Chem Biol Drug Des 2014 Research Article
Selectivity Mechanism of ATP-Competitive Inhibitors for PKB and PKA Ke Wu1, Jingzhi Pang1, Dong Song1, Ying Zhu1, Congwen Wu1, Tianqu Shao1 and Haifeng Chen1,2,* 1
State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, College of Life Sciences and Biotechnology, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai 200240, China 2 Shanghai Center for Bioinformation Technology, 1275 Keyuan Road, Shanghai 200235, China *Corresponding author: Haifeng Chen, [email protected] Protein kinase B (PKB) acts as a central node on the PI3K kinase pathway. Constitutive activation and overexpression of PKB have been identified to involve in various cancers. However, protein kinase A (PKA) sharing high homology with PKB is essential for metabolic regulation. Therefore, specific targeting on PKB is crucial strategy in drug design and development for antitumor. Here, we had revealed the selectivity mechanism for PKB inhibitors with molecular dynamics simulation and 3D-QSAR methods. Selective inhibitors of PKB could form more hydrogen bonds and hydrophobic contacts with PKB than those with PKA. This could explain that selective inhibitor M128 is more potent to PKB than to PKA. Then, 3D-QSAR models were constructed for these selective inhibitors and evaluated by test set compounds. 3D-QSAR model comparison of PKB inhibitors and PKA inhibitors reveals possible methods to improve the selectivity of inhibitors. These models can be used to design new chemical entities and make quantitative prediction of the specific selective inhibitors before resorting to in vitro and in vivo experiment. Key words: 3D-QSAR, molecular dynamics simulation, protein kinase A, protein kinase B, selectivity Received 22 July 2014, revised 16 October 2014 and accepted for publication 29 October 2014
The serine/threonine kinase Akt, also known as protein kinase B (PKB), is a significant component of the PI3K kinase pathway (1). PKB acts as a central node on the pathway by phosphorylating a large number of downstream proteins, such as mammalian target of rapamycin (mTOR), Bcl-xL/Bcl-2-associated death factor (Bad), and ª 2014 John Wiley & Sons A/S. doi: 10.1111/cbdd.12472
glycogen synthase kinase 3 (GSK-3) to regulate cell growth, protein translation, apoptosis, and cell-cycle progression (2). Constitutive activation and overexpression of PKB activity have been identified to involve in various forms of cancer (3). Therefore, inhibitors of PKB could be powerful anticancer agents. PKB as a member of the AGC family of kinase shares high homology with protein kinase A (PKA) and protein kinase C (PKC). The previous works have reported that PKA plays a key role in metabolic regulation (4) and activates almost all metabolic regulation based on cAMP. Thus, specific targeting on PKB plays a key role to reduce the side-effects of drug. Therefore, the selectivity of inhibitor will be crucial for drug design. Several type inhibitors of PKB have been reported these years, including ATP-competitive inhibitors (5,6), activesite-directed inhibitors, and non-ATP-competitive allosteric compounds. Furthermore, some of them have already been tested in clinical trail for the treatment of cancers (7). To improve the efficiency and selectivity of drug discovery, molecular dynamics (MD) simulation and three-dimensional quantitative structure activity relationship (3D-QSAR) methods were used in this study. Here, we researched the binding mode between PKB and a series of ATP-competitive inhibitors in literature (7–9). To furthermore reveal the selectivity of inhibitors, comparative research was performed on PKA inhibitors. These results illustrate the different binding modes and the selectivity mechanism of PKA and PKB inhibitors.
Methods and Materials Data set The structure and bioactivity (IC50) of the molecules were extracted from the literature (7–9). The bioactivity IC50 (nM) was determined with enzymatic assay by the previous work and converted to the logarithmic scale pIC50 (nM). The structure and bioactivity are listed in Tables S4–S6.
Molecular docking The complexes of PKB and M326 (PDB code: 3OW4), PKA and M326 (PDB code: 3OW3) were downloaded from Brookhaven Protein Databank. Their binding pockets for 1
Wu et al. Table 1: Partial least square parameters of 3D-QSAR for PKB and PKA CoMSIA
PKB Q2 Component R2 SEE F S E H D A PKA Q2 Component R2 SEE F S E H D A
CoMFA SE
SE
SHE
0.711 6 0.961 0.214 186.612 0.782 0.214 – – –
0.664 5 0.901 0.342 68.118 0.639 0.361 – – –
0.679 6 0.936 0.274 110.505 0.183 0.397 0.419 – –
0.615 4 0.865 0.400 47.901 0.179 0.444 – 0.377 –
0.588 4 0.888 0.364 59.427 0.234 0.407 – – 0.359
0.620 5 0.846 0.426 41.292 0.143 0.293 – 0.339 0.226
0.647 6 0.930 0.288 99.183 0.130 0.242 0.375 – 0.253
0.635 6 0.913 0.321 78.325 0.119 0.309 0.326 0.247 –
0.597 6 0.899 0.345 66.905 0.095 0.204 0.287 0.214 0.201
0.663 4 0.959 0.175 144.875 0.849 0.151 – – –
0.582 5 0.878 0.307 34.637 0.424 0.576 – – –
0.610 5 0.953 0.192 96.406 0.154 0.288 0.558 – –
0.668 6 0.975 0.143 146.683 0.115 0.243 – 0.642 –
0.628 5 0.971 0.149 163.239 0.174 0.305 – – 0.521
0.620 6 0.979 0.131 177.721 0.093 0.200 – 0.436 0.271
0.636 5 0.978 0.130 213.581 0.108 0.210 0.349 – 0.333
0.637 5 0.974 0.142 178.613 0.077 0.175 0.297 0.451 –
0.641 6 0.985 0.109 255.004 0.068 0.149 0.237 0.342 0.205
SED
SEA
SEDA
SEHA
SEHD
SEHDA
S, steric field; E, electrostatic field; H, hydrophobic field; D, hydrogen bond donor field; A, hydrogen bond acceptor field.
PKB and PKA are shown in Figure 1. Tripos force field was used to optimize these inhibitors. All calculations were performed on the in-house Xeon (1.86 GHz) cluster. The routine module of ‘Glide SP’ for Schrodinger was used to dock inhibitors with PKB and PKA. Firstly, the receptor structures of PKB and PKA were extracted from the complexes (PDB code: 3OW4 and 3OW3). Then, the Protein Preparation Wizard was used to add hydrogen and optimize these hydrogens. Receptor grid was generated, and the center and size of the enclosing box were determined with the ligand M326. Secondly, the ligands were constructed in the SYBYL and added partial charges with Ga€ckel method. Thirdly, the ligands were imported steiger–Hu into Schrodinger and processed by LigPrep including ionization and stereoisomers. To identify suitable parameters of docking, co-crystal structure of M326 was docked with PKB. The RMSD between M326 and the original ligand
A
from the co-crystal structure of PKB (3OW4) is 0.334 A. This suggests that similar parameters can be used to dock these inhibitors with PKB or PKA. For each ligand, top 20 conformers were extracted as the data set for 3D-QSAR. The docking free energy between ligand and receptor is listed in supplementary Table S1.
Molecular modeling and alignment Three-dimensional structures of inhibitors in Tables S4–S6 were built and optimized with SYBYL (10). Tripos force field with a distance-dependent dielectric constant and the Powell conjugate gradient algorithm with a convergence criterion of 0.01 kcal/(mol A) were used to optimize the structure of inhibitors. M128 with the most selectivity was chosen as the template of alignment. Molecular alignment was applied with the SYBYL routine of ‘database align’.
B
Figure 1: Binding site of PKB and PKA. (A) Binding pocket of PKB and M326. (B) Binding pocket of PKA and M326.
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CoMFA models CoMFA method was proposed by Cramer et al. (11). After consistently aligning the molecules within a molecular lattice, which extended 4 A units beyond the aligned molecules in all directions, a probe sp3 carbon atom with +1 net charge was employed. The steric and electrostatic interactions between the probe atom and the atom of the molecule were calculated. The steric and electrostatic fields were then scaled by the CoMFA-STD method in SYBYL (10) with default cutoff of 30 kcal/mol. Coulomb potential was used to model the electrostatic interactions and Lennard–Jones potential for van der Waals interactions. Partial least squares (PLS) (12) method was performed for regression analysis. Optimum number of components that yields the highest qcv was used to construct the final model. Total data set of molecules was randomly divided into training and test sets in the approximate ratio 3:1 (for example, 50 in training set to 17 in the test set).
CoMSIA models CoMSIA was derived with the method proposed by Klebe et al. (13) and Klebe and co-workers (14). Same lattice box was used with CoMFA model. Five physicochemical properties related to steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor fields were evaluated using the probe atom. Gaussian potential function was employed to calculate these interactions. The potential function in CoMSIA led to much smoother sampling of the fields around the molecules than CoMFA. A default value of 0.3 was used as attenuation factor.
Molecular dynamics simulations AMBER 8.0 simulation package (15) and the ff99SB force field (16) with the TIP3P water model (17) were used to
perform MD simulations and energy minimizations. The initial co-ordinates of PKB and PKA were extracted from PDB database (PDB code: 3OW4 and 3OW3) (18). The complexes for the most selective molecule M128 and other representative molecules with PKB or PKA were extracted from molecular docking. SWISS-MODEL was used to model the missing structure from Pro451 to Ser457 of PKB. The hydrogen atoms of complex were added using the LEAP module of AMBER 8.0. Antechamber was used to handle the force field of ligands. To maintain the neutrality of complex, Na+ or Cl ions were placed around the complex. The SHAKE algorithm (18) was used to constrain bonds involving hydrogen atoms. The complex was solvated in a octahedron box of TIP3P water model, and the shortest distance between the edge of the box and any complex atom is at least 10 A. Particle mesh Ewald (PME) (19) was used to calculate long-range electrostatic interactions. Then, the complex was minimized by the PMEMD module of AMBER 8.0, which included 1000 steps with the steepest descent method. After the minimization, the complex was equilibrated at 298K for 20 ps. After the equilibration, molecular dynamics simulation was employed to record the trajectory. Each complex was simulated for 5 ns at 298K and the time step was 2 fs. A total of 65 ns trajectories were collected for these solvated systems.
Data analysis The hydrophobic, electrostatic, and hydrogen bond interactions between ligand and protein during the simulation were handled with in-house software (20–26). These residues and ligands are in hydrophobic contact when the distance between the center mass of side chain for hydrophobic residue and the ligand is closer than 6.5 A for the complex.
A
B
Figure 2: RMSD variation for PKA and PKB. (A) PKA. (B) PKB.
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B
C
D
Figure 3: Intramolecular interaction between PKB and M128. (A) Electrostatic interaction; (B) Hydrophobic interaction; (C) Hydrogen bond interaction. (D) Interaction plot between PKB and M128.
Results Binding mode of inhibitors with PKB Thirteen complexes of PKB-M103, PKB-M126, PKBM127, PKB-M128, PKB-M129, PKB-M135, PKB-M137, PKB-M144, PKB-M335, PKA-M128, PKA-M129, PKA135, and PKA-M144 were simulated for 5.0 ns each, respectively. The root-mean-squared deviation (RMSD) relative to the initial structure was calculated and is shown in Figure 2. The results show that 5-ns simulations are sufficient for the equilibration of all systems at 298K. To reveal the binding mode, the hydrophobic interactions, electrostatic interactions, and hydrogen bonding interactions were calculated. The population of intramolecular interaction for complex PKB-M128 (the most selective one) is illustrated in Figure 3. In summary, there are 8 hydrophobic interactions, 10 electrostatic interactions, and 3 hydrogen bonds between PKB and M128, with population higher than 50%. To explain the different bioactivities of M128 and M129, the distance and angle of hydrogen bond for OH(M128)/O (Glu228) and OH(M129)/O(Glu228) are shown in Figure S11. This figure shows that the distance of hydrogen bond for M128 is significant shorter than that for M129 and the angle of hydrogen bond for M128 is significant larger than that for M129. This indicates that the hydrogen bond of M128/Glu228 is stronger than that of M129/
A
Glu228. At the same time, the distances between hydrophilic Thr211 and hydroxyl group for M128 and M129 are also shown in Figure S11. The distance of M128/Thr211 is shorter than that of M129/Thr211. This indicates that the hydrophilic interaction for M128 is also stronger than that for M129. Therefore, the different chirality of hydroxyl group for M128 and M129 forms different interactions with PKB. Table S7 illustrates the interactions for nine inhibitors with PKB. This indicates that the interactions between M128 and PKB are stronger than those between other inhibitors and PKB. It is consistent with the experimental observation that the bioactivity of M128 is the most active among these inhibitors. The hydrogen bond for M335 is the less among these inhibitors. Figure 4 illustrates that the substituent of –CH2NHCH(CH3)2 in M335 is within the binding pocket of PKB, which is different from other inhibitors. The distance between amine of M335 and Glu234 is about 5.1 A, and the hydrogen bond between Glu234 and M335 disappears. In general, there is a hydrophobic interaction between Val64 and R3 group of M128. To insight into the effect of Val164 on the bioactivity of different inhibitors, the distance between Val164 and group R3 of M103 and M126 was calculated and is shown in Figure S12. This figure shows that the distance for M103 is larger than that for M126. This indicates that the hydrophobic interaction between
B
Figure 4: Alignment of M128 (in green) and M335 (in magenta) within the binding site of PKB. (A) Binding site of PKB for M128 and M335. (B) The hydrogen bond between Glu234 and M128 marked in yellow dash line.
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Val164 and M103 is weaker than that for M126/Val164. This is consistent with the bioactivity experiment that the IC50 of M126 is smaller than that of M103. Similarly, the R3 group (–CH3OH) of M127 is hydrophilic substituent, which can explain M127 with the lowest bioactivity among M103 and M126.
Selectivity mechanism of inhibitors The interactions between PKA and M128 are illustrated in supplementary Figure S1. This figure shows that hydrophobic interactions and electrostatic interactions of PKAM128 are similar to those of PKB-M128. However, there is only one hydrogen bond between Glu107 and O32 of M128 in PKA-M128. According to sequence and structure alignment, residue Glu121 in PKA is corresponding to residue Glu228 in PKB. This suggests that two hydrogen bonds of Glu234/M128 and Ala230/M128 in PKB are destroyed in PKA. To further reveal the effect of –OH(M128) on its selectivity, the alignment of four average structures for M128/M129/ PKB and M128/M129/PKA is shown in Figure S13 This figure suggests that Thr211 in PKB is corresponding to Val104 in PKA. This indicates that the –OH of M128 locates at hydrophobic environment in PKA instead of hydrophilic environment in PKB. This can explain that the activity of M128 with PKB is much better than that with PKA. Figure S13 illustrates the distance between O32 and Val104 for M128-PKA and M129-PKA. It suggests that – OH of M128 is closer to hydrophobic residue Val104 than that of M129, which can explain the activity of M129 better than that of M128 with PKA. Furthermore, the activity of M128 is better than that of M129 with PKB. Therefore, the
selectivity of M128 for PKB (pIC50 for PKB/pIC50 for PKA) is better than that of M129. The bioactivity experiment indicates that M135, M136, M137, and M144 have similar activities for PKB and different bioactivities for PKA. To reveal the selectivity of these inhibitors, the conformers of the R1 substituent for these inhibitors were compared. Figure S14 illustrates the superposition of M135 within PKB and PKA pockets. This figure indicates that the amine of M135 is turned over and near the hydrophobic residue Phe327 in PKA. However, the amine of M135 is straight in PKB and far away from the residue Phe438 (Phe438 of PKB is corresponding to Phe327 of PKA). The distances between C33 of M135 and Phe438 of PKB, between C33 of M135 and Phe327 of PKA are also shown in Figure S14. The distance between C33 of M135 and Phe438 of PKB is larger than that between C33 of M135 and Phe327 of PKA. This can explain that the selectivity of M135 is worse than that of M128. The charge of amine is another factor to influence the selectivity. The substituent of amine for M135 is near polar residues of Glu127 and Asp328 in PKA because of its overturn, while the substituent (-CH2NHCH2CH2F) of M136 is far away from these residues. This can explain that the PKA activity of M136 is lower than that of M135. The substituent of amine is –F in M136 and has repelling interaction with negative residues of Glu127 and Asp328. The substituent of amine is –CF3 for M137, which is strong electronegativity group. The repelling interactions between M137 and negative charge residues are stronger than those for M136. Therefore, the PKA inhibit activity of M137 is lower than that of M136.
A
B
C D
Figure 5: Superposition of M128 and M129 within the binding pockets of PKB and PKA. (A) Binding pocket of PKB. (B) Surface of binding pocket for PKB of M128 and M129. Interaction residues are shown in stick. (C) Binding pocket of PKA. (D) Surface of binding pocket for PKA of M128 and M129. Interaction residues are shown in stick.
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10-fold cross-validated q2 of CoMFA model for PKB inhibitors is 0.711 and 0.663 for PKA inhibitors. And the non-cross-validated parameter r2 of the model for PKB inhibitors is 0.961 and 0.959 for PKA inhibitors. The correlation between predicted activities (PA) and experimental activities (EA) of training and test sets with CoMFA model is shown in supplementary Figure S2. The correlation coefficient r2 between EA and PA of test set for PKB inhibitors’ CoMFA model is 0.967 and 0.917 for PKA inhibitors’ CoMFA model. In CoMSIA model, the 10-fold cross-validated q2 for PKB inhibitors is 0.679 and 0.641 for PKA inhibitors. The correlation between PA and EA of training and test sets for CoMSIA model is also shown in supplementary Figure S2. The correlation coefficient r2 between EA and PA of test set for PKB inhibitors’ CoMSIA-SEH model is 0.942 and 0.897 for PKA inhibitors’ CoMSIA-SEHDA model. The high value of correlation coefficient0 r2 suggests that the CoMFA and CoMSIA models for PKB inhibitors and PKA inhibitors are robust and have good prediction ability.
Figure 5A illustrates the superposition of M128 and M129 within PKB-binding pocket. This figure shows three common hydrogen bonds with Phe438 and Ala230 and ten common hydrophobic interactions with residues Met281, Ala230, Met281, Phe236, Phe438, Leu156, Ala177 for M128 and M129 for M128 and M129. Figure 5C illustrates the superposition of M128 and M129 within PKA-binding pocket. There are also one common hydrogen bond and some hydrophobic interactions with similar residues for M128 and M129. In summary, M128 and M129 have similar binding modes to PKB or PKA, respectively. Therefore, we could construct common 3D-QSAR models for these selective PKB and PKA inhibitors.
3D-QSAR model Two methods, CoMFA and CoMSIA, were used to construct 3D-QSAR models for PKB inhibitors and PKA inhibitors. The parameters of these models are given in Table 1. The experimental activity and predicted activity are shown in Tables S2 and S3.
Analysis of CoMFA and CoMSIA models for PKB inhibitors The contribution of steric and electrostatic fields of CoMFA model was 0.782 and 0.214, respectively. The contribution of steric, electrostatic, and hydrophobic fields of CoMSIA model was 0.183, 0.397, and 0.419, respectively. Hydrophobic field has a predominant contribution for CoMSIA model. The contour maps of CoMFA and CoMSIA models for PKB inhibitors with structure M128 are shown in Figure 6. The steric and electrostatic fields of CoMSIA model are similar to those of CoMFA model, which could be analyzed jointly. Green-colored regions near the R3 group suggest that bulky groups could be favorable to the bioactivity (shown in supplementary Figure S3). This could explain that the activities have the sequence: M126 (CH=CH2) > M103 (-CH3), M319 (-CH3) > M317 (-H). Bluecolored regions near the isopropyl of R1 group show that positive groups are favorable to activity (shown in supple-
Combination of CoMSIA fields CoMSIA model includes 5 fields, such as steric field, electrostatic field, hydrophobic field, hydrogen bond donor field, and hydrogen bond acceptor field. The combination of fields will influence the performance of model. The optimal model was built by systemical combination of these fields. The best model includes the best non-cross-validation coefficient, the smallest standard error, and the largest F value. According to this rule, the CoMSIA-SEH was chosen as the best model for PKB and CoMSIA-SEHDA for PKA. The contours will be analyzed using these two models.
Evaluation of CoMFA and CoMSIA models Table 1 lists the statistical parameters of CoMFA and CoMSIA models for PKB inhibitors and PKA inhibitors. The
A
B
Figure 6: Contour plot with M128 of PKB inhibitor. (A) CoMFA. (B) CoMSIA-SEH. Yellow contours indicate regions where bulky groups are unfavorable for activity, and green contours indicate regions where bulky groups are favorable for activity. Red contours indicate regions where groups with negative charge could increase activity, whereas blue contours indicate regions where groups with positive charge could increase activity. Orange contours indicate regions where hydrophobic groups are favorable for activity, and gray contours indicate regions where hydrophilic groups are favorable for activity.
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mentary Figure S4). This could explain that the activity of M134 (-CH2NH2) is higher than that of M137 (CH2NHCH2CF3). The orange-colored regions near the R3 group suggest that hydrophobic groups are favorable to activity (shown in supplementary Figure S5). This is consistent with the experiment activity of M103 (-CH3) > M127 (CH2OH). In summary, the CoMFA and CoMSIA models for PKB inhibitors could explain the different activity of training and test sets.
Analysis of CoMFA and CoMSIA models for PKA inhibitors The contribution of steric and electrostatic fields of CoMFA model was 0.849 and 0.151, respectively. The contribution of steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor fields of CoMSIA model was 0.068, 0.149, 0.237, 0.342, and 0.205, respectively. Hydrophobic field has also a predominant contribution to CoMSIA model. The contour maps of CoMFA and CoMSIA models for PKA inhibitors with structure M128 are shown in supplementary Figure S6. Green-colored regions near the R2 group of benzene ring suggest that bulky groups could be favorable to the inhibit activity. It could explain the experiment bioactivity of M149 > M134, M152 > M139, M153 > M135, M154 > M144. The red–colored regions near the benzene ring indicate that negative groups are favorable to activity (shown in supplementary Figure S7). This could explain the activity of M134 (-Ph-Cl) is lower than that of M149 (-Ph-CF3). The orange-colored regions near the R4 group suggest that hydrophobic groups are favorable to activity (shown in supplementary Figure S8). This is consistent with the experiment activity of M128 (CH2OH) > M103 (-CH3). The cyan-colored contours near the isopropyl indicate that hydrogen bond donor groups are favorable to activity (shown in supplementary Figure S9). This is consistent with the activity of M139 (-C (CH2)2CH2OH) > M128 (-CH(CH2)2). The stone-colored contours near the five-membered ring indicate that hydrogen bond acceptor groups are not favorable to activity (shown in supplementary Figure S10). This could explain that activity of M103 is higher than that of M128. In summary, the CoMFA and CoMSIA models for PKA inhibitors could explain the different activity of training and test sets and be used to design the selective inhibitors.
Discussion Comparison of 3D-QSAR models for PKB and PKA Figure S15 illustrates the comparison between CoMISA models for PKB inhibitors and PKA inhibitors. For PKB inhibitors, green-colored contours (marked in G) are near benzene, while there are no contour plots in PKA model. This suggests that bulky substituent of benzene is favorable to the PKB inhibit activity, no guide information for the PKA inhibit activity. Gray-colored regions (marked in W) near –OH (R4 group) of model for PKB inhibitors, and Chem Biol Drug Des 2014
Figure 7: Detail guide for how to improve the selectivity of inhibitor.
orange-colored regions (marked in O) near –OH of model for PKA inhibitors suggest that hydrophobic groups are favorable to the bioactivity of PKA inhibitor and unfavorable to that of PKB inhibitor. This is agreement with the experimental observation that the selectivity of M128 is stronger than that of M103. The detail guide for how to improve the selectivity of inhibitor is shown Figure 7.
Comparison of 3D-QSAR and molecular dynamics simulation results MD simulation can illustrate the binding mode between inhibitors and PKB (PKA), while 3D-QSAR mainly focuses the effect of substituent on the bioactivity of inhibitors and predicts the activity of new inhibitors. Figure S16 illustrates the alignment between 3D-QSAR model of M128 and PKB. This figure shows that hydrophobic amino acids of Va164, Met227, Ala177, Leu156, and Leu181 cover the R3 group and the benzene ring. The contour plot of hydrophobic favorable orange-colored region is also near the R3 group and the benzene ring. This suggests that the results of MD simulation are consistent with those of 3D-QSAR. The contour plot of CoMSIA model for PKB inhibitors illustrates that blue-colored contours are near the R4 group. This suggests that positive charge groups are favorable to the PKB inhibit activity. MD simulation shows that the negative charge residue of Glu228 can form electrostatic interaction with the R4 group of inhibitors. That is, MD results are consistent with those of 3D-QSAR. Similarly, red-colored contours of CoMSIA model for PKB inhibitors near R3 group show that negative charge substituent of R3 is favorable to the bioactivity. MD simulation indicates that the positive charge residue of Lys179 can form electrostatic interaction with negative charge substituent of R3. CoMSIA hydrogen bond acceptor field indicates that 7
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hydroxyl and amine of M128 are covered in hydrogen bond favorable regions. At the same time, Glu228 and Glu234 form two hydrogen bonds with hydroxyl and amine substituent of M128. In summary, the results of 3D-QSAR are consistent with those of MD simulation.
Conflict of Interest There is not conflict of interest.
References Comparison with previous works The X-ray structure shows that M128 forms one hydrogen bond with Ala230. (7) This hydrogen bond was also found in MD simulations at room temperature. Furthermore, experimental data indicate that the replacement of Thr211 (PKB) to Val (PKA), Met281 (PKB) to Leu(PKA), and Ala230 (PKB) to Val(PKA) leads to a narrower and less polar cavity in PKA (7.) The volume of binding pocket for average structure is calculated with CASTp (27) and 664.5 A3 for 3 PKB and 278.5 A for PKA, respectively. The volume of binding pocket for PKB is more than twice than that of PKA and consistent with the experimental observation. MD simulation also found that the –OH of M128 was nearer hydrophilic Thr211 of PKB than hydrophobic Val104 of PKA. Polar cavity is favorable to the binding of with PKB. These findings might be helpful to improve the selectivity of inhibitor for PKB.
Conclusion CoMFA and CoMSIA methods were used to build 3DQSAR models on PKB inhibitors and PKA inhibitors. Correlation coefficient r2 of test set was 0.980, 0.967 for PKB inhibitors and 0.906, 0.918 for PKA inhibitors. The result shows that these models process good prediction ability. The comparison of 3D-QSAR between PKB inhibitors and PKA inhibitors illustrates the way to improve the selectivity of inhibitors. At the same time, molecular dynamics simulation was used to research the binding mode between inhibitor and PKB, PKA. The result suggests that the most selective compound (M128) has 8 hydrophobic interactions, 10 electrostatic interactions, and 1 hydrogen bond with PKA. Comparison with PKA, two more hydrogen bonds were found between M128 and PKB. These hydrogen bonds can change the binding mode of M128 with PKB and improve the selectivity.
Acknowledgments This work was supported by Center for HPC at Shanghai Jiao Tong University, by grants from the Ministry of Science and Technology of China (2012CB721003), by the National High-tech R&D Program of China (863 Program) (2014AA021502), the National Natural Science Foundation of China (J1210047 and 31271403), by the Innovation Program of the Shanghai Education Committee (Grants No. 12ZZ023), and by Medical Engineering Cross Fund of Shanghai Jiaotong University (YG2013MS68).
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13. Klebe G., Abraham U., Mietzner T. (1994) Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem;37:4130–4146. €hm M., Stu €rzebecher J., Klebe G. (1999) Three14. Bo dimensional quantitative structure-activity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa. J Med Chem;42:458– 477. 15. Weiner S.J., Kollman P.A., Nguyen D.T., Case D.A. (1986) An all atom force field for simulations of proteins and nucleic acids. J Comput Chem;7:230–252. 16. Hornak V., Abel R., Okur A., Strockbine B., Roitberg A., Simmerling C. (2006) Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins;65:712–725. 17. Jorgensen W.L., Chandrasekhar J., Madura J.D., Impey R.W., Klein M.L. (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys;79:926–935. 18. Ryckaert J.-P., Ciccotti G., Berendsen H.J. (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of i n/i-alkanes. J Comput Phys;23:327–341. 19. Darden T., York D., Pedersen L. (1993) Particle mesh Ewald: an N log (N) method for Ewald sums in large systems. J Chem Phys;98:10089–10092. 20. Chen H.-F. (2008) Mechanism of coupled folding and binding in the siRNA-PAZ complex. J Chem Theory Comput;4:1360–1368. 21. Chen H.-F. (2009) Aggregation mechanism investigation of the GIFQINS cross-b amyloid fibril. Comput Biol Chem;33:41–45. 22. Chen H.-F. (2009) Molecular dynamics simulation of phosphorylated KID post-translational modification. PLoS ONE;4:e6516. 23. Chen H.-F., Luo R. (2007) Binding induced folding in p53-MDM2 complex. J Am Chem Soc;129:2930–2937. 24. Qin F., Chen Y., Li Y.-X., Chen H.-F. (2009) Induced fit for mRNA/TIS11d complex. J Chem Phys;131:115103. 25. Qin F., Chen Y., Wu M., Li Y., Zhang J., Chen H.-F. (2010) Induced fit or conformational selection for RNA/ U1A folding. RNA;16:1053–1061. 26. Wang W., Ye W., Jiang C., Luo R., Chen H.-F. (2014) New force field on modeling intrinsically disordered proteins. Chem Biol Drug Des;84:253–269. 27. Binkowski T.A., Naghibzadeh S., Liang J. (2003) CASTp: computed atlas of surface topography of proteins. Nucleic Acids Res;31:3352–3355.
Figure S1. Intramolecular interaction between PKA and M128. Figure S2. Correlations between experimental and predictive activity for PKB and PKA. Figure S3. Steric field of CoMFA model for PKB-inhibitors. Figure S4. Electrostatic field of CoMFA model for PKBinhibitors. Figure S5. Hydrophobic field of CoMSIA model for PKBinhibitors. Figure S6. Contour plot with M128 of PKA inhibitor. Figure S7. Electrostatic field of CoMFA model for PKAinhibitors. Figure S8. Hydrophobic field of CoMSIA model for PKAinhibitors. Figure S9. Hydrogen bond donor field of CoMSIA model for PKA-inhibitors. Figure S10. Hydrogen bond acceptor field of CoMSIA model for PKA-inhibitors Figure S11. The binding site of PKB for M128 and M129. (A) Binding site of PKB for M128 and M129. (B) The binding surface for M128 and M129. (C) The angle of hydrogen bond between Glu228 and M128, Glu228 and M129. (D) The distance of hydrogen bond between Glu228 and M128, Glu228 and M129. (E) The distance between Thr211 and O25 of M128 and O32 of M129. Figure S12. Binding site of PKB for M126 and M103. (A) Binding site of PKB. (B) Hydrophobic contact for Val164/ M103 and Val164/M126 marked in yellow dash line. (C) The distance for Val164/M103(C25) and Val164/C32(M126) Figure S13. Alignment between PKB and PKA with M128 and M129. (A) Alignment between PKB (in blue) and PKA (in orange) with M128 (in green) and M129 (in magenta). (B) Surface of binding sites for PKB and PKA. Thr211 of PKB and Val104 of PKA are shown as stick. (C) The distance between Val104 of PKA and O32 for M128 and M129.
Supporting Information
Figure S14. Alignment of PKB and PKA for M135. (A) Alignment of M135 with PKB (in magenta) and M135 with PKA (in green). (B) Surface of binding sites for PKB and PKA. The Phe438 of PKB and Phe327 of PKA are shown in stick. (C) The distance between C33 of M135 and Phe438 of PKB, Phe327 of PKA.
Additional Supporting Information may be found in the online version of this article:
Figure S15. Comparison of CoMSIA field with M128 and different regions marked in different characters. G repre-
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sents green, Y for yellow, W for white, and O for orange. (A) Steric field for PKB. (B) Steric field for PKA. (C) Hydrophobic field for PKB. (D) Hydrophobic field for PKA. Figure S16. Superposition of 3D-QSAR and complex for M128 and PKB. M128 is marked in green. (A) Hydrophobic field of CoMSIA and key residues of PKB. (B) Electrostatic field of CoMSIA and key residues of PKB. (C) Hydrogen bond acceptor field of CoMSIA and key residues of PKB.. Table S1. Binding free energy between ligand and receptor.
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Table S2. Experimental activity and predicted activity for PKB. Table S3. Experimental activity and predicted activity for PKA. Table S4–S6. Structure and activity. Table S7. Summary of interaction between inhibitor and PKB.
Chem Biol Drug Des 2014
Selectivity Mechanism of ATP-Competitive Inhibitors for PKB and PKA Ke Wu1, Jingzhi Pang1, Dong Song1, Ying Zhu1, Congwen Wu1, Tianqu Shao1 and Haifeng Chen1,2,* 1
State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, College of Life Sciences and Biotechnology, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai 200240, China 2 Shanghai Center for Bioinformation Technology, 1275 Keyuan Road, Shanghai 200235, China *Corresponding author: Haifeng Chen, [email protected] Protein kinase B (PKB) acts as a central node on the PI3K kinase pathway. Constitutive activation and overexpression of PKB have been identified to involve in various cancers. However, protein kinase A (PKA) sharing high homology with PKB is essential for metabolic regulation. Therefore, specific targeting on PKB is crucial strategy in drug design and development for antitumor. Here, we had revealed the selectivity mechanism for PKB inhibitors with molecular dynamics simulation and 3D-QSAR methods. Selective inhibitors of PKB could form more hydrogen bonds and hydrophobic contacts with PKB than those with PKA. This could explain that selective inhibitor M128 is more potent to PKB than to PKA. Then, 3D-QSAR models were constructed for these selective inhibitors and evaluated by test set compounds. 3D-QSAR model comparison of PKB inhibitors and PKA inhibitors reveals possible methods to improve the selectivity of inhibitors. These models can be used to design new chemical entities and make quantitative prediction of the specific selective inhibitors before resorting to in vitro and in vivo experiment. Key words: 3D-QSAR, molecular dynamics simulation, protein kinase A, protein kinase B, selectivity Received 22 July 2014, revised 16 October 2014 and accepted for publication 29 October 2014
The serine/threonine kinase Akt, also known as protein kinase B (PKB), is a significant component of the PI3K kinase pathway (1). PKB acts as a central node on the pathway by phosphorylating a large number of downstream proteins, such as mammalian target of rapamycin (mTOR), Bcl-xL/Bcl-2-associated death factor (Bad), and ª 2014 John Wiley & Sons A/S. doi: 10.1111/cbdd.12472
glycogen synthase kinase 3 (GSK-3) to regulate cell growth, protein translation, apoptosis, and cell-cycle progression (2). Constitutive activation and overexpression of PKB activity have been identified to involve in various forms of cancer (3). Therefore, inhibitors of PKB could be powerful anticancer agents. PKB as a member of the AGC family of kinase shares high homology with protein kinase A (PKA) and protein kinase C (PKC). The previous works have reported that PKA plays a key role in metabolic regulation (4) and activates almost all metabolic regulation based on cAMP. Thus, specific targeting on PKB plays a key role to reduce the side-effects of drug. Therefore, the selectivity of inhibitor will be crucial for drug design. Several type inhibitors of PKB have been reported these years, including ATP-competitive inhibitors (5,6), activesite-directed inhibitors, and non-ATP-competitive allosteric compounds. Furthermore, some of them have already been tested in clinical trail for the treatment of cancers (7). To improve the efficiency and selectivity of drug discovery, molecular dynamics (MD) simulation and three-dimensional quantitative structure activity relationship (3D-QSAR) methods were used in this study. Here, we researched the binding mode between PKB and a series of ATP-competitive inhibitors in literature (7–9). To furthermore reveal the selectivity of inhibitors, comparative research was performed on PKA inhibitors. These results illustrate the different binding modes and the selectivity mechanism of PKA and PKB inhibitors.
Methods and Materials Data set The structure and bioactivity (IC50) of the molecules were extracted from the literature (7–9). The bioactivity IC50 (nM) was determined with enzymatic assay by the previous work and converted to the logarithmic scale pIC50 (nM). The structure and bioactivity are listed in Tables S4–S6.
Molecular docking The complexes of PKB and M326 (PDB code: 3OW4), PKA and M326 (PDB code: 3OW3) were downloaded from Brookhaven Protein Databank. Their binding pockets for 1
Wu et al. Table 1: Partial least square parameters of 3D-QSAR for PKB and PKA CoMSIA
PKB Q2 Component R2 SEE F S E H D A PKA Q2 Component R2 SEE F S E H D A
CoMFA SE
SE
SHE
0.711 6 0.961 0.214 186.612 0.782 0.214 – – –
0.664 5 0.901 0.342 68.118 0.639 0.361 – – –
0.679 6 0.936 0.274 110.505 0.183 0.397 0.419 – –
0.615 4 0.865 0.400 47.901 0.179 0.444 – 0.377 –
0.588 4 0.888 0.364 59.427 0.234 0.407 – – 0.359
0.620 5 0.846 0.426 41.292 0.143 0.293 – 0.339 0.226
0.647 6 0.930 0.288 99.183 0.130 0.242 0.375 – 0.253
0.635 6 0.913 0.321 78.325 0.119 0.309 0.326 0.247 –
0.597 6 0.899 0.345 66.905 0.095 0.204 0.287 0.214 0.201
0.663 4 0.959 0.175 144.875 0.849 0.151 – – –
0.582 5 0.878 0.307 34.637 0.424 0.576 – – –
0.610 5 0.953 0.192 96.406 0.154 0.288 0.558 – –
0.668 6 0.975 0.143 146.683 0.115 0.243 – 0.642 –
0.628 5 0.971 0.149 163.239 0.174 0.305 – – 0.521
0.620 6 0.979 0.131 177.721 0.093 0.200 – 0.436 0.271
0.636 5 0.978 0.130 213.581 0.108 0.210 0.349 – 0.333
0.637 5 0.974 0.142 178.613 0.077 0.175 0.297 0.451 –
0.641 6 0.985 0.109 255.004 0.068 0.149 0.237 0.342 0.205
SED
SEA
SEDA
SEHA
SEHD
SEHDA
S, steric field; E, electrostatic field; H, hydrophobic field; D, hydrogen bond donor field; A, hydrogen bond acceptor field.
PKB and PKA are shown in Figure 1. Tripos force field was used to optimize these inhibitors. All calculations were performed on the in-house Xeon (1.86 GHz) cluster. The routine module of ‘Glide SP’ for Schrodinger was used to dock inhibitors with PKB and PKA. Firstly, the receptor structures of PKB and PKA were extracted from the complexes (PDB code: 3OW4 and 3OW3). Then, the Protein Preparation Wizard was used to add hydrogen and optimize these hydrogens. Receptor grid was generated, and the center and size of the enclosing box were determined with the ligand M326. Secondly, the ligands were constructed in the SYBYL and added partial charges with Ga€ckel method. Thirdly, the ligands were imported steiger–Hu into Schrodinger and processed by LigPrep including ionization and stereoisomers. To identify suitable parameters of docking, co-crystal structure of M326 was docked with PKB. The RMSD between M326 and the original ligand
A
from the co-crystal structure of PKB (3OW4) is 0.334 A. This suggests that similar parameters can be used to dock these inhibitors with PKB or PKA. For each ligand, top 20 conformers were extracted as the data set for 3D-QSAR. The docking free energy between ligand and receptor is listed in supplementary Table S1.
Molecular modeling and alignment Three-dimensional structures of inhibitors in Tables S4–S6 were built and optimized with SYBYL (10). Tripos force field with a distance-dependent dielectric constant and the Powell conjugate gradient algorithm with a convergence criterion of 0.01 kcal/(mol A) were used to optimize the structure of inhibitors. M128 with the most selectivity was chosen as the template of alignment. Molecular alignment was applied with the SYBYL routine of ‘database align’.
B
Figure 1: Binding site of PKB and PKA. (A) Binding pocket of PKB and M326. (B) Binding pocket of PKA and M326.
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CoMFA models CoMFA method was proposed by Cramer et al. (11). After consistently aligning the molecules within a molecular lattice, which extended 4 A units beyond the aligned molecules in all directions, a probe sp3 carbon atom with +1 net charge was employed. The steric and electrostatic interactions between the probe atom and the atom of the molecule were calculated. The steric and electrostatic fields were then scaled by the CoMFA-STD method in SYBYL (10) with default cutoff of 30 kcal/mol. Coulomb potential was used to model the electrostatic interactions and Lennard–Jones potential for van der Waals interactions. Partial least squares (PLS) (12) method was performed for regression analysis. Optimum number of components that yields the highest qcv was used to construct the final model. Total data set of molecules was randomly divided into training and test sets in the approximate ratio 3:1 (for example, 50 in training set to 17 in the test set).
CoMSIA models CoMSIA was derived with the method proposed by Klebe et al. (13) and Klebe and co-workers (14). Same lattice box was used with CoMFA model. Five physicochemical properties related to steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor fields were evaluated using the probe atom. Gaussian potential function was employed to calculate these interactions. The potential function in CoMSIA led to much smoother sampling of the fields around the molecules than CoMFA. A default value of 0.3 was used as attenuation factor.
Molecular dynamics simulations AMBER 8.0 simulation package (15) and the ff99SB force field (16) with the TIP3P water model (17) were used to
perform MD simulations and energy minimizations. The initial co-ordinates of PKB and PKA were extracted from PDB database (PDB code: 3OW4 and 3OW3) (18). The complexes for the most selective molecule M128 and other representative molecules with PKB or PKA were extracted from molecular docking. SWISS-MODEL was used to model the missing structure from Pro451 to Ser457 of PKB. The hydrogen atoms of complex were added using the LEAP module of AMBER 8.0. Antechamber was used to handle the force field of ligands. To maintain the neutrality of complex, Na+ or Cl ions were placed around the complex. The SHAKE algorithm (18) was used to constrain bonds involving hydrogen atoms. The complex was solvated in a octahedron box of TIP3P water model, and the shortest distance between the edge of the box and any complex atom is at least 10 A. Particle mesh Ewald (PME) (19) was used to calculate long-range electrostatic interactions. Then, the complex was minimized by the PMEMD module of AMBER 8.0, which included 1000 steps with the steepest descent method. After the minimization, the complex was equilibrated at 298K for 20 ps. After the equilibration, molecular dynamics simulation was employed to record the trajectory. Each complex was simulated for 5 ns at 298K and the time step was 2 fs. A total of 65 ns trajectories were collected for these solvated systems.
Data analysis The hydrophobic, electrostatic, and hydrogen bond interactions between ligand and protein during the simulation were handled with in-house software (20–26). These residues and ligands are in hydrophobic contact when the distance between the center mass of side chain for hydrophobic residue and the ligand is closer than 6.5 A for the complex.
A
B
Figure 2: RMSD variation for PKA and PKB. (A) PKA. (B) PKB.
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B
C
D
Figure 3: Intramolecular interaction between PKB and M128. (A) Electrostatic interaction; (B) Hydrophobic interaction; (C) Hydrogen bond interaction. (D) Interaction plot between PKB and M128.
Results Binding mode of inhibitors with PKB Thirteen complexes of PKB-M103, PKB-M126, PKBM127, PKB-M128, PKB-M129, PKB-M135, PKB-M137, PKB-M144, PKB-M335, PKA-M128, PKA-M129, PKA135, and PKA-M144 were simulated for 5.0 ns each, respectively. The root-mean-squared deviation (RMSD) relative to the initial structure was calculated and is shown in Figure 2. The results show that 5-ns simulations are sufficient for the equilibration of all systems at 298K. To reveal the binding mode, the hydrophobic interactions, electrostatic interactions, and hydrogen bonding interactions were calculated. The population of intramolecular interaction for complex PKB-M128 (the most selective one) is illustrated in Figure 3. In summary, there are 8 hydrophobic interactions, 10 electrostatic interactions, and 3 hydrogen bonds between PKB and M128, with population higher than 50%. To explain the different bioactivities of M128 and M129, the distance and angle of hydrogen bond for OH(M128)/O (Glu228) and OH(M129)/O(Glu228) are shown in Figure S11. This figure shows that the distance of hydrogen bond for M128 is significant shorter than that for M129 and the angle of hydrogen bond for M128 is significant larger than that for M129. This indicates that the hydrogen bond of M128/Glu228 is stronger than that of M129/
A
Glu228. At the same time, the distances between hydrophilic Thr211 and hydroxyl group for M128 and M129 are also shown in Figure S11. The distance of M128/Thr211 is shorter than that of M129/Thr211. This indicates that the hydrophilic interaction for M128 is also stronger than that for M129. Therefore, the different chirality of hydroxyl group for M128 and M129 forms different interactions with PKB. Table S7 illustrates the interactions for nine inhibitors with PKB. This indicates that the interactions between M128 and PKB are stronger than those between other inhibitors and PKB. It is consistent with the experimental observation that the bioactivity of M128 is the most active among these inhibitors. The hydrogen bond for M335 is the less among these inhibitors. Figure 4 illustrates that the substituent of –CH2NHCH(CH3)2 in M335 is within the binding pocket of PKB, which is different from other inhibitors. The distance between amine of M335 and Glu234 is about 5.1 A, and the hydrogen bond between Glu234 and M335 disappears. In general, there is a hydrophobic interaction between Val64 and R3 group of M128. To insight into the effect of Val164 on the bioactivity of different inhibitors, the distance between Val164 and group R3 of M103 and M126 was calculated and is shown in Figure S12. This figure shows that the distance for M103 is larger than that for M126. This indicates that the hydrophobic interaction between
B
Figure 4: Alignment of M128 (in green) and M335 (in magenta) within the binding site of PKB. (A) Binding site of PKB for M128 and M335. (B) The hydrogen bond between Glu234 and M128 marked in yellow dash line.
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Val164 and M103 is weaker than that for M126/Val164. This is consistent with the bioactivity experiment that the IC50 of M126 is smaller than that of M103. Similarly, the R3 group (–CH3OH) of M127 is hydrophilic substituent, which can explain M127 with the lowest bioactivity among M103 and M126.
Selectivity mechanism of inhibitors The interactions between PKA and M128 are illustrated in supplementary Figure S1. This figure shows that hydrophobic interactions and electrostatic interactions of PKAM128 are similar to those of PKB-M128. However, there is only one hydrogen bond between Glu107 and O32 of M128 in PKA-M128. According to sequence and structure alignment, residue Glu121 in PKA is corresponding to residue Glu228 in PKB. This suggests that two hydrogen bonds of Glu234/M128 and Ala230/M128 in PKB are destroyed in PKA. To further reveal the effect of –OH(M128) on its selectivity, the alignment of four average structures for M128/M129/ PKB and M128/M129/PKA is shown in Figure S13 This figure suggests that Thr211 in PKB is corresponding to Val104 in PKA. This indicates that the –OH of M128 locates at hydrophobic environment in PKA instead of hydrophilic environment in PKB. This can explain that the activity of M128 with PKB is much better than that with PKA. Figure S13 illustrates the distance between O32 and Val104 for M128-PKA and M129-PKA. It suggests that – OH of M128 is closer to hydrophobic residue Val104 than that of M129, which can explain the activity of M129 better than that of M128 with PKA. Furthermore, the activity of M128 is better than that of M129 with PKB. Therefore, the
selectivity of M128 for PKB (pIC50 for PKB/pIC50 for PKA) is better than that of M129. The bioactivity experiment indicates that M135, M136, M137, and M144 have similar activities for PKB and different bioactivities for PKA. To reveal the selectivity of these inhibitors, the conformers of the R1 substituent for these inhibitors were compared. Figure S14 illustrates the superposition of M135 within PKB and PKA pockets. This figure indicates that the amine of M135 is turned over and near the hydrophobic residue Phe327 in PKA. However, the amine of M135 is straight in PKB and far away from the residue Phe438 (Phe438 of PKB is corresponding to Phe327 of PKA). The distances between C33 of M135 and Phe438 of PKB, between C33 of M135 and Phe327 of PKA are also shown in Figure S14. The distance between C33 of M135 and Phe438 of PKB is larger than that between C33 of M135 and Phe327 of PKA. This can explain that the selectivity of M135 is worse than that of M128. The charge of amine is another factor to influence the selectivity. The substituent of amine for M135 is near polar residues of Glu127 and Asp328 in PKA because of its overturn, while the substituent (-CH2NHCH2CH2F) of M136 is far away from these residues. This can explain that the PKA activity of M136 is lower than that of M135. The substituent of amine is –F in M136 and has repelling interaction with negative residues of Glu127 and Asp328. The substituent of amine is –CF3 for M137, which is strong electronegativity group. The repelling interactions between M137 and negative charge residues are stronger than those for M136. Therefore, the PKA inhibit activity of M137 is lower than that of M136.
A
B
C D
Figure 5: Superposition of M128 and M129 within the binding pockets of PKB and PKA. (A) Binding pocket of PKB. (B) Surface of binding pocket for PKB of M128 and M129. Interaction residues are shown in stick. (C) Binding pocket of PKA. (D) Surface of binding pocket for PKA of M128 and M129. Interaction residues are shown in stick.
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10-fold cross-validated q2 of CoMFA model for PKB inhibitors is 0.711 and 0.663 for PKA inhibitors. And the non-cross-validated parameter r2 of the model for PKB inhibitors is 0.961 and 0.959 for PKA inhibitors. The correlation between predicted activities (PA) and experimental activities (EA) of training and test sets with CoMFA model is shown in supplementary Figure S2. The correlation coefficient r2 between EA and PA of test set for PKB inhibitors’ CoMFA model is 0.967 and 0.917 for PKA inhibitors’ CoMFA model. In CoMSIA model, the 10-fold cross-validated q2 for PKB inhibitors is 0.679 and 0.641 for PKA inhibitors. The correlation between PA and EA of training and test sets for CoMSIA model is also shown in supplementary Figure S2. The correlation coefficient r2 between EA and PA of test set for PKB inhibitors’ CoMSIA-SEH model is 0.942 and 0.897 for PKA inhibitors’ CoMSIA-SEHDA model. The high value of correlation coefficient0 r2 suggests that the CoMFA and CoMSIA models for PKB inhibitors and PKA inhibitors are robust and have good prediction ability.
Figure 5A illustrates the superposition of M128 and M129 within PKB-binding pocket. This figure shows three common hydrogen bonds with Phe438 and Ala230 and ten common hydrophobic interactions with residues Met281, Ala230, Met281, Phe236, Phe438, Leu156, Ala177 for M128 and M129 for M128 and M129. Figure 5C illustrates the superposition of M128 and M129 within PKA-binding pocket. There are also one common hydrogen bond and some hydrophobic interactions with similar residues for M128 and M129. In summary, M128 and M129 have similar binding modes to PKB or PKA, respectively. Therefore, we could construct common 3D-QSAR models for these selective PKB and PKA inhibitors.
3D-QSAR model Two methods, CoMFA and CoMSIA, were used to construct 3D-QSAR models for PKB inhibitors and PKA inhibitors. The parameters of these models are given in Table 1. The experimental activity and predicted activity are shown in Tables S2 and S3.
Analysis of CoMFA and CoMSIA models for PKB inhibitors The contribution of steric and electrostatic fields of CoMFA model was 0.782 and 0.214, respectively. The contribution of steric, electrostatic, and hydrophobic fields of CoMSIA model was 0.183, 0.397, and 0.419, respectively. Hydrophobic field has a predominant contribution for CoMSIA model. The contour maps of CoMFA and CoMSIA models for PKB inhibitors with structure M128 are shown in Figure 6. The steric and electrostatic fields of CoMSIA model are similar to those of CoMFA model, which could be analyzed jointly. Green-colored regions near the R3 group suggest that bulky groups could be favorable to the bioactivity (shown in supplementary Figure S3). This could explain that the activities have the sequence: M126 (CH=CH2) > M103 (-CH3), M319 (-CH3) > M317 (-H). Bluecolored regions near the isopropyl of R1 group show that positive groups are favorable to activity (shown in supple-
Combination of CoMSIA fields CoMSIA model includes 5 fields, such as steric field, electrostatic field, hydrophobic field, hydrogen bond donor field, and hydrogen bond acceptor field. The combination of fields will influence the performance of model. The optimal model was built by systemical combination of these fields. The best model includes the best non-cross-validation coefficient, the smallest standard error, and the largest F value. According to this rule, the CoMSIA-SEH was chosen as the best model for PKB and CoMSIA-SEHDA for PKA. The contours will be analyzed using these two models.
Evaluation of CoMFA and CoMSIA models Table 1 lists the statistical parameters of CoMFA and CoMSIA models for PKB inhibitors and PKA inhibitors. The
A
B
Figure 6: Contour plot with M128 of PKB inhibitor. (A) CoMFA. (B) CoMSIA-SEH. Yellow contours indicate regions where bulky groups are unfavorable for activity, and green contours indicate regions where bulky groups are favorable for activity. Red contours indicate regions where groups with negative charge could increase activity, whereas blue contours indicate regions where groups with positive charge could increase activity. Orange contours indicate regions where hydrophobic groups are favorable for activity, and gray contours indicate regions where hydrophilic groups are favorable for activity.
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mentary Figure S4). This could explain that the activity of M134 (-CH2NH2) is higher than that of M137 (CH2NHCH2CF3). The orange-colored regions near the R3 group suggest that hydrophobic groups are favorable to activity (shown in supplementary Figure S5). This is consistent with the experiment activity of M103 (-CH3) > M127 (CH2OH). In summary, the CoMFA and CoMSIA models for PKB inhibitors could explain the different activity of training and test sets.
Analysis of CoMFA and CoMSIA models for PKA inhibitors The contribution of steric and electrostatic fields of CoMFA model was 0.849 and 0.151, respectively. The contribution of steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor fields of CoMSIA model was 0.068, 0.149, 0.237, 0.342, and 0.205, respectively. Hydrophobic field has also a predominant contribution to CoMSIA model. The contour maps of CoMFA and CoMSIA models for PKA inhibitors with structure M128 are shown in supplementary Figure S6. Green-colored regions near the R2 group of benzene ring suggest that bulky groups could be favorable to the inhibit activity. It could explain the experiment bioactivity of M149 > M134, M152 > M139, M153 > M135, M154 > M144. The red–colored regions near the benzene ring indicate that negative groups are favorable to activity (shown in supplementary Figure S7). This could explain the activity of M134 (-Ph-Cl) is lower than that of M149 (-Ph-CF3). The orange-colored regions near the R4 group suggest that hydrophobic groups are favorable to activity (shown in supplementary Figure S8). This is consistent with the experiment activity of M128 (CH2OH) > M103 (-CH3). The cyan-colored contours near the isopropyl indicate that hydrogen bond donor groups are favorable to activity (shown in supplementary Figure S9). This is consistent with the activity of M139 (-C (CH2)2CH2OH) > M128 (-CH(CH2)2). The stone-colored contours near the five-membered ring indicate that hydrogen bond acceptor groups are not favorable to activity (shown in supplementary Figure S10). This could explain that activity of M103 is higher than that of M128. In summary, the CoMFA and CoMSIA models for PKA inhibitors could explain the different activity of training and test sets and be used to design the selective inhibitors.
Discussion Comparison of 3D-QSAR models for PKB and PKA Figure S15 illustrates the comparison between CoMISA models for PKB inhibitors and PKA inhibitors. For PKB inhibitors, green-colored contours (marked in G) are near benzene, while there are no contour plots in PKA model. This suggests that bulky substituent of benzene is favorable to the PKB inhibit activity, no guide information for the PKA inhibit activity. Gray-colored regions (marked in W) near –OH (R4 group) of model for PKB inhibitors, and Chem Biol Drug Des 2014
Figure 7: Detail guide for how to improve the selectivity of inhibitor.
orange-colored regions (marked in O) near –OH of model for PKA inhibitors suggest that hydrophobic groups are favorable to the bioactivity of PKA inhibitor and unfavorable to that of PKB inhibitor. This is agreement with the experimental observation that the selectivity of M128 is stronger than that of M103. The detail guide for how to improve the selectivity of inhibitor is shown Figure 7.
Comparison of 3D-QSAR and molecular dynamics simulation results MD simulation can illustrate the binding mode between inhibitors and PKB (PKA), while 3D-QSAR mainly focuses the effect of substituent on the bioactivity of inhibitors and predicts the activity of new inhibitors. Figure S16 illustrates the alignment between 3D-QSAR model of M128 and PKB. This figure shows that hydrophobic amino acids of Va164, Met227, Ala177, Leu156, and Leu181 cover the R3 group and the benzene ring. The contour plot of hydrophobic favorable orange-colored region is also near the R3 group and the benzene ring. This suggests that the results of MD simulation are consistent with those of 3D-QSAR. The contour plot of CoMSIA model for PKB inhibitors illustrates that blue-colored contours are near the R4 group. This suggests that positive charge groups are favorable to the PKB inhibit activity. MD simulation shows that the negative charge residue of Glu228 can form electrostatic interaction with the R4 group of inhibitors. That is, MD results are consistent with those of 3D-QSAR. Similarly, red-colored contours of CoMSIA model for PKB inhibitors near R3 group show that negative charge substituent of R3 is favorable to the bioactivity. MD simulation indicates that the positive charge residue of Lys179 can form electrostatic interaction with negative charge substituent of R3. CoMSIA hydrogen bond acceptor field indicates that 7
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hydroxyl and amine of M128 are covered in hydrogen bond favorable regions. At the same time, Glu228 and Glu234 form two hydrogen bonds with hydroxyl and amine substituent of M128. In summary, the results of 3D-QSAR are consistent with those of MD simulation.
Conflict of Interest There is not conflict of interest.
References Comparison with previous works The X-ray structure shows that M128 forms one hydrogen bond with Ala230. (7) This hydrogen bond was also found in MD simulations at room temperature. Furthermore, experimental data indicate that the replacement of Thr211 (PKB) to Val (PKA), Met281 (PKB) to Leu(PKA), and Ala230 (PKB) to Val(PKA) leads to a narrower and less polar cavity in PKA (7.) The volume of binding pocket for average structure is calculated with CASTp (27) and 664.5 A3 for 3 PKB and 278.5 A for PKA, respectively. The volume of binding pocket for PKB is more than twice than that of PKA and consistent with the experimental observation. MD simulation also found that the –OH of M128 was nearer hydrophilic Thr211 of PKB than hydrophobic Val104 of PKA. Polar cavity is favorable to the binding of with PKB. These findings might be helpful to improve the selectivity of inhibitor for PKB.
Conclusion CoMFA and CoMSIA methods were used to build 3DQSAR models on PKB inhibitors and PKA inhibitors. Correlation coefficient r2 of test set was 0.980, 0.967 for PKB inhibitors and 0.906, 0.918 for PKA inhibitors. The result shows that these models process good prediction ability. The comparison of 3D-QSAR between PKB inhibitors and PKA inhibitors illustrates the way to improve the selectivity of inhibitors. At the same time, molecular dynamics simulation was used to research the binding mode between inhibitor and PKB, PKA. The result suggests that the most selective compound (M128) has 8 hydrophobic interactions, 10 electrostatic interactions, and 1 hydrogen bond with PKA. Comparison with PKA, two more hydrogen bonds were found between M128 and PKB. These hydrogen bonds can change the binding mode of M128 with PKB and improve the selectivity.
Acknowledgments This work was supported by Center for HPC at Shanghai Jiao Tong University, by grants from the Ministry of Science and Technology of China (2012CB721003), by the National High-tech R&D Program of China (863 Program) (2014AA021502), the National Natural Science Foundation of China (J1210047 and 31271403), by the Innovation Program of the Shanghai Education Committee (Grants No. 12ZZ023), and by Medical Engineering Cross Fund of Shanghai Jiaotong University (YG2013MS68).
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Figure S1. Intramolecular interaction between PKA and M128. Figure S2. Correlations between experimental and predictive activity for PKB and PKA. Figure S3. Steric field of CoMFA model for PKB-inhibitors. Figure S4. Electrostatic field of CoMFA model for PKBinhibitors. Figure S5. Hydrophobic field of CoMSIA model for PKBinhibitors. Figure S6. Contour plot with M128 of PKA inhibitor. Figure S7. Electrostatic field of CoMFA model for PKAinhibitors. Figure S8. Hydrophobic field of CoMSIA model for PKAinhibitors. Figure S9. Hydrogen bond donor field of CoMSIA model for PKA-inhibitors. Figure S10. Hydrogen bond acceptor field of CoMSIA model for PKA-inhibitors Figure S11. The binding site of PKB for M128 and M129. (A) Binding site of PKB for M128 and M129. (B) The binding surface for M128 and M129. (C) The angle of hydrogen bond between Glu228 and M128, Glu228 and M129. (D) The distance of hydrogen bond between Glu228 and M128, Glu228 and M129. (E) The distance between Thr211 and O25 of M128 and O32 of M129. Figure S12. Binding site of PKB for M126 and M103. (A) Binding site of PKB. (B) Hydrophobic contact for Val164/ M103 and Val164/M126 marked in yellow dash line. (C) The distance for Val164/M103(C25) and Val164/C32(M126) Figure S13. Alignment between PKB and PKA with M128 and M129. (A) Alignment between PKB (in blue) and PKA (in orange) with M128 (in green) and M129 (in magenta). (B) Surface of binding sites for PKB and PKA. Thr211 of PKB and Val104 of PKA are shown as stick. (C) The distance between Val104 of PKA and O32 for M128 and M129.
Supporting Information
Figure S14. Alignment of PKB and PKA for M135. (A) Alignment of M135 with PKB (in magenta) and M135 with PKA (in green). (B) Surface of binding sites for PKB and PKA. The Phe438 of PKB and Phe327 of PKA are shown in stick. (C) The distance between C33 of M135 and Phe438 of PKB, Phe327 of PKA.
Additional Supporting Information may be found in the online version of this article:
Figure S15. Comparison of CoMSIA field with M128 and different regions marked in different characters. G repre-
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sents green, Y for yellow, W for white, and O for orange. (A) Steric field for PKB. (B) Steric field for PKA. (C) Hydrophobic field for PKB. (D) Hydrophobic field for PKA. Figure S16. Superposition of 3D-QSAR and complex for M128 and PKB. M128 is marked in green. (A) Hydrophobic field of CoMSIA and key residues of PKB. (B) Electrostatic field of CoMSIA and key residues of PKB. (C) Hydrogen bond acceptor field of CoMSIA and key residues of PKB.. Table S1. Binding free energy between ligand and receptor.
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Table S2. Experimental activity and predicted activity for PKB. Table S3. Experimental activity and predicted activity for PKA. Table S4–S6. Structure and activity. Table S7. Summary of interaction between inhibitor and PKB.
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