCONH-
5-chorothiophene-2-yl
6.252
6.392
0.140
6.540
0.288
23
X
R1
R2
Actual pIC50
Predicted CoMFA
Residual
Predicted CoMSIA
Residual
S
morpholin-3-one-4-yl
5-chlorothiophene-2-yl
8.600
8.427
-0.173
8.598
-0.002
3
24
S
piperidin-3-one-4-yl
5-chlorothiophene-2-yl
8.311
8.435
0.124
8.290
-0.021
25
SO
morpholin-3-one-4-yl
5-chlorothiophene-2-yl
7.195
7.343
0.148
7.212
0.017
26
SO2
piperidin-3-one-4-yl
5-chlorothiophene-2-yl
7.297
7.169
-0.128
7.305
0.008
27
C
morpholin-3-one-4-yl
5-chlorothiophene-2-yl
8.327
8.352
0.025
8.369
0.042
28
C
piperidin-3-one-4-yl
5-chlorothiophene-2-yl
8.181
8.313
0.132
8.095
-0.086
29
(Boc)N
piperidin-3-one-4-yl
5-chlorothiophene-2-yl
5.087
5.054
-0.033
5.109
0.022
30
N
morpholin-3-one-4-yl
5-chlorothiophene-2-yl
8.296
7.979
-0.317
8.214
-0.082
31
N
piperidin-3-one-4-yl
5-chlorothiophene-2-yl
7.818
8.009
0.191
7.924
0.106
Test6
S
morpholin-3-one-4-yl
4-chlorophenyl
7.345
8.293
0.948
8.484
1.139
Test7
SO2
morpholin-3-one-4-yl
5-chlorothiophene-2-yl
7.760
7.177
-0.583
7.607
-0.153
Fig. 1 The alignment of all molecules , compound 23 is used as the template. a The common substructure (shown in bold) for the alignment of all molecules. b The alignment of all the compounds. Molecules are colored in white for common C, blue for N, red for O, yellow for S, cyan for H atoms, respectively.
The three-dimensional structures of the molecules were drawn and all molecular modeling calculations were performed in Sybyl 2.0.21,22 All molecules charges were calculated by the GasteigerHuckel method,23 energy minimization and conformational search were performed using Tripos force field24 by Powell method.25 In order to obtain stable conformation, the energy gradient limit was set to 0.005 kcal/ (mol·Ǻ) and the maximum iterations were set to 10000. The highest bioactivity compound 23 was selected as the template molecule. The bold atoms in Fig. 1a was chosen as a common structural. The alignment of training set is shown in Fig. 1. Both ligand- and receptor- based alignment rules were adopted to build statistical model.26
Fig. 2 R1 fragment is represented by the blue color and R 2 fragment is represented by red color.
Docking and scoring technology is applied to drug discovery process for predicting the binding mode of a known active ligand.31,32 In this work, three molecules were selected as the ligand. The X-ray crystal structure of FXa inhibitor (PDB code: 2W26) was retrieved from the RCSB Protein Data Bank. It is necessary to do preparatory work before docking. We analyzed the protein and repaired the side chains and the terminal of the main chain firstly. Then, the protein was added hydrogen atoms and charges. Finally, we specified the type of the atoms of the protein. The docking area was defined by protomol, the molecular was flexibly docked into the active pocket.
Topomer CoMFA27-29 is the second generation of CoMFA. It is a 3D-QSAR technique that automates the creation of models for predicting the biological activity of compounds. The original CoMFA uses only one column. In this study, each molecule was divided into two sets of fragments shown as R1 (blue) and R2 (red) groups (Fig. 2). Steric and electrostatic interaction energies were calculated using the carbon sp3 probe.30 Partial least-squares (PLS) regression was used to generate a Topomer CoMFA model.
The MD simulations were performed using the dynamics module of SYBYL. Compound 23 was used as the template molecule to explain the MD simulations. Then, energy minimization was performed for the complex molecule with AMBER7 FF99 force field and Gasteiger-Huckel charge without
4
water using Boltzmann initial velocity. The simulations were executed using normal temperature and volume (NTV) ensemble 300 K with coupling 100 fs. Besides, we perform a 5 ns simulation with a time step of 1 fs and snapshot the conformation every 100 fs.22
Fraction
The structural alignment of compounds plays in the development of successful 3D-QSAR models. In this study, we used the ligand-based alignment rule. The alignment result is shown in Figure 1b. To obtain an effective 3D-QSAR model, a number of statistical parameters (including q2, r2, standard error of estimate (SEE) and F-statistic values) should be analyzed. The statistical results are listed in Table 2.
CoMSIA
q2
0.511
0.700
noc
6
9
r2
0.984
0.993
SEE
0.174
0.119
F
240.836
348.096
0.197
Electrostatic
0.404
0.151
Hydrophobic
-
0.296
H-donor
-
0.094
H-acceptor
-
0.263
（b）
（a） 9
9
training set test set
8.5 8
8
7.5
7.5
7 6.5 6 5.5
7 6.5 6 5.5
5
5
4.5
4.5
4 4
4.5
5
training set test set
8.5
The predictive pIC50
The predictive pIC50
0.596
The CoMFA model resulted in a cross-validated q2 of 0.511, a non-cross-validated correlation coefficient r2 of 0.984, a SEE value of 0.174, and an F value of 240.836. For the CoMSIA model, the q2, r2, SEE, and F values were 0.700, 0.993, 0.119, and 348.096, respectively. The CoMFA model showed that the contributions of the steric and electrostatic fields were 59.6% and 40.4%, respectively. The steric field was the main contribution in this model. To obtain the optimal CoMSIA model, the steric, electrostatic, hydrophobic, H-donor, and acceptor field contribution were 19.7%, 15.1%, 29.6%, 9.4%, and 26.3%, respectively. Steric, electrostatic, hydrophobic, and H-acceptor were important contributions in this model. The actual and predicted pIC50 values of the training and test set molecules are illustrated in Fig. 3.
Table 2. Statistical results for the CoMFA and CoMSIA models CoMFA
Steric
5.5
6
6.5
7
7.5
8
8.5
4
9
4
The actual pIC50
4.5
5
5.5
6
6.5
7
7.5
8
8.5
9
The actual pIC50
Fig. 3 Plot of actual activity against activity predicted by the best CoMFA (a) and CoMSIA (b) models.
In the Topomer CoMFA analysis, all of the models were investigated using the full cross validated (q2) PLS leave-one-out (LOO) method with CoMFA standard options for variable scaling. This model provided a cross-validated q2 value of 0.377, an optimized component of 4, and an r2 value of 0.886. These data indicate that the model has a predictive ability of (q2 > 0.2). The StDev*Coeff contour maps were built by the optimal CoMFA and CoMSIA and Topomer models. The compound 23 is used to illustrate all contour maps of the optimal CoMFA and CoMSIA models. From Figures 4–6, we can explain the key structural features required for inhibitory activity.
Fig. 4 Steric contour of the CoMFA (a) and CoMSIA (b) models. Topomer CoMFA contour maps around R1(c) and R2(d).The green color shows the favored steric area and the yellow color show steric area.
5
The steric fields of CoMFA, CoMSIA, and Topomer CoMFA are shown in Fig. 4, where the green (favorable) and yellow (unfavorable) contours represent 80% and 20% level contributions, respectively. Figs. 4(a) and 4(b) show that the steric contour map of CoMFA is similar to that of CoMSIA. Figs. 4(c) and 4(d) depict that the steric contour map of Topomer CoMFA is similar to those of the CoMFA and CoMSIA models. The steric contour maps confirmed the reliability of the models. All steric contour maps show that the yellow contour maps appear near R1 while the green maps appear near R2. This observation is consistent with the results. The order of inhibitory activity is as follows: morpholin > piperidin, chlorophenyl > chlorothiophene. For example, 23 > 24, test7 > 26, 27 > 28, 30 > 31, 20 > 19, 22 > test5.
Fig. 6 Hydrophobic (a), hydrogen bond donor field (b) and hydrogen bond acceptor field(c) contours of the CoMSIA models. Yellow represents the favored hydrophobic area, white represents the disfavored hydrophobic area, magenta represents the favored H-acceptor area, red represents the disfavored H-acceptor area, cyan represents the favored H-donor area, and purple represents the disfavored H-donor area.
The hydrophobic and hydrogen bonding contour map of CoMSIA is shown in Fig. 6, where the yellow (hydrophobic favorable) and white (hydrophobic unfavorable) contours represent 80% and 20% level contributions, respectively. Fig. 6a shows that a large white (hydrophilic favorable) contour is near R2. This result suggests that introducing hydrophilic substituents into the R2 group enhances the inhibitory activity. One small white contour is in R1, and one small white contour is near the peptide. For example, test5 < 19, 22 < 20, 31 < 30. Fig. 6(b) shows a cyan (H-bond donor favorable) map near R2. This result suggests that the electropositive H-bond donors in these regions improve the inhibitory activity. A purple (H-bond donor unfavorable) contour is near the peptide. As shown in Fig. 6(c), three large purple (H-bond receptor favorable) contour maps and one red (H-bond receptor unfavorable) contour map are distant from compound 23. This result indicates that the H-bond acceptor elicits minimal effect on inhibitory activity.
Fig. 5 Electrostatic contour of the CoMFA (a) and CoMSIA (b) models. Topomer CoMFA contour maps around R1(c) and R2(d). The red color shows the favored negative electrostatic area and the blue color shows the favored positive electrostatic area.
Fig. 5(a) shows the CoMFA electrostatic contours. A mediumsized red contour appears near O of the morpholin. A mediumsized blue contour is near R2 and a small blue contour is also near the S. As a result, the order of inhibitory activity is: morpholin > piperidin. The potency of the compounds will be decreased by adding electronegative groups into R2. For example, 16 < test3, 15 < test3. The CoMSIA and Topomer CoMFA electrostatic contour maps are basically consistent with those of CoMFA, and are thus not discussed here.
Molecular docking is the most widespread method for calculating protein-ligand interactions. This method is effective in predicting the potential ligand binding site(s) on the whole protein target. The crystal structure of the protein (PDB code: 2W26) with the cognate ligand was re-docked. The docking result is shown in Fig. 7. From Fig. 7, it can be seen that some
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key amino acids (Gly219, Gln192, Lys96, Tyr99, Ala190, Cys191, Ser195, Val213, Ser214, Gly216, Trp215, Glu217, Cys220, Gly226, Ile227, Tyr228, Glu97, Thr98, Phe174, Met180 and Asp189) interacted with the inhibitor at the binding site. The hydrogen bond distances were observed to be 2.02 Å(Gly219=O…H-N).
binding site. The hydrogen bond distance was observed to be 1.98 Å(Gly219=O … H-N). In addition, some amino acids (Gly219, Gln192, Ala190, Cys220, Glu217, Gly216, Trp215, Lys96, Tyr99 and Glu97) interacted with the compound through electrostatic interaction.
Compounds 23, 27, and 18 were selected to dock into the binding site of 2W26. The results are shown in Fig. 8. As shown in Fig. 8a, some key amino acids (Gly219, Gln192, Arg143, Tyr99, Glu97, Ala190, Cys220, Glu217, Ser195, Lys96, Gly216, Met180, Asp189 and Trp215) interacted with compound 23 at the
Fig. 7 Re-docking result of the cognate ligand into the binding site of the protein (PDB code: 2W26). Hydrogen bonds are shown as yellow dashed lines, with distance unit of Å.
Fig. 8 Docking result of the compounds 23 (a), 27 (b) and 18(c) into the binding site of 2W26. Hydrogen bonds are shown as yellow li nes, with distance unit of Å. The inhibitor and the important residues are shown as stick model.
As shown in Fig. 8b, some key amino acids (Gly219, Gln192, Lys96, Tyr99, Ser195, Gly216, Cys220, Trp215, Ile227, Gly226, Ala190 and Cys191) interacted with compound 27 at the binding site. The hydrogen bond distances observed were observed to be 2.23Å(Gly219=O…H-N), 2.12Å(Gly219N-H…O-). In addition, some amino acids (Gln192, Ser195, Gly216, Cys220, Trp215, Ser214 and Ile227) interacted with the compound by electrostatic interaction. As shown in Fig. 8c, some key amino acids (Gln192,
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Tyr99, Arg222, Gly219, Gly216, Glu217, Trp215, and Ser214) interacted with compound 18 at the binding site. The hydrogen bond distances were observed to be 2.02Å (Arg222-HN-H…O=), 2.62Å (Arg222-HN-H…O=), 1.94Å (Gln192--HN-H…O-), and 1.84Å (Tyr99-N-H … O-). However, significantly fewer amino acids electrostatically interacted with compound 18 compared with compounds 23 and 27. In addition, the key residue Gly219 was not observed in the binding pocket. To further confirm the reliability of docking results, MD simulations, were used to explore the probable binding modes between the ligand and the receptor protein. In this study, a 5 ns simulation of the ligand-receptor complex was run to obtain a stable conformation. The root mean square deviation (RMSD) of the receptors and the ligands is shown in Fig. 9. After 2 ns, the RMSD of the complex reaches about 4.3 Å and maintains similar value at last, which indicates that the docked complex can reach metastable conformation after 2 ns of simulation. The MD-simulated structure and the original docked structure is shown in. From Fig. 10, the green and red ribbon represents the original docked structure and the lowest energy structure, respectively. The compound 23 were docked into the same binding site of the original docked structure and the lowest energy structure. Besides, their structures are basically similar.
Fig. 9 The root-mean-square deviation (RMSD) of the docked complex versus the MD simulation structures.
Fig. 10 View of superimposed backbone atoms of the lowest energy structure of the MD simulation (red) and the initial structure (green) for the docked complex.
Fig. 11 Docking result of the MD-simulated structure. Hydrogen bonds are shown as yellow dashed lines, with distance unit of Å. Molecular surface is shown as cyan.
After 5 ns MD simulation studies, we found the residues are basically similar between the inhibitor and the protein. But, hydrogen bonds at the binding site increase. As shown in Fig. 11, there are four acid residues Gly219, Gln192, Tyr99 and Lys96 forming 4 hydrogen bonds with the compound 23. It can be seen that some residues in this binding are important H-bond donors, which is consistent with the CoMSIA model. Furthermore, the H-bonds between the ligand and Gly219 provide stability. The docking result and MD simulation both show that residue Gly219 in the protein is a key residue to confer the inhibitory activity.
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The analysis described above suggests that there are no significant differences between the original docked structure and the MDsimulated structure. Therefore, the docking results is reliable and valid. In this study, a series of computer-aided drug design processes, such as 3D-QSAR studies, Topomer CoMFA, molecular docking, and molecular dynamics were used to build models and explore the probable binding modes between the ligand and the receptor protein. The built models show good internal and external predictive ability and can be extrapolated to predict novel and more potent molecules. The contour maps obtained from the CoMFA, CoMSIA and Topomer CoMFA analysis could be used to guide designing new compounds with high FXa inhibitory activity. To study the binding modes of inhibitors at the active site of the protein, molecular docking studies of representative compounds were conducted. To validate the docking results, MD simulation is performed. Some key residues (Gly219, Gln192, Lys96, Tyr99, Ala190, Cys220, Glu217, Gly216, Trp215 and Glu97) and four hydrogen bonds (Gly219, Gln192, Tyr99 and Lys96) were detected in the binding site. These results show that the model is useful to predict new FXa inhibitors and can offer guidelines for further research. References and notes 1.
2.
3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.
17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32.
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Molecular modeling studies of [6,6,5] Tricyclic Fused Oxazolidinones as FXa inhibitors using 3D-QSAR, Topomer CoMFA, molecular docking simulations and molecular dynamics simulations
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Cheng Xua, Yujie Rena,*
Docking result of the MD-simulated structure. Hydrogen bonds are shown as yellow dashed lines, with distance unit of Å. Molecular surface is shown as cyan.
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