A Specific Pharmacophore Model of Aurora B Kinase Inhibitors and Virtual Screening Studies Based on...

A Specific Pharmacophore Model of Aurora BKinase Inhibitors and Virtual Screening StudiesBased on it

Hui-Yuan Wang1,2, Lin-Li Li2, Zhi-XingCao1, Shi-Dong Luo1, Yu-Quan Wei1 andSheng-Yong Yang1,*

1State Key Laboratory of Biotherapy and Cancer Center, West ChinaHospital, West China Medical School, Sichuan University, Sichuan610041, China2West China School of pharmacy, Sichuan University, Sichuan610041, China*Corresponding author: Sheng-Yong Yang, [email protected]

In this study, 3D-pharmacophore models of AuroraB kinase inhibitors have been developed by usingHipHop and HypoGen modules in Catalyst soft-ware package. The best pharmacophore model,Hypo1, which has the highest correlation coeffi-cient (0.9911), consists of one hydrogen-bondacceptor, one hydrogen-bond donor, onehydrophobic aliphatic moiety and one ringaromatic feature. Hypo1 was validated by test setand cross-validation methods. And the specificityof Hypo1 to Aurora B inhibitors was examinedwith the use of selective inhibitors against AuroraB and its paralogue Aurora A. The results clearlyindicate that Hypo1 can differentiate selectiveinhibitors of Aurora B from those of Aurora A, andthe ring aromatic feature likely plays some impor-tant roles for the specificity of Hypo1. Then Hypo1was used as a 3D query to screen several data-bases including Specs, NCI, Maybridge and Chi-nese Nature Product Database (CNPD) foridentifying new inhibitors of Aurora B. The hitcompounds were subsequently subjected to filter-ing by Lipinski’s rule of five and docking studiesto refine the retrieved hits, and some compoundsselected from the top ranked hits have beensuggested for further experimental assay studies.

Key words: Aurora B, drug design, kinase inhibitor, pharmacophoremodeling, virtual screening

Received 22 July 2008, revised and accepted for publication 13 Novem-ber 2008

In the past decade, the Aurora family of serine ⁄ threonine kinaseshas attracted intense attention because of its critical roles playedin the regulation of cell mitosis (1–5). Humans express three Aurora

kinases, namely, Aurora A, B, and C (6). At least two of them (Aur-ora A and B) have been found to be frequently over-expressed inhuman tumors, including breast, lung, colon, ovarian, prostate, col-orectal, and pancreatic cancers (the function of Aurora C is not yetclear) (6–15). Many in vitro and in vivo experimental data show theassociation existing between Aurora A ⁄ B over-expression and can-cer, thus Aurora A and B have been thought to be attractive drugtargets for cancer therapy (16–20). For a long time, much moreattention has been paid to the discovery of Aurora A inhibitors dueto, for examples, the gene amplification of Aurora A and the obser-vation that it can transform cells when over-expressed. Untilrecently, several studies have shown that inhibition of Aurora Bmight be more advantageous in terms of providing therapeutic ben-efit in oncology (16,17,19,21–27). Typically, for example, inhibitorsthat target both Aurora A and B induce a failure in cell divisionand endoreduplication rather than a G2-M arrest, which are typicalphenotypes of the ablation of Aurora B function (21). As a result,the research interest in recent years has been shifted to the discov-ery of Aurora B kinase inhibitors.

Currently, at least 10 pharmaceutical companies have been involvedin the development of Aurora B inhibitors. Although, up to now, theinhibitors targeting Aurora B are not as many as those targetingAurora A, still there have been considerable amount of compoundsreported to be inhibitors of Aurora B. And at least one compound(AZD1152) (28), which selectively targets against Aurora B, hasentered clinical trials. On the other hand, a pre-survey to the chemi-cal structure and bioactivity of these complexes shows that the lim-ited number of inhibitors of Aurora B still bear sufficient diversityof chemical structure and bioactivity. These provide a good basis toelucidate the structure–activity relationship of these diverse com-pounds and further benefit to identify novel Aurora B inhibitors.

In terms of the residue sequence and 3D-structure of catalyticdomain, Aurora B is too similar to its paralogue Aurora A (17,29);this increases the difficulty to find a selective Aurora B inhibitors.Taking full advantage of the information of known Aurora B inhibi-tors, particularly the selective Aurora B inhibitors, may benefit thediscovery of selective Aurora B inhibitors. Thus, in this study, weshall develop specific pharmacophore models of Aurora B inhibitorsbased on common chemical features of Aurora B inhibitors reportedso far by using the HipHop (30) and HypoGen (31) algorithms imple-mented in Catalyst software (Accelrys, San Diego, CA, USA). Thenthe best model will be used as a 3D search query for several che-mical databases, including Specs, NCI, Maybridge, and Chinese

115

Chem Biol Drug Des 2009; 73: 115–126

Research Article

ª 2009 The AuthorsJournal compilation ª 2009 Blackwell Munksgaard

doi: 10.1111/j.1747-0285.2008.00751.x

Chart 1: Chemical structures of the 21 training set compounds together with their experimental inhibitory activities (IC50 values, inparentheses).

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116 Chem Biol Drug Des 2009; 73: 115–126

Chart 2: Chemical structures of the 21 test set molecules together with their experimental inhibitory activities (IC50 values, in parentheses).

A Specific Pharmacophore Model of Aurora B Kinase Inhibitors

Chem Biol Drug Des 2009; 73: 115–126 117

Nature Product Database (CNPD). The hit compounds will be subse-quently subjected to filtering by Lipinski's rule of five (32) and dock-ing study to refine the retrieved hits and as a result to reduce therate of false positive. Finally potential lead compounds will beshifted to in vitro and in vivo, as well as structural modification stu-dies. As far as we know, this is the first report on the specific 3Dpharmacophore modeling of Aurora B kinase inhibitors for rationaldrug design.

Materials and Methods

Pharmacophore modelingAll the parmacophore modeling calculations were carried out byusing CATALYST 4.11 software package (Accelrys). The commonpharmacophore features necessary for potent Aurora B inhibitorswere identified by HipHop program and quantitative pharmaco-phore models were created by HypoGen module within CATA-LYST.

As far as we can collect, there are about 88 human Aurora Bkinase inhibitors which quantitative bioactivity data are publiclyavailable (17–19,29,33–38). After ignoring the inhibitors with verysimilar structure and similar activity, 42 inhibitors were chosen forthe pharmacophore modeling. For the specific quantitative pharma-cophore models by HypoGen, 20 inhibitors who have relativelyhigher inhibitory potency to Aurora B kinase (IC50 < 1 lM) wereselected to form the training set; these compounds have satisfac-tory diversity in both structural features and activity ranges. Thebiological activity values of the selected 20 inhibitors span a rangeof four orders of magnitude (IC50 values ranging from 0.0003 to0.63 lM). Chemical structures as well as experimental IC50 valuesof training set molecules are given in Chart 1-1–20. The remaining22 compounds form the test set, which are shown in Chart 2-21–

42. The eight most active compounds (compounds 1–8) in trainingset were selected for the identification of common chemical fea-tures by HipHop.

All molecules were built in Catalyst 2D ⁄ 3D visualizer in Catalystsoftware package and all of the structures were minimized usingthe Charmm-like force field implemented in the program (39). A ser-ies of energetically reasonable conformational models which repre-sent the flexibility of each compound were generated within theCatalyst catConf module using the Poling Algorithm (39–42). Wechoose a maximum number of 250 conformers, the 'best conforma-tional analysis' method (40–42), and an energy threshold of15 kcal ⁄ mol above the global energy minimum for conformationsearching.

In the HipHop run, compound 1 was considered as 'reference com-pound' specifying a 'principal' value of 2 and a 'MaxOmitFeat' valueof 0, meaning their structures and conformations would have thestrongest influence in the model building phase. The 'principal' and'MaxOmitFeat' values were set to 1 for the remaining compoundsof the training set. The initial features, which were specified basedon an overview of all the training set molecules, include hydrogen-bond acceptor (A), hydrogen-bond donor (D), hydrophobic aliphaticmoiety (L), and ring aromatic feature (R).

In the HypoGen run, the initial pharmacophore features were speci-fied based on the common pharmocophore features identified inthe HipHop run. The quality of the HypoGen models were evaluatedaccording to Debnath (43) in terms of cost functions and other sta-tistical parameters which were calculated by HypoGen module dur-ing hypothesis generation. A good pharmacophore model shouldhave a high correlation coefficient, lowest total cost and RMSDvalues, and the total cost should be close to the fixed cost andaway from the null cost. The best pharmacophore hypothesis wasfurther validated by test set method and Fischer's randomizationtest (44).

Virtual screeningThe best HypoGen pharmacophore hypothesis was used as a 3Dstructural query for retrieving potent molecules from chemical data-bases including Specs, NCI, MayBridge and CNPD. For each mole-cule in the databases, 100 conformers were generated with thefast conformer generation method, allowing a maximum energy of15 kcal ⁄ mol above that of the most stable conformation. All querieswere performed using the Fast Flexible Search Databases ⁄ Spread-sheets method within the Catalyst DBServer module. Only thosecompounds that fit all the features of the pharmocophore model inthe Catalyst queries were retrieved as a hit.

Docking studiesTo reduce the rate of false-positive, structure-based docking studieswere carried out by using LigandFit within Cerius2 program package(Accelrys). The receptor structure was taken from the crystal structureof Aurora B combined with compound 14 (PDB entry: 2bfy) (45,46).Solvent molecules in the crystal structure were removed. Hydrogenatoms were added in a normal scheme by Cerius2. The binding siteof the bound ligand was identified as the active site. The site defini-tion as well as the used docking parameters was firstly validatedthrough docking the bound ligand back into the protein. The bestdocked pose should differ only minimally from the position of the

Figure 1: The best HipHop pharmacophore model mapping withthe most active compound 1. The features are color coded withgreen, hydrogen-bond acceptor; magenta, hydrogen-bond donor;light blue, hydrophobic aliphatic moiety; orange, ring aromaticfeature.

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ligand in the crystal structure (for example, root mean square devia-tion is <0.5 �). Partial charges of all chemical compounds were auto-matically assigned by the Gasteiger scheme implemented in Cerius2program. All molecules were flexibly docked employing the CFF forcefield (which is an all hydrogen force field) for energy minimization.Conformations of each compound were created with Monte Carlosimulation (5000 trials). The grid resolution was set to 0.5 �. Otherparameters were kept at their default values.

Results and Discussion

Pharmacophore modelBefore performing the quantitative pharmacophore modeling, thequalitative HipHop model was generated based on the eight mostactive compounds in the training set (1–8). Figure 1 presents themapping of the best HipHop pharmacophore model onto the mostactive compound 1 in the training set. The best hypothesis containssix features: three hydrogen-bond acceptors (A), one hydrogen-bonddonor (D), one hydrophobic aliphatic moiety (L), and one ring aro-matic feature (R).

The HipHop pharmacophore model is a qualitative model which isnot suitable for virtual screening purpose. But it provides important

Table 1: Statistical parameters of the top 10 pharmacophoremodels generated by the HypoGen

Hypo no. Total costa Cost diffb RMSD Correlation (r) Featuresc

1 73.5097 131.5123 0.5199 0.9911 ADLR2 77.4001 127.6219 0.8113 0.9782 ADLR3 78.2158 126.8062 0.8403 0.9766 ADLR4 81.8615 123.1605 0.9531 0.9702 ADR5 82.3621 122.6599 0.9676 0.9693 ADR6 82.4296 122.5924 1.0784 0.9611 ADLR7 83.6884 121.3336 1.0548 0.9632 ADR8 83.7755 121.2465 1.1385 0.9565 ADLR9 84.8748 120.1472 1.1832 0.9529 ADLR

10 84.9034 120.1186 1.1803 0.9532 ADLR

aThe total cost value of a hypothesis is calculated by summing three costfactors, a weight cost (data not shown), an error cost (data not shown), anda configuration cost (a constant among all the hypotheses).bThe difference between the total cost of a hypothesis and that of the nullhypothesis, roughly correlates with significance. The larger the difference,the greater the significance of the hypothesis. A true correlation in the datawill very likely be estimated by models that exhibit a cost difference (Nullcost)Total cost). (Fixed cost = 70.80, configuration cost = 16.96, nullcost = 205.022). All cost values are in bits.cA = hydrogen-bond acceptor, D = hydrogen-bond donor, L = hydrophobicaliphatic moiety, R = ring aromatic feature.

Figure 2: The best HypoGen pharmacophore model for Aurora B inhibitors. (A) The best pharmacophore model Hypo1. (B) 3D spatial rela-tionship and the distance constraints of the Hypo1. (C) Hypo1 mapping with the most active compound 1. (D) Hypo1 mapping with the leastactive compound 20. The features are color coded with green, hydrogen-bond acceptor; magenta, hydrogen-bond donor; light blue, hydropho-bic aliphatic moiety; orange, ring aromatic feature.



information which is helpful for the HypoGen run. The HipHop phar-macophore model clearly indicates that the chemical featuresincluding hydrogen-bond acceptor, hydrogen-bond donor, hydrophobicfeature, and ring aromatic feature are necessary for potent AuroraB inhibitors. According to this information, the four types of chemi-cal features were specified as the initial pharmacophore features inthe HypoGen run. The quantitative models were generated with the20 compounds in the training set (compounds 1–20, Chart 1). Thetop 10 hypotheses generated by HypoGen together with the resultsof statistical analysis are presented in Table 1. The top rankedpharmacophore model (Hypo1, Figure 2A), which was characterizedby the lowest total cost value (73.5097), the highest cost difference(131.5123), the lowest RMSD (0.5199), and the best correlationcoefficient (0.9911), was chosen for further analysis. The fixed costand null cost are 70.80 and 205.022 bits, respectively. The Hypo1who has the best predictive power and statistical significance con-tains four features, including one hydrogen-bond acceptor, onehydrogen-bond donor, one hydrophobic aliphatic moiety, and onering aromatic feature. The 3D space and distance constraints ofthese pharmacophore features are shown in Figure 2B. Figure 2C, Dpresent the Hypo1 aligned with the most active compound 1 (IC50:0.0003 lM) and the least active compound 20 (IC50: 0.63 lM) inthe training set. Clearly, all hypothesis features are mapped verywell with the corresponding chemical functional groups on com-pound 1, while two hypothesis features, i.e., hydrogen-bond donorand ring aromatic feature, are not mapped very well or completely

Table 3: Experimental and predicted IC50 values of the test setcompounds

MoleculeExptlIC50 (lM)

EstimatedIC50 (lM) Errora

Exptlscaleb

Estimatedscale

21 0.005 0.016 3.2 +++ ++22 0.0075 0.02 2.7 +++ ++23 0.008 0.0036 )2.2 +++ +++24 0.012 0.019 1.6 ++ ++25 0.014 0.017 1.2 ++ ++26 0.015 0.014 )1.1 ++ ++27 0.018 0.013 )1.4 ++ ++28 0.03 0.013 )2.3 ++ ++29 0.039 0.016 )2.5 ++ ++30 0.048 0.023 )2.1 ++ ++31 0.048 0.049 1 ++ ++32 0.049 0.099 2 ++ ++33 0.057 0.05 )1.1 ++ ++34 0.063 0.12 1.9 ++ +35 0.115 0.21 1.8 + +36 0.135 0.16 1.2 + +37 0.172 0.15 )1.1 + +38 0.2 0.53 2.7 + +39 0.437 0.53 1.2 + +40 0.77 0.53 )1.5 + +41 1.21 0.61 )2 + +42 1.3 0.53 )2.5 + +

a+ means that the estimated IC50 is higher than the experimental IC50;) means that the estimated IC50 is lower than the experimental IC50; a valueof 1 indicates that the estimated IC50 is equal to the experimental IC50.bActivity scale: +++, IC50 £ 0.01 lM (highly active); ++, 0.01 lM < IC50 £0.1 lM (moderately active); +, IC50 > 0.1 lM (low active).

Table 2: Experimental and predicted IC50 values of the trainingset compounds

MoleculeExptlIC50 (lM)

EstimatedIC50 (lM) Errora

Fitvalueb

Exptlscalec

Estimatedscale

1 0.0003 0.00035 +1.2 7.09 +++ +++2 0.007 0.0083 +1.2 5.72 +++ +++3 0.007 0.0097 +1.4 5.65 +++ +++4 0.008 0.009 +1.1 5.68 +++ +++5 0.008 0.0093 +1.2 5.67 +++ +++6 0.009 0.0086 )1.1 5.71 +++ +++7 0.009 0.0081 )1.1 5.73 +++ +++8 0.009 0.014 +1.6 5.48 +++ ++9 0.013 0.013 )1 5.53 ++ ++

10 0.018 0.013 )1.4 5.54 ++ ++11 0.025 0.019 )1.3 5.37 ++ ++12 0.028 0.016 )1.7 5.42 ++ ++13 0.042 0.027 )1.5 5.21 ++ ++14 0.25 0.3 +1.2 4.16 + +15 0.3 0.51 +1.7 3.93 + +16 0.44 0.51 +1.2 3.93 + +17 0.53 0.51 )1 3.93 + +18 0.58 0.53 )1.1 3.91 + +19 0.59 0.51 )1.2 3.93 + +20 0.63 0.52 )1.2 3.92 + +

a+ means that the estimated IC50 is higher than the experimental IC50;) means that the estimated IC50 is lower than the experimental IC50; a valueof 1 indicates that the estimated IC50 is equal to the experimental IC50.bFit value indicates how well the features in the pharmacophore map thechemical features in the molecule.cActivity scale: +++, IC50 £ 0.01 lM (highly active); ++, 0.01 lM < IC50 £0.1 lM (moderately active); +, IC50 > 0.1 lM (low active).

Table 4: Results of Fischer's randomization test using CatScram-ble implemented in Catalyst software

Validationno. Total cost Fixed cost RMSD

Correlation(r)

Hypo1 73.5097 70.80 0.5199 0.9911Results for scrambled

Trial1 153.594 69.8206 2.8944 0.6708Trial2 156.908 63.9946 3.0371 0.6282Trial3 173.392 63.6171 3.2656 0.5483Trial4 170.779 69.8778 3.1703 0.5835Trial5 182.63 66.1838 3.4123 0.4853Trial6 153.41 71.5708 2.8285 0.6893Trial7 159.229 64.7401 3.0404 0.6274Trial8 184.613 63.9946 3.4489 0.4689Trial9 129.468 66.287 2.5084 0.7661Trial10 159.969 65.7461 3.0658 0.6189Trial11 178.152 66.1838 3.3354 0.5196Trial12 157.597 63.9946 3.0312 0.6303Trial13 160.665 66.287 3.0593 0.6211Trial14 149.625 60.1091 2.9904 0.6426Trial15 157.647 70.8369 2.9459 0.6559Trial16 163.771 60.1091 3.2043 0.5712Trial17 176.378 66.6524 3.2703 0.5465Trial18 139.431 70.8369 2.5876 0.7488Trial19 150.909 70.424 2.8358 0.6870

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not mapped with any functional group on compound 20. Thesereflect the validity of the pharmacophore model Hypo1 to someextent.

Furthermore, we classified all the training set compounds into threecategories: highly active (IC50 £ 0.01 lM, +++), moderately active(0.01 lM < IC50 £ 0.1 lM, ++), and low active (IC50 > 0.1 lM, +).

Chart 3: Chemical structures of selective Aurora B inhibitors.



Table 2 shows the experimental and estimated inhibitory activities ofthe 20 training set molecules. Obviously, all compounds were cor-rectly predicted except one compound (compound 8) which is highlyactive experimentally but is predicted to be moderately active.

Validation of the HypoGen model

Test set methodAn independent test set composed of 22 compounds (Chart 2-21–42), was used to evaluate the predictive ability of the quantitativeHypoGen model Hypo1. Table 3 shows the predicted values of inhi-bitory activity of the test set compounds together with their corre-sponding experimental values. From Table 3, we can see thatcompounds of all activity scales were predicted properly except foronly three compounds; two compounds which are highly activeexperimentally but are predicted to be moderately active, and onecompound which is moderately active experimentally is predicted tobe low active. Further, a regression analysis of the experimentaland predicted values of inhibitory activity of the test set compoundsgives a fairly good correlation coefficient of 0.9152, indicating agood predictive ability.

Fischer randomization testFischer randomization test was used to further evaluate the statisti-cal relevance of Hypo1. With the aid of the CatScramble programavailable in Catalyst, Fischer randomization test generated 19 ran-dom spreadsheets to construct hypotheses using exactly the sameconditions as used in generating the original pharmacophorehypothesis. In this validation test, we selected the 95% confidencelevel. The results are presented in Table 4 and Figure S1 (see Sup-porting Information). Out of the 19 runs, only two trials have corre-lation values around 0.7, but the RMSD values and total costs aremuch higher than those of the original pharmacophore hypothesis

Hypo1. The results of CatScramble clearly demonstrate that the ori-ginal hypothesis is far more superior to those of the 19 hypothesesrandomly produced, indicating that there is a strong correlationbetween the structures and activities.

Evaluation of the specificity of Hypo1 to AuroraB kinase inhibitorsAs it has been mentioned, Aurora B and A belong to the same sub-family. Their amino acid sequence identity in the catalytic domainreaches �73% (17), whilst the crystal structures of Aurora B and Ashow that their active sites (ATP binding site) are very similar(17,29). Thus, generally inhibitors targeting against Aurora B arealso likely to target against Aurora A. However, there are still somesmall differences in the amino acid residues and 3D structures oftheir active sites. Therefore, there is some possibility to discoversome inhibitors selectively targeting against Aurora B or A. Indeed,currently, there have been some selective Aurora B or Aurora Akinase inhibitors reported. Logically one may wonder whether theHypo1 is specifically suitable for Aurora B kinase inhibitor. To exam-ine this point, we collected two compound sets: one for the selec-tive Aurora B inhibitors (Chart 3), the other one for the selectiveAurora A inhibitors (Chart 4). Then for each compound in the com-pound sets, the best fit analysis using the Hypo1 was performed.Table 5 presents the experimental IC50 values (lM) for both the Aur-ora B and A, selectivity (denoted as the ratio between the IC50values against Aurora B and Aurora A) and fit values, as well asthe mapped features. Clearly, selective inhibitors of Aurora B gener-ally have higher fit values (5.76–7.69) over those (3.94–5.53) of Aur-ora A. This situation can also be reflected from the mappedfeatures of inhibitors. The selective inhibitors of Aurora B aremapped very well with Hypo1, while the selective inhibitors of Aur-ora A miss at least one feature in general. And in most cases, themissed feature corresponds to the ring aromatic feature. Thisreveals that the ring aromatic feature might play a key role in

Chart 4: Chemical structures of selective Aurora A inhibitors.

Wang et al.


differentiating selective inhibitors of Aurora B from those of AuroraA. Figure 3A and B shows the Hypo1 mapped with several selectiveinhibitors of Aurora B and Aurora A, respectively.

By the way, an alternative strategy to identify which features conferthe selectivity of Aurora B and A kinase inhibitors is to use thecrystal structural information of Aurora B and A since the crystalstructures of Aurora B and A have been known. However, the resi-due sequences (sequence identity �73%) and 3D structures of Aur-ora B and A are so similar that it is difficult to draw a correctconclusion about the selectivity. In contrast, the strategy of usingknown inhibitors to identify which features confer Aurora B selectiv-ity is a good choice as it does not need any receptor information.And also it is easy to be evaluated with other known inhibitors.

A comparison between the best pharmacophoremodel of Aurora B and Aurora A kinaseinhibitorsAs has already been mentioned, both the residue sequences and3D structures of Aurora B and A are very similar, and many com-pounds are dual inhibitors of Aurora B and A. One may be con-cerned about the similarity of pharmacophore models of Aurora Band A kinase inhibitors. In previous study, we have developed phar-macophore models of Aurora A kinase inhibitors (47). Thus, a com-parison between the best pharmacophore models of Aurora B andAurora A kinase inhibitors will be made here. Figure 4 presents thebest pharmacophore model of Aurora A kinase inhibitors (Hypo1-A).Comparing with Hypo1 for Aurora B (Figure 2A), they both containone hydrogen-bond acceptor and one hydrogen-bond donor. Buttheir spatial arrangement in Hypo1 and Hypo1-A is different. Inaddition, Hypo1-A contains another three chemical features: twohydrophobic features and one hydrophobic aromatic moiety, as wellas one excluded volume. And Hypo1 includes another two features:one hydrophobic aliphatic moiety and one ring aromatic feature.These indicate that the pharmacophore models derived from ligandsof Aurora A and B are different from each other, which is under-standable as it has been demonstrated that the pharmacophoremodels are strongly dependent on the training set compounds (48).

Virtual screeningThe validated Hypo1 was used as a 3D structural query for retrievingpotent compounds from chemical databases including Specs(135 556 molecules), NCI (238 819 molecules), MayBridge (59 652molecules) and CNPD (43 055 molecules). Only those compoundscompletely mapped onto the Hypo1 were selected. Number of thetotal hits is 1859, in which 346 hits (hit rate: 0.26%) are from theSpecs, 1299 hits (hit rate: 0.54%) from the NCI, 88 hits (hit rate:0.15%) from the Maybridge, and 126 from the CNPD (hit rate:0.29%). These hits were then screened by using the Lipinski's rule offive to make them more drug-like. A total of 202 molecules whichshow very good mapping with the Hypo1 passed this filtration.

Docking studiesTo further refine the retrieved hits, the 202 compounds were dockedinto the inhibitor binding site of Aurora B by using LigandFit withinCerius2 program package. The 3D structure of Aurora B was takenfrom the crystal structure of Aurora B combined with compound 14

(PDB entry: 2bfy). Several scoring functions are available withinLigandFit, including LigScore1, LigScore2 (49), PLP1, PLP2 (50), Jain(51), Ludi (52,53), and PMF (54), as well as a consensus score (55).However, a benchmark test to the scoring functions with the use ofthe known inhibitors indicated that the Ludi scoring functionshowed better performance than others. Thus, the hit compoundswere ranked based on the Ludi scoring function. Finally, more than20 compounds were selected from the top ranked hits, which havebeen suggested for further in vitro and in vivo, as well as structuralmodification studies. And 10 of them are shown in Table S1 (seeSupporting Information). The predicted binding affinity values(BestEst) by using the pharmacophore model Hypo1 (calculated byCatalyst) as well as the Ludi scoring function values (calculated byLigandFit) are also given in Table S1. From Table S1, we can see

Table 5: The experimental IC50 values (lM) for both the AuroraB and A, selectivity (denoted as the ratio between the IC50 valuesagainst Aurora B and Aurora A) and fit values, as well as themapped features

Compound no.

IC50(lM) SelectivityAurora B ⁄Aurora A

Fitvalue

Mappedfeatuer

Aurora A Aurora B A D L R

1 (AZD1152) 0.013 0.0003 1 ⁄ 43 7.69 + + + +6 1.9 0.009 1 ⁄ 211 5.8 + + + )

22 0.8 0.0075 1 ⁄ 107 5.83 + + + +25 1.4 0.014 1 ⁄ 100 6.28 + + + +27 1.6 0.018 1 ⁄ 89 6.47 + + + +52 0.45 0.002 1 ⁄ 225 7.67 + + + +53 0.22 <0.001 <1 ⁄ 220 5.79 + + ) +54 0.41 0.002 1 ⁄ 205 5.87 + ) + +55 0.23 0.001 1 ⁄ 230 5.88 + ) + +56 0.094 0.001 1 ⁄ 94 5.8 + + ) +57 0.28 <0.001 <1 ⁄ 280 6.26 + + + +58 0.11 0.001 1 ⁄ 110 6.73 + + + +59 0.69 0.001 1 ⁄ 690 5.76 + + ) +60 0.16 <0.001 <1 ⁄ 160 6.15 + + + +61 0.19 0.001 1 ⁄ 190 7.31 + + + +62 0.22 0.002 1 ⁄ 110 7.35 + + + +64 0.087 <0.001 <1 ⁄ 87 7.33 + + + +65 0.35 <0.001 <1 ⁄ 350 7.12 + + + +66 0.95 <0.001 <1 ⁄ 950 6.61 + + + +67 1.4 <0.001 <1 ⁄ 1400 6.71 + + + +68 0.98 <0.001 <1 ⁄ 980 7.68 + + + +69 1.4 <0.001 <1 ⁄ 1400 6.57 + + + +16 0.00067 0.44 657 3.94 ) ) + +17 0.0009 0.53 589 3.94 ) + + )19 0.00097 0.59 608 3.94 + ) + )37 (MLN8054) 0.004 0.172 43 4.79 + + ) +38 0.0005 0.2 400 3.99 + + + )40 0.0011 0.77 700 4.29 + + + )42 0.0018 1.3 722 4.02 + + + )43 0.00044 0.089 202 3.94 ) ) + +44 0.00049 0.092 188 3.94 + ) + )45 0.00089 1.1 1236 5.3 + + + )46 0.00099 1.9 1919 5.53 + + + )47 0.0021 2.9 1381 5.29 + + + )48 0.0022 5.4 2455 5.34 + + + )49 0.0029 1.5 517 4.22 + + + )50 0.0048 9.9 2063 5.24 + + + )51 0.0061 1.4 230 3.99 + + + )



that the predicted binding affinity values are not correlated wellwith the Ludi scoring function values. A plausible explanation forthis could be that possible steric forbidden zones due to that theprotein receptors are not considered in the pharmacophore model.Other factors, such as the protein flexibility and the accuracy ofdocking scoring functions, might also be responsible for this incon-sistency.

Conclusions

In this study, chemical feature-based specific pharmacophore mod-els of Aurora B inhibitors have been developed with the aid of

HipHop and HypoGen modules in Catalyst program package. Thebest quantitative pharmacophore model corresponds to Hypo1,which was characterized by the best correlation coefficient (0.9911),the lowest total cost value (73.5097), the highest cost difference(131.5123), and the lowest RMSD (0.5199). Hypo1 includes four fea-tures: one hydrogen-bond acceptor, one hydrogen-bond donor, onehydrophobic aliphatic moiety and one ring aromatic feature. Thispharmacophore model was further validated by test set and cross-validation methods. Results obtained by the test set method showa fairly good correlation between the experimental and estimatedIC50 values (correlation coefficient of 0.9152), indicating a good pre-dictive capacity. And results of cross-validation by using CatScram-ble program within Catalyst further confirmed the statisticalconfidence of Hypo1. On the other hand, in order to evaluate thespecificity of Hypo1 to Aurora B, Hypo1 was mapped with selectiveinhibitors of Aurora A and B, respectively. The results clearlydemonstrate that Hypo1 can distinguish selective inhibitors of Aur-ora B from those of Aurora A, and the ring aromatic feature islikely to be essential to the specificity of Hypo1. Then the modelHypo1 was used as a 3D query to screen several databases includ-ing Specs, NCI, Maybridge and CNPD for retrieving new potent inhi-bitors of Aurora B. The hit compounds were subsequently subjectedto filtering by Lipinski's rule of five and docking studies to refinethe retrieved hits, and some of the hit compounds have beenshifted for further experimental assay studies.

Acknowledgment

This work was supported by the National Natural Science Founda-tion of China (30772651 and 20872100) and the Youth Foundationof Sichuan Province (08ZQ026-030).

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Figure 3: (A) Pharmacophore model Hypo1 aligned with selective Aurora B inhibitors 25 (green), 27 (red), and 57 (dark green). Allhypothesis features are mapped with the corresponding chemical functional groups on the inhibitors. (B) Hypo1 aligned with selective AuroraA inhibitors 43 (red), 44 (blue), and 49 (brown). All ligands miss at least two hypothesis features of the hydrogen-bond donor and ringaromatic feature. The pharmacophore features are color coded with green, hydrogen-bond acceptor; magenta, hydrogen-bond donor; light blue,hydrophobic aliphatic moiety; orange, ring aromatic feature. Compound numbers and chemical structures are depicted in Charts 2–4.

Figure 4: The best pharmacophore model of Aurora A kinaseinhibitors, Hypo1-A, taken from Ref. (47). The features are colorcoded with green, hydrogen-bond acceptor; purple, hydrogen-bonddonor; light blue, hydrophobic feature; dark blue, hydrophobic aro-matic moiety; gray, excluded volume.

Wang et al.


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Supporting Information

Additional Supporting Information may be found in the online ver-sion of this article:

Figure S1. The difference in costs between HypoGen runs andCatScramble runs. The 95% confidence level was selected.

Table S1. Part of the hits obtained from virtual screening.

Please note: Wiley-Blackwell are not responsible for the content orfunctionality of any supporting materials supplied by the authors.Any queries (other than missing material) should be directed to thecorresponding author for the article.

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