Structure-Based Kinase Profiling To Understand the

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Cite This: J. Chem. Inf. Model. XXXX, XXX, XXX−XXX

Structure-Based Kinase Profiling To Understand the Polypharmacological Behavior of Therapeutic Molecules Devawati Dutta,† Ranjita Das,†,§ Chhabinath Mandal,*,‡ and Chitra Mandal*,† †

Cancer Biology and Inflammatory Disorder Division, Council of Scientific and Industrial ResearchIndian Institute of Chemical Biology, Kolkata 700032, India ‡ National Institute of Pharmaceutical Education and Research, Kolkata 700032, India S Supporting Information *

ABSTRACT: Several drugs elicit their therapeutic efficacy by modulating multiple cellular targets and possess varied polypharmacological actions. The identification of the molecular targets of a potent bioactive molecule is essential in determining its overall polypharmacological profile. Experimental procedures are expensive and time-consuming. Therefore, computational approaches are actively implemented in rational drug discovery. Here, we demonstrate a computational pipeline, based on reverse virtual screening technique using several consensus scoring strategies, and perform structure-based kinase profiling of 12 FDAapproved drugs. This target prediction showed an overall good performance, with an average AU-ROC greater than 0.85 for most drugs, and identified the true targets even at the top 2% cutoff. In contrast, 10 non-kinase binder drugs exhibited lower binding efficiency and appeared in the bottom of ranking list. Subsequently, we validated this pipeline on a potent therapeutic molecule, mahanine, whose polypharmacological profile related to targeting kinases is unknown. Our target-prediction method identified different kinases. Furthermore, we have experimentally validated that mahanine is able to modulate multiple kinases that are involved in cross-talk with different signaling molecules, which thereby exhibits its polypharmacological action. More importantly, in vitro kinase assay exhibited the inhibitory effect of mahanine on two such predicted kinases’ (mTOR and VEGFR2) activity, with IC50 values being ∼12 and ∼22 μM, respectively. Next, we generated a comprehensive drug−protein interaction fingerprint that explained the basis of their target selectivity. We observed that it is controlled by variations in kinase conformations followed by significant differences in crucial hydrogen-bond and van der Waals interactions. Such structure-based kinase profiling could provide useful information in revealing the unknown targets of therapeutic molecules from their polypharmacological behavior and would assist in drug discovery.

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INTRODUCTION Combinatorial chemistry and high-throughput screening (HTS) technology have revolutionized the development of therapeutic molecules for disease eradication. Although several drugs were initially designed and developed as highly selective and target-specific agents, they modulate multiple cellular targets to elicit their therapeutic efficacy.1,2 For example, imatinib (Gleevec), rationally designed for chronic myeloid leukemia by specifically targeting BCR-ABL1 kinase, also inhibits other kinases.3,4 Consequently, the notion of “one target, one drug” has been gradually shifting to multi-targeted therapy, known as “polypharmacology”, and is currently gaining momentum in drug discovery.2,5,6 The usefulness of studying the polypharmacological profile of a therapeutic molecule is best appreciated in cancer treatments, as it is associated with a complex multiplicity of genetic © XXXX American Chemical Society

determinants, molecular heterogeneity, and multiple oncogenic pathways.7 With the appearance of several kinase mutations in cancer, multi-targeted drugs can compensate the effects of inhibiting one target by over-activating another target or cellular pathway. Many of today’s chemotherapeutics target multiple oncoproteins and therefore exhibit varied polypharmacological actions causing both beneficial and adverse effects.1,5,8 In imatinib-resistant leukemic patients, dasatinib (Sprycel) has been demonstrated as a better drug, as it can activate alternate signaling pathways by targeting different kinases.3,4,9 Methotrexate is used not only as an anti-cancer drug but also to treat psoriasis and rheumatoid arthritis.10,11 In contrast, staurosporine was excluded in clinical practice due to Received: April 24, 2017

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DOI: 10.1021/acs.jcim.7b00227 J. Chem. Inf. Model. XXXX, XXX, XXX−XXX

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Journal of Chemical Information and Modeling

Figure 1. Chemical structures of the drugs used for polypharmacological profiling: (a) imatinib, (b) sorafenib, (c) dasatinib, (d) axitinib, (e) sunitinib, (f) nilotinib, (g) pazopanib, (h) regorafenib, (i) ponatinib, (j) lenvatinib, (k) bosutinib, and (l) cabozantinib. The kinase profiling used a reverse virtual screening method. Some physicochemical features of these molecules are also given: Mwt = molecular weight, HBD = hydrogen bond donors, HBA = hydrogen bond acceptors, RB = rotatable bonds.

its interaction with multiple kinases.2,4 Therefore, it is essential to study the polypharmacological profile of potent therapeutic molecules to understand their overall mode of action. In order to understand the polypharmacological profile of a particular drug, the most significant step is to identify its multiple molecular targets in order to depict the complex interactions between them. Such profiling will also reveal novel targets with so far unknown mechanisms of action and “offtargets” which may be responsible for the undesired biological activity.12,13 Exhaustive experiments for profiling all drug− target pairs are still expensive and time-consuming.14 Computational methods offer an alternative and quick means of biological target identification and are being actively implemented in drug-discovery programs.15,16 Chemogenomics, similarity ensemble approach (SEA), systems biology, and ligand-based in silico screening approaches were applied to predict the polypharmacological profiles of potent bioactive molecules.17−20 The ligand- and network-based methods mainly depend upon the chemical similarity index between the test molecules and others available in chemical databases (i.e., similar molecules bind to the same targets).21,22 The major drawback is that these methods fail to predict if the molecules in a chemical database are not similar to the test molecule. Although high-throughput virtual screening has been widely used to identify new lead molecules of an established druggable target,23 surprisingly, the opposite question is still an issue and a very challenging task. Therefore, we asked, Is it possible to predict all the targets of known drugs or newly identified lead molecules with high accuracy and specif icity? A new target identification approach termed “reverse virtual screening”, based on the application of physics-based docking methods, has been recently introduced.24,25 In this approach, a bioactive small molecule is docked to a panel of three-dimensional structures of proteins to recognize its putative molecular targets. The primary advantage of a structure-based target prediction method is that simultaneous prediction of the strength of binding of the molecules to their targets and their

mode of binding can be achieved. It can search for the key pharmacophore and molecular interaction fingerprints, which provide valuable insights for rational drug selectivity.26 Accordingly, one can design drugs for the enhancement of their potency. Moreover, this method can better detect the activity cliffs of compounds that are structurally similar but have high differences in their activity through their molecular interaction fingerprint.27 Further optimization of such compounds can be subsequently achieved through structure− activity relationship analyses. Although a few target-fishing studies have been reported, the main problem that persists is associated with prediction accuracy and specificity.24,25,28−36 Moreover, many of the past studies were based on the implementation of a single-scoring function. The first docking-based target prediction method is Invdock, based on the DOCK docking algorithm.24 It evaluated the screening of eight compounds against nine proteins present in the therapeutic target database. Out of 43 experimentally documented protein−ligand interactions, about 38 were retrieved by Invdock.24 TarFisDock, another program based on DOCK, screened the 698 structures of 371 targets with vitamin E and 4H-tamoxifen using the Potential Drug Target Database.28 About 50% of true targets were ranked among the first 10% and 5% cutoffs for vitamin E and 4H-tamoxifen, respectively. Paul et al. performed reverse screening of five chemically diverse ligands with 2148 binding sites of 1045 different proteins incorporated in the sc-PDB database using the GOLD docking program.25 However, poor performance was reported with 1% enrichment. Kellenberger et al. observed area under the curve (AUC) scores between 0.7 and 0.95 (Goldscore) and interaction fingerprint scoring within the range 0.45−0.9 for four compounds with 1550 different proteins.30 Recently, inverse Rapid Index-based Screening Engine (iRAISE) was developed, in which a scoring cascade and active site coverage for each individual protein pocket were applied.31,32 On a test set with 7915 protein structures and 117 query ligands, iRAISE ranked more than 35% of the targets to B


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Figure 2. Schematic diagram of the drug−target interaction network of FDA-approved multi-kinase inhibitor drugs. The drug−target network was generated using known experimental interactions of kinases with the FDA-approved drugs obtained from DrugBank. A drug node (cyan) and a target node (light orange) are connected to each other by a gray edge. Targets of (a) imatinib, (b) sorafenib, (c) dasatinib, (d) axitinib, (e) sunitinib, (f) nilotinib, (g) pazopanib, (h) regorafenib, (i) ponatinib, (j) lenvatinib, (k) bosutinib, and (l) cabozantinib. (m) Network of drug−target interaction of multi-kinase inhibitor drugs that modulate multiple common targets (green). The network was prepared by Cytoscape.

the first position, predicted more than 80% of the binding modes with a root-mean-square deviation (RMSD) accuracy of axitinib (0.804 ± 0.02) > imatinib (0.784 ± 0.02) > bosutinib (0.783 ± 0.02) > sorafenib (0.750 ± 0.02) > nilotinib (0.724 ± 0.02) > cabozantinib (0.720 ± 0.02) > pazopanib (0.716 ± 0.02) > regorafenib (0.687 ± 0.02) > ponatinib (0.642 ± 0.02) > lenvatinib (0.628 ± 0.02) (Figure 4a−l, Table S4). Interestingly, ranking criterion 2 demonstrated a significantly better performance over the criterion 1. The average AU-ROC values for the most drugs (AU-ROC ≈ 0.9) were significantly higher than the average AU-ROC values for criterion 1 (AUROC ≈ 0.7). In this case, the average AU-ROC values were in the order of axitinib (0.940 ± 0.02) > imatinib (0.921 ± 0.02) > nilotinib (0.904 ± 0.02) > sorafenib (0.900 ± 0.02) > regorafenib (0.878 ± 0.02) > sunitinib (0.840 ± 0.02) > G


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Figure 6. Predicted molecular targets of the selected drugs. (a) Heat map of predicted targets of (1) sorafenib, (2) imatinib, (3) dasatinib, (4) axitinib, (5) sunitinib, (6) nilotinib, (7) pazopanib, (8) regorafenib, (9) ponatinib, (10) lenvatinib, (11) bosutinib, and (12) cabozantinib using the reverse screening method (Rank-by-number strategy, Criterion 2). The gradation of color on the heat map is from red to green, where dark red represents the top rank, and light red and green represent the bottom ranks of the drug candidates among 233 human kinases according to their respective ranks in top 10% cutoff. (b) Scatter plot of consensus score and ranking order of the true kinase targets of 12 multi-kinase inhibitors taking the top ranking position (cutoff = 10% of the total data set).The experimental activity values (Kd) of these kinases in the range from 0.01 nM to >10 μM for all the 12 test molecules were taken from the existing literature.4,45

cabozantinib (0.805 ± 0.02) > ponatinib (0.782 ± 0.02) > bosutinib (0.779 ± 0.02) > pazopanib (0.778 ± 0.02) > dasatinib (0.742 ± 0.02) > lenvatinib (0.625 ± 0.02) (Figure 4a′−l′, Table S4). However, the average AU-ROC value for dasatinib, using criterion 1 (0.805 ± 0.02) was a little higher than that of criterion 2 (0.742 ± 0.02). For all the 12 compounds, the prediction performance was good. The consensus scoring approach gave a better performance measure in comparison to single scoring functions (Table S5). However, in some individual cases also single scoring provides similar good results, which is not unexpected. It was observed that, in the cases of imatinib and dasatinib, the Aspscore scoring function using ranking criterion 2 also showed comparable results like the consensus score. However, considering all the molecules, the employed consensus scoring overall resulted into more robust predictions and showed better performance than the individual scoring function. From the AU-ROC values, it can be stated that the reverse virtual screening using all the consensus scoring strategies by ranking criterion 2 showed an overall better performance for the target prediction of these drugs. Sensitivity and Specificity. Sensitivity (or hit-rate) measures the proportion of actives (TP) while specificity measures the proportion of inactive (TN). The values identified by the target prediction showed how good the method is at predicting the true molecular targets of a small molecule. A maximum hit-rate was obtained at top 10% cutoff for all the 12 drugs in comparison to 2% and 5% cutoff (Figure 5a). At top 10% cutoff, the recovered true targets were 90% for imatinib; 80% for axitinib, sunitinib, and nilotinib; 75% for sorafenib; 70% for regorafenib and cabozantinib; 65% for pazopanib and

bosutinib; 60% for ponatinib; and 50% for dasatinib and lenvatinib. The target prediction method was so sensitive that it recovered the actives of these drugs even at top 2% cutoff. Moreover, all of the inactives were predicted successfully for all these drugs at top 2% cutoff of the data set. At 10% cutoff, about 90% of true negatives could be predicted for these drugs (Figure 5b). Precision and Accuracy. Another statistical measure, precision, depicts the measure of the probability of how many of the samples predicted as true positives are actually positive, and it is inversely related to the standard error. It was observed that at top 2% cutoff, an average precision of 80% was obtained for most of the 12 drugs, which implies very less standard error of prediction. This showed that method has substantially predicted their true targets for most of the drugs even at 2% cutoff of the data set (Figure 5c). For sorafenib, the precision value was 100% with all the true targets retrieved at 2% cutoff. However, in case of dasatinib, sunitinib, pazopanb, regorafenib, and ponatinib, the precision value was 80%.With gradually increasing of cutoffs, i.e., 5% and 10%, the obtained precisions were reduced for these drugs due to the inclusion of some false positives (Figure 5c). Accuracy measures the degree of reliability of the prediction of both actives and inactives for a particular condition. The accuracy of the prediction method was more than 90% for all the 12 drugs and thus can specifically predict their true positive and true negative (Figure 5d). Based on the analysis of evaluation metrics, it suggested a good overall performance of the target prediction for these drugs. Putative molecular Targets of 12 Test Drugs. The identified molecular targets of the 12 drugs obtained by their reverse virtual screening toward a panel of 233 kinase proteins H


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were ABL1, ABL2, BMX, BTK, CSF1R, DDR1, HCK, KIT, and SRC. For axitinib and nilotinib, 90% of the true targets that were retrieved through our pipeline were KDR, KIT, CSF1R, ABL1 and ABL1, KIT, DDR1, MAPK11, respectively (Figure 6a-4,6). Only FLT1 (axitinib) and LCK (nilotinib) could not be predicted in top 10% cutoff. In spite of these, some putative kinase that were predicted to be strong binders for axitinib were FGFR1, CDKL2, TNIK, EIF2AK3, MAPK10, AURKB, BRAF, NTRK1, FGFR4, and DDR1, while the strong binders predicted for nilotinib were KDR, BRAF, CDK8, NTRK3, NTRK1, CSF1R, and MAPK14. Similarly for sunitinib, multiple kinases such as KIT, CSF1R, KDR, FLT3, ABL1, EIF2AK3, CDK2, BRAF, NTRK3, MAP3K9, AURKB, and FGFR1 were predicted as its strong binders (Figure 6a-5). Interestingly, about the entire true molecular targets of sunitinib, i.e., KIT, KDR, FLT3, CSF1R were retrieved, except FLT1, at 10% cutoff, thus signifying a good prediction. However, so far there is no experimental evidence of the rest of the predicted kinases to be its true targets. Out of 233 human kinases, the predicted strong binders of pazopanib were KDR, KIT, DDR1, CSF1R, FGFR1, MAPK10, NTRK1, FGFR3, BRAF, MAP4K4, and FGFR4. However, most of these kinases, namely, KDR, KIT, FGFR3, FGFR1, and CSF1R, have been established as its true targets (Figure 6a-7). Apart from these, there are no reports of other predicted strong binders (i.e., DDR1, FGFR4, NTRK1, MAPK10, BRAF, and MAP4K4) as its true targets, and further experimental validation is required. Other drugs, such as ponatinib and regorafenib, also showed varied polypharmacological profiles for multiple kinases with high affinity (Figure 6a-8,9). The predicted strong binders of ponatinib were ABL1, KDR, BRAF, CSF1R, KIT, DDR1, FLT3, FGFR4, EIF2AK3, RIPK2, ABL2, LCK, and NTRK1. Most of these kinases namely ABL1, KIT, FLT3, FGFR4, LCK, KDR, DDR1, CSF1R, and ABL2 have been established as true targets of ponatinib (Figure 6a-9). On the other hand, the predicted strong binders of regorafenib were KDR, ABL1, BRAF, MAPK14, CSF1R, KIT, CDK8, NTRK1, DDR1, EIF2AK3, FGFR4, and FLT3. Out of these putative binders, KDR, KIT, FGFR1, NTRK1, BRAF, and ABL1 were its experimentally established true targets (Figure 6a-8). In the cases of bosutinib and cabozantinib, at top 10% cutoff of the data set, the predicted strong binders that were its true molecular targets were ABL1, HCK, SRC, MAP2K1 and KIT, KDR, TEK, MET, FLT3, respectively (Figure 6a-11,12). However, prediction accuracy of lenvatinib was 50%, as only KDR, FGFR4, FGFR1, and KIT were recovered in 10% cutoff while the prediction could not retrieve FLT1, FGFR2, FGFR3, and RET as its targets (Figure 6a-10). Therefore, through the target prediction method, it could be stated that all these 12 drugs modulate multiple kinases and have varied polypharmacological profiles related to targeting human kinases. Ranking Order of the Predicted Targets of 12 Test Drugs. A good computational target prediction pipeline of a molecule implies that the top-scoring protein targets are likely to be the true hits and are always positioned at higher ranks among all. Figure 6b shows the ranking order of the true targets of these multikinase drugs in the top 10% cutoff. For sorafenib, 9 out of 12 experimentally established targets were ranked at the top positions in the ranking order: KDR (1st), MAPK14 (2nd), CDK8 (3rd), BRAF (4th), ABL1 (5th), DDR1 (6th), CSF1R (7th), FLT3 (9th), and KIT (15th) (Figure 6b, Table

are represented as a colored heat map (Figure 6a. For defining a true molecular target, we have used a cutoff of top 10% of the total kinase data set to distinguish between the actives (TP) from the inactives (TN). This is because most of the drugs used in the study are multi-kinase inhibitors having multiple targets. Therefore, the cutoff should be such that we can recover more true targets (TP) of the drugs through our reverse screening approach. To define the actives and the inactives of a drug for the 233 kinases, we considered the experimental activity values (Kd) of these kinases in the range from 0.01 nM to >10 μM for all the 12 test molecules from the existing literature.4,45 During the reverse screening for an individual test molecule, the binding efficiency of the 233 kinases were also calculated and correlated well with the reported experimental results, as also observed by the high sensitivity and specificity of the method. The kinases having high binding efficiency (in nM range) appeared in the top ranking list and hence may be considered as the active ones. On the other hand, those kinases which have low binding efficiency (in μM) appeared in the bottom ranking list were considered as the inactive ones. Moreover, the range of binding efficiency also depends upon the nature of the small molecule toward the kinases. The active versus inactive predicted targets of these drugs were gradually colored from dark red (top rank) to green (bottom rank) according to their respective ranks in top 10% cutoff. Moreover, the binding profile of these 12 drugs was plotted using KINOMEscan dendrogram (http://kinhub.org/ kinmap/) on the basis of obtained decreasing order of absolute binding energy (kcal/mol) in the range of −10 kcal/mol to −6 kcal/mol.63 In most of the cases, the predicted strong binders of the 12 set of drugs have binding energy more than −9 kcal/ mol, i.e., good binders (Figure S2). All the consensus scoring strategies (Rank-by-number, Rankby-rank, Vote-by-number, Vote-by-rank, and Vote-by-percent) showed a similar trend of results for the predicted targets of these drugs (Figure S3). From the obtained consensus score, using Rank-by-number strategy, it was observed that all the 12 drugs have a strong binding affinity toward multiple kinases, rather than a single kinase (Figure 6a (1−12), Figure S2). Out of 233 human kinases, the predicted strong binders of sorafenib were ABL1, BRAF, CDK8, CSF1R, DDR1, EIF2AK3, EPHA3, FGFR4, FLT3, ITK, KDR, KIT, MAPK14, NTRK1 PTK2B, and TEK. Interestingly, most of these kinases namely ABL1, BRAF, CDK8, CSF1R, DDR1, FLT3, KDR, KIT, and MAPK14 have been established as true targets of sorafenib (Figure 6a-1, Table S6). Apart from these, there are no reports of other predicted strong binders of sorafenib (i.e., EIK2AK3, ITK, NTRK1, FGFR4, PTK2, PTK2B, and TEK) as its true targets. However, rest of these predicted kinases need to be validated experimentally to claim as its true targets. In case of imatinib, multiple kinases such as ABL1, ABL2, BRAF, CDKL2, CSF1R, CSNK2A1, CSNK2A2, DDR1, EIF2AK3, GSG2, KDR, KIT, MAPK14, NTRK1, PLK1, and PTK2B were predicted as its strong binders (Figure 6a-2, Table S6). Interestingly, about all the true molecular targets of imatinib, i.e., ABL1, ABL2, CSF1R, DDR1, KIT, and NTRK1 were retrieved, thus signifying a good prediction. However, there is no experimental evidence of the rest of the predicted kinases to be its true targets. Dasatinib also showed varied polypharmacological profile, and its strong binders were ABL1, ABL2, ACVR1, BMX, BTK, CSF1R, DDR1, EIF2AK3, HCK, KIT, MELK, NTRK1, SRC, PRKACA, and WEE1 (Figure 6a-3, Table S6). Out of these, the true molecular targets retrieved I


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Figure 7. Predicted polypharamacological profile of mahanine. (a) Chemical structure of mahanine, (b) Heat map of the structure-based profiling of mahanine for 233 kinases using five consensus scoring strategies. The gradation of color on the heat map is from dark red to green, based on top 10% cutoff of the total 233 kinases, where dark red represents kinases in top ranking position, while light red shows moderate binders and green symbolizes weak binders or true negatives of mahanine. (c) Specific activity of total kinases of mahanine-treated HeLa cells. HeLa cells were treated with 17.5 μM of mahanine. Cell lysates were used to measure the kinase activity as described in the Materials and Methods section. Phosphatase alone was used for the standard. Based on the phosphate standard curve, the slope of the linear regression line representing the amount of phosphate was calculated, termed as conversion factor (CF). The specific activity of total kinases was calculated using the CF and the equation [specific activity = slope × (CF/time)] and expressed as μmol/min/ng. Bars represent means ± SD (n = 3). (d) Mahanine impeded the activation of VEGFR2 (KDR), mTOR, c-RAF (RAF1), CSNK2A1, and CDK2 at gene levels. Mahanine showed a concentration-dependent decrease in mRNA levels of KDR, MTOR, RAF-1, CSNK2A2, and CDK2 in two representative ovarian (PA1) and cervical (HeLa) cancer cell lines. Cells were treated with mahanine (0, 12.5, and 17.5 μM) for 24 h. Total mRNA was extracted using anRNeasy Mini Kit and reverse transcribed into cDNA. PCR assays of targeted genes were carried out with specific forward and reverse primers. The PCR products were electrophoresed on an agarose gel (1%), stained with ethidium bromide, and visualized under UV light as described in Materials and Methods. (e) Mahanine reduced the activation of VEGFR2, CK2α, and CDK2 at protein level. Western Blot analysis revealed that mahanine induced deactivation of VEGFR2, CDK2, and CK2α in protein levels in dose-dependent manner within 24 h. HeLa cells were treated with mahanine (0, 12.5, and 17.5 μM) for 24 h. The cell lysates were electrophoresed on SDS-PAGE, electroblotted on PVDF, and developed as described in Materials and Methods. (f) Mahanine down-regulated mTOR signaling cascades. Mahanine in a concentration-dependent manner in HeLa cells reduced mTOR and a few other key regulatory proteins of this pathway after 24 h. (g) Mahanine modulated Ras/c-Raf/ERK signaling cascades. Mahanine (0, 12.5, and 17.5 μM)-treated PA1 cells after 24 h were sonicated, whole-cell lysates were electrophoresed and analyzed by Western blot using anti-VEGFR2, p-VEGFR2 (Tyr 1175), mTOR, pmTOR (Ser 2481), c-Raf, p-c-Raf, CDK2, and CK2α antibodies. It reduced Ras/c-Raf/ERK pathway-mediated signaling molecules. (h,i) IC50 of mTOR and VEGFR2 inhibitory activity by mahanine were determined using human recombinant mTOR and VEGFR2. Inhibitory percentages of kinase activity in mahanine-treated sample were calculated by considering the untreated sample as 100%. (j) Immunoprecipitation kinase activity assay of p-mTOR and p-VEGFR2. Total mTOR and VEGFR2 was pulled down from mahanine (17.5 μM)-treated HeLa cell lysates followed by incubation with kinase buffer supplemented with ATP for 60 min at 30 °C. The samples were resolved by SDS-PAGE, transferred, and identified by specific anti-phospho-mTOR (Ser2448) and anti phospho-VEGFR2 (Tyr1175) antibodies. Bar diagram represents relative fold change of phosphomTOR and phospho-VEGFR2. (k) Schematic diagram of experimental validation of a few predicted kinases and their cross talk in the modulation of cellular signaling process by mahanine. (l,m) Molecular interactions of mahanine bound in the active site of kinases. The ligand represented in stick form where C = orange, O = red, N = blue, and H = white. The protein is represented both as ribbon form with helices and sheets. Amino acid residues of mTOR (l) and VEGFR2 (m) involving H-bond and van der Waals interactions with mahanine are represented. The closed interaction residues of protein are displayed in stick form (olive green color).

S7). However, some of its other true targets such as RET, FLT1, and RAF1 could not be well predicted (Table S7). In case of imatinib, 90% of its true molecular targets were ranked at the top positions in the ranking order: ABL1 (1st), DDR1

(2nd), KIT (3rd), ABL2 (8th), NTRK1 (9th), CSF1R (10th), and LCK (23rd) (Figure 6b, Table S7). It was observed that most of the true targets of sorafenib and imatinib appeared in the top 10 positions. Screening of the targets of dasatinib J


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score with the above 12 multi-kinase inhibitors for some human kinases such as ABL1, KDR, KIT, and CSF1R (Figure S5). It was observed that in all the mentioned kinases, the binding efficiency of the kinase inhibitors which are actually their true target is higher and they ranked in the top ranking order. While the efficiency of the kinase non-binders appeared in the lower ranking list in the binding profile (Figure S5). In ABL1, all its true targets (ponatinib, nilotinib, regorafenib, imatinib, bosutinib, dasatinib, axitinib, sorafenib, cabozantinib) appeared within the top 10 positions while the non-binders of ABL1 are in the lower raking position (Figure S5a). Lenvatinib and pazopanib, the two multi-kinase inhibitors, have low affinity toward ABL1, hence appeared in low ranking position. Similarly, all the VEGFR2 (KDR) and KIT inhibitors are listed in the top ranking and their non-binders in the lower positions (Figure S5b,c). Similarly, in case of CSF1R, most of its true targets appeared in the top ranking order, and the nonbinders are in the bottom of the list (Figure S5d). Therefore, it can be stated that our method can sensitively distinguish between the kinase binders (true targets) from the non-kinase binders. Predicted Polypharmacological Profile of Mahanine. This structure-based target prediction pipeline was experimentally validated using a carbazole alkaloid, mahanine (Figure 7a), having anti-cancer activity in different types of cancer with various mutations.56,57,61,62,64−66 We established its polypharmacological profile related to kinase signaling. Figure 7b represents the heat map of the identified molecular targets of mahanine obtained by the reverse virtual screening toward a panel of 233 kinases using the different consensus scoring strategies as mentioned above. The predicted active versus inactive targets were gradually colored from dark red (top ranks) to green (bottom ranks) according to their respective ranks. Based on their ranks and colored region, we categorized into strong binders, moderate binders, and weak binders. Mahanine showed the high binding affinity toward ABL1, CDKL2, CSNK2A1, CSNK2A2, BRAF, CDK2, DDR1, EIF2AK3, FLT3, GSG2, KDR, MTOR, PIM1, PIK3CA, PIK3CG, and RAF1, and they occupied the top ranking position in the list. Moreover, all the consensus scoring strategies used in our study showed a similar trend of results for the predicted targets of mahanine (Figure 7b). To establish the accuracy of the target prediction, initially we performed kinase activity assay using Universal kinase assay kit (R&D system) followed by calculation of specific activity of total kinases in mahanine-treated HeLa cells. We demonstrated that mahanine has the potency to decrease the specific activity of total kinases from ∼1200 μmol/min/ngto ∼780 μmol/min/ ng. From this result, it can be presumed that ∼60% specific activity of total kinases has been reduced after mahanine treatment (Figure 7c). Based on this prediction, we selected a few strong binders such as tyrosine kinase KDR and serine/threonine kinase mTOR, RAF1, CSNK2A1, CSNK2A2, PIM1, and CDK2 for their experimental validation to find out where these predicted kinases really get modulated by this compound in both genetic and protein levels (Figure 7d). Accordingly, we selected two representative cancer cell lines, namely, ovarian (PA1) and cervical (HeLa). They were treated with mahanine (0, 12.5, and 17.5 μM) for 24 h. We found that mahanine showed a concentration-dependent decrease in transcriptional levels of KDR (VEGFR2), mTOR, RAF1, CSNK2A2, and CDK2 in PA1 cells. In HeLa cells, mahanine also reduced the gene

showed that 12 out of 21 experimentally established targets were ranked at the top 10% of the total data set with the ranking order: ABL1 (1st), DDR1 (2nd), BTK (4th), CSF1R (5th), HCK (6th), KIT (7th), ABL2 (9th), SRC (10th), EPHB4 (15th), BMX (17th), EPHA3 (24th), and LCK (25th) (Figure 6b, Table S7). However, the other targets of dasatinib, i.e., FYN, CSK, EPHA2, EPHA4, LYN, EPHA8, EPHA5, EPHB1, EPHB2 could not be retrieved by the reverse screening in the top ranking list (Table S7). For axitinib, sunitinib, and nilotinib, 4 out of 5 experimentally established targets were ranked at the top positions at 10% cutoff. The ranking order of targets of axitinib was KDR (1st), ABL1 (4th), KIT (8th), and CSF1R (16th), for sunitib were KIT (1st), CSF1R (2nd), KDR (3rd), and FLT4 (4th), and that for nilotinib was ABL1 (1st), KIT (2nd), DDR1 (9th), and MAPK11 (19th) (Figure 6b, Table S7). However, in all these drugs, only one target could not be retrieved, i.e., FLT1 (sunitinib and axitinib) and LCK (nilotinib) (Table S7). Screening of the targets of pazopanib exhibited that 5 out of 8 experimentally established targets were ranked at the top 10% cutoff with the ranking order: KDR (1st), KIT (2nd), CSF1R (4th), FGFR1 (5th), and FGFR3 (8th) (Figure 6b, Table S7). However, the other targets of pazopanib, i.e., FLT1, LCK, and ITK, could not be retrieved by the reverse screening in the top ranking list (Table S7). Similarly, target prediction of regorafenib and cabozantinib highlighted that 6 out of 11 and 5 out of 7 true targets were ranked at the top position in 10% cutoff, respectively. The ranking order of the targets of regorafenib was KDR (1st), ABL1 (2nd), BRAF (3rd), KIT (6th), NTRK1 (8th), and FGFR1 (21st), and that for cabozantinib was KIT (1st), KDR (2nd), TEK (6th), MET (10th), and FLT3 (15th) (Figure 6b, Table S7). The true molecular targets of bosutinib that were ranked in top position were in the ranking order: ABL1 (1st), MAP2K1 (11th), HCK (16th), and SRC (23rd) (Figure 6b, Table S7).However, the other two targets such as LYN and MAP2K2 could not be well predicted (Table S6). Ponatinib also have showed varied polypharmacological profile where 9 out of 15 experimentally established targets were ranked at the top 10% of the total data set, with the ranking order ABL1 (1st), KDR (2nd), CSF1R (4th), KIT (5th), DDR1 (6th), FLT3 (7th), FGFR4 (8th), ABL2 (11th), and LCK (15th) (Figure 6b, New Table S7). However, the other targets of ponatinib, i.e., FGFR1, SRC, RET, FGFR3, LYN, FGFR2 could not be retrieved by the reverse screening in the top ranking list (Table S7). Only in case of lenvatinib, the target prediction was not satisfactory as 50% of the targets were predicted in the ranking order: KDR (1st), KIT (2nd), FGFR4 (5th), FGFR1 (18th). However, FLT1, FGFR2, FGFR3, and RET could not recovered in the 10% cutoff (Figure 6b, Table S7). It was observed that most of the true targets of these drugs appeared in the top ten ranking positions. Ligand Efficiency and Binding Profile of Kinase Binders with Kinase Non-binders. Ligand efficiency of a drug quantifies how effectively the molecule binds to the target using their binding energy per atom. Depending on the binding affinity and physical properties of a lead molecule, ligand efficiencies may increase or decrease. Using our method, we generated the consensus score using five consensus scoring strategies for another ten set of drugs (belinostat, gemcitabine, cytarabine, nelarabine, topotecan, irinotecan, tamoxifen, paclitaxel, etoposide, methotrexate) which have no report to be a kinase binder (Figure S4). We compared their consensus K


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Figure 8. Sequence variation of ATP-binding domain of tyrosine family of kinases. Multiple sequence alignment of the catalytic domain of tyrosine kinase family is shown. The active site residues were aligned with Clustal Omega using the default settings. The sequence numbering is not a continuous sequence, where gaps in between the residues show the presence of a fragment of amino acid residues of the kinase domain. The full conserved residues are colored in dark blue, while partially conserved residues in light blue, while invariant and differential residues are shown as white color. The highlighted red box shows the hinge region residues of ATP binding site.

and CDK2 were also down-regulated after the treatment (Figure 7e). We also demonstrated mahanine-mediated inhibition of mTOR pathway indicated by the down-regulation of p-mTOR (Ser 2448), p70S6 kinase, and 4E-BP1 (Figure 7f). Though mahanine did not exhibit any change in gene expression level of RAF1, it decreased in total protein and phosphorylation level (Figure 7g). Moreover, inhibition of upstream Ras and downstream activation of ERK were also demonstrated in mahanine-treated HeLa cells (Figure 7g). To address the question whether mahanine directly exhibited its inhibitory effect on kinases, we carefully selected two important predicted kinases, namely, mTOR and VEGFR2 present in the upstream signaling pathway. Accordingly, in vitro kinase assay was performed using these two human recombinant kinases. We observed that pre-incubation of these two kinases with mahanine for 1 h, inhibits their activity in a concentration-dependent manner. IC50 values of mahanine against recombinant mTOR and VEGFR2 were ∼12 and ∼22 μM, respectively (Figure 7h,i. To further establish involvement of mahanine, we carried out immunoprecipitation kinase assay.60 It has been earlier reported that activity of mTOR can successfully be measured by phosphorylations of mTOR predominantly on S2448 giving rise mTORC1 formation.71Accordingly, we performed a pulldown experiment using mTOR with mahanine-treated or

expression levels of KDR, mTOR, CSNK2A2, and CDK2. However, the mRNA expression of RAF1 remained unchanged in HeLa cells, and CSNK2A1 slightly increased in both the treated cells. These results validated mahanine-mediated inhibition of these above-mentioned predicted kinases at the transcriptional level (Figure 7d). Thus, it is apparent that mahanine is able to modulate multiple kinases as predicted by our pipeline. Earlier reports revealed that activation of VEGFR-2 contributes to phosphorylation of multiple downstream signals including ERK, AKT, P70S6K, and furthermore RAS/RAF/ ERK/MAPK pathway.67,68 It is also reported that CSNK2A1 can phosphorylate Akt which in downstream activates mTOR.69 mTOR phosphorylates eukaryotic initiation factor 4E-binding protein1/2 (4E-BP1/2) and p70 ribosomal S6 protein kinase (p70S6K) in the downstream pathway.70 Therefore, next obvious question is to address how such deregulation of these kinases influences cross-talk between the cellular signaling pathways to induce cell death in these cancer cells. Accordingly, we also checked mahanine-mediated regulation of the above-mentioned predicted kinases at the protein level (Figure 7e−g). Mahanine exhibited the hindrance of the total as well as Tyr1175 phosphorylation of VEGFR2 at its post-transcriptional level in concentration dependent manner in HeLa cells. Furthermore, CK2α (casein kinase) L


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Figure 9. Structural comparison of the binding orientation of imatinib, sorafenib, and dasatinib bound in the DFG-in and DFG-out conformations of CSF1R kinase (PDB IDs: 3LCD, 4R7I). Two kinases conformations (DFG-out = pink and DFG-in = blue) are superposed with each other to show the differences of the two conformations represented as ribbon. The activation segment of the kinase main chains are colored as red (DFG-out) and navy blue (DFG-in). The hinge region is represented as Conolly surface (purple), while the allosteric site as the yellow color. The binding pose of (a) imatinib, (b) sorafenib, and (c) dasatinib represented in stick in the DFG-out (green) and DFG-in (pink). Magnified view of (a′) imatinib (carbon = green), (b′) sorafenib (carbon = magenta) represented as stick models interacting with both hinge (purple) and allosteric (yellow) sites. (c′) Dasatinib (carbon = cyan) interacting with the only hinge (purple).

untreated HeLa cells followed by kinase assay.60 We observed decreased Ser2448 phosphorylation of mTOR after treatment (Figure 7j). This experiment clearly demonstrated mahaninemediated inhibition of mTOR kinase. Similarly, we also tested another predicted kinase namely VEGFR2. Total VEGFR2 was pull-down with mahaninetreated and untreated HeLa cells, and kinase assay was performed which showed impairment in Tyr1175 phosphorylation of VEGFR2 in mahanine-treated cells further establishing that mahanine have the potency to inhibit this kinase also (Figure 7j). Based on these results we could hypothesize that the abovementioned successfully predicted kinases VEGFR2 (KDR), mTOR, c-Raf (RAF1), CDK2, and CSNK2A1 (CK2a) might be the putative targets of mahanine. Taken together, we have demonstrated that mahanine is able to modulate multiple kinases which are involved in cross-talk with different signaling molecules and thereby exhibit its polypharmacological action assigning it as a potent therapeutic molecule (Figure 7k). Moreover, we explored the molecular interaction of mahanine with mTOR and KDR to analyze the nature of binding. Our result showed similar interaction fingerprint with known ligand-bound crystal structures with these two kinases (Figure 7l,m).72,73 The long carbon tail points toward the surface-exposed portion of the hinge region, and the carbazole moiety rests on the hinge region. In the docked complex of mahanine with mTOR, it forms hydrogen bonds with Asp2244 and Cys2243 in the hinge region of the ATP binding site. These hydrogen bonds are complemented by hydrophobic interactions over the whole length of the mahanine, specifically with the hinge region residues such as Trp2239, Val2240, Cys2243, Asp2244, Thr2245, and Arg2251 (Figure 7l). In the docked complex of mahanine with KDR (VEGFR2), it forms hydrogen-bonding with the hinge region residues, i.e., Cys919 and Lys920, and hydrophobic interaction with Val899, Phe921,

Leu889, Phe918, Ile915, Ala866, Glu885, and Phe1047 (Figure 7m). Basis of Kinase Selectivity and Specificity of Three Representative Drugs toward Its Multiple Targets. Sequence-Based Analysis. To examine the basis of specificity and selectivity of these multi-kinase inhibitor drugs toward different kinases, out of 12 drugs, we selected three representative drugs, namely, imatinib, sorafenib, and dasatinib, to understand their basis of selectivity toward kinases. We observed that, out of 60 different tyrosine (Tyr) kinases, only 7 are the known molecular targets of sorafenib while 8 are the targets of imatinib whereas 21 are identified targets for dasatinib. Accordingly, we calculated the sequence similarity among these Tyr kinases, using multiple sequence alignment. A percentage identity matrix was generated and observed that more than 70% of amino acid residues in the ATP-binding site were functionally conserved (Figure S6). Next obvious question came in our mind that despite such high conservation, why did these drugs show a variation in the strength of binding with all these 60 Tyr kinases? What is the basis of the selectivity and specificity of these drugs to drive their polypharmacological action by targeting only a few Tyr kinases, but not the others? In order to understand such variation of binding affinity with different Tyr kinases, it is important to compare the key residues of the ATP-binding catalytic domain. The ATP binding pocket is lined by 36 amino acid residues that are involved in hydrogen-bonding and van der Waals interactions with the kinase inhibitors.74 The kinase catalytic domain folds into N-terminal (mostly β-sheets) and C-terminal lobes (mostly α-helices) connected by a hinge region. The hinge region of the ATP-binding pocket is mainly significant for the interaction with the kinase inhibitors, which are also mostly ATP competitors in nature. Interestingly, we also observed variations of amino acid residues mainly exist in the hinge M


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Journal of Chemical Information and Modeling region (highlighted in red box) of these 60 Tyr kinases (Figure 8). One of the key residues in the hinge region that form critical hydrogen bond with its side chain hydroxyl group to ATP and other kinase inhibitors is the gatekeeper polar residue, threonine (Thr).75 Crystallographic structure of ABL1 with imatinib and dasatinib showed a critical hydrogen bond with gatekeeper residue Thr315 in the hinge region.75,76 We also observed that the Thr residue is conserved in the known Tyr kinase targets of imatinib and dasatinib (Figure 8). On the contrary, in their respective weak binders, Thr is substituted by either of Val/Met/Phe/Iso/Leu (Figure 8). All these residues are hydrophobic with high molecular mass and van der Waals radius. Therefore, they do not ionize or participate in the formation of hydrogen bonds. Accordingly, they might partially block the cavity, providing steric hindrance, and thus impede binding. Crystallographic structure of sorafenib-KDR complex showed that the ligand forms a critical hydrogen bond with Cys919 of the hinge region.73 We observed that this Cys residue is conserved in known Tyr kinase targets of sorafenib (Figure 8). However, in weak binders of sorafenib, the Cys residue is substituted by either of Met/Val/Iso/Ala/Leu. Therefore, it can be substantiated that the variability of the amino acid residue in the hinge region may eliminate essential hydrogen bonds and might not favor the strong association of these drugs with the other Tyr kinases which are weak binders. Therefore, it could be one of the factors for the selectivity of these drugs toward their targets. Structure-Based Analysis. The ligand selectivity for targeting multiple kinases also depends upon their distinct structural conformations. The active DFG-in conformation and the inactive DFG-out conformation are reported based on the position of the activation segment (A-loop) in the kinase structure. In DFG-in state, the activation segment interacts with the mouth of the protein (blue color, in Figure 9a). The phenylalanine (Phe) side chain of the conserved DFG-motif located in the activation segment, flips “in” and located inside the ATP-binding pocket, while the aspartate (Asp) side chain is located on the outside of the pocket.77 In contrast, in DFG-out conformation, the activation segment folds on the side of the C-terminal lobe (red color in Figure 9a). There is an interchange of positions the Asp and Phe residues. The Asp side chain flips inside the ATP-binding pocket, and the Phe side chain flips outside the ATP-binding pocket. More than 74% of the kinases exist in DFG-in conformations, 10.1% in DFG-out only, and 16.2% were found in both the DFG-in and -out conformations.77,78 Next, we tried to address the question which kinase conformation is strongly selective and favored by imatinib, sorafenib, and dasatinib. Accordingly, we performed the molecular docking both in the DFG-in and DFG-out conformations of the Tyr kinases (Table 1, Figure 9a−c). The binding orientations of these drugs to the threedimensional structures of kinases were in line with experimental binding mode. The RMSD values for the best-scored docking poses of the three multi-target drugs with their respective crystal structure are shown in Figure S7. It was observed that the binding orientation of imatinib, sorafenib, and dasatinib in their docked complexes have RMSD value less than 1 Å to the bioactive pose in their respective crystal structures. For instance, using Glide, docking imatinib to ABL1, KIT, and CSF1R (DFG-out state) generated the best binding pose with

Table 1. Binding Energy Values of the Tyr Kinases with Three Representative Drugs (Sorafenib, Imatinib, and Dasatinib) in DFG-In and DFG-Out Conformations best binding energy (kcal/mol) kinase name

sorafenib

imatinib

dasatinib

ABL1 (DFG-in) ABL1 (DFG-out) ABL2 (DFG-in) ABL2 (DFG-out) BMX (DFG-in) BTK (DFG-in) CSF1R (DFG-in) CSF1R (DFG-out) CSK (DFG-in) DDR1 (DFG-out) EGFR (DFG-in) EPHA2 (DFG-in) EPHA3 (DFG-in) EPHA4 (DFG-in) EPHA8 (DFG-in) EPHB4 (DFG-in) FLT1 (DFG-out) FLT3 (DFG-out) FYN (DFG-in) HCK (DFG-in) KDR (DFG-out) KIT (DFG-in) KIT (DFG-out) LCK (DFG-out) LCK (DFG-in) LYN (DFG-in) NTRK1 (DFG-out) NTRK1 (DFG-in) RET (DFG-in) SRC (DFG-in) SRC (DFG-out)

−8.32 −10.42 −6.74 −6.78 −6.22 −7.39 −6.54 −11.73 −7.01 −10.45 −7.64 −5.33 −6.04 −7.08 −4.3 −9.43 −10.13 −10.25 −7.47 −9.25 −13.09 −5.74 −12.23 −8.44 −6.3 −4.6 −9.33 −8.4 −6.12 −6.78 −9.31

−10.39 −16.42 −7.7 −16.24 −7.15 −6.98 −7.13 −11.18 −6.56 −15.11 −6.72 −7.2 −5.41 −6.44 −7.57 −9.65 −5.54 −9.13 −9.64 −4.15 −10.54 −5.51 −11.35 −14.06 −6.35 −6.81 −10.27 −9.09 −8.01 −6.51 −7.95

−13.44 −9.14 −8.04 −9.49 −11.43 −13.32 −9.59 −9.13 −9.29 −9.09 −8.04 −7.44 −6.3 −11.86 −7.19 −10.1 −6.24 −8.26 −9.62 −9.02 −7.5 −8.05 −9.93 −6.27 −9.12 −8.35 −7.14 −7.56 −5.71 −9.44 −8.70

RMSD values of 0.35, 0.57, and 0.55 Å from their X-ray structures (2HYY, 1T46, 4R7I), respectively (Figure S7a,b,e). Similarly, docking sorafenib to BRAF, KDR (DFG-out state) generated the best pose with RMSDs of 0.37 and 0.30 Å from the X-ray structures (1UWH, 3WZE), respectively (Figure S7f, g). Moreover, dasatinib-BTK and dasatinib-BMX (DFG-in state) gave RMSDs of 0.74 and 0.72 Å from the X-ray structures (3SXR, 3LCD), respectively (Figure S7h,i). An RMSD threshold value of 2.0 Å is widely accepted to distinguish success and failure in reproducing a known binding mode. The favorable binding orientations of these drugs were significantly different from each other in both the structural conformations. In the DFG-out state, these drugs were extended throughout the ATP-binding cleft. On the contrary, these drugs were less close and mostly placed near the entrance region of the ATP-binding cleft in the DFG-in state (Figure 9a−c). Moreover, in the DFG-in conformation, dasatinib showed a higher strength of binding than imatinib and sorafenib. On the contrary, imatinib and sorafenib prefer to bind in DFG-out conformation rather than the DFG-in form (Table 1). In the DFG-out form, both sorafenib and imatinib extend themselves to bind to the hinge region of the ATPbinding site and additionally into an allosteric site (Figure 9a′,b′, Table 1). This allosteric site is mainly formed due to reN


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Figure 10. Comparison of the interaction fingerprints of the drugs bound with the tyrosine kinases: (a) Imatinib-ABL1 (DFG-out) complex, (a′) Sorafenib-ABL1 (DFG-out) complex and (a″) Dasatinib-ABL1 (DFG-out) complex (PDB ID: 2HYY); (b) Imatinib-ABL1 (DFG-in) complex, (b′) Sorafenib-ABL1 (DFG-in) complex and (b″) Dasatinib-ABL1 (DFG-in) complex (PDB ID: 2GQG); (c) Imatinib-LCK (DFG-out) complex, (c′) Sorafenib-LCK (DFG-out) complex, (c″) Dasatinib-LCK (DFG-out) complex (PDB ID: 2PL0); (d) Imatinib-LCK (DFG-in) complex (d′) Sorafenib-LCK (DFG-in) complex, (d″) Dasatinib-LCK (DFG-in) complex (PDB ID: 3AC1); (e) Imatinib-SRC (DFG-out) complex, (e′) Sorafenib-SRC (DFG-out) complex, (e″) Dasatinib-SRC (DFG-out) complex (PDB ID: 2OIQ); (f) Imatinib-SRC (DFG-in) complex, (f′) Sorafenib-SRC (DFG-in) complex, (f″) Dasatinib-SRC (DFG-in) complex (PDB ID: 1YOL); (g) Imatinib-BMX (DFG-in) complex, (g′) SorafenibBMX (DFG-in) complex, (g″) Dasatinib- BMX (DFG-in) complex (PDB ID: 3SXR); (h) Imatinib-HCK (DFG-in) complex, (h′) Sorafenib-HCK (DFG-in) complex, (h″) Dasatinib-HCK (DFG-in) complex (PDB ID: 3VS1); (i) Imatinib-KDR (DFG-out) complex, (i′) Sorafenib-KDR (DFGout) complex, (i″) Dasatinib- KDR (DFG-out) complex (PDB ID: 3WZE); (j) Imatinib-FLT3 (DFG-out) complex, (j′) Sorafenib-FLT3 (DFG-out) complex and (j″) Dasatinib-FLT3 (DFG-out) complex (PDB ID: 4RT7); (k) Imatinib-KIT (DFG-out) complex, (k′) Sorafenib-KIT (DFG-out) complex, (k″) Dasatinib-KIT (DFG-out) complex (PDB ID: 1T46); (l) Imatinib-KIT (DFG-in) complex, (l′) Sorafenib-KIT (DFG-in) complex, (l″) Dasatinib-KIT (DFG-in) complex (PDB ID: 1PKG); (m) Imatinib-CSF1R (DFG-out) complex, (m′) Sorafenib-CSF1R (DFG-out) complex, (m″) Dasatinib-CSF1R (DFG-out) (PDB ID: 4R7I); (n) Imatinib-CSF1R (DFG-in) complex, (n′) Sorafenib-CSF1R (DFG-in) complex, (n″) Dasatinib-CSF1R (DFG-in) (PDB ID: 3LCD).

orientation of the activation segment toward the C-lobe (highlighted in red). In the DFG-in form, dasatinib binds close to the hinge region of the ATP binding site and has no close association with the allosteric site (Figure 9c′). Thus, it can be deciphered that the kinase conformation plays a key role in determining the selectivity of these drugs. Subsequently, we wanted to find out which interacting amino acids in the ATP-binding domain play a key role in determining the selectivity profile of these drugs toward their targets? Accordingly, we have done a comparative analysis of hydrogenbonding, stacking, and hydrophobic interactions of these drugs toward their respective targets. Remarkably, there was a significant variability in the pattern of interaction fingerprints (Figure 10, Figure 11, Figure S8). From the reverse screening result, we observed that all the three drugs have high selectivity toward ABL1 kinase. Both imatinib and sorafenib preferentially bind in the DFG-out state and involve multiple hydrogen-bond interactions with Met318, Thr315 of the hinge region, Glu286 from C-helix, and Asp810

of the conserved DFG motif (Figure 10a,a′). However, sorafenib, have no interaction with gatekeeper Thr residue (Figure 10a′). Additionally, it has close interactions with Ile360 and His361 of the allosteric site (Figure 9a,b, Figure S8). Lack of these crucial hydrogen bonds disrupts and weakens their strength of binding in the DFG-in state (Figure 10b,b′). Dasatinib preferentially binds in the DFG-in and forms crucial hydrogen bonds with the gatekeeper residue Thr315 and other residues, Met318 and Thr319, in the hinge region (Figure 10b″). But it did not show any specific interaction with these residues present in the hinge region when it is in the DFG-out state of ABL1. Rather it is forming hydrogen bonds with Glu286 of C-helix and Ile360 of the allosteric site and thereby reducing the binding strength (Figure 10a″). The ligands and interacting amino acids residues are represented in stick model with carbon color of imatinib = green, sorafenib = magenta, and dasatinib = cyan, amino acid = tan. H-bonds are in dotted line. O


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Figure 11. Detail comparative analysis of different molecular interactions of sorafenib, imatinib, and dasatinib with their respective molecular targets in DFG-in/out conformation. Residues are within 4 Å of the binding site. Hydrogen-bond interaction residues (yellow), π−π stacking interacting residues (green), hydrophobic residues (light pink), hydrophilic residues (light blue), aromatic residues (gray), and glycine (white) are shown.

conserved DFG motif and Glu640 of C-helix (Figure 10j,j′). Imatinib forms another hydrogen bond with gatekeeper Thr670 residue (Figure 10j). Moreover, the extended configuration of both of the molecules forms additional hydrophobic interactions especially with the residues of the allosteric region Ile789 and His790. Likewise, CSF1R bound to these two drugs also revealed a similar pattern of molecular interactions with Cys666, Glu633, Thr663, and Asp796 (Figure 10m,m′). However, dasatinib is much more stable in DFG-in state of KIT. In KIT-dasatinib complex, it forms two crucial hydrogenbond interactions with Cys673 and Thr670 (Figure 10l″). On the other hand, CSF1R bound to dasatinib formed hydrogenbond interactions with Cys666, Cys667, Tyr665, and gatekeeper residue Thr663 (Figure 10n″). All these hydrogen bonds were complemented by extensive hydrophobic interactions, over the entire length of these drugs. There was a significant variability in the pattern of molecular interaction fingerprints of these drugs with the amino acids residues of the respective kinase targets (Figure 11, Figure S8). Thus, such detailed comparative analysis and interpretation of the overall molecular fingerprints of these kinase-ligand complexes (in DFG-in and DFG-out forms) established that the ligand selectivity profile toward the kinases depends upon the different fingerprints of molecular interactions between these drugs and the channel that configured in the neighboring amino acid residues of ATP-binding pocket.

Next, we tried to address the question how SRC-family of kinases such as HCK, LCK, SRC, BMX, and BTK behaved with imatinib, sorafenib, and dasatinib. Dasatinib preferentially binds these kinases in the DFG-in conformation through hydrogenbond interaction with the hinge region. The only exception is LCK which also exists in the DFG-out form. Dasatinib did not show any hydrogen-bond interaction and thereby reduced the strength of binding in the DFG-out form of LCK (Figure 10c″). However, in the DFG-in conformation, it forms crucial hydrogen bonds with Thr316, Met319, and Asn321 (Figure 10d″). A similar pattern of hydrogen-bonding was observed with SRC, HCK, and BMX in the hinge region (Figure 10f″, g″, h″). However, in dasatinib-BMX complex, apart from hydrogen bond with Thr489, it forms another hydrogen bond with Ile492 instead of methionine (Figure 10g″). On the contrary, imatinib shows strong association with the DFG-out form of the LCK kinase and exhibits similar fingerprint of molecular interactions like ABL1 (Figure 10c). However, in the DFG-in form of SRCfamily of kinases, both imatinib and sorafenib did not exhibit a strong interaction with the residues of the hinge region. Allosteric site residues were also far from these drugs, which ultimately lead to low strength of binding (Figure 10d,d′; f,f′; g,g′; h,h′). Sorafenib showed high selectivity and affinity toward the VEGFR-family of kinases such as KDR, FLT1, KIT, CSF1R, and FLT3 in the DFG-out conformation (Table 1). It has the solved crystallographic structure only with KDR (DFG-out) which showed crucial hydrogen-bond interactions with Cys919, Glu885, and Asp1046.73 Our docked complex also showed a similar mode of binding with RMSD of 0.34 Å with the X-ray structure and similar pattern of hydrogen-bond interactions (Figure 10i′, Figure S7g). In the sorafenib-FLT3 complex, it forms the hydrogen bond with Cys695, Glu611, and Asp829 (Figure 10j′). Among these VEGFR-family of kinases, imatinib and dasatinib showed high strength of binding to only KIT and CSF1R and did not exhibit preferable molecular interactions with the rest (Figure 10k,l″,m, n″). The detailed molecular fingerprints revealed that in DFG-out state of KIT, both imatinib and sorafenib have hydrogen-bond interactions with Cys673 of the hinge region, Asp810 of the

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DISCUSSION A crucial step in assessing the polypharmacological behavior of a drug is mainly through identification and confirmation of its molecular targets. Although, different experimental methods exist for drug target identification, but due to their laborious and expensive nature, in silico methods are quick and efficient for the prediction of drug−target interactions and have drawn more attention in recent years.14,24,25,28−36 The major achievement of the present study is to establish a computational approach for target identification based on reverse virtual screening using several consensus scoring strategies. By structure-based profiling of kinases, playing an P


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the lowest AUC score was achieved for only lenvatinib (∼0.6 ± 0.02). Even, the top 1% scorers were significantly enriched with the true targets with high enrichment factor (data not shown). At top 2% cutoff, an average enrichment factor of 80% was achieved. It can be suggested that the sensitivity of the method is such that if a therapeutic molecule had just one known binder, it would still be among the top 2% of docking hits. However, our prediction could retrieve more than 80% targets for most of the drugs (sorafenib, imatinib, axitinib, sunitinib, and nilotinib), more than 65% for regorafenib, cabozantinib, pazopanib, bosutinib, and ponatinib, for but only half of the targets for dasatinib and lenvatinib. There may be several factors for such variation like different chemical nature of these drugs mainly the torsional flexibility, their varying affinity for the targets and possibly due to the lack of the correct structural kinase conformation (DFG-in and -out) from our data set for the proper binding of these drugs. All these drugs have high affinity to bind in a specific kinase conformation, either DFG-in and DFG-out. For example, most of the drugs such as sorafenib, imatinib, sunitinib, lenvatinib, axitinib, and pazopanib bind in DFG-out kinase conformation.40 The two primary targets of these that were not retrieved through our prediction pipeline are RET and FLT1. The structure of these two kinases exists in DFG-in conformation, so it can be hypothesized that improper binding of these set of drugs in DFG-in conformation of these kinases gave as false negative result. This method can perceptively distinguish between the kinase binder and the nonkinase binders against a set of kinase targets, and therefore the binding efficiency of these drugs could be achieved. The next question that was addressed is that how the modulation of multiple kinases by these drugs results in their polypharmacological action. Several in vitro studies reported inhibition of multiple kinases to understand the mechanism of action of these drugs in the cellular system.3,4,9,37−40 Imatinib, a well-known drug for chronic myeloid leukemia (CML), was discovered as the target for ABL1 kinase.3 However, increased imatinib-resistance in CML patients developed second generation BCR-ABL inhibitor, nilotinib. This drug has 30fold increased potency over imatinib and has a favorable safety profile.79 Due to the dual mechanism of action inhibiting both Abl and Src kinases; bosutinib is active in resistant CML disease, other myeloid malignancies, and solid tumors.80 Since KIT constitutively activated in most of gastrointestinal stromal tumors (GISTs), some drugs like imatinib, sunitinib, nilotinib have also been approved for GISTs as they also inhibits cKIT.81 Moreover, nilotinib also inhibits mutated variant of KIT and is used in the treatment of mastocytosis.82 Due to their polypharmacological nature, imatinib and nilotinib also strongly bind to several carbonic anhydrases (CAs), a totally distinct family of enzymes form kinases.83 On the other hand, sorafenib and sunitinib specifically target the VEGFR-family of kinase and are used for advanced renal cell and hepatocellular carcinoma. Recently, sorafenib has been used for acute myeloid leukemia due to its ability to potently inhibit ABL1.84 Regorafenib, a structural analogue of sorafenib, is more potent and used for the treatment of patients with metastatic colorectal cancer previously treated with fluoropyrimidine-, oxaliplatin-, and irinotecan-based chemotherapy and advanced gastrointestinal stromal tumors previously treated with imatinib mesylate and sunitinib malate.85,86 Due to the multi-targeted action of axitinib, it is effective in treating many cancers like sorafenibresistant metastatic renal cell carcinoma, kidney cell cancer,

important role in cancer, we demonstrated that 12 clinically used drugs selected as test molecules for our study, have a strong binding affinity toward multiple kinases, thus confirming their polypharmacological behavior. Our approach using consensus scoring strategies could successfully recover most of their true kinase targets. Moreover, through our target-prediction method, we have established that mahanine, a potent therapeutic molecule isolated from a natural product, is able to modulate multiple kinases, which are involved in cross-talk with different signaling molecules and thereby demonstrating its polypharmacological action by means of experimental strategies. Furthermore, we have shown that the target selectivity of the test drugs is controlled not only by variations in kinase structural conformations but also by crucial hydrogen bonds and van der Waals interactions. Biologically annotated small molecule libraries are easier to set up than a collection of multiple protein binding sites of varied nature. Docking algorithms and scoring functions have made substantial developments in structure-based virtual screening. Moreover, structure-based target prediction will provide substantial insights for rational drug selectivity and designing of better drugs. This method can also better detect the activity cliffs between compounds.26,27 However, it is difficult to handle multiple proteins with multiple crystal structures per protein, and with multiple binding pockets per structure. Therefore, it is a very rigorous process to determine the final protein-drug scores as each drug needs to be docked to all the binding pockets of the proteins. For such reason, the reverse screening approach has been less applied. Moreover, application of single scoring function in reverse screening suffers from several limitations to prioritize the targets.30 Our target prediction pipeline tried to overcome all these limitations by the implementation of several consensus scoring strategies based on a combination of multiple scoring functions and ranking criteria. It enabled us to get the optimal hit rate for most of the drugs by reducing the number of false positives and negatives with appreciable accuracy. All these consensus scoring strategies effectively led to higher hit rates and AUC score than using a single scoring function. The main advantage of the use of consensus score is that each of the scoring function brings out the true hits, however, in some cases, it may also miss out the hits. But the combination of different scores suggests that all the multiple scoring functions will not be inaccurate together. In our strategy, we have utilized all the three types of scoring functions, i.e., empirical, force-field, and knowledge-based for the better enrichment of our analysis.41,42,53,54 Several kinases have been reported as molecular targets of these 12 FDA approved drugs.3,4,9,37−40 We successfully recovered them as the true target using our computational strategy. Our target prediction showed an overall good performance (AU-ROC value ∼0.9) with appreciable accuracy for most drugs. However, the average AU-ROC value for dasatinib, using criterion 1 (0.805 ± 0.02) was a little higher than that of criterion 2 (0.742 ± 0.02). This may be attributed to the difference in the data sets of the two criteria, where in ranking criterion 1; each 233 kinases have multiple number PDB entries with a total number of 1080 kinase structures. In ranking criterion 2, averages of three best scoring PDBs of each individual kinase were taken to make a data set of 233 kinases. Moreover, since dasatinib has 21 known targets, the target selectivity of dasatinib is low. Moreover, out of the 12 drugs, Q


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VEGFR2 after kinase assay further established that mahanine might also have the potency to inhibit these kinases. More importantly, inhibition of mTOR and VEGFR2 kinase activity by mahanine further validates them as true targets of mahanine. Thus, such experimental validation of the predicted kinase association strongly suggests the robustness of our prediction pipeline. Moreover, CSNK2A1 is reported to activate Akt either deactivating PTEN or by physically interacting.69 Mahanineinduced deactivation of VEGFR2 (KDR) and CSNK2A1 might lead to PTEN stabilization, followed by down-regulation of Akt, consequently resulting in mTOR inhibition.57,70 Demonstration of mahanine-mediated CDK2 down-regulation further supported the potent capacity of mahanine to restrict G0/G1 phase with inhibition of CDK4/CDK6 and CDC25A.64 Promotion of G1 phase through activation of CDK2 by Akt additionally indicated the involvement of VEGFR2-mediated regulation.91 Furthermore, mahanine exhibited deactivation of RAS/RAF/ERK pathway which also represented an association with VEGFR2. Thus, the modulation of multiple kinases and their cross-talks in response to mahanine interrupt several signaling pathways to induce cell death in several cancer cells with various oncogenic mutations. Therefore, our study further establishes mahanine as a novel bioactive molecule with polypharmacological behavior. Since the majority of kinase inhibitors bind to a conserved ATP-binding site of the kinase enzyme, it is important to understand the selectivity profile of such inhibitors to avoid toxicity issues. Despite the high conservation in the ATPbinding domain of the Tyr kinases, the question arises: What is the basis of selectivity and specificity of these drugs toward their targets? The binding specificity of these drugs was driven by inherent variation in binding affinity when bound to DFG-in and -out conformations of the Tyr kinases followed by a significant difference in the entire molecular interaction fingerprint, mainly the hydrogen-bond interactions. Both sorafenib and imatinib preferentially showed high binding strength toward DFG-out kinase conformation. Some crucial H-bond interactions with the polar amino acid residues in the hinge region, C-helix, the highly conserved DFG motif, and van der Waals interactions with an allosteric site serve as determining factors in the selectivity profile of these drugs toward the multiple kinases. On the contrary, dasatinib mainly interacts with the amino acid residues of the hinge region of the ATP-binding site, especially with threonine gatekeeper residue in the DFG-in kinase conformation. Additional molecular interactions with the allosteric site confer much more kinase selectivity to imatinib and sorafenib than dasatinib, which has low kinase selectivity and has many targets. Therefore, deciphering such molecular interaction fingerprints of the ATP binding site of kinases with their inhibitors would help us in the rational design of new lead molecules as better multi-targeted drugs with diverse polypharmacological action. Such structure-based kinase profiling could provide useful information in revealing the unknown and secondary therapeutic targets of a known drug for repositioning. Thousands of small molecules, either synthesized or isolated from natural sources are waiting for their positioning as a potential drug. This general methodology can be successfully applied for the molecular target identification of such lead bioactive molecules.

pancreatic cancer, and thyroid cancer. Its polypharmacological action inhibit Wnt/β-catenin signaling by targeting E3 ubiquitin ligase SHPRH (SNF2, histone-linker; PHD and RING, finger domain-containing helicase).87 However, the polypharmacological action of drugs may also lead to unexpected off-target binding resulting in various side effects. Both KIT and CSF1R, belonging to VEGFR family of kinases, are required for maintaining the cardiac homeostasis during ischemic and cardiomyocyte terminal differentiation. These kinases are inhibited by imatinib, sorafenib, dasatinib, sunitinib which potentially resulted in cardiotoxicity.88 Moreover, sunitinib is also associated with hepatotoxicity.89 Since bosutinib is not an inhibitor of c-KIT or PDGF receptor, it has less hematologic toxicities and rare cases of clinically apparent acute liver injury.90 Therefore, it is important to identify all the possible targets of a drug, to understand its wide therapeutic application. One of the important applications of our structure-based target prediction pipeline is to predict unknown molecular targets of a newly identified, non-toxic, potential bioactive molecule effective against different types of cancer with several oncogenic mutations.61,62,64−66 Our previous studies depict that it modulates redox potential by targeting mitochondrial complex-III and destabilizes Hsp90 chaperone activity.56,64 It also induces Fas/FasL and mitochondrial activation mediated apoptosis, activates p53/p73, triggers the G0/G1 phase cell cycle arrest, and is a DNA minor groove binder.57,61,64 Despite these mechanisms of action, the overall polypharmacological profile in relation to kinases-signaling was not completely known. Our approach successfully predicted a few strong binders of mahanine such as tyrosine kinase KDR and serine/ threonine kinase mTOR, RAF1, CSNK2A1, CSNK2A2, PIM1, and CDK2 in both gene and protein levels. Literature studies revealed that KDR (VEGFR-2) activation contributes to the phosphorylation of multiple downstream kinases including Akt, which in turn activates mTOR.60 It has been reported that the activity of mTOR can successfully be measured by phosphorylation of mTOR predominantly on Ser2448 for mTORC1 formation.71 Based on this observation, we earlier performed the experiment with mahanine using brain cancer cells (glioblastoma, GBM) as a model system and demonstrated that mahanine reduced both Ser2448 and Ser2481 phosphorylation of mTOR.65 It is also known that when Rictor binds to mTOR, then only this kinase gets activated. Accordingly, we knocked down the Rictor and found no activation of mTOR and subsequent deactivation of Akt. Similar observation was made in mahanine-treated cells, suggesting direct association of mTOR with mahanine in GBM.65 Furthermore, Chatterjee et al. also reported similar finding in lung cancer cells.70 This again confirmed that mahanine irrespective of the type of cancer is able to inhibit this kinase. Taken together, all these above-mentioned evidence convincingly suggested that mahanine is a true direct inhibitor of mTOR kinase in different type of cancer cells as predicted by our computational method. In our system, we also demonstrated that mahanine successfully reduce the phosphorylation of mTOR at Ser2448 and consequently deactivate its downstream molecules (pp70S6 kinase 4EBP1) in HeLa cells. Additionally, pull-down of mTOR followed by kinase assay resulted in decreased Ser2448 phosphorylation of mTOR in mahanine-treated HeLa cells confirmed mahanine-mediated direct inhibition of mTOR kinase. Similarly, reduced Tyr1175 phosphorylation of R


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CONCLUSION We established a reverse virtual screening method using several consensus scoring strategies to identify multiple targets of a drug. Our structure-based target profiling of 12 clinically used test drugs recovered most of their true kinase targets, thus confirming their polypharmacological behavior. Additionally, we also have included ten more drugs that do not have any report to be a kinase binder (inactive to kinase). Using our method, we have observed that the binding efficiency of these kinase non-binders drugs is low. Taken together, our method can sensitively distinguish between the kinase binder and the non-kinase binders against a set of kinase targets. Next, we successfully validated our target prediction method through experimental strategies not only to identify the putative targets of a potent therapeutic molecule, mahanine but also demonstrated the kinase activity and binding with the predicted kinases. We have observed that it also modulates multiple kinases involved in cross-talk with different signaling molecules, thereby demonstrating its polypharmacological action. Furthermore, we established that target selectivity of these drugs is driven by different molecular interaction fingerprints and is dependent upon the variations in kinase structural conformations. Taken together, such structure-based kinase profiling could provide useful information to study the overall polypharmacological behavior of therapeutic molecules in revealing their unknown targets, which may be either ontargets or sometimes unexpected off-targets, and will assist in future drug design and discovery.

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each pair of aligned sequences of ATP-binding region of tyrosine family of kinases (Figure S6); RMSDs of the binding pose of sorafenib, imatinib, and dasatinib in the respective tyrosine kinases with the known crystallographic structures to check the docking accuracy (Figure S7); molecular interactions of the docked complex of sorafenib, imatinib, and dasatinib with their respective predicted targets in the ATP-binding pocket (in 4 Å) (Figure S8) (PDF)

AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. *E-mail: [email protected]. ORCID

Chitra Mandal: 0000-0001-8275-3978 Present Address §

R.D.: Bose Institute, Kolkata 700054, India

Author Contributions

D. Dutta, Chhabinath Mandal, and Chitra Mandal conceived and designed the study, analyzed and interpreted the data, and wrote the manuscript. R. Das performed the wet lab experiments. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The work is supported in part by Council of Scientific and Industrial Research (ESC 0103), Department of Science and Technology (GAP 336 and GAP 339), and Department of Biotechnology (GAP 346), Govt. of India. We sincerely acknowledge Mr. Eswara M. Satyavarapu for providing the purified mahanine, Mr. Anindyajit Banerjee for professional discussion, and Mr. Asish Mallick and Ms. Rita Maity for their technical help. Director (CSIR-IICB) is kindly acknowledged for providing all institutional facilities. D.D. is Senior Research Fellow of DBT, and R.D. is currently a SERB N-PDF. Chitra Mandal is grateful to financial support by Sir J.C. Bose National Fellowship, Department of Science and Technology, Govt. of India, and DBT-Distinguished Biotechnology Research professorship award.

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jcim.7b00227. Comparative evaluation of the existing structure-based prediction methods (Table S1); asymmetric distribution of PDB entries of 233 human kinases (Figure S1); total human protein kinase data sets used in the reverse virtual screening of the therapeutic molecules (Table S2); detailed methodology for calculation of scores and ranking of targets using the consensus scoring strategies taking a drug and a small kinase data set (as an example) (Methodology S1); primers used to investigate the gene expression of a few predicted kinases mediated by mahanine (Table S3); AUC score of the target prediction of 12 therapeutic molecules using five consensus scoring strategies and two ranking criterion (Table S4); comparison of performance evaluation measure (ROCScore) using single scoring and consensus scoring functions for target prediction of three representative drugs, namely imatinib, sorafenib, and dasatinib (Table S5); KINOMEscan dendrogram for binding affinity of 12 drugs against 233 kinases using their calculated binding energy values (glidescore) (Figure S2); heat map of the three representative drugs for all strategies (Figure S3); true positives and true negatives of multi-kinase inhibitors of three representative clinically approved drugs, namely imatinib, sorafenib, and dasatinib (Table S6); ranking order of the true predicted targets of 12 drugs (Table S7); chemical structures of the non-kinase binder drugs (Figure S4); scatter plot showing the binding efficiency of kinase binders and non-binders for a set of kinases (Figure S5); percent identity matrix for

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