Mol Divers DOI 10.1007/s11030-014-9556-0

FULL-LENGTH PAPER

3D-QSAR modeling and molecular docking study on Mer kinase inhibitors of pyridine-substituted pyrimidines Zhuang Yu · Xianchao Li · Cuizhu Ge · Hongzong Si · Lianhua Cui · Hua Gao · Yunbo Duan · Honglin Zhai

Received: 21 March 2014 / Accepted: 13 October 2014 © Springer International Publishing Switzerland 2014

Abstract Mer kinase is a novel therapeutic target for many cancers, and overexpression of Mer receptor tyrosine kinase has been observed in several kinds of tumors. To deeply understand the structure–activity correlation of a series of pyridine/pyrimidine analogs as potent Mer inhibitors, a combined molecular docking and three-dimensional quantitative structure–activity relationship modeling was carried out. A comparative molecular similarity indices analysis model was developed based on the maximum common substructure alignment. The optimum model exhibited statistically significant results: the cross-validated correlation coefficient q 2 was 0.599, and non-cross-validated r 2 value was 0.984. Furthermore, the results of internal validation such as bootstrapping, Y-randomization as well as external validation (the external 2 = 0.728) confirmed predictive correlation coefficient rext Z. Yu (B) Department of Oncology, The Affiliated Hospital of Medical College Qingdao University, Qingdao University, Qingdao 266071, Shandong, China e-mail: [email protected] X. Li · C. Ge · H. Gao Department of Pharmacy, Qingdao University, Qingdao 266071, Shandong, China H. Si · Y. Duan Laboratory of New Fibrous Materials and Modern Textile, The Growing Base for State Key Laboratory, Department of Pharmacy, Institute for Computational Science and Engineering, Qingdao University, Qingdao 266071, Shandong, China L. Cui The School of Public Health, Qingdao University, Qingdao 266071, Shandong, China H. Zhai Department of Chemistry, Lanzhou University, Lanzhou 730000, Gansu, China

the rationality and good predictive ability of the model. Using the crystal structure of Mer kinase, the selected pyridine/pyrimidine compounds were docked into the enzyme active site. Some key amino acid residues were determined, and hydrogen bonding and hydrophobic interactions between Mer kinase and inhibitors were identified. The satisfactory results from this study may aid in the research and development of novel potent Mer kinase inhibitors. Keywords Mer kinase inhibitors · Pyridine · Pyrimidines · Cancer · 3D-QSAR · CoMSIA · Molecular docking

Introduction Tyro-3, Axl, and Mer constitute the TAM family of receptor tyrosine kinases (RTKs). Axl and Mer share the same ligand Gas6 which binds Mer with threefold to tenfold lower affinity than Axl [1–3]. Other ligands, including protein S [1], Tubby [4], TULP-1 [4], and Galectin-3 [5], can also stimulate Mer. Mer and Axl can mediate the functions of macrophages such as cytokine secretion, clearance of apoptotic cells, and platelet aggregation [6]. Axl, Mer, and Gas6 are also found in erythroid cells and are proven to be in regulation of erythropoiesis [7,8]. Additionally, Axl and Mer are natural required in killer cells differentiation and maturation [9,10]. Some studies have shown that Axl and Mer are overexpressed in many human cancers, such as acute myeloid leukemia (AML), breast cancer, non-small cell lung cancer (NSCLC), gastric cancer, and pancreatic cancer [11– 15]. Moreover, the ligands Gas6 and Protein S are secreted by multiple organs, and are also upregulated in some tumors [16,17]. Recent studies have identified Axl and Mer as potential therapeutic targets for NSCLC [18–20]. High level expres-

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sion of Axl and Mer and of their ligands Gas6 and Protein S were found in NSCLC cell lines [21,22]. Just like in glioblastoma multiforme (GBM) tumors, Mer and Axl may be also continuously activated in NSCLC by autocrine or paracrine mechanisms [23]. Indeed, Axl and Mer are the most likely phosphorylated RTKs in NSCLC cell lines [23]. Furthermore, Axl expression is usually correlated with lymph node involvement and always found in higher clinical stage, so Axl expression leads to poor prognosis in NSCLC [13]. The molecular mechanisms of Axl and Mer for the development and progression of NSCLC have been studied. The silencing of Axl mediated by RNAi inhibits the viability of NSCLC cells and tumor growth in vitro and xenograft models, respectively [18]. Axl expression has been concerned with the invasiveness and migration of NSCLC cells [13,18], and these may be mediated by the Axl-dependent upregulated MMP-9 expression [24]. Decreased expression of Mer also can increase the death of cancer cell [22]. In vitro experiment data indicate that inhibition of Axl or Mer increases the sensitivity of NSCLC cells to numerous chemotherapy drugs significantly [25]. Recent literature indicates that Axl and Mer play a key role in mediating the survival, proliferation, and migration of NSCLC cells. So, inhibition of Axl and/or Mer may be a feasible route to improve therapeutic efficacy in NSCLC chemotherapy. Several Axl and Mer inhibitors have emerged. Although they exhibit potent inhibitory activity for Axl and Mer kinases, most of them were designed as inhibitors of other tyrosine kinases, such as Src/ABL, c-Kit, and Met, and not specifically to target Axl or Mer. Amuvatinib (MP-470, Supergen, Inc.) was the first reported compound exhibiting potent activity against Axl kinase (IC50 = 1 μM) [26], even though it was originally designed as a c-Kit and Met inhibitor. Bosutinib (SKI-606, Wyeth now PF5208763, Pfizer) was originally developed as a second generation Src/ABL kinase inhibitor and also exhibited inhibition of Axl (IC50 = 0.4 μM) [27]. Foretinib (GSK1363089, GlaxoSmithKline) was recently identified as a potent inhibitor of Axl (IC50 = 11 nM) [28], while it was initially developed as a dual Met-VEGFR2 inhibitor. BMS-777607 (BristolMyers Squibb) and PF-2341066 (Pfizer) were also originally developed as Met inhibitors exhibiting inhibitory activity for Axl kinases (IC50 = 1.1 nM and 300–320 nM, respectively) [29]. R428 (Rigel) is an Axl inhibitor (IC50 = 14 nM) that also shows cross-reactivity with Mer, Tyro3, VEGFR family members, Ret, Tie2, and Abl kinases [30]. A new family of small molecule Mer inhibitors, pyrazolopyrimidinesulfonamides, has been discovered, and of which UNC1062 was identified as a potent (IC50 = 1.1 nM) and selective Mer inhibitor [31,32]. Overall, considering the high sequence homology of the tyrosine kinase domains of Axl and Mer [33], a great challenge will be to design highly selective inhibitors against Axl over Mer and vice versa.

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To overcome this selectivity challenge, new strategies need to be considered and explored for research and development of novel Axl and/or Mer kinase inhibitors. With advancement in computer techniques and simulation software, molecular simulation methods have become very useful tools for drug discovery and design, such as QSAR (quantitative structure–activity relationship) modeling, protein modeling, molecular docking, and molecular dynamics that are widely used now [34–37]. In this study, a QSAR model was built based on the structures of selected pyridine– pyrimidine compounds that potently inhibit activity of Mer kinase. Our QSAR model shows the relationship between structure and activity, and provides guidance to make further structure modification for more potent inhibition. We also performed in silico docking studies to gain deeper insight into the binding interactions of the inhibitors and the Mer kinase at the catalytic site.

Materials and methods Dataset The dataset used for our molecular modeling study contains 43 structures which were designed by Weihe Zhang to explore new pyridine/pyrimidine analogs as potent Mer inhibitors [38]. The structures of the compounds as well as their IC50 and pIC50 (−logIC50 ) values are shown in Table 1. These data were divided into a training set (35 compounds) for 3D-QSAR model generation and a test set (8 compounds) for model validation. All structures were constructed using ChemDraw Ultra 8.0 [39], and then optimized in Sybyl 7.3 [40]. Structural energy minimization was performed using the Tripos force field and Powell gradient algorithm with a convergence criterion of 0.05 kcal/mol and a maximum of 1,000 iterations, and the minimized structure was used as the initial conformation for CoMSIA and molecular docking [41]. Partial atomic charges were calculated using the Gasteiger–Hückel method [42]. The X-ray structure of Mer kinase domain (PDB ID: 4M3Q) was obtained from the Protein Database Bank (PDB) [43], and prepared for the docking analyses. Molecular docking To investigate the binding modes between the Mer kinase and its inhibitors, the Surflex-Dock program interfaced with Sybyl 7.3 was used to dock compounds into the Mer kinase domain [44]. Prior to initiating the docking simulations, the cocrystallized ligand and structural water molecules were removed from the crystal structure, and polar hydrogen atoms were added and Kollman-all atom charges were assigned to protein atoms. Surflex-Dock supplies a fully automated

Mol Divers Table 1 Structures and activity of pyridine-substituted pyrimidines as Mer kinase inhibitors HO NH R1

N N H

Comp.

N

pIC50

IC50

R1

[nM]

Obs.

Pred.

Res.

17

7.770

7.417

0.353

570

6.240

6.265

–0.025

950

6.020

7.213

–1.193

15200

4.820

4.825

–0.005

6.3

8.200

7.839

0.361

2.8

8.550

8.679

–0.129

3.9

8.410

8.432

–0.022

1.7

8.770

8.783

–0.013

12

7.920

7.946

– 0.026

18

7.740

7.356

0.384

1.1

8.960

N

3*

N

4 F

5* 6

Emptya F

N

7

N N

N

8*

N

N

N

9

10

12

N

N O

N

O N

N 13*

O

N

N

N F

14

8.990

– 0.030

F

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Mol Divers Table 1 continued N

15

N

O N

16

S

N O

18*

0.021

0.70

9.150

9.105

0.045

0.69

9.160

9.106

0.054

3.4

8.470

7.953

0.517

0.69

9.160

9.218

– 0.058

1.3

8.890

0.81

9.090

N

H N

N

8.749

N N H

N

8.770

O

O

17

1.7

O

19

N

N

20

21

N H

N H

– 0.046

OH

N H

N

8.936

R2

NH

9.108

– 0.081

N

N N H

Comp.

2

N

pIC50

IC50

R2NH

[nM]

Obs.

Pred.

Res.

18

7.740

7.751

– 0.011

1250

5.900

5.962

– 0.062

1.8

8.740

8.851

– 0.111

4.5

8.350

7.885

0.465

NH2

H N H N

22 H N

23 NH2

24*

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H N NH2

Mol Divers Table 1 continued H N

25

18

7.740

7.728

0.012

34

7.470

7.422

0.048

72

7.140

7.063

0.077

160

7.800

7.772

0.028

200

6.700

6.702

–0.002

1130

5.950

5.977

–0.027

600

6.220

7.320

–1.100

NH2 H N

26

27

28

NH NH

H N H N

OH

H N

29

OH

30 31*

H N

OH

H N

OH

HO NH

N

N R3

Compound

R3

33 34 35 36

pIC50

IC50 [nM]

Obs.

Pred.

Res.

S

2700

5.570

5.653

– 0.083

N H

540

6.270

6.408

– 0.138

170

6.770

6.564

0.206

50

7.300

7.441

– 0.141

19

7.720

7.822

– 0.102

44

7.360

7.415

– 0.055

380

6.420

6.337

0.083

16500

4.780

5.490

– 0.710

N H N H N H

37

38

N H

39

N

40*

N

N

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Mol Divers Table 1 continued

41

N H N H

42

43

44 45 46 a

N H HO

HO

HO

N H N H

N H

83

7.080

7.134

– 0.054

110

6.960

7.030

– 0.070

14

7.850

7.779

0.071

320

6.490

6.442

0.048

220

7.660

7.568

0.092

44

8.360

8.408

– 0.048

there is no substituent group in this compound

* test set

flexible docking procedure for the ligand and relies on the rigid-receptor approximation to simulate ligand–receptor binding modes [45]. In Sybyl 7.3, the intended binding site where ligands could fit and make potential interactions can be created based on three modes: automatic, ligand, and residues modes [46]. Considering the structural similarity of the cocrystallized ligand and target compounds in our study, the ligand-based mode was applied in the present work. The ProtoMol bloat and ProtoMol threshold parameters were set to their default values of 0 and 0.50 Å [47], and then a binding pocket for the studies was created. After docked into the binding pocket, the top ten options of binding conformation of each ligand were ranked by total scores. The docking conformation assumed to represent the possible bioactive conformation of ligands was selected based on the following two criteria [48]: (i) the conformation possesses the highest docking score, and (ii) the orientation of the conformation is similar with that of the cocrystallized ligand. CoMSIA models generation Based on selected pyridine–pyrimidine structures and their pIC50 data, a CoMSIA (comparative molecular similarity indices analysis) study was performed using Sybyl 7.3. CoMSIA is a reliable tool for 3D-QSAR studies that can avoid singularities at the atomic positions arising from the Lennard–Jones and dramatic changes of Coulomb potentials fields used in CoMFA (comparative molecular field analysis, another tool of 3D-QSAR) [40]. In 3D-QSAR studies, alignment rule and bioactive conformation selection are two important factors to construct a reliable model. Two common methods used for alignment are maximum common

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substructure (MCS) alignment and alignment based on docked poses. When structures have the common fragment, the highest active compound can be used as a template molecule, and the rest of the compounds were aligned to it. If a kinase crystal structure is available and the docked conformations of compounds are coincident, alignment based on docked poses is preferred [49]. In CoMSIA, five molecular fields were calculated: steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor. The CoMSIA descriptors were calculated by a 3D cubic lattice with grid spacing of 2 Å and extending 4 Å units beyond the aligned molecules in all directions. The default value of 0.3 was used for the attenuation factor α [50]. In the process of building a statistically significant 3DQSAR model, the partial least squares (PLS) analysis was used to correlate the CoMSIA fields to the pIC50 values. PLS was performed in two stages [51,52]. First, a leaveone-out (LOO) cross-validation analysis was performed to determine the optimum number of components (ONC) and cross-validated correlation coefficient (q 2 ). Second, noncross-validation analysis was developed using the ONC to generate the final PLS regression models for CoMSIA. The non-cross-validation results were evaluated by several statistical parameters, such as the non-cross-validated correlation coefficient (r 2 ), the standard error of estimate (SEE), and F value. To further assess the robustness and the statistical liability of the derived model, bootstrap analysis for 100 runs was performed [53], and Y vector (pIC50 ) was randomized 20 times [54]. A parameter Rp2 was introduced to evaluate the Y-randomization results. The parameter penalized the model for the difference between squared mean correlation coef-

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ficient (Rr2 ) of randomized models and squared correlation coefficient (R 2 ) of the non-randomized model [55] and was calculated by the following equation:  Rp2 = R 2 ∗ R 2 − Rr2 . Parameter Rp2 ensures that the models are not obtained by chance. For an acceptable model, the value of Rp2 should be greater than 0.5. In addition, the external predictive ability of the CoMSIA model was assessed by predicting the activity of the test set, and the external predictive correlation coefficient was deter2 was calculated using mined by calculation. The external rext the following equation [56]: ntest 2 i=1 (yi − y˜i ) 2 rext = 1 − ntest , 2 i=1 (yi − y¯tr ) where ntest is the number of compounds that constitute the validation data set (test set), y¯tr is the averaged value of the dependent variable for the training set, and yi , y˜i , (i = 1, . . . , ntest) are the experimental values and the 3D-QSAR model predictions of the test set over the available validation set. To further validate the predictive ability of the model, 2 a modified r 2 (rm (overall) ) was introduced as the following equation [55]:  2 2 = r ∗ (1 − r 2 − r02 ). rm (overall) In the equation, r 2 is squared correlation coefficient between observed and predicted values of the compounds, and r02 is squared correlation coefficient with intercept set to zero. The parameter determines whether the range of predicted activity values for the whole dataset is really close to 2 the observed activity or not. The value of rm (overall) should be greater than 0.5 for an acceptable model.

Results and discussion Molecular docking The docking process was performed to investigate the probable binding modes of selected compounds and Mer kinase, and provided a deeper insight into the binding interactions of the inhibitors at Mer’s catalytic site. Figure 1a shows the structure of compound 19, and the position of R1 , R2 , and R3 substituents on it, and docked poses with the best docking score of all structures are shown in Fig. 1b. From the results, we can find that the conformations of most molecules in the binding site of Mer kinase agree with that of 19, except 6 compounds (6, 15, 16, 17, 33, and 39). To further investigate the difference of the inhibitors’ binding modes, the high

Fig. 1 a The structure of compound 19 and the position of the R1 , R2 , and R3 substituents on it; b docked poses of all structures in the binding pocket of Mer kinase domain: conformation of compounds 19 (red) and 17 (green). (Color figure online)

active compounds 17 and 19 were selected for more detailed analysis. To compare the best score conformation with that of the cocrystallized ligand, the structure of compound 3 was constructed, minimized, and then docked in the binding site. The surface of the binding site of Mer kinase and detail interactions of these three compounds in the binding site are shown in Fig. 2. Figure 2a shows the surface of the binding site and conformation with the best socking score (green) of compound 3 and original conformation of the cocrystallized ligand. Compared to the original cocrystallized conformation, the version of compound 3 we built matches with high resemblance when docked. Concerning the two criteria mentioned above [48], the conformations of these selected compounds determined using the proposed docking approach in our work could represent the bioactive conformation in the binding site of Mer kinase. The detailed binding mode of compound 3 with Mer depicted in Fig. 2b shows three hydrogen bond interactions (red dashes) formed with residues PRO672, MET674, and GLU595. It can also be observed that a hydrophobic clamp exists involving the hydrophobic residues ILE650, LEU671, PRO672, and MET674, an area that could be accommodate

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Fig. 2 Docking results: a the surface of the binding site and the conformation comparison of compound 3 (green) and cocrystallized ligand (orange), and the key residues (yellow) at the binding site; b interaction between compound 3 (green) and residues (yellow); c interac-

tion between compound 19 (green) and residues (yellow); d interaction between compound 17 (green) and residues (yellow). (Color figure online)

hydrophobic groups. As shown in Fig. 2b, the R3 substituent, which is mainly composed by a carbon chain, is placed just in this hydrophobic area. The R2 substituent with a hydrophobic ring is close to LEU593 and a hydrogen bond formed between the hydroxyl of the ring and GLU595. Figure 2c shows the binding mode of the most potent inhibitor considered in our study, compound 19. Three hydrogen bonds are observed between compound and the residues PRO672, MET674, and LYS675. The “–NH–” in R3 substituent and the “N” in the pyrimidine ring have the same hydrogen bond interaction with PRO672 and MET674 in both 3 and 19, so these 2 functional groups are considered very important for Mer inhibition. In R1 , the “–NH–” group forms a hydrogen bond with residue LYS675. As seen in the Fig. 2a, R1 substituent would be oriented toward the solvent region of Mer kinase in which substitutions can expose to the solvent and may influence the physical or pharmacokinetic (PK) properties of these analogs [38,57]. So we could conclude that R1 substituent could be utilized for tuning sol-

ubility and other physical or PK properties. R2 substituent is close to and may have hydrophobic interaction with residue LEU593. When docked into the pocket, compound 17 has a different binding mode with that of 19. Two hydrogen bonds are found between the amide group and PRO672, MET674, and the hydrophobic ring of R1 substituent is placed in the hydrophobic clamp. R2 substituent is oriented to the other side of the binding pocket and formed a hydrogen bond with residue LYS675. R3 substituent is exposed to the solvent area and is closed to the hydrophobic residue VAL804. Considering the high pIC50 value of 17, this binding mode may also contribute to inhibitory activity and is waiting to be validated by further studies.

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Selection of alignment method All selected pyridine–pyrimidine compounds have the common fragment (Fig. 3a), and when docked into the binding

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Fig. 3 Common fragment of all compounds (a) and the alignment of all the compounds in training set (b)

pocket of Mer, the conformations of them were consistent with that of 19, and the common fragment was aligned well (Fig. 1). So the maximum common substructure (MCS) alignment method was chosen in our CoMSIA study. Compound 19 was selected as a template, and the rest of the compounds in the training set were aligned on it using Align Database command in Sybyl [44] (Fig. 3b). CoMSIA statistical results The statistical results of our optimal CoMSIA model are summarized in Table 2, and three molecular fields are included: the hydrophobic, the hydrogen bond donor, and the hydrogen bond acceptor field. The CoMSIA model was built on seven components (ONC = 7), and showed a cross-validated q 2 of 0.599. A non-cross-validated r 2 of 0.984, a SEE of 0.106, and an F value of 566.746 indicated a good correlation between the experimental and predicted pIC50 values. The relative field contributions of the hydrophobic, hydrogen bond donor, and hydrogen bond acceptor fields were 42.0, 30.8, and 27.2 % (Table 2), respectively. Validation of our CoMSIA model To measure the bias of the original calculations, a bootstrapping analysis for 100 runs was performed, and an aver2 value of 0.997 and a SEEboot value of 0.069 were age rboot 2 and lower SEEboot conobtained. The higher value of rboot firmed the robustness of the model. To further test the robustness and eliminate the possibility of chance correlation, the Y vector (pIC50 ) was randomized 20 times. The q 2 (−0.179

Fig. 4 Plot of predicted pIC50 values versus experimental ones for the CoMSIA model

to 0.324) and Rp2 (0.581) values demonstrated that the constructed 3D-QSAR model was stable and not affected by chance correlations. The external test set of 8 compounds was used to further assess the reliability and predictability of the built model. The 2 = 0.728, and r 2 statistic parameters, rext m (overall) = 0.821, suggested a good predictive ability of our CoMSIA model. Table 1 lists the experimental, predicted activities, and the residual values of the dataset, and Fig. 4 shows a good relationship between predicted pIC50 values and experimental ones. To visualize the results of our CoMSIA model, 3D colorcoded contour maps were generated. The CoMSIA results were mapped using the “SD*Coeff” option in Sybyl. The contributions of favorable and unfavorable levels were maintained as the default value of 80 and 20 %, respectively. The contour maps of CoMSIA fields superimposed with compound 19 are depicted in Fig. 5. The CoMSIA hydrophobic contour map is displayed in Fig. 5a, where the hydrophobic favorable regions are represented in yellow and the unfavorable regions in white. As seen from the picture, the most part of R1 , R2 , and R3 substituents are surrounded by a large yellow contour map suggesting that hydrophobic groups at these positions are crucial for Mer inhibition. Combined with our docking results, we anticipate that the R3 substituent will most

Table 2 Statistical results of CoMSIA model Model

CoMSIA

q2

0.599

r2

0.984

N

7

SEE

0.106

F

566.746

Field contribution % H

D

A

42.0

30.8

27.2

H hydrophobic, D hydrogen bond donor, A hydrogen bond acceptor

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Fig. 5 CoMSIA SD*Coeff contour maps around the most active compound 19 a for the hydrophobic field (yellow hydrophobic favorable, white: hydrophilic favorable) b for hydrogen bond donor field (cyan hydrogen bond donor group is favored, purple: hydrogen bond donor

group is disfavored); c for H-bond acceptor field (magenta groups with H-bond acceptor atoms are favored, red groups with H-bond acceptor atoms are disfavored). (Color figure online)

likely occupy the hydrophobic clamp around the hydrophobic residues ILE650, LEU671, PRO672, and MET674, and the R2 substituent could interact with hydrophobic residue LEU593 (Fig. 2c). From the docking results, we have concluded that R1 substituent will be oriented to solvent and may influence the physical and pharmacokinetic (PK) properties, so concerning a yellow contour surrounding R1 substituent, hydrophobic groups may be beneficial to the physical and pharmacokinetic (PK) properties of these pyridine– pyrimidine analogs. The white contour map on the groups in R2 substituent means that these groups could engage in interactions with hydrophilic residues like GLU595. The CoMSIA hydrogen bond donor and acceptor contour maps are depicted in Fig. 5b and c. The hydrogen bond donor field is represented in cyan and purple contours (Fig. 5b), in which cyan contours indicate regions where a hydrogen bond donor group would be beneficial for activity, while the purple contours represent areas where a hydrogen bond donor

group would be unfavorable. In the hydrogen bond acceptor field (Fig. 5c), the favored hydrogen bond acceptor regions are shown in magenta and the disfavored regions in red. In Fig. 5b, all substituents, especially R1 and R2 , are surrounded by large purple contours that indicating a hydrogen bond acceptor in these groups is significant for activity. The purple contours on the “–NH–” group in R3 substituent and “N” atom in the pyrimidine ring show the importance of the two groups that can form key hydrogen bonds with the residue PRO672 and MET674, respectively (Fig. 2b and c). In Fig. 5c, a red contour has also been observed located around the ring of the R2 substituent, implying that hydrogen bond acceptor groups are preferred here. The hydroxyl group in R2 substituent acts as a hydrogen bond donor to form a hydrogen bond with GLU595, which is consistent with the observations obtained from previous molecular docking analysis. Moreover, a magenta contour has also been observed on top of the R2 substituent indicating that H-bond

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Mol Divers Table 3 R2 substituent modifications and predicted pIC50 values R2

N

R2'

N

N H

N N H Comp.

N

R2R2'N

Pred. pIC50

H N

47

6.630 F F

48

49

H N

H N

9.238

F

8.518

binding mode. From the results shown in Fig. 6, we can see that the conformation of compound 48 (green) is consistent with that of compound 3 (magenta), and they have the similar hydrogen bonding interactions with the residues. So, the compound 48 may be a potential candidate of Mer inhibitor waiting for being identified in pharmacological experiments.

Conclusion

Fig. 6 Conformational comparison and hydrogen bonds of compound 48 (green) and compound 3 (magenta). (Color figure online)

acceptor atoms at this position may also increase the activity. The red contour near the “–NH–”group in R1 substituent reveals that the H-bond donor is essential at this position that can generate hydrogen bond interaction with residues just like the LYS675 (Fig. 2c). The “N–H” in R2 substituent is also under a red contour revealing the importance of the proton which may form an intramolecular hydrogen bond with the “N” of the pyridine ring in R1 substituent and then maintain the overall conformation of the molecule [36]. As a validation of the conclusion from CoMSIA model that H-bond acceptor groups in R2 substituent may also increase the activity, the R2 substituent was modified with a fluorine atom replacement on cyclohexane ring, and the modified structures and their predicted pIC50 values are shown in Table 3. We docked compound 48 with a higher predicted pIC50 into the binding site for a further investigation of its

Based on the structures of selected pyridine-substituted pyrimidine derivatives, a combined molecular docking and CoMSIA modeling was carried out. We obtained an optimal CoMSIA model based on maximum common substructure (MCS) alignment method, and the model showed high q 2 (0.599) and r 2 (0.984) values indicating a good predictive ability of the developed model. A series of validation methods, including bootstrapping, Y-randomization as well as external validation of a test set, were applied to validate our model, and the satisfied results of them further confirmed that the model is reliable and has high predictive ability. The hydrophobic, hydrogen bond donor, and hydrogen bond acceptor fields in our CoMSIA model are essential for the inhibition activity of Mer, which are consistent with the interactions between the ligands and the amino acid residues identified by molecular docking. In the docking process, we found several key residues that have hydrogen bonding interactions with ligands, such as GLU595, PRO672, MET674, and LYS675. And there are several hydrophobic residues LEU593, ILE650, LEU671, PRO672, MET674 and VAL804 that could interact with hydrophobic groups in Mer inhibitors. Around the residues ILE650, LEU671, PRO672,

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and MET674, a hydrophobic clamp exists that could also be occupied by hydrophobic groups. Our proposed approach based on molecular docking and CoMSIA aims to aid in the research and development of novel potent Mer kinase inhibitors. Acknowledgments This work was supported by the Science and Technology Development Project of Shandong Province: No. 2012YD18038 and No. 2012YD18042.

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