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Molecular docking and 3D-QSAR-based virtual screening of flavonoids as potential aromatase inhibitors against estrogen-dependent breast cancer a
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Manika Awasthi , Swati Singh , Veda P. Pandey & Upendra N. Dwivedi a
Department of Biochemistry, Bioinformatics Infrastructure Facility, Centre of Excellence in Bioinformatics, University of Lucknow, Lucknow 226007, Uttar Pradesh, India Accepted author version posted online: 04 Apr 2014.Published online: 28 Apr 2014.
To cite this article: Manika Awasthi, Swati Singh, Veda P. Pandey & Upendra N. Dwivedi (2014): Molecular docking and 3DQSAR-based virtual screening of flavonoids as potential aromatase inhibitors against estrogen-dependent breast cancer, Journal of Biomolecular Structure and Dynamics, DOI: 10.1080/07391102.2014.912152 To link to this article: http://dx.doi.org/10.1080/07391102.2014.912152
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Journal of Biomolecular Structure and Dynamics, 2014 http://dx.doi.org/10.1080/07391102.2014.912152
Molecular docking and 3D-QSAR-based virtual screening of flavonoids as potential aromatase inhibitors against estrogen-dependent breast cancer Manika Awasthi, Swati Singh, Veda P. Pandey and Upendra N. Dwivedi* Department of Biochemistry, Bioinformatics Infrastructure Facility, Centre of Excellence in Bioinformatics, University of Lucknow, Lucknow 226007, Uttar Pradesh, India Communicated by Ramaswamy H. Sarma
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(Received 13 June 2013; accepted 2 April 2014) Aromatase, catalyzing final step of estrogen biosynthesis, is considered a key target for the development of drug against estrogen-dependent breast cancer (EDBC). Identification and development of naturally occurring compounds, such as flavonoids, as drugs against EDBC is in demand due to their lesser toxicity when compared to those of synthetic ones. Thus, a three-dimensional quantitative structure–activity relationship, using comparative molecular field analysis (CoMFA) was done on a series of 45 flavonoids against human aromatase. A significant cross-validated correlation coefficient (q2) of 0.827 was obtained. The best predictive CoMFA model explaining the biological activity of the training and test sets with correlation coefficient values (r2) of 0.916 and 0.710, respectively, when used for virtual screening of a flavanoids database following molecular docking revealed a flavanone namely, 7-hydroxyflavanone beta-D-glucopyranoside showing highest predicted activity of 1.09 μM. In comparison to a well-established inhibitor of aromatase, namely 7-hydroxyflavanone (IC50: 3.8 μM), the derivative identified in the present study, namely 7-hydroxyflavanone beta-D-glucopyranoside exhibited about 3.5 folds higher inhibitory activity against aromatase. The result of virtual screening was further validated using molecular dynamics (MD) simulation analysis. Thus, a 25 ns MD simulation analysis revealed high stability and effective binding of 7-hydroxyflavanone beta-D-glucopyranoside within the active site of aromatase. To the best of our knowledge, this is the first report of CoMFA-based QSAR model for virtual screening of flavonoids as inhibitors of aromatase. Keywords: aromatase; breast cancer; 3D-QSAR; flavonoids; 7-hydroxyflavanone beta-D-glucopyranoside; molecular dynamics simulation
1. Introduction Breast cancer represents most frequently diagnosed cancer in females and ranks second as the leading cause of cancer death in women (American Cancer Society [ACS], 2013). According to the American Cancer Society report (2012), more than 2.9 million women in US suffered with history of invasive breast cancer (ACS, 2012). Around 50–80% of female breast cancers have been found to be estrogen-dependent where estrogen binding to receptor has been reported to stimulate tumor cell proliferation (Brueggemeier, Richards, Joomprabutra, Bhat, & Whetstone, 2001; Elledge & Osborne, 1997; Hulka & Moorman, 2001; Kelsey, Gammon, & John, 1993). Aromatase, a mitochondrial cytochrome P450 family enzyme produced at high levels in breast tissues, catalyzes the conversion of androgens to estrogens and acts as a key target in estrogen receptor-positive breast cancer therapy (Brodie & Njar, 1998; Brueggemeier, Hackett, & Diaz-Cruz, 2005). One of the effective modes of regulating adverse effects of estrogen has been suggested to be through the inhibition of aromatase thereby lowering the *Corresponding author. Email: [email protected] © 2014 Taylor & Francis
levels of circulating estrogen (Banting, Nicholls, Shaw, & Smith, 1989; Brueggemeier et al., 2005; Pasqualini, 2004). Both steroidal (e.g. exemestane) and non-steroidal (e.g. anastrozole) inhibitors of aromatase block the biosynthesis of estrogens by inhibiting aromatase in an irreversible or a reversible (competitive) manner, respectively, with non-steroidal inhibitors being more effective (Campos, 2004; Miller et al., 2008). The general mode of action of aromatase inhibitors has been suggested to be due to coordination of the inhibitor with iron atom of the catalytic heme group (Favia, Cavalli, Masetti, Carotti, & Recanatini, 2006; Guallar, Baik, Lippard, & Friesner, 2003). Though, both categories of aromatase inhibitors are promising but their long-term administration lead to development of several side effects. This has motivated the development of a safer substitute of steroidal and nonsteroidal inhibitors, based on natural products and their derivatives such as coumarin, lignin, and flavonoids (Bhatnagar, 2007; Sakamoto, Horiguchi, Oguma, & Kayama, 2010). Thus, along with the development of
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synthetic compounds, various classes of natural products also need evaluation in order to discover novel and relatively safer aromatase inhibitors for application as chemopreventive agents against breast cancer (Wessjohann, Ruijter, Garcia-Rivera, & Brandt, 2005). A large number of natural compounds belonging to wide variety of classes have been evaluated for their inhibitory potential towards aromatase. Thus, a total of 282 natural compounds consisting of 125 flavonoids, 10 alkaloids, 36 terpenoids, 18 lignans, 16 xanthones, 19 peptides, 15 fatty acids, and 43 miscellaneous compounds have been tested for their inhibitory potential against aromatase (Balunas, Su, Brueggemeier, & Kinghorn, 2008). Plant-derived flavonoids, also known as phytoestrogens, contribute a significant proportion to our daily diet including food supplements and neutraceuticals. Dietary flavonoids have been reported to significantly reduce incidence of breast cancer among women of Asian and Oriental origin (Adlercreutz, 1995; Fink et al., 2006; Jordan, Mittal, Gosden, Koch, & Lieberman, 1985; Peterson et al., 2003). Several naturally occurring flavonoids and their derivatives have been reported to possess inhibitory effects on aromatase and therefore, have invited considerable attention in development of these molecules as drugs against breast cancer (Di Carlo, Mascolo, Izzo, & Capasso, 1999; Jeong, Shin, Kim, & Pezzuto, 1999; Kao, Zhou, Sherman, Laughton, & Chen, 1998). Thus, a detailed quantitative structure–activity relationship analysis of these compounds would provide insight into identification and optimization of the lead molecules and in combinatorial library-based drug designing. Computer-aided drug discovery (CADD) involves two main strategies, namely target-based and ligandbased approaches (Costanzi, Tikhonova, Harden, & Jacobson, 2009; Vilar, Cozza, & Moro, 2008). Targetbased drug discovery (TBDD) depends on the structure of the target and its interactions with the ligands. On the other hand, ligand-based drug discovery (LBDD) is dependent on the structural information and molecular properties of known ligands only. It does not require the knowledge of target structure or its interactions with ligands. Both these methods have their limitations (Vilar & Costanzi, 2012). Therefore, in order to complement the advantages and limitations of each other, a combined approach utilizing both the methods would provide a better approach for virtual screening of a large set of diverse chemical libraries leading to an unambiguous identification of a lead compound. Though molecular dynamics (MD) and pharmacophore-based QSAR analyses for lead identification in rational drug designing against estrogen-dependent breast cancer (EDBC) with respect to aromatase and flavonoids have been reported earlier (Mirzaie, Chupani, Asadabadi, Shahverdi, & Jamalan, 2013; Neves, Dinis, Colombo, & Sá e Melo, 2009; Schuster et al., 2006), however,
comparative molecular field analysis (CoMFA)-based QSAR analyses have not yet been done for this purpose. Thus, in the present paper, we report results of molecular docking of a series of 45 flavonoids to the crystal structure of human placental aromatase. The best binding modes were utilized for alignment of the ligands and analyzed by three-dimensional quantitative structureactivity relationship (3D-QSAR) studies using reported inhibitory activity (IC50 values) of the selected flavonoids (Balunas et al., 2008). The resulting 3DQSAR model was further used to screen a database of 6850 flavonoids (http://www.metabolome.jp/download/ flavonoid/) for predicting their inhibitory potential against aromatase and to provide further insights into the design of novel inhibitors of aromatase. The results of screening were further validated using MD simulation analysis. To the best of our knowledge, this is the first report of CoMFA-based QSAR model for virtual screening of flavonoids as inhibitors of aromatase. 2. Materials and methods 2.1. Computational software All computational analyses were done using various modules of Accelrys Discovery Studio (DS) 3.1 as described in the following sections. 2.2. Preparation of protein structure The X-ray crystal structure of human placental aromatase in complex with androstenedione (ASD) (PDB ID: 3EQM) was retrieved from the Protein Data Bank (PDB) (Berman et al., 2000) and necessary changes were done. 2.3. Preparation and optimization of ligand structures Forty-five flavonoids including 24 flavones, 14 flavanones, 3 isoflavanones, 1 isoflavan, 1 catechin, 1 flavanol, and 1 coumestan with their IC50 values against aromatase were selected (Balunas et al., 2008) and their structures were sketched. The IC50 values were converted to pIC50 [i.e. −log (IC50)] for normalization of the data. Energy minimization was performed using CHARMm forcefield and conformations were generated for each compound using the BEST algorithm. Furthermore, the compounds were classified in four categories namely strongly active (IC50: 50 μM). 2.4. Molecular docking and binding energy calculations Aromatase active site was predicted based on the PDB site record using ‘Define and Edit Binding Site module’
Virtual screening of flavonoids as aromatase inhibitors against breast cancer and subsequently docking of the selected 45 flavonoids was performed using ‘LibDock module.’ Binding energy of different docked conformations was calculated using ‘Calculate Binding Energy module.’ Based on the binding energy and docking score, the best conformation of each compound was screened out for further 3D-QSAR analysis.
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2.6. Virtual screening For virtual screening, both the CADD strategies (TBDD and LBDD) were combined to predict inhibitory potential of compounds from a flavonoid database and provide further insights into the design of novel aromatase inhibitors. The step-wise strategy for structure and ligand-based virtual screening of flavonoid database is represented in Figure 1.
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2.5. 3D-QSAR analysis 2.5.1. Molecular alignment
2.6.1. Target-based screening (docking-based scoring)
Molecular alignment was done by ‘Align to Selected Substructure module’ using isolicoflavonol, the most active among selected compounds, as template.
Flavonoids database consisting of 6850 flavonoids from various plant sources was used for target-based screening (TBS). Flavonoids were docked with aromatase (PDB ID: 3EQM) as target using ‘LibDock module.’ The docked poses of all database molecules were subjected to consensus scoring based on 10 scoring functions, LigScore1, LigScore2, PLP1, PLP2, Jain, PMF, PMF04, Ludi Energy Estimate 1, Ludi Energy Estimate 2, and Ludi Energy Estimate 3, to screen molecules having more than five consensus score.
2.5.2. Data-set generation Based on the structural diversity and wide range of activity, 31 compounds were selected as training set and remaining 14 as test set. 2.5.3. CoMFA The aim of CoMFA is to derive a correlation between the biological activity of a set of molecules and their three-dimensional shape with electrostatic and hydrogenbonding characteristics (Cramer, Patterson, & Bunce, 1988). The pre-aligned ligands were placed in a threedimensional grid of 2 Å spacing with 6 Å extension and the energy potentials were calculated by applying CHARMm forcefield. A+1 point charge and a sp3hybridized carbon atom with 1.73 Å radius are used as the electrostatic potential probe and Van der Waals potential probe, respectively, and the distance-dependent dielectric constant was applied for solvation effect. Partial least square (PLS) model was generated using steric and electrostatic descriptors as independent variables and the predicted IC50 values as dependent variables.
2.6.2. Ligand-based screening (Activity prediction using CoMFA 3D-QSAR model) The database compounds screened out through the TBS were further subjected to molecular alignment and fingerprint-based similarity screening. Molecules exhibiting consensus score of >5 were superimposed over the reference data-set of 45 selected flavonoids using ‘Align to Selected Substructure module.’ The resulting aligned structures were further subjected to fingerprint-based similarity screening and compounds exhibiting more than 50% similarity with the reference data-set were subjected to activity prediction through earlier generated 3D-QSAR model using ‘Calculate Molecular Properties module.’ Screened-out molecules were ranked according to their pIC50 values.
Figure 1. Step-wise strategy for target- and ligand-based virtual screening of flavonoids database (TBS = Target-based screening; LBS = Ligand-based screening).
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Table 1.
Names and structures of the selected 45 flavonoids along with their IC50 values.
Compound number
Compound name
1
Isolicoflavonol
.1
2
(2S)-2′,4′-Dihydroxy-2′′-(1-hydroxy-1-methylethyl)dihydrofuro[2,3-h] flavanone
.1
3
7-Hydroxyflavone
.2
4
8-Prenylnaringenin
.2
5
(2S)-Abyssinone II
.4
6
Chrysin
.5
7
Apigenin
1.2
8
7,4′-Dihydroxyflavone
2
9
(2S)-5,7,2′,4′-Tetrahydroxyflavanone
2.2
10
Naringenin
2.9
11
7-Methoxyflavone
3.2
Structure
IC50 (μm)
(Continued)
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Table 1.
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(Continued).
Compound number
Compound name
12
(2S)-Euchrenone a7
3.4
13
7-Hydroxyflavanone
3.8
14
Eriodictyol
5.3
15
5,7,4′-Trihydroxy-3′-Methoxyflavone
7.2
16
7,8-Dihydroxyflavone
8
17
Flavone
8
18
Flavanone
8
19
7-Methoxyflavanone
8
20
Luteolin
8.6
21
Broussoflavonol F
9.7
22
4′-Hydroxyflavanone
10
23
5,7-Dihydroxyflavanone
10
Structure
IC50 (μm)
(Continued)
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Table 1.
(Continued).
Compound number
Compound name
24
Quercetin
12
25
Methylequol
20
26
5,7,2′,4′-Tetrahydroxy-3-geranylflavone
24
27
5,7,4′-Trihydroxy-3′-methoxyflavanone
25
28
Coumestrol
25
29
5,7,3′-Trihydroxy-4′-Methoxyflavone
27
30
7,3′,4′-Trihydroxyflavone
38
31
3′,4′-Dimethoxyflavone
42
32
7,3′,4′,5′-Tetrahydroxyflavone
45
33
3′-Hydroxyflavone
73
34
6-Hydroxyflavone
80
35
6,4′-Dihydroxyflavone
90
Structure
IC50 (μm)
(Continued)
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Table 1.
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(Continued).
Compound number
Compound name
36
5-Hydroxyflavone
100
37
(−)-Epigallocatechin
100
38
5,4′-Dihydroxyflavone
120
39
Isoflavanone
120
40
Flavan-4-ol
120
41
3-Hydroxyflavone
140
42
3′,4′-Dihydroxyflavanone
160
43
4′-Hydroxyisoflavanone
160
44
2-Hydroxyisoflavanone
170
45
4′-Hydroxyflavone
180
2.7. MD simulation Interactions of human aromatase with the best hit identified from virtual screening of flavonoid database were investigated through 25 ns of MDsimulation using GROMACS 4.5.5 package with GROMOS96 43a1 force field. A parallel MD simulation analysis of aromatase with its co-crystallized ligand, ASD was also done. The docking poses of aromatase with its ligands were prepared for MD simulation through mild minimization and
Structure
IC50 (μm)
solvation within a water filled three-dimensional cube of 1 Å spacing. System was neutralized and further minimized. The complex structure was heated to 300 K and equilibrated for 100 ps in NVT ensemble and another 100 ps in NPT ensemble. After heating and equilibration the complex structure of aromatase with its ligands was subjected to production run of 25 ns in NPT ensemble. PRODRG web server (Schüttelkopf & van Aalten, 2004) was used to generate topologies and coordinations of
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ligands. Default values of GROMACS were assigned for determination of hydrogen-bonding interactions between aromatase and its ligands.
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3. Results and discussion 3.1. Sketching of compounds Chemical structures of 45 selected flavonoids were sketched. Name, structure, and IC50 values of all the 45 flavonoids are presented in Table 1. In order of their IC50 values, each flavonoid was assigned a compound number from 1–45 with compound 1 having lowest while compound 45 having highest IC50 value. It is noteworthy that compound numbers 1–13, 14–23, 24–32 and 33–45, based on their IC50 values, were found to be strongly, moderately, weakly active, and inactive, respectively. 3.2. Molecular docking and binding energy calculations Molecular docking of all the selected 45 flavonoids with aromatase was performed using LibDock module. Top 20 conformations of each of the 45 compounds, based on their LibDock score, were selected and their binding energies were calculated. For the purpose of validation of our docking results, two most active compounds namely isolicoflavonol (Table 1, compound no. 1) and (2S)-2′,4′-Dihydroxy-2′′-(1-hydroxy-1-methylethyl) dihydrofuro[2,3-h] flavanone (Table 1, compound no. 2) were analyzed for the residues involved in binding with aromatase at the active site. Results are shown in Figure 2. Thirteen residues, namely Arg115, Ile133, Phe221, Trp224, Ile305, Ala306, Asp309, Thr310, Val370, Val373, Met374, Leu477, and Ser478 which were
involved in the binding of compounds 1 and 2, at the active site of aromatase, were found to be common. Results of present docking analysis were validated by comparing the interacting residues involved in the binding of the ligand, ASD, co-crystallized with aromatase (PDB ID: 3EQM). Thus, a comparison of the interacting residues involved in binding of the ligands (compounds 1 and 2) in the present study with those of the co-crystallized ligand (ASD) revealed that all the 13 interacting residues were found to be common thereby validating our result of docking (Karkola & Wähälä, 2009; Murthy, Nagaraju, Sastry, Rao, & Sastry, 2006).
Figure 3. Molecular alignment of the best conformations of each of the 45 selected flavonoids as obtained from the results of docking analysis. Atom color scheme: C, blue; O, red.
Figure 2. Three-dimensional view showing the residues involved in the binding of isolicoflavonol (A) and (2S)-2′,4′-dihydroxy-2′′(1-hydroxy-1-methylethyl) dihydrofuro [2,3-h] flavanone (B), the two most active flavonoids among the 45 selected ones, at the active site of aromatase.
Virtual screening of flavonoids as aromatase inhibitors against breast cancer 3.3. 3D-QSAR analysis Conformations with minimum binding energies were selected for 3D-QSAR analysis using the CoMFA modelling method. 3.3.1. Molecular alignment The accuracy of 3D-QSAR analyses is considered to be extremely sensitive to molecular alignment rules and overall orientation of the aligned compounds. One of the most important steps in the alignment-based 3D-QSAR
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methods is the superimposition of the bioactive conformations of all the molecules over each other (Akamatsu, 2002; Kim, 1995). Therefore, molecular alignment of the best selected conformations of all 45 flavonoids retrieved from docking studies was done and results are presented in Figure 3. 3.3.2. PLS regression analysis In order to minimize the number of descriptors in CoMFA, PLS regression technique was used, which combines the
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Table 2. Experimental, predicted and residual pIC50 of the selected training (TR) and test (TE) sets flavonoids using CoMFA 3DQSAR model. Assigned compound numbers (cf. Table 1) are indicated within parenthesis. S. No.
Training (TR)/Test (TE) sets (Compound number)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
TR (2) TR (4) TR (5) TR (6) TR (11) TR (12) TR (15) TR (16) TR (17) TR (18) TR (19) TR (20) TR (21) TR (23) TR (25) TR (26) TR (27) TR (28) TR (30) TR (31) TR (32) TR (33) TR (35) TR (36) TR (37) TR (38) TR (40) TR (41) TR (42) TR (44) TR (45) TE (1) TE (3) TE (7) TE (8) TE (9) TE (10) TE (13) TE (14) TE (22) TE (24) TE (29) TE (34) TE (39) TE (43)
Experimental pIC50
Predicted pIC50
Residual pIC50
7.000 6.698 6.397 6.301 5.494 5.468 5.142 5.096 5.096 5.096 5.096 5.065 5.013 5.000 4.698 4.619 4.602 4.602 4.443 4.376 4.346 4.136 4.045 4.000 4.000 3.920 3.920 3.853 3.795 3.769 3.744 7.000 6.698 5.920 5.698 5.657 5.537 5.420 5.275 5.000 4.920 4.568 4.096 3.920 3.795
7.03907 6.75162 6.18546 6.08957 4.90286 5.42204 5.257 4.55967 5.27055 5.27391 4.79818 4.42191 4.84351 5.46886 4.75781 4.8221 4.61711 4.75156 4.61808 4.62617 4.50526 4.2298 3.93444 4.17235 4.14796 4.16586 4.20564 3.81865 3.79497 3.69449 3.68354 6.896 5.62418 5.66223 5.4691 5.56399 5.35725 5.41959 5.86434 5.035 4.69831 5.11488 5.24827 4.06833 3.48253
−.03907 −.05362 .21154 .21143 .59114 .04596 −.115 .53633 −.17455 −.17791 .29782 .64309 .16949 −.46886 −.05981 −.2031 −.01511 −.14956 −.17508 −.25017 −.15926 −.0938 .11056 −.17235 −.14796 −.24586 −.28564 .03435 .00003 .07451 .06046 .104 1.07382 .25777 .2289 .09301 .17975 .00041 −.58934 .035 .22169 −.54688 −1.15227 −.14833 .31247
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characteristics of two different techniques namely principal component analysis and multiple linear regression (Geladi & Kowalski, 1986; Wold et al., 1984). The CoMFA model was constructed with 31 compounds in the training set, which was validated by the remaining 14 molecules in the test set. The results of 3D-QSAR were interpreted on the basis of the coefficient of correlation between experimental activity and predicted activity of the molecules based on CoMFA analysis. Table 2 shows the predicted activities of the training and test set compounds, respectively, along with their residual values i.e. difference between the experimental and predicted activities of the compounds. The obtained model showed very good predictability as the predicted activity values of all the compounds in the test set were very close to the observed activity values. Correlation between experimental activity and predicted activity of training and test sets were calculated and results are shown in Figure 4(A) and (B), respectively. The best predictive CoMFA model gave cross-validated q2 value equal to 0.827 and non-cross-validated r2 value equal to 0.916. The predicted r2 of 14 compounds in the test set was 0.710, which showed good correlation. Results of the regression statistics obtained from 3D-QSAR CoMFA analysis are given in Table 3.
3.4. Virtual screening For virtual screening, both the target- and ligand-based methods were used.
3.4.1. TBS Target-based virtual screening method uses structural information of the protein to identify the expected conformation and orientation of the ligand into its active site (Klebe, 2006; Shoichet, 2004). Thus, 6850 flavonoids from the flavonoids database were subjected to docking with crystal structure of aromatase. Out of 6850 flavonoids, 5794 flavonoids docked successfully. The successfully docked 5794 flavonoids were subjected to the previously mentioned 10 scoring functions followed by the consensus scoring. Based on the set criteria of consensus score more than five, 1906 flavonoids qualified for further screening. In view of the fact that docking scores are not very effective in the unambiguous ranking of active compounds, based on their affinities or potencies, rather they distinguish between active and inactive compounds, the screened-out flavonoids (1906) were further subjected to ligand-based screening using CoMFA 3D-QSAR model.
Figure 4. Plot of PLS regression analysis showing linear relationship between experimental and predicted activities of the training (A) and test (B) data-sets as analyzed by CoMFA 3D-QSAR model. Activity was expressed in terms of pIC50. Table 3. Statistical results of CoMFA 3D-QSAR model. NOC, number of components; N, number of compounds in training set; r2, coefficient of correlation; q2, cross-validated coefficient of correlation. N
r2
r2(adjusted)
r2test
q2 (cross-validation)
NOC
RMS residual error
31
.916
.8850
.710
.827
6
.2993
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Table 4. Compound ID, names, structures, predicted pIC50 and IC50 values of ten best hits obtained from virtual screening of flavonoids database. All the selected flavonoids exhibited highest consensus score of 10. S. Compound No. ID
Name
Predicted pIC50
Predicted IC50 (μM)
1
FL2F19GS 0001
7-Hydroxyflavanone beta-D-glucopyranoside
5.96245
1.09
2
FL3FAEGS 0010
Diosmetin 3′-glucoside
5.91168
1.23
3
FL2FCCGS 0001
7-O-Methyleriodictyol 3′-O-glucoside
5.8866
1.31
4
FL5FAANI 0008
Glyasperin A; 3,5,7-Trihydroxy-2-[4-hydroxy-3-(3-methyl-2butenyl)phenyl]-6-(3-methyl-2-butenyl)-4H-1-benzopyran-4-one
5.86114
1.38
5
FL2FAANI 0024
Macarangaflavanone A; 5,7,4′-Trihydroxy-3′-geranylflavanone
5.80099
1.55
6
FL2FACNN 0001
Silandrin
5.71098
1.91
7
FL2FABNI 0006
5,7-Dihydroxy-4′-methoxy-8-C-prenyl-3′-(3-hydroxy-3methylbutyl)flavanone
5.62492
2.39
8
FL2FAAGS 0022
Naringenin 4′-O-alpha-L-rhamnopyranoside; 5,7,4′Trihydroxyflavanone 4′-rhamnoside
5.60305
2.50
9
FL3FAAGS 0008
Apigenin 7-galactoside
5.55759
2.80
10
FL2FADGS 0002
Viscumiside A
5.48259
3.29
Structure
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3.4.2. Ligand-based method (LBS) For ligand-based screening (LBS) of 1906 screened-out flavonoids, three different approaches namely molecular alignment, fingerprint similarity, and activity prediction were applied. Thus, 1906 flavonoids obtained after TBS were superimposed on the reference data-set of the 45 selected flavonoids, and 1634 structures obtained after molecular alignment were further subjected to fingerprint similarity-based screening. As a result, 161 flavonoids showing minimum 50% similarity to the reference set of selected 45 flavonoids were obtained which were further subjected to activity prediction using CoMFA 3D-QSAR model and were ranked in order of their predicted activities (pIC50 values). These, flavonoids were found to exhibit the pIC50 values in the range of 3.4568–5.96245 which corresponded to IC50 values in the range of 350 to 1.09 μM. Out of 161 flavonoids, 115 were found to be strongly active (with IC50 value of
Journal of Biomolecular Structure and Dynamics Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/tbsd20
Molecular docking and 3D-QSAR-based virtual screening of flavonoids as potential aromatase inhibitors against estrogen-dependent breast cancer a
a
a
a
Manika Awasthi , Swati Singh , Veda P. Pandey & Upendra N. Dwivedi a
Department of Biochemistry, Bioinformatics Infrastructure Facility, Centre of Excellence in Bioinformatics, University of Lucknow, Lucknow 226007, Uttar Pradesh, India Accepted author version posted online: 04 Apr 2014.Published online: 28 Apr 2014.
To cite this article: Manika Awasthi, Swati Singh, Veda P. Pandey & Upendra N. Dwivedi (2014): Molecular docking and 3DQSAR-based virtual screening of flavonoids as potential aromatase inhibitors against estrogen-dependent breast cancer, Journal of Biomolecular Structure and Dynamics, DOI: 10.1080/07391102.2014.912152 To link to this article: http://dx.doi.org/10.1080/07391102.2014.912152
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Journal of Biomolecular Structure and Dynamics, 2014 http://dx.doi.org/10.1080/07391102.2014.912152
Molecular docking and 3D-QSAR-based virtual screening of flavonoids as potential aromatase inhibitors against estrogen-dependent breast cancer Manika Awasthi, Swati Singh, Veda P. Pandey and Upendra N. Dwivedi* Department of Biochemistry, Bioinformatics Infrastructure Facility, Centre of Excellence in Bioinformatics, University of Lucknow, Lucknow 226007, Uttar Pradesh, India Communicated by Ramaswamy H. Sarma
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(Received 13 June 2013; accepted 2 April 2014) Aromatase, catalyzing final step of estrogen biosynthesis, is considered a key target for the development of drug against estrogen-dependent breast cancer (EDBC). Identification and development of naturally occurring compounds, such as flavonoids, as drugs against EDBC is in demand due to their lesser toxicity when compared to those of synthetic ones. Thus, a three-dimensional quantitative structure–activity relationship, using comparative molecular field analysis (CoMFA) was done on a series of 45 flavonoids against human aromatase. A significant cross-validated correlation coefficient (q2) of 0.827 was obtained. The best predictive CoMFA model explaining the biological activity of the training and test sets with correlation coefficient values (r2) of 0.916 and 0.710, respectively, when used for virtual screening of a flavanoids database following molecular docking revealed a flavanone namely, 7-hydroxyflavanone beta-D-glucopyranoside showing highest predicted activity of 1.09 μM. In comparison to a well-established inhibitor of aromatase, namely 7-hydroxyflavanone (IC50: 3.8 μM), the derivative identified in the present study, namely 7-hydroxyflavanone beta-D-glucopyranoside exhibited about 3.5 folds higher inhibitory activity against aromatase. The result of virtual screening was further validated using molecular dynamics (MD) simulation analysis. Thus, a 25 ns MD simulation analysis revealed high stability and effective binding of 7-hydroxyflavanone beta-D-glucopyranoside within the active site of aromatase. To the best of our knowledge, this is the first report of CoMFA-based QSAR model for virtual screening of flavonoids as inhibitors of aromatase. Keywords: aromatase; breast cancer; 3D-QSAR; flavonoids; 7-hydroxyflavanone beta-D-glucopyranoside; molecular dynamics simulation
1. Introduction Breast cancer represents most frequently diagnosed cancer in females and ranks second as the leading cause of cancer death in women (American Cancer Society [ACS], 2013). According to the American Cancer Society report (2012), more than 2.9 million women in US suffered with history of invasive breast cancer (ACS, 2012). Around 50–80% of female breast cancers have been found to be estrogen-dependent where estrogen binding to receptor has been reported to stimulate tumor cell proliferation (Brueggemeier, Richards, Joomprabutra, Bhat, & Whetstone, 2001; Elledge & Osborne, 1997; Hulka & Moorman, 2001; Kelsey, Gammon, & John, 1993). Aromatase, a mitochondrial cytochrome P450 family enzyme produced at high levels in breast tissues, catalyzes the conversion of androgens to estrogens and acts as a key target in estrogen receptor-positive breast cancer therapy (Brodie & Njar, 1998; Brueggemeier, Hackett, & Diaz-Cruz, 2005). One of the effective modes of regulating adverse effects of estrogen has been suggested to be through the inhibition of aromatase thereby lowering the *Corresponding author. Email: [email protected] © 2014 Taylor & Francis
levels of circulating estrogen (Banting, Nicholls, Shaw, & Smith, 1989; Brueggemeier et al., 2005; Pasqualini, 2004). Both steroidal (e.g. exemestane) and non-steroidal (e.g. anastrozole) inhibitors of aromatase block the biosynthesis of estrogens by inhibiting aromatase in an irreversible or a reversible (competitive) manner, respectively, with non-steroidal inhibitors being more effective (Campos, 2004; Miller et al., 2008). The general mode of action of aromatase inhibitors has been suggested to be due to coordination of the inhibitor with iron atom of the catalytic heme group (Favia, Cavalli, Masetti, Carotti, & Recanatini, 2006; Guallar, Baik, Lippard, & Friesner, 2003). Though, both categories of aromatase inhibitors are promising but their long-term administration lead to development of several side effects. This has motivated the development of a safer substitute of steroidal and nonsteroidal inhibitors, based on natural products and their derivatives such as coumarin, lignin, and flavonoids (Bhatnagar, 2007; Sakamoto, Horiguchi, Oguma, & Kayama, 2010). Thus, along with the development of
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synthetic compounds, various classes of natural products also need evaluation in order to discover novel and relatively safer aromatase inhibitors for application as chemopreventive agents against breast cancer (Wessjohann, Ruijter, Garcia-Rivera, & Brandt, 2005). A large number of natural compounds belonging to wide variety of classes have been evaluated for their inhibitory potential towards aromatase. Thus, a total of 282 natural compounds consisting of 125 flavonoids, 10 alkaloids, 36 terpenoids, 18 lignans, 16 xanthones, 19 peptides, 15 fatty acids, and 43 miscellaneous compounds have been tested for their inhibitory potential against aromatase (Balunas, Su, Brueggemeier, & Kinghorn, 2008). Plant-derived flavonoids, also known as phytoestrogens, contribute a significant proportion to our daily diet including food supplements and neutraceuticals. Dietary flavonoids have been reported to significantly reduce incidence of breast cancer among women of Asian and Oriental origin (Adlercreutz, 1995; Fink et al., 2006; Jordan, Mittal, Gosden, Koch, & Lieberman, 1985; Peterson et al., 2003). Several naturally occurring flavonoids and their derivatives have been reported to possess inhibitory effects on aromatase and therefore, have invited considerable attention in development of these molecules as drugs against breast cancer (Di Carlo, Mascolo, Izzo, & Capasso, 1999; Jeong, Shin, Kim, & Pezzuto, 1999; Kao, Zhou, Sherman, Laughton, & Chen, 1998). Thus, a detailed quantitative structure–activity relationship analysis of these compounds would provide insight into identification and optimization of the lead molecules and in combinatorial library-based drug designing. Computer-aided drug discovery (CADD) involves two main strategies, namely target-based and ligandbased approaches (Costanzi, Tikhonova, Harden, & Jacobson, 2009; Vilar, Cozza, & Moro, 2008). Targetbased drug discovery (TBDD) depends on the structure of the target and its interactions with the ligands. On the other hand, ligand-based drug discovery (LBDD) is dependent on the structural information and molecular properties of known ligands only. It does not require the knowledge of target structure or its interactions with ligands. Both these methods have their limitations (Vilar & Costanzi, 2012). Therefore, in order to complement the advantages and limitations of each other, a combined approach utilizing both the methods would provide a better approach for virtual screening of a large set of diverse chemical libraries leading to an unambiguous identification of a lead compound. Though molecular dynamics (MD) and pharmacophore-based QSAR analyses for lead identification in rational drug designing against estrogen-dependent breast cancer (EDBC) with respect to aromatase and flavonoids have been reported earlier (Mirzaie, Chupani, Asadabadi, Shahverdi, & Jamalan, 2013; Neves, Dinis, Colombo, & Sá e Melo, 2009; Schuster et al., 2006), however,
comparative molecular field analysis (CoMFA)-based QSAR analyses have not yet been done for this purpose. Thus, in the present paper, we report results of molecular docking of a series of 45 flavonoids to the crystal structure of human placental aromatase. The best binding modes were utilized for alignment of the ligands and analyzed by three-dimensional quantitative structureactivity relationship (3D-QSAR) studies using reported inhibitory activity (IC50 values) of the selected flavonoids (Balunas et al., 2008). The resulting 3DQSAR model was further used to screen a database of 6850 flavonoids (http://www.metabolome.jp/download/ flavonoid/) for predicting their inhibitory potential against aromatase and to provide further insights into the design of novel inhibitors of aromatase. The results of screening were further validated using MD simulation analysis. To the best of our knowledge, this is the first report of CoMFA-based QSAR model for virtual screening of flavonoids as inhibitors of aromatase. 2. Materials and methods 2.1. Computational software All computational analyses were done using various modules of Accelrys Discovery Studio (DS) 3.1 as described in the following sections. 2.2. Preparation of protein structure The X-ray crystal structure of human placental aromatase in complex with androstenedione (ASD) (PDB ID: 3EQM) was retrieved from the Protein Data Bank (PDB) (Berman et al., 2000) and necessary changes were done. 2.3. Preparation and optimization of ligand structures Forty-five flavonoids including 24 flavones, 14 flavanones, 3 isoflavanones, 1 isoflavan, 1 catechin, 1 flavanol, and 1 coumestan with their IC50 values against aromatase were selected (Balunas et al., 2008) and their structures were sketched. The IC50 values were converted to pIC50 [i.e. −log (IC50)] for normalization of the data. Energy minimization was performed using CHARMm forcefield and conformations were generated for each compound using the BEST algorithm. Furthermore, the compounds were classified in four categories namely strongly active (IC50: 50 μM). 2.4. Molecular docking and binding energy calculations Aromatase active site was predicted based on the PDB site record using ‘Define and Edit Binding Site module’
Virtual screening of flavonoids as aromatase inhibitors against breast cancer and subsequently docking of the selected 45 flavonoids was performed using ‘LibDock module.’ Binding energy of different docked conformations was calculated using ‘Calculate Binding Energy module.’ Based on the binding energy and docking score, the best conformation of each compound was screened out for further 3D-QSAR analysis.
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2.6. Virtual screening For virtual screening, both the CADD strategies (TBDD and LBDD) were combined to predict inhibitory potential of compounds from a flavonoid database and provide further insights into the design of novel aromatase inhibitors. The step-wise strategy for structure and ligand-based virtual screening of flavonoid database is represented in Figure 1.
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2.5. 3D-QSAR analysis 2.5.1. Molecular alignment
2.6.1. Target-based screening (docking-based scoring)
Molecular alignment was done by ‘Align to Selected Substructure module’ using isolicoflavonol, the most active among selected compounds, as template.
Flavonoids database consisting of 6850 flavonoids from various plant sources was used for target-based screening (TBS). Flavonoids were docked with aromatase (PDB ID: 3EQM) as target using ‘LibDock module.’ The docked poses of all database molecules were subjected to consensus scoring based on 10 scoring functions, LigScore1, LigScore2, PLP1, PLP2, Jain, PMF, PMF04, Ludi Energy Estimate 1, Ludi Energy Estimate 2, and Ludi Energy Estimate 3, to screen molecules having more than five consensus score.
2.5.2. Data-set generation Based on the structural diversity and wide range of activity, 31 compounds were selected as training set and remaining 14 as test set. 2.5.3. CoMFA The aim of CoMFA is to derive a correlation between the biological activity of a set of molecules and their three-dimensional shape with electrostatic and hydrogenbonding characteristics (Cramer, Patterson, & Bunce, 1988). The pre-aligned ligands were placed in a threedimensional grid of 2 Å spacing with 6 Å extension and the energy potentials were calculated by applying CHARMm forcefield. A+1 point charge and a sp3hybridized carbon atom with 1.73 Å radius are used as the electrostatic potential probe and Van der Waals potential probe, respectively, and the distance-dependent dielectric constant was applied for solvation effect. Partial least square (PLS) model was generated using steric and electrostatic descriptors as independent variables and the predicted IC50 values as dependent variables.
2.6.2. Ligand-based screening (Activity prediction using CoMFA 3D-QSAR model) The database compounds screened out through the TBS were further subjected to molecular alignment and fingerprint-based similarity screening. Molecules exhibiting consensus score of >5 were superimposed over the reference data-set of 45 selected flavonoids using ‘Align to Selected Substructure module.’ The resulting aligned structures were further subjected to fingerprint-based similarity screening and compounds exhibiting more than 50% similarity with the reference data-set were subjected to activity prediction through earlier generated 3D-QSAR model using ‘Calculate Molecular Properties module.’ Screened-out molecules were ranked according to their pIC50 values.
Figure 1. Step-wise strategy for target- and ligand-based virtual screening of flavonoids database (TBS = Target-based screening; LBS = Ligand-based screening).
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Table 1.
Names and structures of the selected 45 flavonoids along with their IC50 values.
Compound number
Compound name
1
Isolicoflavonol
.1
2
(2S)-2′,4′-Dihydroxy-2′′-(1-hydroxy-1-methylethyl)dihydrofuro[2,3-h] flavanone
.1
3
7-Hydroxyflavone
.2
4
8-Prenylnaringenin
.2
5
(2S)-Abyssinone II
.4
6
Chrysin
.5
7
Apigenin
1.2
8
7,4′-Dihydroxyflavone
2
9
(2S)-5,7,2′,4′-Tetrahydroxyflavanone
2.2
10
Naringenin
2.9
11
7-Methoxyflavone
3.2
Structure
IC50 (μm)
(Continued)
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Table 1.
5
(Continued).
Compound number
Compound name
12
(2S)-Euchrenone a7
3.4
13
7-Hydroxyflavanone
3.8
14
Eriodictyol
5.3
15
5,7,4′-Trihydroxy-3′-Methoxyflavone
7.2
16
7,8-Dihydroxyflavone
8
17
Flavone
8
18
Flavanone
8
19
7-Methoxyflavanone
8
20
Luteolin
8.6
21
Broussoflavonol F
9.7
22
4′-Hydroxyflavanone
10
23
5,7-Dihydroxyflavanone
10
Structure
IC50 (μm)
(Continued)
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Table 1.
(Continued).
Compound number
Compound name
24
Quercetin
12
25
Methylequol
20
26
5,7,2′,4′-Tetrahydroxy-3-geranylflavone
24
27
5,7,4′-Trihydroxy-3′-methoxyflavanone
25
28
Coumestrol
25
29
5,7,3′-Trihydroxy-4′-Methoxyflavone
27
30
7,3′,4′-Trihydroxyflavone
38
31
3′,4′-Dimethoxyflavone
42
32
7,3′,4′,5′-Tetrahydroxyflavone
45
33
3′-Hydroxyflavone
73
34
6-Hydroxyflavone
80
35
6,4′-Dihydroxyflavone
90
Structure
IC50 (μm)
(Continued)
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Table 1.
7
(Continued).
Compound number
Compound name
36
5-Hydroxyflavone
100
37
(−)-Epigallocatechin
100
38
5,4′-Dihydroxyflavone
120
39
Isoflavanone
120
40
Flavan-4-ol
120
41
3-Hydroxyflavone
140
42
3′,4′-Dihydroxyflavanone
160
43
4′-Hydroxyisoflavanone
160
44
2-Hydroxyisoflavanone
170
45
4′-Hydroxyflavone
180
2.7. MD simulation Interactions of human aromatase with the best hit identified from virtual screening of flavonoid database were investigated through 25 ns of MDsimulation using GROMACS 4.5.5 package with GROMOS96 43a1 force field. A parallel MD simulation analysis of aromatase with its co-crystallized ligand, ASD was also done. The docking poses of aromatase with its ligands were prepared for MD simulation through mild minimization and
Structure
IC50 (μm)
solvation within a water filled three-dimensional cube of 1 Å spacing. System was neutralized and further minimized. The complex structure was heated to 300 K and equilibrated for 100 ps in NVT ensemble and another 100 ps in NPT ensemble. After heating and equilibration the complex structure of aromatase with its ligands was subjected to production run of 25 ns in NPT ensemble. PRODRG web server (Schüttelkopf & van Aalten, 2004) was used to generate topologies and coordinations of
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ligands. Default values of GROMACS were assigned for determination of hydrogen-bonding interactions between aromatase and its ligands.
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3. Results and discussion 3.1. Sketching of compounds Chemical structures of 45 selected flavonoids were sketched. Name, structure, and IC50 values of all the 45 flavonoids are presented in Table 1. In order of their IC50 values, each flavonoid was assigned a compound number from 1–45 with compound 1 having lowest while compound 45 having highest IC50 value. It is noteworthy that compound numbers 1–13, 14–23, 24–32 and 33–45, based on their IC50 values, were found to be strongly, moderately, weakly active, and inactive, respectively. 3.2. Molecular docking and binding energy calculations Molecular docking of all the selected 45 flavonoids with aromatase was performed using LibDock module. Top 20 conformations of each of the 45 compounds, based on their LibDock score, were selected and their binding energies were calculated. For the purpose of validation of our docking results, two most active compounds namely isolicoflavonol (Table 1, compound no. 1) and (2S)-2′,4′-Dihydroxy-2′′-(1-hydroxy-1-methylethyl) dihydrofuro[2,3-h] flavanone (Table 1, compound no. 2) were analyzed for the residues involved in binding with aromatase at the active site. Results are shown in Figure 2. Thirteen residues, namely Arg115, Ile133, Phe221, Trp224, Ile305, Ala306, Asp309, Thr310, Val370, Val373, Met374, Leu477, and Ser478 which were
involved in the binding of compounds 1 and 2, at the active site of aromatase, were found to be common. Results of present docking analysis were validated by comparing the interacting residues involved in the binding of the ligand, ASD, co-crystallized with aromatase (PDB ID: 3EQM). Thus, a comparison of the interacting residues involved in binding of the ligands (compounds 1 and 2) in the present study with those of the co-crystallized ligand (ASD) revealed that all the 13 interacting residues were found to be common thereby validating our result of docking (Karkola & Wähälä, 2009; Murthy, Nagaraju, Sastry, Rao, & Sastry, 2006).
Figure 3. Molecular alignment of the best conformations of each of the 45 selected flavonoids as obtained from the results of docking analysis. Atom color scheme: C, blue; O, red.
Figure 2. Three-dimensional view showing the residues involved in the binding of isolicoflavonol (A) and (2S)-2′,4′-dihydroxy-2′′(1-hydroxy-1-methylethyl) dihydrofuro [2,3-h] flavanone (B), the two most active flavonoids among the 45 selected ones, at the active site of aromatase.
Virtual screening of flavonoids as aromatase inhibitors against breast cancer 3.3. 3D-QSAR analysis Conformations with minimum binding energies were selected for 3D-QSAR analysis using the CoMFA modelling method. 3.3.1. Molecular alignment The accuracy of 3D-QSAR analyses is considered to be extremely sensitive to molecular alignment rules and overall orientation of the aligned compounds. One of the most important steps in the alignment-based 3D-QSAR
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methods is the superimposition of the bioactive conformations of all the molecules over each other (Akamatsu, 2002; Kim, 1995). Therefore, molecular alignment of the best selected conformations of all 45 flavonoids retrieved from docking studies was done and results are presented in Figure 3. 3.3.2. PLS regression analysis In order to minimize the number of descriptors in CoMFA, PLS regression technique was used, which combines the
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Table 2. Experimental, predicted and residual pIC50 of the selected training (TR) and test (TE) sets flavonoids using CoMFA 3DQSAR model. Assigned compound numbers (cf. Table 1) are indicated within parenthesis. S. No.
Training (TR)/Test (TE) sets (Compound number)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
TR (2) TR (4) TR (5) TR (6) TR (11) TR (12) TR (15) TR (16) TR (17) TR (18) TR (19) TR (20) TR (21) TR (23) TR (25) TR (26) TR (27) TR (28) TR (30) TR (31) TR (32) TR (33) TR (35) TR (36) TR (37) TR (38) TR (40) TR (41) TR (42) TR (44) TR (45) TE (1) TE (3) TE (7) TE (8) TE (9) TE (10) TE (13) TE (14) TE (22) TE (24) TE (29) TE (34) TE (39) TE (43)
Experimental pIC50
Predicted pIC50
Residual pIC50
7.000 6.698 6.397 6.301 5.494 5.468 5.142 5.096 5.096 5.096 5.096 5.065 5.013 5.000 4.698 4.619 4.602 4.602 4.443 4.376 4.346 4.136 4.045 4.000 4.000 3.920 3.920 3.853 3.795 3.769 3.744 7.000 6.698 5.920 5.698 5.657 5.537 5.420 5.275 5.000 4.920 4.568 4.096 3.920 3.795
7.03907 6.75162 6.18546 6.08957 4.90286 5.42204 5.257 4.55967 5.27055 5.27391 4.79818 4.42191 4.84351 5.46886 4.75781 4.8221 4.61711 4.75156 4.61808 4.62617 4.50526 4.2298 3.93444 4.17235 4.14796 4.16586 4.20564 3.81865 3.79497 3.69449 3.68354 6.896 5.62418 5.66223 5.4691 5.56399 5.35725 5.41959 5.86434 5.035 4.69831 5.11488 5.24827 4.06833 3.48253
−.03907 −.05362 .21154 .21143 .59114 .04596 −.115 .53633 −.17455 −.17791 .29782 .64309 .16949 −.46886 −.05981 −.2031 −.01511 −.14956 −.17508 −.25017 −.15926 −.0938 .11056 −.17235 −.14796 −.24586 −.28564 .03435 .00003 .07451 .06046 .104 1.07382 .25777 .2289 .09301 .17975 .00041 −.58934 .035 .22169 −.54688 −1.15227 −.14833 .31247
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characteristics of two different techniques namely principal component analysis and multiple linear regression (Geladi & Kowalski, 1986; Wold et al., 1984). The CoMFA model was constructed with 31 compounds in the training set, which was validated by the remaining 14 molecules in the test set. The results of 3D-QSAR were interpreted on the basis of the coefficient of correlation between experimental activity and predicted activity of the molecules based on CoMFA analysis. Table 2 shows the predicted activities of the training and test set compounds, respectively, along with their residual values i.e. difference between the experimental and predicted activities of the compounds. The obtained model showed very good predictability as the predicted activity values of all the compounds in the test set were very close to the observed activity values. Correlation between experimental activity and predicted activity of training and test sets were calculated and results are shown in Figure 4(A) and (B), respectively. The best predictive CoMFA model gave cross-validated q2 value equal to 0.827 and non-cross-validated r2 value equal to 0.916. The predicted r2 of 14 compounds in the test set was 0.710, which showed good correlation. Results of the regression statistics obtained from 3D-QSAR CoMFA analysis are given in Table 3.
3.4. Virtual screening For virtual screening, both the target- and ligand-based methods were used.
3.4.1. TBS Target-based virtual screening method uses structural information of the protein to identify the expected conformation and orientation of the ligand into its active site (Klebe, 2006; Shoichet, 2004). Thus, 6850 flavonoids from the flavonoids database were subjected to docking with crystal structure of aromatase. Out of 6850 flavonoids, 5794 flavonoids docked successfully. The successfully docked 5794 flavonoids were subjected to the previously mentioned 10 scoring functions followed by the consensus scoring. Based on the set criteria of consensus score more than five, 1906 flavonoids qualified for further screening. In view of the fact that docking scores are not very effective in the unambiguous ranking of active compounds, based on their affinities or potencies, rather they distinguish between active and inactive compounds, the screened-out flavonoids (1906) were further subjected to ligand-based screening using CoMFA 3D-QSAR model.
Figure 4. Plot of PLS regression analysis showing linear relationship between experimental and predicted activities of the training (A) and test (B) data-sets as analyzed by CoMFA 3D-QSAR model. Activity was expressed in terms of pIC50. Table 3. Statistical results of CoMFA 3D-QSAR model. NOC, number of components; N, number of compounds in training set; r2, coefficient of correlation; q2, cross-validated coefficient of correlation. N
r2
r2(adjusted)
r2test
q2 (cross-validation)
NOC
RMS residual error
31
.916
.8850
.710
.827
6
.2993
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Table 4. Compound ID, names, structures, predicted pIC50 and IC50 values of ten best hits obtained from virtual screening of flavonoids database. All the selected flavonoids exhibited highest consensus score of 10. S. Compound No. ID
Name
Predicted pIC50
Predicted IC50 (μM)
1
FL2F19GS 0001
7-Hydroxyflavanone beta-D-glucopyranoside
5.96245
1.09
2
FL3FAEGS 0010
Diosmetin 3′-glucoside
5.91168
1.23
3
FL2FCCGS 0001
7-O-Methyleriodictyol 3′-O-glucoside
5.8866
1.31
4
FL5FAANI 0008
Glyasperin A; 3,5,7-Trihydroxy-2-[4-hydroxy-3-(3-methyl-2butenyl)phenyl]-6-(3-methyl-2-butenyl)-4H-1-benzopyran-4-one
5.86114
1.38
5
FL2FAANI 0024
Macarangaflavanone A; 5,7,4′-Trihydroxy-3′-geranylflavanone
5.80099
1.55
6
FL2FACNN 0001
Silandrin
5.71098
1.91
7
FL2FABNI 0006
5,7-Dihydroxy-4′-methoxy-8-C-prenyl-3′-(3-hydroxy-3methylbutyl)flavanone
5.62492
2.39
8
FL2FAAGS 0022
Naringenin 4′-O-alpha-L-rhamnopyranoside; 5,7,4′Trihydroxyflavanone 4′-rhamnoside
5.60305
2.50
9
FL3FAAGS 0008
Apigenin 7-galactoside
5.55759
2.80
10
FL2FADGS 0002
Viscumiside A
5.48259
3.29
Structure
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M. Awasthi et al.
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3.4.2. Ligand-based method (LBS) For ligand-based screening (LBS) of 1906 screened-out flavonoids, three different approaches namely molecular alignment, fingerprint similarity, and activity prediction were applied. Thus, 1906 flavonoids obtained after TBS were superimposed on the reference data-set of the 45 selected flavonoids, and 1634 structures obtained after molecular alignment were further subjected to fingerprint similarity-based screening. As a result, 161 flavonoids showing minimum 50% similarity to the reference set of selected 45 flavonoids were obtained which were further subjected to activity prediction using CoMFA 3D-QSAR model and were ranked in order of their predicted activities (pIC50 values). These, flavonoids were found to exhibit the pIC50 values in the range of 3.4568–5.96245 which corresponded to IC50 values in the range of 350 to 1.09 μM. Out of 161 flavonoids, 115 were found to be strongly active (with IC50 value of
