Full Papers. 1 Introduction. Min Yang a, Lu Zhou a *, Zhili Zuo b *, Ricardo Mancera c, Hang Song a, Xiangyang Tang d, Xiang Ma a

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1 Docking Study and Three-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) Analyses of Min Yang a, Lu Zhou a *, Zhili Zuo b *, Ricardo Mancera c, Hang Song a, Xiangyang Tang d, Xiang Ma a a College of Chemical Engineering, Sichuan University, Sichuan, Chengdu, , P. R. China * zhoulu@scu.edu.cn; Phone: þ b Curtin Health Innovation Research Institute, School of Biomedical Sciences, Curtin University of Technology, GPO Box U1985, Perth WA 6485, Australia * z.zuo@curtin.edu.au c Curtin Health Innovation Research Institute, Western Australian Biomedical Research Institute, School of Pharmacy and School of Biomedical Sciences, Curtin University of Technology, GPO Box U1987, Perth WA 6845, Australia d College of Computer Science, Southeast University for Nationalities, Sichuan, Chengdu, , P. R. China Keywords: 3D-QSAR, Gold, CoMFA, TGF-b RI, Medicinal chemistry, Structure-property relationships Received February 6, 2009,; Accepted: July 10, 2009 DOI: /qsar Abstract Transforming growth factor-b Type I Receptor Kinase (TGF-b RI) is an important and novel drug target for the treatment of fibrosis and cancer. In this study, docking and comparative molecular field analysis (CoMFA) were performed and the structural characteristics of TGF-b RI ligands were rationalized. The genetic algorithm search method in the docking program GOLD was employed to determine the likely binding mode conformations of 70 inhibitors in the active site of TGF-b RI. Based on the binding mode conformations, a highly predictive 3D-QSAR model was developed with a q 2 value of for CoMFA. The predictive ability of this model was validated with a number of compounds that were not included in the original training set. Furthermore, the 3D- QSAR model was mapped back to the binding site of the TGF-b RI to obtain a better understanding of the essential interactions between the inhibitors and TGF-b RI. The robustness, predictive ability and automated alignment generation of this model make it a potential tool for the design and development of new drug leads to inhibit TGF-b RI. 1 Introduction Transforming growth factor-b (TGF-b) is a cytokine that has been postulated to be involved in a number of disease states, including inflammation, fibrosis, cancer, asthma and cardiovascular disease. The complex function of TGF-b, which mediates pathways involving the regulation of gene response and DNA transcription factors, is dependent upon the activation of type I (TGF-b RI) and type II (TGF-b RII) receptors [1 3]. Extensive research has indicated that inhibition of the TGF-b RI may be useful for the treatment of a number of diseases relative to the TGFb signaling pathway, such as fibrosis and cancer [4 8]. Over the past years the development of small molecule inhibitors against TGF-b RI has received a lot of attention in the pharmaceutical industry and the medicinal chemistry community. The combination of high-throughput screening (HTS) and the availability of X-ray structures of TGF-b RI complexed with inhibitors in its active site have contributed to the discovery of potent TGF-b RI inhibitors [9]. Recently, Li et al. disclosed a novel series of TGF-b RI inhibitors [10 13] and the structure of the complex of one of the inhibitors with TGF-b RI was determined by X-ray crystallography [10]. This provided us with insight into the mechanism of interaction of TGF-b RI with its inhibitors and valuable clues for the design of new inhibitors. To identify the physicochemical properties that have a substantial effect on the binding affinity of the ligands, a comparative molecular field analysis (CoMFA) [15, 16] was applied to investigate the three dimensional quantitative structure activity relationships (3D-QSAR) on the basis of the protein-bound conformation of TGF-b RI ligands predicted by the molecular docking program GOLD [14] and experimental data extracted from the literature [10 13]. The combination of docking and 3D- QSAR analysis is advantageous because it allows the di WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim QSAR Comb. Sci. 28, 2009, No ,

2 rect visualization and interpretation of molecular modeling results within the binding site, thereby revealing the interactions contributing positively or negatively to the binding affinity. The results are discussed and compared with the structures and binding modes of TGF-b RI ligands, particularly in light of the recently solved structure of the complex of a high affinity ligand with TGF-b RI [10]. Table 1. Molecular structures, experimental activities (pic 50 ), and predicted activities (pic 50 ) (by CoMFA model) of all the ligands. Ligands belonging to the test data set are indicated with an asterisk (*). a) 2 Computational Details 2.1 Data Set and Biological Activity. A panel of 70 structurally and pharmacologically diverse inhibitors of TGF-b RI was selected from five publications reported by a single laboratory [10 13] (Table 1), whose biological data (represented as IC 50 values) were considered to be comparable because of the similar nature of the experimental method used to determine binding affinities. A series of 4-(quinoline-4-yl)-substituted dihydropyrrolopyrazoles, dihydropyrrolopyrazole-substituted benzimidazoles and dihydropyrrolopyrazole analogues with TGF-b RI inhibitory activity were extracted to define our training and test sets. As a rule of thumb, the pic 50 ( log IC 50 ) values of the training data set should span at least 3 log units. Accordingly, the pic 50 values of the training set described in this manuscript span 3.1 log units. The training set (55 compounds) and the test set (15 compounds, which have been marked with an asterisk in Table 1), were selected considering their structural diversity and full range of biological activities (Table 1). To ensure the accuracy of the CoMFA model built with the training set, the number of test compounds is about 1 = 4 of the number of compounds in the training set. The average values of the inhibitory activity (pic 50 ) in the training and test sets are and 6.686, respectively. The structures of the compounds in the training and test sets are shown in Table Molecular Docking Studies The three-dimensional (3D) structure of each compound was built on the basis of the structure of compound 580 (M15), which was extracted from the crystal structure of its TGF-b RI complex (PDB entry 1RW8) [10], using SYBYL 8.0 (Tripos Associates Inc., St. Louis, MO, USA), followed by energy minimization to a convergence of 0.05 kcal/(mol Š) using the Tripos force field with Gasteiger Hückel charges. The molecular docking program GOLD (version 3.0.1), which uses a powerful genetic algorithm (GA) method for ligand conformational and docking searches [17], was employed to generate an ensemble of docked conformations. The bound ligand was removed from the coordinates set of TGF-b RI while water molecules within 6.5 Š of the active pocket were taken into account during the docking simulations. The GA operators were 100 for the population size, 1.1 for selection, ID five for the number of subpopulations, for the maximum number of GA applications, and two for the size of the niche used to increase population diversity. The weights were chosen so that crossover mutations were applied with equal probability (95/95 for the values) and migration was applied 5% of the time. The ChemScore scoring function, described by Eldridge et al. [18, 19] and encoded in GOLD, was applied to rank the binding poses of the inhibitors. ChemScore values provide an estimate of the total free energy of ligand binding (DG binding ). The final ChemScore value is obtained by including a clash penalty and internal torsional terms, which consider close contacts and poor internal conformations. Covalent and constraint scores may also be included. The fitness score is taken as the negative of the sum of the energy components, so that larger fitness scores are better [20]. 2.3 CoMFA R Compound Chem- Score pic 50 pic 50Pred 1 2 NH *3A NHCOCH 2 N(CH 3 ) B NHCOCH 2 CH 2 N(CH 3 ) C NHCOCH D NHS(O) 2 CH A NHCONH(CH 2 ) 2 OH *4B NHCOO(CH 2 ) 2 OH C NHCONH(CH 2 ) 3 N(CH 3 ) D NHCONH(CH 2 ) 2 N(CH 3 ) E NHCONH(CH 2 ) 2 OCH F NHCOOCH *5A B Steric and electrostatic interactions were calculated using the Tripos force field with a distance-dependent dielectric QSAR Comb. Sci. 28, 2009, No , WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 1301

3 M. Yang et al. Table 1. b) ID Compound R 1 R 2 ChemScore pic 50 pic 50Pred 14 7B 2-Cl H C 6,8-OMe H *7D 8-F Me E 6-Br Me *7F 6-OCF 3 Me COOMe Me CONH(CH 2 ) 2 (CH 3 ) 3 Me OH H A 7-O(CH 2 ) 3 N(CH 2 CH 2 ) 2 NCH 3 H *15 7-O(CH 2 ) 2 Cl H A 7-O(CH 2 ) 2 N(CH 3 ) 2 H *15B 7-O(CH 2 ) 2 N(CH 2 CH 3 )CH 3 H C 7-O(CH 2 ) 2 N(CH 2 CH 2 ) 2 NCH 3 H D 7-O(CH 2 ) 2 N(CH 2 CH 2 ) 2 O H A 7- (CH 2 ) 5 N(CH 3 ) 2 H A 7-OCH 2 CON(CH 2 CH 2 ) 2 NCH 3 H H H constant for all intersections and a regularly spaced (2 Š) grid, taking an sp 3 carbon atom as the steric probe and a þ 1 charge as the electrostatic probe. The cutoff for the energy value was set to 30 kcal/mol. Using standard options for the scaling of variables, a regression analysis was carried out using the leave-one-out cross-validated partial least squares (PLS) method [20, 21]. The optimum number of components with the lowest standard error of prediction was considered for further analysis. Equal weights were assigned to the steric and electrostatic fields using the CoM- FA scaling option. The minimum sigma (column filtering) was set to 2.0 kcal/mol to improve the signal-to-noise ratio by omitting those lattice points whose energy variation was below this threshold. Typically, whenever the increase in q 2 with an additional component is less than 5%, the model with fewer components is preferred over the model with a slightly higher q 2 [22]. The final model using noncross-validated conventional analysis was developed with an optimum number of components to yield a noncrossvalidated r 2 value. 3. Results and Discussion 3.1 Binding Conformations and Docking Study GOLD is one of the most reliable molecular docking programs used for the prediction of the interactions of ligands bound to proteins [23 25]. In order to determine the likely binding conformations of the inhibitors, GOLD was used to dock all compounds into the active site of TGF-b RI. Docking accuracy was validated using the known X- ray structure of TGF-b RI in complex with M15. The ligand M15 was docked into the binding site of TGF-b RI and the docked conformation corresponding to the highest ChemScore value was selected as the most likely binding conformation. The root-mean-square deviation (RMSD) between the conformation of M15 in the crystal structure and that in the predicted binding mode is Š, suggesting that GOLD is able to successfully reproduce the experimentally observed binding mode for TGF-b RI inhibitors, and that the parameters set for the GOLD simulations are appropriate for this docking study. As shown in Figure 1a, the conformation of M15 in the crystal structure and that WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim QSAR Comb. Sci. 28, 2009, No ,

4 Table 1. c) ID Compound R 1 R 2 R 3 ChemScore pic 50 pic 50Pred 32 M3 A H H H M4 A H Me H *M5 A H Et H M6 A H Pr i H M7 A H H F M8 A Cl H H *M9 A Cl Me H M10 A OEt Me H M11 A SEt H H M12 A SO 2 Et H H M13 B Me H Me *M14 B F H H M15 B F H Me M16 B F F Me M17 B F H CF M18 B F H CH 2 OH *M19 B SO 2 Me H Me M20 B OMe H H M21 B OMe H Me M22 B OH H Me predicted by docking are almost the same in the active site of TGF-b RI. Therefore, the GOLD program can be used to search the binding conformations of all the other inhibitors of TGF-b RI. As shown in Figure 1, M15 binds to the active site of TGF-b RI and makes several interactions with the hinge region of the enzyme. For example, the pyridyl nitrogen of M15 forms a hydrogen bond with a water molecule as well as hydrogen bonds with Asp351, Glu245 and Tyr249. The fluorine atom of M15 acts as a hydrogen bond acceptor for the backbone NH of His283. Strong hydrophobic interactions are formed between the CH 3 in the 3-position of the pyridine ring of M15 and the nonpolar side chains of Phe262, Tyr249 and Leu278. Figure 1. a) A. Comparison of the conformation of M15 in the crystal structure of TGF-b RI and that from the Gold docking calculations. Protein residues at a distance of less than 3.6 Š from the inhibitor are represented as sticks, while the inhibitor is shown as a ball and stick model mm ( dpi). b) Predicted protein-bound conformations of compounds 15D (red), M15 (white), L11B (yellow) and L17E (green). Residues at a distance of less than 3.6 Š from the inhibitors are represented as sticks, while the inhibitors are shown as ball and stick models. 3.2 Alignment Generation The quality of the spatial alignment of the compounds is very important in 3D-QSAR analysis. This challenging step is often impeded when there is not enough data on the biologically active conformations of the compounds in complex with their target protein. Since M15 is similar to many other ligands in this study, the predicted bound conformations of these ligands using GOLD were taken to be their biologically active conformations. Ten conformations were generated for each ligand using GOLD (version 3.0.1). The conformation with the strongest predicted binding affinity to TGF-b RI was extracted from the optimized inhibitor-tgf-b RI complex. To ob- QSAR Comb. Sci. 28, 2009, No , WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 1303

5 M. Yang et al. Table 1. d) ID Compound R 1 R 2 ChemScore pic 50 pic 50Pred 52 L5A A Me L5B A H L8A A Me *L8B A H L11A B Me Me L12A B H Me L15A B Me (CH 2 ) 3 OH L15B B H (CH 2 ) 3 OH *L17A B Me (CH 2 ) 3 N(CH 3 ) L17B B Me (CH 2 ) 3 N(CH 2 CH 3 ) L17C B Me (CH 2 ) 3 N(CH 2 ) 4 O L17D B Me (CH 2 ) 3 N(CH 2 ) L17E B Me (CH 2 ) 3 N(CH 2 ) L17F B H (CH 2 ) 3 N(CH 2 ) *L17G B H (CH 2 ) 3 N(CH 2 ) L11B C Me Me *L12B C H Me L13B C Me (CH 2 ) 3 OTHP L14B C H (CH 2 ) 3 OTHP tain a well-superposed set of ligands, the alignment based on GOLD scores was improved by manually selecting docking poses for ligands that showed deviations from the alignment. These conformations were aligned together inside the binding pocket of TGF-b RI and used directly for CoMFA analyses to explore the specific contributions of the electrostatic and steric effects to the biological activities. The set of best-ranked conformations according to GOLD contained 6 ligands (5A, 14A, M17, M21, L5A and L8A) whose structural features did not superimposed well with the rest of the ligands. For these 6 ligands, conformations with lower scores could be aligned with the majority of the other ligands. An average decrease of 2.0 in the GOLD fitness score was measured between the bestranked conformations and the conformations with optimum alignment with the rest of the ligands. These differences in the GOLD score can be considered to be of minor importance. Figure 2 illustrates the resulting conformational alignment of the 70 compounds taken from the docked conformations. All of the inhibitors were placed in the active site WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim QSAR Comb. Sci. 28, 2009, No ,

6 ChemScore ¼ 11.6þ2.73 pic 50 (1) (N ¼70, r 2 ¼0.531, F¼75.39, S¼2.02) This good correlation demonstrates that the binding conformations and binding model of the inhibitors are reasonable and reliable. Figure 2. Superimposition of 70 TGF-b RI inhibitors used for the 3D-QSAR studies. of TGF-b RI in a conformation similar to that of M15. CoMFA was performed using this set of aligned binding conformations. 3.3 Correlation Between Predicted Binding Affinity and Inhibitory Activity The predicted binding affinity (ChemScore) and the corresponding experimental pic 50 values are also listed in Table 1. A correlation was found between binding affinity and the pic 50 values through linear regression analysis, as shown in Figure 3. The correlation coefficient (r 2 ) was found to be CoMFA Statistical Results The statistical values obtained from the PLS analysis allow the evaluation of the quality of the model. PLS analysis was carried out for the 55 binding conformations of the training set of compounds, and the statistical quality and robustness of the model were determined with internal cross-validation procedures. Internal validation using leave-one-out cross-validation (LOO-CV) gave a q 2 of and a standard error of prediction (SDEP LOO )of A more rigorous cross-validation yielded an average q 2 10 of and SDEP 10 of using 10 random groups, and an average q 2 5 of and SDEP 5 of using five random groups. The nonvalidated PLS analysis gave a r 2 of and a standard error of estimate (SEE) of These q 2 and r 2 values reflect a statistically significant and robust model. These results are listed in Table 2. Steric field descriptors explain 53.8% of the variance while electrostatic descriptors explain 46.2%. The predicted activities of the 55 inhibitors and their experimental activities are listed in Table 1, and the correlation between them is depicted in Figure 4. Our results show that the activities predicted by the CoMFA model are in good agreement with the experimental data. The most rigorous test for the predictive ability of the model was done with the 15 compounds in the test set, which were completely excluded during the model build- Figure 3. Correlation between the predicted binding free energies (ChemScore) and the experimental activities (pic 50). Figure 4. Correlation between the experimental and predicted activities (pic 50 ). QSAR Comb. Sci. 28, 2009, No , WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 1305

7 M. Yang et al. 3.5 CoMFA Contour Maps Figure 5. CoMFA contour maps. Sterically favorable areas (contribution level of 80%) are shown in green (G). Sterically unfavorable areas (contribution level of 20%) are shown in yellow (Y). Compounds 15D, M15, L11B and L17E were chosen as the reference molecules in the maps. Figure 5 illustrates the contours of the steric fields, shown in green (bulky groups favored) and yellow (bulky groups not favored) colors. Figure 6 illustrates the electrostatic field contours, shown in red (electronegative substituents favored) and blue (electropositive substituents favored) colors. The individual contributions of the steric and the electrostatic favorable and unfavorable levels are initially fixed at 80% and 20%, respectively. To get a better understanding of the relationship between the binding pocket residues of TGF-b RI and the ligands, the contours of CoMFA and docked compounds 15D, M15, L11B and L17E were mapped back into the binding pocket. The contour plots confirmed the validity of the constructed models. For example, a small green contour region G1 around the C2-position of the pyridine ring indicates that certain bulkiness is favored in this region as it increases the activity of the inhibitor. The SAR also explained why good enzyme inhibition activity was observed with the unsubstituted pyridyl-2-yl analogue, as large branched groups at this position lead to reduced activity. The large green contour region G3 suggests that there is a requirement for bulky substituents in this region for potent TGF-b RI inhibitory activity. Compounds with large substitutions at N-3 of the benzimidazole ring, such as L17C and L17D, showed strong TGF-b RI inhibition, indicating ing process. The test set provides a good representation of the structures and activities of the compounds in the training set used to build the model. Both high-affinity and low-affinity compounds were predicted close to their experimentally measured values, yielding a predictive r 2 of with a SEE of Hence, apart from good statistical quality, the model also shows excellent predictive ability. Table 2. CoMFA results. Parameters CoMFA PLS statistics q 2 (CV LOO correlation coefficient) r 2 (correlation coefficient) S standard error of estimate ( SEE) F Optimal comp. 5 Field distribution (%) Steric 53.8 Electrostatic 46.2 Testing set r S Figure 6. CoMFA contour maps. Positively charged favorable areas (contribution level of 80%) are shown in blue (B). Positively charged unfavorable areas (contribution level of 20%) are shown in red (R). Compounds 15D, M15, L11B and L17E were chosen as the reference molecules in the?maps WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim QSAR Comb. Sci. 28, 2009, No ,

8 that the N-3 substituent may point away from the binding pocket and into the solvent. In general, the N-3 position of benzimidazoles tolerates many different polarity substitutions (Table 1). Two large yellow Y1 and Y2 regions in the steric contour maps are present in the vicinity of the R substituent, indicating that occupancy of this unfavorable position reduces TGF-b RI inhibitory activity. We investigated the substituent requirement at the 2-position of quinoline and found that chlorine along with various other groups (-OMe, -SEt) exhibited weak activity, with IC 50 values > 20 mm (Table 1). Small substituents are favored at the quinoline 6-position, while large substitutions diminish significantly the inhibitory activity. Most of the active compounds do not orient their R groups into this yellow contour region. A yellow contour region Y4 was observed around the 2-position of the pyrrole ring, NH of the amide linkage and bulky R substituents, suggesting that compounds with substituents in these regions will be less active than those compounds without substituents positioning this region. This is in good agreement with the fact that the imidazole moiety is too close to Asp351 to tolerate additional substituents. The yellow contour region Y3 suggests that increasing the bulkiness of the substituent at the 1-position of the pyridine ring will decrease the inhibitory activity because of increasing steric hindrance. The electrostatic properties (electron distribution) of the molecules and their complementarities with the binding site structure were considered in the interpretation of the electrostatic fields. On the left hand side of the binding pocket there is a volume R3 which is indicated by the CoMFA model to be favorable for negative partial charges. This prediction is in agreement with the presence of a positively charged Arg221 positioning the binding pocket, thus forming attractive interactions with the negatively charged moieties of the ligands. A large red R1 contour region is found near the 5-position of the 4- phenyl in compound M15, which indicates a preference for negatively charged substituents in this area. As we know, the hydrogen in the NH group is electrophilic, and further docking studies also showed that the NH groups of His283 may form H-bonds with the fluorine, carbonyl or OH group of compounds M15, M16, M17 and M22, which act as hydrogen bond acceptors. In addition, these groups may form strong electrostatic interactions with polar residues in the binding pocket. The B1 volume indicates that compounds with positively charged substituents at 6-position of the quinoline ring are favored for improving the biological activity. Electron-deficient and relatively small groups result in potent activities, such as those of compounds 7D, 7E and 7F. At the other end of the binding pocket, the model displays a favorable volume R2 for negative charges. This region is able to interact with electron-rich groups in the ligands. Compounds with electron-donating groups at this position have good activity in vitro. Further expansion of the SAR at the 7-position using novel amino alkoxy side chains (such as in compounds 14A 19) resulted in potent inhibitors in the TGF-b RI assay with IC 50 values ranging from 6 to 129 nm. One blue contour B2 is also found below the 7-position, indicating that negatively charged substituents in this area are unfavorable, which is one of the reasons why compounds 3D, M12 and M19 have low biological aactivities. Volume R6 is found next to the protonated residue Lys213, which can interact with electron-rich groups in the ligands, in agreement with the receptor structure. A small green contour (G1) is found above the 2-position and 3-position of the pyridine ring (Fig. 5). G1 is surrounded by Phe262, Leu278 and Val279, which form a hydrophobic region in the binding pocket of TGF-b RI. Compounds 7D, 7E, 7F, 8, 9, M4 and M5 all contain small hydrophobic 2-substituents or 3-substituents such as methyl or ethyl groups and may form hydrophobic interactions with these residues. A volume favoring hydrophilic groups was found at the bottom of the binding pocket, formed by the side chains of Tyr282, His283, Glu284, His285, Lys342 and Lys213, as well as a number of structural water molecules. This is one of the most hydrophilic surfaces of the ligand binding domain of TGF-b RI and is therefore able to interact with polar portions of the ligands. In addition, the solvation effect on potency was also evident as compounds with basic amines at the terminal position of the side chain have slightly higher activity in comparison with other substituted analogues. This is another reason why compounds 15A, 15B, 15D, 16A, 17A and 19 have higher biological activities. 3.6 Further Test of the 3D-QSAR Model Additionally, we analyzed a number of compounds reported earlier [26] to further test the predictive ability of the model. The results are shown in Table 3, showing that the 3D-QSAR model is reliable and useful at predicting the biological activity of other compounds. 4 Conclusions Molecular docking and the 3D-QSAR method CoMFA were used to explore the interactions influencing binding affinity of TGF-b RI ligands. The compounds considered were superimposed for 3D-QSAR analysis using molecular docking. The CoMFA was carried out on a series of 70 TGF-b RI binding compounds, representing a variety of pharmacological functionalities and several structural scaffolds. A 3D-QSAR model generated from such set of structurally diverse compounds has the potential of identifying TGF-b RI binding compounds with different scaffolds and functional groups. A statistically significant 3D-QSAR model and steric and electrostatic contour maps were built, allowing the visualization of the structural features that explain the differ- QSAR Comb. Sci. 28, 2009, No , WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 1307

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