DOI: 10.1002/minf.201100101
From Molecular Docking to 3D-Quantitative StructureActivity Relationships (3D-QSAR): Insights into the Binding Mode of 5-Lipoxygenase Inhibitors Gokcen Eren,[a] Antonio Macchiarulo,[b] and Erden Banoglu*[a]
Abstract: Pharmacological intervention with 5-Lipoxygenase (5-LO) is a promising strategy for treatment of inflammatory and allergic ailments, including asthma. With the aim of developing predictive models of 5-LO affinity and gaining insights into the molecular basis of ligand-target interaction, we herein describe QSAR studies of 59 diverse nonredox-competitive 5-LO inhibitors based on the use of molecular shape descriptors and docking experiments. These studies have successfully yielded a predictive model
able to explain much of the variance in the activity of the training set compounds while predicting satisfactorily the 5-LO inhibitory activity of an external test set of compounds. The inspection of the selected variables in the QSAR equation unveils the importance of specific interactions which are observed from docking experiments. Collectively, these results may be used to design novel potent and selective nonredox 5-LO inhibitors.
Keywords: 5-LO · Nonredox 5-LO inhibitor · 3D-QSAR · Molecular docking
1 Introduction Leukotriens (LTs) are critical mediators of immediate hypersensitivity reactions and inflammation.[1] 5-Lipoxygenase (5LO) is the key enzyme in the biosynthesis of LTs which are formed from arachidonic acid (AA) in two successive steps.[2] Upon cell activation, 5-LO converts AA to 5-hydroperoxy-6,8,11,14-eicosatetraenoic acid (5-HPETE) and subsequently into the LTA4 which is then transformed into LTB4 by LTA4 hydrolase. The metabolism of LTB4 by LTC4 synthase yields the cysteinyl-LTs (LTC4, D4, and E4). Thus formed LTs are involved in numerous inflammatory diseases and allergic disorders, such as allergic rhinitis, asthma, arthritis, and psoriasis.[3] In addition, the role of 5-LO products in carcinogenesis and cell survival, and in atherosclerosis has also recently been demonstrated.[4,5] Regarding these diverse biological properties, 5-LO represents a valuable target for the therapy of such ailments, and the development of 5-LO inhibitors is little advanced since zileuton is the only 5-LO inhibitor currently in clinical use.[6] 5-LO inhibitors can be classified into three main groups: (i) redox-active compounds which reduce the active-site iron, (ii) iron-ligand inhibitors that are excellent ligands for the iron of the enzyme active site,[7] and (iii) the nonredox type inhibitors which compete AA for binding to 5-LO without redox properties.[8] Unfortunately, despite the strong efforts in the development of 5-LO inhibitors, most potential candidates failed due to lack of efficacy in clinical studies or due to severe side effects.[9] Hence, the improvement of the pharmacological properties of 5-LO inhibitors in terms of efficacy and safety has been a major challenge and requires new leads with novel modes of molecular action. Mol. Inf. 2012, 31, 123 – 134
In the present work, our aim was to reveal beneficial information for designing functional inhibitors of 5-LO and to quantify the importance of each structural feature which may contribute to the activity of the ligand. Therefore, docking experiments of a collection of fifty-nine diverse nonredox 5-LO inhibitors have been carried out to get insights into the binding mode of the compounds at the enzyme active site and generate a molecular alignment according to the relative bioactive conformation. The alignment thus obtained has been instrumental to calculate molecular shape descriptors, and generate predictive models of target affinity after splitting the dataset into a training set of fifty compounds and a test set of nine small molecules. The best model is endowed with good regression (r2) and predictive (q2) coefficients as well as predictive performance on the external test set of compounds. Molecular descriptors selected in the QSAR equation are discussed in the lights of the results of docking experiments and al[a] G. Eren, E. Banoglu Gazi University, Faculty of Pharmacy, Department of Pharmaceutical Chemistry 06330 Ankara, Turkey tel.: + 90-312-2023236; fax: + 90-312-2235018 *e-mail: [email protected] [b] A. Macchiarulo Dipartimento di Chimica e Tecnologia del Farmaco, Universit di Perugia Via del Liceo 1, 06123 Perugia, Italy Supporting Information for this article is available on the WWW under http://dx.doi.org/10.1002/minf.201100101.
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lowed to disclose key interactions of nonredox 5-LO inhibitors.
2 Material and Methods 2.1 Dataset and Biological Data
The dataset of 59 structurally diverse compounds with nonredox 5-LO inhibitory activities (IC50) in cell-free systems was selected from the literature.[10–26] The IC50 values were converted into corresponding pIC50 values by the formula pIC50 = log(IC50), where IC50 represents the half maximal (50 %) inhibitory concentration of a molecule. The calculated pIC50 values ranged from 5.70 to 8.41. Structures of the compounds were sketched by using builder module of Molecular Operating Environment (MOE 2010.10) software[27] and energy minimized upto root mean square gradient of 0.05 kcal mol1 using MMFF94x force field. The energyminimized structures were stored in MOE database for descriptor calculation. 2.2 Docking Studies
The recently reported x-ray crystal structure of human 5-LO (PDB code: 3O8Y)[28] was downloaded from the Protein Data Bank of Brookhaven (PDB, www.rcsb.org/pdb) and the A chain was selected for docking studies using MOE. The protein was prepared by removing water molecules, optimizing hydrogen bonding and minimizing by AMBER99 force field (RMSD between Ca of two protein is 1.079 ; RMSD between the residues of binding site before and after the minimization procedure is 0.859 ). The molecular docking method was performed using the GOLD 5.0.1 software[29] that uses the GA. The binding region for the docking study was defined as a list of residues (Trp147, Phe177, Tyr181, Thr364, His367, Leu368, His372, Leu373, Ile406, Asn407, Leu414, Leu420, Phe421, His432, His550, Asn554, Trp599, His600, Ala603, Ala606). For each of the GA run, a maximum number of 100000 operations were performed with a population size of 100 individuals. Default cutoff values were employed. When the top three solutions attained RMSD values within 1.5 , GA docking was terminated. The scoring function GoldScore implemented in GOLD was used to rank the docking positions of the molecules, which were clustered together when differing by more than 0.75 RMSD. The conformation of molecules based on the best fitness score was further analyzed.
ordinate information but also require an absolute frame of reference. In order to reduce the number of descriptors, firstly discarded those showing constant values and having zero values for more than 5 % of compounds. To further improve the performance of the models avoiding over-fitting, multicollinear and redundant descriptors were discarded (pairwise correlation coefficient higher than 0.9). As a result, the total number of descriptors to be used for the generation of the QSAR models was 62, including potential energy descriptors (9), MOPAC descriptors (4), surface area, volume and shape descriptors (37), and conformation dependent charge descriptors (10). Stepwise regression analyses were used to identify the best equation relating the descriptors to the biological activity that was expressed as pIC50 (log IC50). 2.4 Training and Test Sets
With the aim of designing a training set and test set, kmeans cluster analysis (k-MCA)[30] was performed. As a result, the whole dataset was classified into five clusters from each of which about 15 % compounds were used to define the test set (9 compounds) and about 85 % compounds were used to define the training set (50 compounds). In order to identify potential outlier in the generation of predictive models, a principal component analysis (PCA)[31] was carried out. As external validation, models developed from the training set were used to predict the activity of the test set compounds. 2.5 Model Development and Validation
The correlation analysis of 3D shape descriptors and 5-LO inhibitory activity data was accomplished by different statistical methods, including genetic algorithm multiple linear regression analysis (GA-MLR), partial least squares analysis (PLS) and principal component regression analysis (PCR). The resulted QSAR models were validated by both leaveone-out (LOO) cross-validation procedure (internally) and test set predictions (externally). The quality of each regression model was evaluated using a squared correlation coefficient (r2), cross-validated squared correlation coefficient (q2), lack of fit (LOF), root mean square error (RMSE), crossvalidated root mean square error (RMSECV), Akaike’s information criterion (AIC), and Fisher value (F).
3 Results and Discussion 2.3 Molecular Descriptors
Molecular descriptors were calculated using QuaSAR module of MOE. They comprised: (i) internal 3D (i3D) descriptors (total number = 138), which are based on 3D coordinate information about each molecule and invariant to rotations and translations of the conformation; (ii) external 3D ( 3D) descriptors (total number = 10), which use 3D co124
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The entire dataset of molecules was docked into the very recently disclosed crystal structure of human 5-LO (PDB code: 3O8Y).[28] After docking experiments, the best and the most energetically favorable pose of each ligand was selected. The protein-ligand complexes were visualized and analyzed to identify both the most conserved and unconserved interactions.
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Leu368, Leu373, Leu414, Leu607 and Ile406 are conserved in all AA-metabolizing lipoxygenases and they form a structural region of conserved hydrophobic side chains. This region accommodates the pentadiene moiety of the substrate for catalysis.[28] Tyr181, Thr364, His600, Ala603, Ala606 and Trp599 are present only in 5-LO wherein they contribute to ligand binding. Above all Trp599 plays an important role in buttressing the cork at the end of the active site cavity. By forming interaction with Trp599, the ligand prevents the movement of this amino acid, thus the substrate may not be able to gain access to the catalytic site of the enzyme. According to the results of docking studies, CHp or pp contacts between the ligand and the side chain of Phe421 and Trp599 are the most conserved interactions (Figure 1), which are observed by the majority of dataset molecules. Likewise, another rather conserved interaction is the hydrophobic contact with Leu420. Additional contacts with Tyr181, Gln363, Thr364, Leu414, and His600 are also present and observed as hydrogen bonding or
pp interactions. Furthermore, some compounds are able to form specific but not conserved hydrogen bonding interactions with the side chain of Asn425 and the backbone of Ile673 (see Supporting Information, Figure S1). The most active molecule of the dataset (16) is able to make strong interactions through hydrogen bonding with the side chain of His600 and the backbone of Ile673, as well as CHp and pp contacts with Leu420, Phe421 and Trp599 (Figure 1). Remarkably, the coumarin moiety of 16 fits the edge of the active site, forming CHp and pp contacts with Phe421, a residue that seems to play a role in the catalytic activity of 5-LO. Among the collected 5-LO inhibitors, they could be clustered into subsets on the basis of their scaffold as shown in Figure 2. The overlay binding poses of the inhibitors from each subgroup suggest the pivotal role of the interactions with Leu420, Phe421 and Trp559 for enzyme inhibition (For detailed presentation of the most conserved interactions between dataset compounds and 5-LO with respect to the
Figure 1. (A) Interactions of 16 with the amino acids at the active site of 5-LO. (B) Ligand interaction diagram showing 16 docked.
Figure 2. The subsets of 5-LO inhibitors collected on the basis of their chemical scaffold.
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Table 1. Chemical structures of collected nonredox 5-LO inhibitors. Training set Compound
Compound
1
2
3
4
5
6
7
8
9
10
11
12
13
14
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Table 1. (Continued) Training set Compound
Compound
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
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Table 1. (Continued) Training set Compound
Compound
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
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Table 1. (Continued) Training set Compound
Compound
51
52
53
54
55
56
57
58
59
chemical class of the compounds, see Supporting Information, Figure S2). QSAR is a powerful approach to study relationships between biological activities and molecular properties and/or chemical structures.[32] As a consequence, QSAR studies are instrumental in enabling the prioritization of analogues before synthesis resulting from next in round virtual screening, and the design of focused small molecule libraries around active ligands.[33] Furthermore, being mostly ligandbased approaches, their results may provide additional clues on the molecular basis of ligand recognition of 5-LO inhibitors that complement those arising from structurebased docking experiments.
In order to obtain robust QSAR models, the dataset of 59 nonredox 5-LO inhibitors (Table 1) was clustered into five groups (Table 2) that were instrumental in defining the training and test sets of compounds as detailed in Material and Methods. 3D shape descriptors available in MOE were computed using the molecular alignment which resulted from docking experiments. In order to identify potential outlier in the generation of predictive models, PCA was carried out. The PCA score plot provides an estimate of the descriptor space of the training and test set molecules.[34] The cumulative percentage of variance explained by the first three principal components (PCs) of the dataset was 64.4 %. The
Table 2. Cluster analysis showing compounds under different clusters. Cluster no
Number of compounds in cluster
Serial numbers of compounds
1 2 3 4 5
16 5 15 5 18
4, 7, 8, 25, 33, 34, 35, 36, 39, 41, 42, 43, 45, 51, 55, 58 27, 44, 49, 50, 59 1, 2, 26, 28, 29, 30, 31, 32, 37, 40, 46, 47, 48, 56, 57 12, 14, 15, 16, 18 3, 5, 6, 9, 10, 11, 13, 17, 19, 20, 21, 22, 23, 24, 38, 52, 53, 54
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Table 3. The statistical parameters for the QSAR model obtained by using the PLS and PCR methods. Model
Method
Number of descriptor
r2
q2
LOF
RMSE
RMSECV
Test r2
1 2 3 4 5
PLS PLS PCR PCR GA-MLR
8 9 6 9 9
0.77 0.78 0.73 0.75 0.81
0.67 0.66 0.63 0.59 0.71
0.25 0.27 0.24 0.32 0.23
0.33 0.34 0.37 0.36 0.31
0.42 0.42 0.44 0.46 0.39
0.74 0.75 0.63 0.69 0.86
test set molecules occupy approximately the same descriptor space as that of the training set molecules, suggesting a good balance of chemical diversity in both sets of compounds. Accordingly, predictions of the test set compounds would be expected as relatively accurate (see Supporting Information, Figure S3). Different QSAR methods were explored to generate predictive models of 5-LO inhibition, including GA-MLR, PLS and PCR. In particular, 5-LO inhibitory activity of compounds was set as dependent variable, while 3D surface descriptors were the predictor variables. The statistical significance of the models was assessed using LOO cross-validation technique and external test set prediction. The statistical results of QSAR models are reported in Table 3. As a result, the best statistical model was obtained using the GA-MLR method (Model 5). The QSAR equation (Equation 1) associated with Model 5, is reported in Table 4, along with a brief description of the 9 descriptors that compose it.
pIC 50 ¼ 43:6516ð15:6417Þ0:0024ð0:0014Þ ðASA PÞ0:0001ð0:00004Þ ðpmiÞ þ 0:0001ð0:00005Þ ðpmiYÞ15:9066ð11:3547Þ ðvsurf CW1Þ þ4:8673ð3:9987Þ ðvsurf CW2Þ þ 1:0024ð0:3068Þ
ð1Þ
ðvsurf EDmin1Þ þ 0:4148ð0:1154Þ ðvsurf EWmin2Þ þ0:0051ð0:0035Þ ðvsurf HB1Þ þ 0:1412ð0:0643Þ ðvsurf IW8Þ As mentioned in Section, to design the training and the test set k-mean cluster analysis was used. To confirm that all structural classes in the test set are well represented in the training set, the distribution was checked and 2 compounds were moved from training set to test set in order to comply with the required chemical diversity in the test set. For comparison, the equation with the same descriptors was developed using random-based training-test set separation, and as shown in Table 5, the final model gener-
Figure 3. The hydrophobic and hydrophilic contour maps of 16 as docked into the active site of 5-LO. Hydrophobic regions are color coded in green and hydrophilic regions are color coded in magenta at 0.2 kcal/mol (A) and 0.5 kcal/mol (B) energy levels.
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Figure 4. Plot of experimental vs. predicted activity of cross-validated model obtained by GA-MLR.
Table 4. Coefficients and brief description of the descriptors that compose the QSAR equation of model 5. Descriptor
Coefficient
Definition
Class
Constant ASA_P pmi pmiY vsurf_CW1 vsurf_CW2 vsurf_EDmin1 vsurf_EWmin2 vsurf_HB1 vsurf_IW8
43.6516 0.0024 0.0001 0.0001 15.9066 4.8673 1.0024 0.4148 0.0051 0.1412
Total polar surface area Principal moment of inertia Principal moment of inertia (Y) Capacity factor at 0.2 kcal/mol Capacity factor at 0.5 kcal/mol Lowest hydrophobic energy The 2nd lowest hydrophilic energy H-bond donor capacity at 0.2 Hydrophilic integy moment at 6.0
Conformation dependent charge descriptors Surface area, volume and shape Descriptors
Table 5. The comparison of the equations and their statistical data obtained from random-based and design-based separation of the training and the test set. Random-based seperation
Design-based seperation
Equation pIC50 = 45.51270.0016 (ASA_P)0.0001 (pmi) + 0.0001 (pmiY)15.9652 (vsurf_CW1) + 4.2879 (vsurf_CW2) + 1.2174 (vsurf_EDmin1) + 0.4723 (vsurf_EWmin2) + 0.0059 (vsurf_ HB1) + 0.1716 (vsurf_IW8) 0.83 r2 0.75 q2 RMSE 0.30 RMSECV 0.38 LOF 0.23 AIC 44.77 F 21.94 0.69 Test r2
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pIC50 = 43.65160.0024 (ASA_P)0.0001 (pmi) + 0.0001 (pmiY)15.9066 (vsurf_CW1) + 4.8673 (vsurf_CW2) + 1.0024 (vsurf_EDmin1) + 0.4148 (vsurf_EWmin2) + 0.0051 (vsurf_ HB1) + 0.1412 (vsurf_IW8) 0.81 0.71 0.31 0.39 0.23 46.29 19.50 0.86
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ated using “design-based” separation gave statistically more robust results, (especially in test r2) indicating that the technique applied has increased the reliability of the equation. The equation associated with Model 5, is statistically significant and explains 81 % of the activity variance. The inspection of the coefficients reveals that positive values of vsurf_CW2, vsurf_Edmin1, vsurf_EWmin2 and vsurf_IW8 contribute significantly to the inhibition activity of the compounds, albeit to a different extend. Conversely, the values of vsurf_CW1 negatively affect the 5-LO inhibitory activity. The remaining descriptors (ASA_P, pmi, pmiY, vsurf_HB1) show only negligible values of coefficients, suggesting their marginal role in affecting the activity of the compounds. Since the coefficients of four descriptors mentioned above are negligible in size in comparison to the other descriptors, it was thought that these descriptors could be removed from the equation to have more meaningful statistical data as well as improvement of the model. However, contrary to the expected result, removal of the descriptors pmi and pmiY with the most negligible values of coefficients, did not improve the quality of the final model (Table 6), yielding slightly worsening of the statistical parameters. vsurf descriptors give informations about the surface polarity, hydrogen bond donor properties and hydrophilic contact surface of the molecules, and are of significant importance in the construction of the QSAR model. Within
Table 6. The comparison of the statistical parameters for the QSAR models obtained by leaving out the descriptors with the most negligible values of coefficients. Statistical data
Including “pmi” and “pmi” dis“pmiY” carded
“pmi” and “pmiY” discarded
r2 q2 RMSE RMSECV LOF AIC F Test r2
0.81 0.71 0.31 0.39 0.23 46.29 19.50 0.86
0.69 0.56 0.40 0.56 0.31 68.42 13.16 0.61
0.79 0.68 0.33 0.41 0.23 50.56 19.23 0.70
the vsurf descriptors, the most significant correlation was observed for the combination of CW1 and CW2 which are the capacity factors representing the ratio between hydrophilic regions and the molecular surface of the molecules. The hydrophilic capacity factor at 0.2 kcal/mol (CW1) has large negative loading while the hydrophilic capacity factor at 0.5 kcal/mol (CW2) takes positive weight in the correlation. The presence of a negative coefficient for CW1 and a positive coefficient for EDmin1 suggests that at the first energy level, large hydrophobic regions per surface unit contribute positively to the activity. On the contrary, the model include CW2, EWmin2 and IW8 with positive signs revealing that at the second energy level hydrophilic re-
Table 7. Experimental and predicted activity of compounds againts human 5-LO in cell-free systems. Training set
Test set
Compd
pIC50(exp)
pIC50(pred)
Compd
pIC50(exp)
pIC50(pred)
Compd
pIC50(exp)
pIC50(pred)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
6.10 6.52 7.58 7.22 7.63 7.65 7.16 7.28 7.57 7.82 7.82 8.04 7.57 8.15 7.92 8.40 7.85 8.00 7.79 7.85 7.83 7.79 7.83 7.68 7.08
6.65 7.03 7.23 7.61 7.32 7.10 7.19 7.38 7.52 7.95 7.38 7.93 7.95 8.07 7.69 7.70 7.88 7.86 7.56 7.81 7.78 8.01 8.24 7.25 7.38
26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
6.10 6.05 6.10 6.19 6.09 6.52 6.62 7.08 7.02 7.34 7.31 6.48 7.72 7.25 6.52 7.04 6.96 6.88 5.92 6.89 6.24 6.64 6.52 5.72 5.70
6.25 6.10 6.28 6.18 6.07 6.62 6.85 7.15 6.63 7.09 7.16 6.63 8.12 7.05 6.34 6.79 7.25 6.31 6.46 6.57 6.61 6.35 6.78 5.96 5.93
51 52 53 54 55 56 57 58 59
7.12 7.90 7.85 7.50 6.82 6.20 6.19 6.85 5.80
6.80 7.59 8.05 7.41 6.59 6.30 6.54 6.60 6.20
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gions are important for the activity. It should be mentioned that the first and the second levels of hydrophobic and hydrophilic energies correspond to different regions of the ligand. Those are shown by mapping the hydrophobic/hydrophilic surface at different contour levels (0.2 kcal/mol and 0.5 kcal/mol, Figure 3) on the most active ligand (16). The predicted values of pIC50 for the training set and the external test set as well as the correlation between experimental and predicted activity for Model 5 are reported in Table 7 and Figure 4, respectively. As a result, Model 5 provides information about the importance of hydrophobic interactions over the hydrophilic contacts for the inhibitory activity against 5-LO. Remarkably, these results are in agreement with those resulting from docking studies that highlight the importance of CH p and/or pp interactions of the compounds with Leu420, and especially Phe421 and Trp599.
4 Conclusions In the attempt to correlate the 5-LO inhibitory activity with molecular properties, docking experiments and QSAR studies were performed. The best predictive model was obtained with the GA-MLR method (Model 5). The inspection of the QSAR equation (Eq. 1) revealed key hydrophobic interactions that were in agreement with those observed from docking experiments. Our results demonstrated the power of docking and QSAR approach to explore the probable binding conformations and key interactions of ligands belonging to different structural classes at the active site of target 5-LO, and further provided useful information to understand the structural features in designing and identification of novel potent and selective nonredox inhibitors targeting human 5-LO. The future of this work now lies in the design of focused libraries of compounds that, being compliant to the key interactions evidenced by this study, may eventually lead to disclose novel nonredox 5-LO inhibitors.
Acknowledgements We would like to thank Guido Kirsten (Sr. Applications Scientist, Chemical Computing Group) for helpful suggestions. This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK No. 108S210) and Gazi University (BAP 02-2010/13).
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[27] MOE 2010.10 (Molecular Operating Environment), Chemical Computing Group, Inc., 1010 Sherbrooke Street West, Suite 910, Montreal, Quebec, Canada. [28] N. C. Gilbert, S. G. Bartlett, M. T. Waight, D. B. Neau, W. E. Boeglin, A. R. Brash, M. E. Newcomer, Science 2011, 331, 217 – 219. [29] M. L. Verdonk, J. C. Cole, M. J. Hartshorn, C. W. Murray, R. D. Taylor, Proteins 2003, 52, 609 – 623. [30] D. MacKay, in An Example Inference Task: Clustering. Information Theory, Inference and Learning Algorithms, Cambridge University Press, New York, 2003, ch. 20. [31] A. Khandelwal, P. Bahadduri, C. Chang, J. E. Polli, P. Swaan, S. Ekins, Pharm. Res. 2007, 24, 2249 – 2262.
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[32] Y. Yu, R. Su, L. Wang, W. Qi, Z. He, Med. Chem. Res. 2010, 19, 1233 – 1244. [33] P. Gedeck, R. A. Lewis, Curr. Opin. Drug Discov. Devel. 2008, 11, 569 – 575. [34] A. Khandelwal, M. D. Krasowski, E. J. Reschly, M. W. Sinz, P. W. Swaan, S. Ekins, Chem. Res. Toxicol. 2008, 21, 1457 – 1467.
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Received: June 20, 2011 Accepted: October 3, 2011 Published online: January 20, 2012
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From Molecular Docking to 3D-Quantitative StructureActivity Relationships (3D-QSAR): Insights into the Binding Mode of 5-Lipoxygenase Inhibitors Gokcen Eren,[a] Antonio Macchiarulo,[b] and Erden Banoglu*[a]
Abstract: Pharmacological intervention with 5-Lipoxygenase (5-LO) is a promising strategy for treatment of inflammatory and allergic ailments, including asthma. With the aim of developing predictive models of 5-LO affinity and gaining insights into the molecular basis of ligand-target interaction, we herein describe QSAR studies of 59 diverse nonredox-competitive 5-LO inhibitors based on the use of molecular shape descriptors and docking experiments. These studies have successfully yielded a predictive model
able to explain much of the variance in the activity of the training set compounds while predicting satisfactorily the 5-LO inhibitory activity of an external test set of compounds. The inspection of the selected variables in the QSAR equation unveils the importance of specific interactions which are observed from docking experiments. Collectively, these results may be used to design novel potent and selective nonredox 5-LO inhibitors.
Keywords: 5-LO · Nonredox 5-LO inhibitor · 3D-QSAR · Molecular docking
1 Introduction Leukotriens (LTs) are critical mediators of immediate hypersensitivity reactions and inflammation.[1] 5-Lipoxygenase (5LO) is the key enzyme in the biosynthesis of LTs which are formed from arachidonic acid (AA) in two successive steps.[2] Upon cell activation, 5-LO converts AA to 5-hydroperoxy-6,8,11,14-eicosatetraenoic acid (5-HPETE) and subsequently into the LTA4 which is then transformed into LTB4 by LTA4 hydrolase. The metabolism of LTB4 by LTC4 synthase yields the cysteinyl-LTs (LTC4, D4, and E4). Thus formed LTs are involved in numerous inflammatory diseases and allergic disorders, such as allergic rhinitis, asthma, arthritis, and psoriasis.[3] In addition, the role of 5-LO products in carcinogenesis and cell survival, and in atherosclerosis has also recently been demonstrated.[4,5] Regarding these diverse biological properties, 5-LO represents a valuable target for the therapy of such ailments, and the development of 5-LO inhibitors is little advanced since zileuton is the only 5-LO inhibitor currently in clinical use.[6] 5-LO inhibitors can be classified into three main groups: (i) redox-active compounds which reduce the active-site iron, (ii) iron-ligand inhibitors that are excellent ligands for the iron of the enzyme active site,[7] and (iii) the nonredox type inhibitors which compete AA for binding to 5-LO without redox properties.[8] Unfortunately, despite the strong efforts in the development of 5-LO inhibitors, most potential candidates failed due to lack of efficacy in clinical studies or due to severe side effects.[9] Hence, the improvement of the pharmacological properties of 5-LO inhibitors in terms of efficacy and safety has been a major challenge and requires new leads with novel modes of molecular action. Mol. Inf. 2012, 31, 123 – 134
In the present work, our aim was to reveal beneficial information for designing functional inhibitors of 5-LO and to quantify the importance of each structural feature which may contribute to the activity of the ligand. Therefore, docking experiments of a collection of fifty-nine diverse nonredox 5-LO inhibitors have been carried out to get insights into the binding mode of the compounds at the enzyme active site and generate a molecular alignment according to the relative bioactive conformation. The alignment thus obtained has been instrumental to calculate molecular shape descriptors, and generate predictive models of target affinity after splitting the dataset into a training set of fifty compounds and a test set of nine small molecules. The best model is endowed with good regression (r2) and predictive (q2) coefficients as well as predictive performance on the external test set of compounds. Molecular descriptors selected in the QSAR equation are discussed in the lights of the results of docking experiments and al[a] G. Eren, E. Banoglu Gazi University, Faculty of Pharmacy, Department of Pharmaceutical Chemistry 06330 Ankara, Turkey tel.: + 90-312-2023236; fax: + 90-312-2235018 *e-mail: [email protected] [b] A. Macchiarulo Dipartimento di Chimica e Tecnologia del Farmaco, Universit di Perugia Via del Liceo 1, 06123 Perugia, Italy Supporting Information for this article is available on the WWW under http://dx.doi.org/10.1002/minf.201100101.
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lowed to disclose key interactions of nonredox 5-LO inhibitors.
2 Material and Methods 2.1 Dataset and Biological Data
The dataset of 59 structurally diverse compounds with nonredox 5-LO inhibitory activities (IC50) in cell-free systems was selected from the literature.[10–26] The IC50 values were converted into corresponding pIC50 values by the formula pIC50 = log(IC50), where IC50 represents the half maximal (50 %) inhibitory concentration of a molecule. The calculated pIC50 values ranged from 5.70 to 8.41. Structures of the compounds were sketched by using builder module of Molecular Operating Environment (MOE 2010.10) software[27] and energy minimized upto root mean square gradient of 0.05 kcal mol1 using MMFF94x force field. The energyminimized structures were stored in MOE database for descriptor calculation. 2.2 Docking Studies
The recently reported x-ray crystal structure of human 5-LO (PDB code: 3O8Y)[28] was downloaded from the Protein Data Bank of Brookhaven (PDB, www.rcsb.org/pdb) and the A chain was selected for docking studies using MOE. The protein was prepared by removing water molecules, optimizing hydrogen bonding and minimizing by AMBER99 force field (RMSD between Ca of two protein is 1.079 ; RMSD between the residues of binding site before and after the minimization procedure is 0.859 ). The molecular docking method was performed using the GOLD 5.0.1 software[29] that uses the GA. The binding region for the docking study was defined as a list of residues (Trp147, Phe177, Tyr181, Thr364, His367, Leu368, His372, Leu373, Ile406, Asn407, Leu414, Leu420, Phe421, His432, His550, Asn554, Trp599, His600, Ala603, Ala606). For each of the GA run, a maximum number of 100000 operations were performed with a population size of 100 individuals. Default cutoff values were employed. When the top three solutions attained RMSD values within 1.5 , GA docking was terminated. The scoring function GoldScore implemented in GOLD was used to rank the docking positions of the molecules, which were clustered together when differing by more than 0.75 RMSD. The conformation of molecules based on the best fitness score was further analyzed.
ordinate information but also require an absolute frame of reference. In order to reduce the number of descriptors, firstly discarded those showing constant values and having zero values for more than 5 % of compounds. To further improve the performance of the models avoiding over-fitting, multicollinear and redundant descriptors were discarded (pairwise correlation coefficient higher than 0.9). As a result, the total number of descriptors to be used for the generation of the QSAR models was 62, including potential energy descriptors (9), MOPAC descriptors (4), surface area, volume and shape descriptors (37), and conformation dependent charge descriptors (10). Stepwise regression analyses were used to identify the best equation relating the descriptors to the biological activity that was expressed as pIC50 (log IC50). 2.4 Training and Test Sets
With the aim of designing a training set and test set, kmeans cluster analysis (k-MCA)[30] was performed. As a result, the whole dataset was classified into five clusters from each of which about 15 % compounds were used to define the test set (9 compounds) and about 85 % compounds were used to define the training set (50 compounds). In order to identify potential outlier in the generation of predictive models, a principal component analysis (PCA)[31] was carried out. As external validation, models developed from the training set were used to predict the activity of the test set compounds. 2.5 Model Development and Validation
The correlation analysis of 3D shape descriptors and 5-LO inhibitory activity data was accomplished by different statistical methods, including genetic algorithm multiple linear regression analysis (GA-MLR), partial least squares analysis (PLS) and principal component regression analysis (PCR). The resulted QSAR models were validated by both leaveone-out (LOO) cross-validation procedure (internally) and test set predictions (externally). The quality of each regression model was evaluated using a squared correlation coefficient (r2), cross-validated squared correlation coefficient (q2), lack of fit (LOF), root mean square error (RMSE), crossvalidated root mean square error (RMSECV), Akaike’s information criterion (AIC), and Fisher value (F).
3 Results and Discussion 2.3 Molecular Descriptors
Molecular descriptors were calculated using QuaSAR module of MOE. They comprised: (i) internal 3D (i3D) descriptors (total number = 138), which are based on 3D coordinate information about each molecule and invariant to rotations and translations of the conformation; (ii) external 3D ( 3D) descriptors (total number = 10), which use 3D co124
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The entire dataset of molecules was docked into the very recently disclosed crystal structure of human 5-LO (PDB code: 3O8Y).[28] After docking experiments, the best and the most energetically favorable pose of each ligand was selected. The protein-ligand complexes were visualized and analyzed to identify both the most conserved and unconserved interactions.
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Leu368, Leu373, Leu414, Leu607 and Ile406 are conserved in all AA-metabolizing lipoxygenases and they form a structural region of conserved hydrophobic side chains. This region accommodates the pentadiene moiety of the substrate for catalysis.[28] Tyr181, Thr364, His600, Ala603, Ala606 and Trp599 are present only in 5-LO wherein they contribute to ligand binding. Above all Trp599 plays an important role in buttressing the cork at the end of the active site cavity. By forming interaction with Trp599, the ligand prevents the movement of this amino acid, thus the substrate may not be able to gain access to the catalytic site of the enzyme. According to the results of docking studies, CHp or pp contacts between the ligand and the side chain of Phe421 and Trp599 are the most conserved interactions (Figure 1), which are observed by the majority of dataset molecules. Likewise, another rather conserved interaction is the hydrophobic contact with Leu420. Additional contacts with Tyr181, Gln363, Thr364, Leu414, and His600 are also present and observed as hydrogen bonding or
pp interactions. Furthermore, some compounds are able to form specific but not conserved hydrogen bonding interactions with the side chain of Asn425 and the backbone of Ile673 (see Supporting Information, Figure S1). The most active molecule of the dataset (16) is able to make strong interactions through hydrogen bonding with the side chain of His600 and the backbone of Ile673, as well as CHp and pp contacts with Leu420, Phe421 and Trp599 (Figure 1). Remarkably, the coumarin moiety of 16 fits the edge of the active site, forming CHp and pp contacts with Phe421, a residue that seems to play a role in the catalytic activity of 5-LO. Among the collected 5-LO inhibitors, they could be clustered into subsets on the basis of their scaffold as shown in Figure 2. The overlay binding poses of the inhibitors from each subgroup suggest the pivotal role of the interactions with Leu420, Phe421 and Trp559 for enzyme inhibition (For detailed presentation of the most conserved interactions between dataset compounds and 5-LO with respect to the
Figure 1. (A) Interactions of 16 with the amino acids at the active site of 5-LO. (B) Ligand interaction diagram showing 16 docked.
Figure 2. The subsets of 5-LO inhibitors collected on the basis of their chemical scaffold.
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Table 1. Chemical structures of collected nonredox 5-LO inhibitors. Training set Compound
Compound
1
2
3
4
5
6
7
8
9
10
11
12
13
14
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Table 1. (Continued) Training set Compound
Compound
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
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Table 1. (Continued) Training set Compound
Compound
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
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Table 1. (Continued) Training set Compound
Compound
51
52
53
54
55
56
57
58
59
chemical class of the compounds, see Supporting Information, Figure S2). QSAR is a powerful approach to study relationships between biological activities and molecular properties and/or chemical structures.[32] As a consequence, QSAR studies are instrumental in enabling the prioritization of analogues before synthesis resulting from next in round virtual screening, and the design of focused small molecule libraries around active ligands.[33] Furthermore, being mostly ligandbased approaches, their results may provide additional clues on the molecular basis of ligand recognition of 5-LO inhibitors that complement those arising from structurebased docking experiments.
In order to obtain robust QSAR models, the dataset of 59 nonredox 5-LO inhibitors (Table 1) was clustered into five groups (Table 2) that were instrumental in defining the training and test sets of compounds as detailed in Material and Methods. 3D shape descriptors available in MOE were computed using the molecular alignment which resulted from docking experiments. In order to identify potential outlier in the generation of predictive models, PCA was carried out. The PCA score plot provides an estimate of the descriptor space of the training and test set molecules.[34] The cumulative percentage of variance explained by the first three principal components (PCs) of the dataset was 64.4 %. The
Table 2. Cluster analysis showing compounds under different clusters. Cluster no
Number of compounds in cluster
Serial numbers of compounds
1 2 3 4 5
16 5 15 5 18
4, 7, 8, 25, 33, 34, 35, 36, 39, 41, 42, 43, 45, 51, 55, 58 27, 44, 49, 50, 59 1, 2, 26, 28, 29, 30, 31, 32, 37, 40, 46, 47, 48, 56, 57 12, 14, 15, 16, 18 3, 5, 6, 9, 10, 11, 13, 17, 19, 20, 21, 22, 23, 24, 38, 52, 53, 54
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Table 3. The statistical parameters for the QSAR model obtained by using the PLS and PCR methods. Model
Method
Number of descriptor
r2
q2
LOF
RMSE
RMSECV
Test r2
1 2 3 4 5
PLS PLS PCR PCR GA-MLR
8 9 6 9 9
0.77 0.78 0.73 0.75 0.81
0.67 0.66 0.63 0.59 0.71
0.25 0.27 0.24 0.32 0.23
0.33 0.34 0.37 0.36 0.31
0.42 0.42 0.44 0.46 0.39
0.74 0.75 0.63 0.69 0.86
test set molecules occupy approximately the same descriptor space as that of the training set molecules, suggesting a good balance of chemical diversity in both sets of compounds. Accordingly, predictions of the test set compounds would be expected as relatively accurate (see Supporting Information, Figure S3). Different QSAR methods were explored to generate predictive models of 5-LO inhibition, including GA-MLR, PLS and PCR. In particular, 5-LO inhibitory activity of compounds was set as dependent variable, while 3D surface descriptors were the predictor variables. The statistical significance of the models was assessed using LOO cross-validation technique and external test set prediction. The statistical results of QSAR models are reported in Table 3. As a result, the best statistical model was obtained using the GA-MLR method (Model 5). The QSAR equation (Equation 1) associated with Model 5, is reported in Table 4, along with a brief description of the 9 descriptors that compose it.
pIC 50 ¼ 43:6516ð15:6417Þ0:0024ð0:0014Þ ðASA PÞ0:0001ð0:00004Þ ðpmiÞ þ 0:0001ð0:00005Þ ðpmiYÞ15:9066ð11:3547Þ ðvsurf CW1Þ þ4:8673ð3:9987Þ ðvsurf CW2Þ þ 1:0024ð0:3068Þ
ð1Þ
ðvsurf EDmin1Þ þ 0:4148ð0:1154Þ ðvsurf EWmin2Þ þ0:0051ð0:0035Þ ðvsurf HB1Þ þ 0:1412ð0:0643Þ ðvsurf IW8Þ As mentioned in Section, to design the training and the test set k-mean cluster analysis was used. To confirm that all structural classes in the test set are well represented in the training set, the distribution was checked and 2 compounds were moved from training set to test set in order to comply with the required chemical diversity in the test set. For comparison, the equation with the same descriptors was developed using random-based training-test set separation, and as shown in Table 5, the final model gener-
Figure 3. The hydrophobic and hydrophilic contour maps of 16 as docked into the active site of 5-LO. Hydrophobic regions are color coded in green and hydrophilic regions are color coded in magenta at 0.2 kcal/mol (A) and 0.5 kcal/mol (B) energy levels.
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Figure 4. Plot of experimental vs. predicted activity of cross-validated model obtained by GA-MLR.
Table 4. Coefficients and brief description of the descriptors that compose the QSAR equation of model 5. Descriptor
Coefficient
Definition
Class
Constant ASA_P pmi pmiY vsurf_CW1 vsurf_CW2 vsurf_EDmin1 vsurf_EWmin2 vsurf_HB1 vsurf_IW8
43.6516 0.0024 0.0001 0.0001 15.9066 4.8673 1.0024 0.4148 0.0051 0.1412
Total polar surface area Principal moment of inertia Principal moment of inertia (Y) Capacity factor at 0.2 kcal/mol Capacity factor at 0.5 kcal/mol Lowest hydrophobic energy The 2nd lowest hydrophilic energy H-bond donor capacity at 0.2 Hydrophilic integy moment at 6.0
Conformation dependent charge descriptors Surface area, volume and shape Descriptors
Table 5. The comparison of the equations and their statistical data obtained from random-based and design-based separation of the training and the test set. Random-based seperation
Design-based seperation
Equation pIC50 = 45.51270.0016 (ASA_P)0.0001 (pmi) + 0.0001 (pmiY)15.9652 (vsurf_CW1) + 4.2879 (vsurf_CW2) + 1.2174 (vsurf_EDmin1) + 0.4723 (vsurf_EWmin2) + 0.0059 (vsurf_ HB1) + 0.1716 (vsurf_IW8) 0.83 r2 0.75 q2 RMSE 0.30 RMSECV 0.38 LOF 0.23 AIC 44.77 F 21.94 0.69 Test r2
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pIC50 = 43.65160.0024 (ASA_P)0.0001 (pmi) + 0.0001 (pmiY)15.9066 (vsurf_CW1) + 4.8673 (vsurf_CW2) + 1.0024 (vsurf_EDmin1) + 0.4148 (vsurf_EWmin2) + 0.0051 (vsurf_ HB1) + 0.1412 (vsurf_IW8) 0.81 0.71 0.31 0.39 0.23 46.29 19.50 0.86
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ated using “design-based” separation gave statistically more robust results, (especially in test r2) indicating that the technique applied has increased the reliability of the equation. The equation associated with Model 5, is statistically significant and explains 81 % of the activity variance. The inspection of the coefficients reveals that positive values of vsurf_CW2, vsurf_Edmin1, vsurf_EWmin2 and vsurf_IW8 contribute significantly to the inhibition activity of the compounds, albeit to a different extend. Conversely, the values of vsurf_CW1 negatively affect the 5-LO inhibitory activity. The remaining descriptors (ASA_P, pmi, pmiY, vsurf_HB1) show only negligible values of coefficients, suggesting their marginal role in affecting the activity of the compounds. Since the coefficients of four descriptors mentioned above are negligible in size in comparison to the other descriptors, it was thought that these descriptors could be removed from the equation to have more meaningful statistical data as well as improvement of the model. However, contrary to the expected result, removal of the descriptors pmi and pmiY with the most negligible values of coefficients, did not improve the quality of the final model (Table 6), yielding slightly worsening of the statistical parameters. vsurf descriptors give informations about the surface polarity, hydrogen bond donor properties and hydrophilic contact surface of the molecules, and are of significant importance in the construction of the QSAR model. Within
Table 6. The comparison of the statistical parameters for the QSAR models obtained by leaving out the descriptors with the most negligible values of coefficients. Statistical data
Including “pmi” and “pmi” dis“pmiY” carded
“pmi” and “pmiY” discarded
r2 q2 RMSE RMSECV LOF AIC F Test r2
0.81 0.71 0.31 0.39 0.23 46.29 19.50 0.86
0.69 0.56 0.40 0.56 0.31 68.42 13.16 0.61
0.79 0.68 0.33 0.41 0.23 50.56 19.23 0.70
the vsurf descriptors, the most significant correlation was observed for the combination of CW1 and CW2 which are the capacity factors representing the ratio between hydrophilic regions and the molecular surface of the molecules. The hydrophilic capacity factor at 0.2 kcal/mol (CW1) has large negative loading while the hydrophilic capacity factor at 0.5 kcal/mol (CW2) takes positive weight in the correlation. The presence of a negative coefficient for CW1 and a positive coefficient for EDmin1 suggests that at the first energy level, large hydrophobic regions per surface unit contribute positively to the activity. On the contrary, the model include CW2, EWmin2 and IW8 with positive signs revealing that at the second energy level hydrophilic re-
Table 7. Experimental and predicted activity of compounds againts human 5-LO in cell-free systems. Training set
Test set
Compd
pIC50(exp)
pIC50(pred)
Compd
pIC50(exp)
pIC50(pred)
Compd
pIC50(exp)
pIC50(pred)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
6.10 6.52 7.58 7.22 7.63 7.65 7.16 7.28 7.57 7.82 7.82 8.04 7.57 8.15 7.92 8.40 7.85 8.00 7.79 7.85 7.83 7.79 7.83 7.68 7.08
6.65 7.03 7.23 7.61 7.32 7.10 7.19 7.38 7.52 7.95 7.38 7.93 7.95 8.07 7.69 7.70 7.88 7.86 7.56 7.81 7.78 8.01 8.24 7.25 7.38
26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
6.10 6.05 6.10 6.19 6.09 6.52 6.62 7.08 7.02 7.34 7.31 6.48 7.72 7.25 6.52 7.04 6.96 6.88 5.92 6.89 6.24 6.64 6.52 5.72 5.70
6.25 6.10 6.28 6.18 6.07 6.62 6.85 7.15 6.63 7.09 7.16 6.63 8.12 7.05 6.34 6.79 7.25 6.31 6.46 6.57 6.61 6.35 6.78 5.96 5.93
51 52 53 54 55 56 57 58 59
7.12 7.90 7.85 7.50 6.82 6.20 6.19 6.85 5.80
6.80 7.59 8.05 7.41 6.59 6.30 6.54 6.60 6.20
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From Molecular Docking to (3D-QSAR)
gions are important for the activity. It should be mentioned that the first and the second levels of hydrophobic and hydrophilic energies correspond to different regions of the ligand. Those are shown by mapping the hydrophobic/hydrophilic surface at different contour levels (0.2 kcal/mol and 0.5 kcal/mol, Figure 3) on the most active ligand (16). The predicted values of pIC50 for the training set and the external test set as well as the correlation between experimental and predicted activity for Model 5 are reported in Table 7 and Figure 4, respectively. As a result, Model 5 provides information about the importance of hydrophobic interactions over the hydrophilic contacts for the inhibitory activity against 5-LO. Remarkably, these results are in agreement with those resulting from docking studies that highlight the importance of CH p and/or pp interactions of the compounds with Leu420, and especially Phe421 and Trp599.
4 Conclusions In the attempt to correlate the 5-LO inhibitory activity with molecular properties, docking experiments and QSAR studies were performed. The best predictive model was obtained with the GA-MLR method (Model 5). The inspection of the QSAR equation (Eq. 1) revealed key hydrophobic interactions that were in agreement with those observed from docking experiments. Our results demonstrated the power of docking and QSAR approach to explore the probable binding conformations and key interactions of ligands belonging to different structural classes at the active site of target 5-LO, and further provided useful information to understand the structural features in designing and identification of novel potent and selective nonredox inhibitors targeting human 5-LO. The future of this work now lies in the design of focused libraries of compounds that, being compliant to the key interactions evidenced by this study, may eventually lead to disclose novel nonredox 5-LO inhibitors.
Acknowledgements We would like to thank Guido Kirsten (Sr. Applications Scientist, Chemical Computing Group) for helpful suggestions. This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK No. 108S210) and Gazi University (BAP 02-2010/13).
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Received: June 20, 2011 Accepted: October 3, 2011 Published online: January 20, 2012
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