Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=irst20 Download by: [Gyeongsang National Uni] Date: 18 October 2016, At: 01:11 Journal of Receptors and Signal Transduction ISSN: 1079-9893 (Print) 1532-4281 (Online) Journal homepage: http://www.tandfonline.com/loi/irst20 Novel virtual lead identification in the discovery of hematopoietic cell kinase (HCK) inhibitors: application of 3D QSAR and molecular dynamics simulation Rohit Bavi, Raj Kumar, Shailima Rampogu, Yongseong Kim, Yong Jung Kwon, Seok Ju Park & Keun Woo Lee To cite this article: Rohit Bavi, Raj Kumar, Shailima Rampogu, Yongseong Kim, Yong Jung Kwon, Seok Ju Park & Keun Woo Lee (2016): Novel virtual lead identification in the discovery of hematopoietic cell kinase (HCK) inhibitors: application of 3D QSAR and molecular dynamics simulation, Journal of Receptors and Signal Transduction, DOI: 10.1080/10799893.2016.1212376 To link to this article: http://dx.doi.org/10.1080/10799893.2016.1212376 Published online: 02 Aug 2016. Submit your article to this journal Article views: 48 View related articles View Crossmark data
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Full Terms & Conditions of access and use can be found athttp://www.tandfonline.com/action/journalInformation?journalCode=irst20
Download by: [Gyeongsang National Uni] Date: 18 October 2016, At: 01:11
Novel virtual lead identification in the discoveryof hematopoietic cell kinase (HCK) inhibitors:application of 3D QSAR and molecular dynamicssimulation
Rohit Bavi, Raj Kumar, Shailima Rampogu, Yongseong Kim, Yong Jung Kwon,Seok Ju Park & Keun Woo Lee
To cite this article: Rohit Bavi, Raj Kumar, Shailima Rampogu, Yongseong Kim, YongJung Kwon, Seok Ju Park & Keun Woo Lee (2016): Novel virtual lead identification in thediscovery of hematopoietic cell kinase (HCK) inhibitors: application of 3D QSAR andmolecular dynamics simulation, Journal of Receptors and Signal Transduction, DOI:10.1080/10799893.2016.1212376
To link to this article: http://dx.doi.org/10.1080/10799893.2016.1212376
Novel virtual lead identification in the discovery of hematopoietic cell kinase(HCK) inhibitors: application of 3D QSAR and molecular dynamics simulation
aDivision of Applied Life Science (BK21 Plus Program), Systems and Synthetic Agrobiotech Center (SSAC), Plant Molecular Biology andBiotechnology Research Center (PMBBRC), Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), Jinju, Republicof Korea; bDepartment of Science Education, Kyungnam University, Masan, Republic of Korea; cDepartment of Chemical Engineering,Kangwon National University, Chunchon, Republic of Korea; dDepartment of Internal Medicine, College of Medicine, Busan Paik Hospital,Inje University, Republic of Korea
ABSTRACTHigh level of hematopoietic cell kinase (Hck) is associated with drug resistance in chronic myeloid leu-kemia. Additionally, Hck activity has also been connected with the pathogenesis of HIV-1 and chronicobstructive pulmonary disease. In this study, three-dimensional (3D) QSAR pharmacophore models weregenerated for Hck based on experimentally known inhibitors. A best pharmacophore model, Hypo1,was developed with high correlation coefficient (0.975), Low RMS deviation (0.60) and large cost differ-ence (49.31), containing three ring aromatic and one hydrophobic aliphatic feature. It was further vali-dated by the test set (r¼ 0.96) and Fisher’s randomization method (95%). Hypo 1 was used as a 3Dquery for screening the chemical databases, and the hits were further screened by applying Lipinski’srule of five and ADMET properties. Selected hit compounds were subjected to molecular docking toidentify binding conformations in the active site. Finally, the appropriate binding modes of final hitcompounds were revealed by molecular dynamics (MD) simulations and free energy calculation studies.Hence, we propose the final three hit compounds as virtual candidates for Hck inhibitors.
ARTICLE HISTORYReceived 11 May 2016Revised 29 June 2016Accepted 29 June 2016Published online 1 August2016
KEYWORDSHck; 3D QSAR; virtualscreening; moleculardocking; moleculardynamics simulation; freeenergy calculations
Introduction
The hematopoietic cell kinase (Hck) is a cytoplasmic or non-receptor tyrosine kinase (TK) from Src family kinases (SFKs),which comprises of eight members in humans, i.e. Blk, Fgr,Fyn, Hck, Lck, Lyn, Src and Yes (1). All these TKs control mul-tiple signal transduction pathways associated with growth,proliferation, differentiation, migration, metabolism and apop-tosis, by interaction with different molecules such as growthfactor receptors, cell–cell adhesion receptors, integrins andsteroid hormone receptors (2). Human Hck kinase comprisesof 526-amino acid and is divided into five different domains:N-terminal domain, the SH3 and SH2 are the regulatorydomains, followed by a catalytic domain and a negativeregulatory C-terminal tail (3,4). In detail, the SH4 region ofN-terminal domain contains 9–12 amino acid residuesinvolved in membrane attachment (5). The SH4 region is fol-lowed by 40–70 residues which are unique and convey dis-tinct localization properties to individual family members. Thehighly conserved SH3 and SH2 domain is attached to thisunique region which binds to specific proline-rich motifs andphosphotyrosine motifs, correspondingly. The approximately260 amino acids kinase domain is responsible for the catalytic
activity and comprises of two lobes that form a cleft depict-ing the active site of the kinase. The two lobes are theamino-terminal lobe and carboxyl-terminal lobe which moverelative to each other so as to close or open the cleft (6,7).SFKs undergo large conformational change, in response tocellular signals by switching between active and inactivestates.
Hck is mainly expressed in myeloid cells and is predomin-antly involved in inflammatory signaling (8). The two isoformsof Hck are expressed in humans as p59Hck and p61Hck,which are produced concurrently and in same quantities byalternative translation of a single mRNA (9). Like other mem-bers of the Src family, Hck is also involved in immune signal-ing and cell proliferation in hematopoietic cells and is linkedto cancer. The drug resistance in chronic myeloid leukemia(CML) is linked with a high level of Hck (10–13), and its iso-forms induce solid tumors in mice (14). Fusion of Abl geneon chromosome 9 to Bcr gene on chromosome 22 gives riseto the Philadelphia chromosome, which is the characteristicof CML (15). Bcr-Abl is the oncogene causing CML, 90% ofadult CML patients showed the presence of Philadelphiachromosome while its percentage is 15–30 in adult acutelymphoblastic leukemia (ALL) patients (15). Hck
CONTACT Keun Woo Lee [email protected] Division of Applied Life Science (BK21 Plus Program), Systems and Synthetic Agrobiotech Center (SSAC), PlantMolecular Biology and Biotechnology Research Center (PMBBRC), Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), 501 Jinjudaero, Jinju 660701, Republic of Korea*These authors contributed equally to this work.
Supplemental data for this article can be accessed here.
� 2016 Informa UK Limited, trading as Taylor & Francis Group
JOURNAL OF RECEPTORS AND SIGNAL TRANSDUCTION, 2016http://dx.doi.org/10.1080/10799893.2016.1212376
phosphorylates Bcr-Abl and induces the binding of growthfactor receptor-bound protein 2 (Grb2) to Tyr177 within theBcr region, thus providing a link to the Ras pathway (16),whose activation is involved in several malignancies (17). Formore than a decade, imatinib, a potential Bcr-Abl tyrosine kin-ase inhibitor has been used as the first line therapy for CML(18). However, the targeted therapy for CML is been effi-ciently performed by the utilization of the second-generationBcr-Abl inhibitor, nilotinib and the dual Src/Abl inhibitor, dasa-tinib (19). Regardless of the type of the therapy offered,resistance to the inhibitors is significantly observed more spe-cifically towards the advanced stages of the diseases that canbe due to several reasons such as, mutation that might occurin the kinase domain, amplification of the gene coding Bcr-Abl or also might be due to the mechanism that do notinvolve Bcr-Abl (e.g. Src family kinase activation) (20).
Additionally, viral infections such as HIV-1 are also linkedwith Hck activity (14). Negative regulatory factor (Nef) is aHIV-1 accessory protein involved in activation of Hck. Thismultifunctional HIV-1 protein hastens the development toAIDS and boosts the infectivity of progeny viruses (21). Nefbinding to Hck regulates the displacement of the SH3 domainfrom its negative regulatory interaction with the catalyticdomain, leading to kinase activation, which is significant inAIDS pathogenesis (22). As Hck and its isoforms are involvedin several signaling pathways in both physiological andpathological conditions of AIDS, cancers and inflammatorydiseases, supports the idea that Hck and its isoforms may actas promising targets for the development of future pharma-cological strategies.
Due to less number of Hck inhibitors and resistance devel-opment to these drugs, there is a need for identification ofnew scaffolds, which are more selective and possess highinhibitory activity. Therefore, we used computer-aided drugdesign approaches to identify potent and novel inhibitorswhich can cause inhibition of Hck. A three-dimensional (3D)QSAR pharmacophore model was built from the chemical fea-tures present in already known inhibitors. The best model,Hypo 1, was validated and used for database screening. Thepotential compounds were filtered by checking their drug-likeproperties. Binding conformations of the selected hit com-pounds were predicted by molecular docking studies. Finally,the appropriate binding modes of final hit compounds wererevealed by molecular dynamics (MD) simulations and freeenergy calculation studies.
Materials and methods
Collection of dataset
A dataset of 54 experimentally tested Hck inhibitors with theirIC50 values was retrieved from several literature resources(23–25) to perform pharmacophore modeling calculations.The selection of training set compounds is judgmental forpharmacophore model generation which consequently deter-mines the quality of the generated pharmacophores. Of these54 compounds, 20 different compounds were selected as thetraining set (Figure 1) based on their experimental activityvalues and structural diversity. The remaining 34 compounds
were used as a test set for validating the hypothesis.The inhibitory activity of these compounds was in the rangeof 0.5–30270 nmol/L. The compounds of the data set wereclassified into active (IC50<100 nmol/L,þþþ), moderatelyactive (100 nmol/L� IC50<10,000 nmol/L,þþ) and inactive(IC50�10,000 nmol/L,þ) based on their IC50 value. Two-dimensional structures of all the compounds were drawnusing ChemSketch (ACD Inc., Toronto, Canada) and was con-sequently exported to Discovery Studio v3.5 (DS) (San Diego,CA) for their corresponding 3D structure generation.
Generation of pharmacophore model
In this study, 3D QSAR-based pharmacophore approach wasused to produce pharmacophore models that can be used topredict the activity of newly designed compounds. All the 20structurally diverse conformational compounds in the trainingsets were exploited in hypotheses generation using HypoGenalgorithm. It creates and ranks the pharmacophores from thebest 3D arrangement of features in the given training setcompounds with their known activity values (IC50). This pro-cedure is achieved in three different steps: the constructivephase, the subtractive phase and the optimization phase (26).The constructive phase, considers all possible pharmacophorehypotheses that are common to the most active compounds.In the subtractive phase, all possible pharmacophore confor-mations of the constructive phase are either retained or dis-carded depending on the number of least active set ofmolecules sharing a common pattern (26). In the optimizationphase, activity, error, fit value and cost values are calculated(26). The next step was selecting probable features to be con-sidered in the generation of pharmacophore hypotheses forwhich Feature Mapping protocol offered by DS was used toidentify the important chemical features of the training setcompounds. As identified by the feature mapping protocol,hydrophobic aliphatic (HY-ali), ring aromatic (RA), hydrogenbond acceptor (HBA) and hydrogen bond donor (HBD) fea-tures were used with other default values to generatepharmacophore models. BEST algorithm was used to generatelow energy conformation of the compounds. Uncertaintyvalue was set to 3 while other parameters had default values.Ten hypotheses were generated with their resultant statisticalparameters such as cost values (fixed cost and null cost), cor-relation (R2), root mean square deviation (RMSD) and fit val-ues. Cost values were analyzed as per Debnath’s method (27).
Pharmacophore validation
Hypothesis validation is one of the vital methods in pharma-cophore modeling procedures. A good pharmacophore modelshould satisfy some of the standards such as low RMS values,high cost difference, good correlation coefficient, the lowesttotal cost, as well as the total cost should be close to thefixed cost and it should be away from the null cost (28).Several methods are available to check the quality of pharma-cophore like Fischer’s randomization method, test set valid-ation, goodness of fit (GF) and enrichment factor (EF) etc. Thegenerated hypotheses were mainly validated to assess its
2 R. BAVI ET AL.
ability to discriminate the active from inactive compounds, toretrieve active compounds from chemical databases and itsaccurate prediction of the activity of compounds. The statis-tical significance of the model was computed by employingFischer’s randomization method (29). Fischer’s randomizationmethod creates pharmacophore hypotheses through random-izing the activity of these compounds by using the samefeatures and parameters used to generate the originalpharmacophore hypothesis. The confidence level was set to95% and 19 random spreadsheets were created by DS (30). Inthis process, if the randomized data sets showed similar orbetter correlation, RMSD and cost values than Hypo1, thenthe original hypothesis was generated by chance (31). Thetest set was used to reveal whether the generated hypothesis
could predict and classify the compounds correctly in theiractivity scale. Low energy conformations were generatedusing the same protocols used for the training set com-pounds. Ligand Pharmacophore Mapping procedure with theBest algorithm and Flexible fitting option was used to deter-mine different conformations of compounds for pharmaco-phore mapping.
Virtual screening and drug-likeness prediction
Virtual screening of chemical databases is done to find newscaffolds that inhibits the activity of numerous targets as wellas to find the suitable potential leads for further develop-ment. Chemical database searching approach offers the
Figure 1. Chemical structures of HCK inhibitors in the training set along with their biological activity value (IC50 nmol/L).
JOURNAL OF RECEPTORS AND SIGNAL TRANSDUCTION 3
benefit that the hit compounds can be gained easily for bio-logical testing as compared to de novo design methods (32).So, with the motive of finding novel lead compounds, thefour-feature pharmacophore model was used as a 3D queryfor virtual screening of four different chemical databases:Chembridge, NCI, Asinex and Maybridge. A chemical com-pound that fits all the features of best pharmacophore wasselected as hit. The database was screened by using LigandPhamacophore Mapping protocol of DS with Fast and Flexibleoptions. Estimated activity values, geometric fit values anddrug-like properties were used as a filter for further refine-ment of mapped compounds. To confirm drug-like physico-chemical properties Lipinski’s rule of five (33) was calculatedfor all the mapped compounds so as to discard the nondrug-like compounds. ADMET Descriptors protocol available in DSwas used to calculate the ADMET properties of each retrievedcompounds. The compounds having better estimated activityvalues and filtered by drug-like properties was concededfurther for molecular docking.
Molecular docking
Molecular-docking studies were performed using GeneticOptimization for Ligand Docking (GOLD) program version5.2.2. GOLD uses a genetic algorithm which allows partialflexibility of the protein and full range of flexibility for theligands (34). For molecular docking calculation, a high reso-lution (2.0 Å) crystal structure (PDB code 2HK5) of Hck boundwith an inhibitor was selected as protein molecule (35). Thehetero atoms other than the bound inhibitor and water mole-cules present in the protein were removed and by usingCHARMm force field hydrogen atoms were added to the pro-tein molecule. The training set compounds and all hit com-pounds were docked into the binding site of protein. All theatoms within 10 Å of the co-crystallized ligand in the crystalstructure were defined as the binding site of the protein.Protonation states of histidine tautomers were changed tothe ND1H as observed in the crystal structure (35). To predictthe binding affinity of the ligand to the target protein,Goldscore was used as the default scoring function whilerescoring was done using Chemscore. Ten docking poseswere generated for each ligand and best poses were selectedbased on high Goldscore and low Chemscore. Additionally,the docked poses were also analyzed for molecular interac-tions and the formation of hydrogen bonds between theligand and the active site residues.
Molecular dynamics simulations
The molecular dynamic (MD) simulations for Hck in complexwith the most active compound from the training set andfinal hit compounds obtained from docking studies were per-formed using GROMACS 4.5.7 package with CHARMm27 forcefield (36). SwissParam was used to generate the topology filesfor ligands (37). The system was solvated with TIP3P watermodel in a dodecahedron box to form an aqueous environ-ment and neutralized with Naþ counter ions. The system wasinitially energy minimized by steepest descent algorithm to
remove possible bad contacts from initial structures until tol-erance of 2000 kJ/mol. The energy minimized system wasthen subjected to equilibration in three different steps. A con-stant temperature controlled by V-rescale thermostat (38) wasapplied for 100 ps at 300k in the first phase of equilibration.Later, 100 ps NPT ensemble was applied at 1 bar of pressurefollowed by 20 ns of production run under the same ensem-bles. During this process, Parrinello-Rahman barostat wasused to maintain the pressure of the system (39). In theequilibration process, the protein backbone was restrainedand solvent molecules with counter ions were allowed tomove. LINCS algorithm was applied to constrain all bondlengths to maintain the geometry of molecule (40) while thegeometry of water molecules was constrained by usingSETTLE algorithm (41). Long range electrostatic interactionswere calculated using Particle Mesh Ewald (PME) method (42).A cut off distance of 9 Å and 10 Å was set for Coulombic andvan der waals interactions. Periodic boundary conditions wereapplied to avoid edge effects. The time step of 2 fs was usedthroughout the simulation and coordinate data was stored atevery picosecond (ps). All the analysis of MD simulations wascarried out by VMD (43) and DS software.
Binding free energy calculations
Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) methodology has been employed for computing thebinding free- energy (DGbind) for individual system, that areconducted for 40 snapshots. The last 5 ns from the stable MDtrajectory are utilized to generate the same. The protocol for(MM/PBSA) methodology remains the same as elucidatedbefore (44–47).
In the solvent, the binding free energy for protein ligandcomplex is generally depicted as:
DGbinding ¼ Gcomplex � Gprotein þ Gligand� �
In the equation, the total free energy of the complex isrepresented by Gcomplex, while the total energy of thedetached protein and ligand in the solvent are depicted byGprotein and Gligand respectively. Free energy calculations foreach of them can be obtained by
GX ¼ EMM þ Gsolvation:
Where, X represents a protein, ligand, or its complex, theaverage molecular mechanics potential energy in vacuum isdetermined by EMM and the Gsolvation infers the free energyin the solvation.
Further, the molecular mechanics potential energy in avacuum can be computed by:
EMM ¼ Ebonded þ Enon�bonded ¼ Ebonded
þ Evdw þ Eelecð Þ:
The bonded interaction such as, bond distance, bondangle and dihedral angle are represented by Ebonded, thenon-bonded interactions, that include van der Waal (Evdw)
4 R. BAVI ET AL.
and electrostatic (Eelec) are denoted by Enon-bonded. Usually theDEbonded is taken as zero (48). The solvation freeenergy (Gsolvation) is calculated by adding the electrostaticsolvation free energy (Gpolar) and apolar solvation free energy(Gnon-polar) that can be demonstrated by
Gsolvation ¼ Gpolar þ Gnon�polar;
Poisson-Boltzmann (PB) equation is employed to calculatethe Gpolar while, the solvent-accessible surface area (SASA) isrecruited to calculate Gnon-polar which can be obtained asfollows:
Gnon polar ¼ cSASAþ b:
The c explains the coefficient of the surface tension of thesolvent and b refers to its fitting parameter whose values are0.02267 kJ/Mol/Å2 or 0.0054 kcal/Mol/Å2 and 3.849 kJ/Mol or0.916 Kcal/Mol, respectively.
Additionally, the solvation contribution (DEsol), van derwaals contribution (DEvdw) and the electrostatic contribution(DEele) have been employed to quantify the binding interac-tions between the protein and ligand.
Results and discussion
Pharmacophore modeling
To build the quantitative hypotheses, HypoGen algorithm wasused to correlate the estimated and the experimental activityvalues of the HCK inhibitors. The training set of 20 chemicallydiverse inhibitors of Hck (Figure 1) with IC50 values rangingfrom 0.5 to 30270 nmol/L was used to generate ten hypothe-ses using the statistical parameters values such as cost values,fit values, correlationVR and RMSD (Table 1). The best hypothe-ses should have lowest total cost values, the highest cost dif-ference, least RMS and good correlation coefficient (33). Inthis study, the first hypothetical pharmacophore (Hypo1) isthe best as it fulfills all the statistical parameters, having low-est total cost value (91.14), the highest cost difference (49.31),the lowest RMSD value (0.60) and the highest correlationcoefficient (0.975). Furthermore, Hypo1 showed the highest fitvalue of 9.62 as compared to the other hypotheses (Table 1).The lower RMSD value signifies a low deviation of theexpected activity from the experimental activity, while
the high correlation reveals the better predictive ability of themodel. The RMSD values of all the hypotheses are below 1,showing the good predictive quality of these hypotheticalstructures. The correlation coefficient is depended on linearregression derived from the geometric fit index; Hypo1 exhib-ited the highest correlation coefficient (0.97), proving its highpredictive ability. By considering all the above parameters itwas revealed that the statistical values of Hypo1 was best ascompared to the other hypothetical structures. As a result,Hypo1, which comprised three ring aromatic features (RA),and one hydrophobic aliphatic feature (Hy-Ali), was selectedas the best hypothesis for further analysis (Figure 2). The 3Dspatial relationship and distance constraints of Hypo1 aredepicted in Figure 2. The predictive accuracy of Hypo1 wastested by estimating the inhibitory activities of 20 training setcompounds. Hypo1 estimated the inhibitory activity value inthe same order of magnitude for all the training set com-pounds (Table 2). Four moderately active compounds wereoverestimated and underestimated as active and inactivecompounds, respectively. Hypo 1 aligned with the most active(IC50¼0.5 nmol/L) compound and the least active(IC50¼30270 nmol/L) compound in the training set isdepicted in Figure 3. Clearly, the most active compoundmapped well on all of the hypothetical features (Figure 3(A)),whereas the least active compound missed two hypotheticalfeatures (Figure 3(B)), particularly two ring aromatic (RA),representing the significance of these features. This revealsthe difference in activities among the most active and theleast active compounds. This analysis suggests that Hypo1was able to differentiate the compounds based on the activ-ity values with high accuracy (Table 2). Hypo1 was furthervalidated using the test-set and Fischer’s randomizationmethod.
Pharmacophore validation
Test set validationTo validate Hypo1 the external validation process was per-formed by 34 structurally diverse compounds other than thetraining set compounds (Table S1). The test set validation wasdone to check the ability of Hypo1 to predict and classify thecompounds according to their activities scale. The test setcompounds were classified into active, moderately active andinactive respectively. Except few compounds, all the other
Table 1. Statistical data of ten pharmacophore hypotheses generated by HypoGen.
Hypo No. Total cost Cost differencea RMSDb Correlation (R2) Max fit Featuresc
Hypo 1 91.14 49.31 0.60 0.975 9.62 1 Hy-Ali, 3 RAHypo 2 91.25 49.20 0.60 0.975 9.45 1 Hy-Ali, 3 RAHypo 3 93.46 46.99 0.79 0.955 8.83 1 Hy-Ali, 3 RAHypo 4 93.94 46.51 0.83 0.950 8.50 1 HBD, 1 Hy-Ali, 2 RAHypo 5 94.01 46.44 0.83 0.950 8.66 1 HBD, 1 Hy-Ali, 2 RAHypo 6 94.19 46.26 0.82 0.952 9.19 1 HBD, 1 Hy-Ali, 2 RAHypo 7 94.54 45.91 0.82 0.952 9.57 1 HBD, 1 Hy-Ali, 2 RAHypo 8 94.56 45.89 0.86 0.947 8.77 1 Hy-Ali, 3 RAHypo 9 95.32 45.13 0.89 0.944 9.27 1 Hy-Ali, 3 RAHypo 10 96.00 44.45 0.94 0.937 8.83 1 Hy-Ali, 3 RAaCost difference, difference between the null cost and the total cost. The null cost of ten scored hypotheses is 140.46, the fixed cost value is86.83. All costs are represented in bit units.
bRMSD: deviation of the log (estimated activities) from the log (experimental activities) normalized by the log (uncertainties).cHBD: hydrogen bond donor; RA: ring aromatic and; Hy-Ali: hydrophobic aliphatic.
JOURNAL OF RECEPTORS AND SIGNAL TRANSDUCTION 5
compounds were estimated correspondingly by Hypo1 asdepicted in Table 3. Thus, Hypo1 was able to predict theactivities of compounds in their own activity scales. The linearregression between the experimental inhibitory activities andpredicted inhibitory activities of the 34 test-set compounds
showed a correlation coefficient (R2) value of 0.96 (Figure 4).This result revealed that Hypo1 fits the training set com-pounds as well as the test set compounds; this result alsoshowed the predictive capacity of Hypo1 to discriminatebetween the active and moderately active compounds.
Figure 2. ‘Hypo 1’ is shown with distance constraints. ‘Hypo 1’ consists of three ring aromatic (RA: orange) and one hydrophobic aliphatic (Hy-Ali: blue) features.(Color figure online.)
Table 2. Experimental and estimated activity of training set compounds based on Hypo 1.
Compound no. Fit Value Exp IC50 nmol/L Pred IC50 nmol/L Errora Experimental scaleb Predicted scaleb
Figure 3. The best pharmacophore model Hypo1 aligned to training set compounds: (A) most active compound 1 (IC50 0.5 nmol/L) and (B) least activity compound20 (IC50 30,270 nmol/L). The most active compound mapped to all four features in Hypo 1, whereas the least active compound mapped only two features.
6 R. BAVI ET AL.
Table 3. Evaluation of estimated and experimental activity (IC50) values of test set compounds using Hypo 1.
Compoundnumber Fit value Experimental IC50 (nmol/L) Predicted IC50 (nmol/L) Errora Experimental scaleb Predicted scaleb
Figure 4. Correlation plot derived between predicted and experimental activities of test set and training set compounds based on Hypo1.
JOURNAL OF RECEPTORS AND SIGNAL TRANSDUCTION 7
Fischer’s randomization methodFischer’s test was applied to estimate the statistical relevanceof Hypo1. 95% confidence level was set; as a result 19 ran-dom spreadsheets was generated by arbitrarily reassign theexperimental activity values to each compound in the trainingset, and a hypothesis was created for each spreadsheet(Figure 5). The formula used to calculate the significance ofthe hypothesis is S¼ [1–(1þX)/Y]� 100, where X denotestotal number of hypotheses with total cost that are lowerthan the original hypothesis, and Y represents the total num-ber of HypoGen runs (initialþ random runs). Here, X¼ 0 andY¼ (1þ 19), hence 95%¼ {1–[(1þ 0)/(19þ 1)]}� 100. The totalcost value of Hypo1 was least as compared to other hypoth-esis in the generated random spreadsheets, which indicatesthat Hypo1 is far more superior to all other random hypothe-ses and was not generated by chance.
Virtual screeningChemical features of Hypo1 play an important role in map-ping and screening out novel compounds from a database.Therefore Hypo1 was used to screen NCI, Maybridge,Chembridge and Asinex databases which contains 238,819,59,652, 50,000, and 213,262 compounds, respectively. Hypo 1mapped a total of 57,811 compounds; of these, 1817 com-pounds were selected for further analysis like ADMET andLipinski’s Rule of Five, by applying maximum fit value greaterthan 8 (active compounds having the highest fit value in thetraining set). To evaluate the pharmacokinetics of a drug inthe human body the ADMET properties were calculated. Inthis level, the hit compounds were checked for low bloodbrain barrier penetration, non-inhibition to CYP2D6, optimalsolubility, good absorption, and non-hepatotoxicity. The val-ues of 3, 3 and 0 were set for BBB, solubility and absorption,respectively. Out of 1817 molecules, 198 molecules passedthe ADMET criteria. These molecules were subjected toLipinski’s Rule of Five for further filtration, which states that amolecular weight should be less than 500, partition co-effi-cient (LogP) value must be less than 5, the number of
rotatable bonds less than 10, less than five hydrogen bonddonors, and the number of hydrogen bond acceptors lessthan 10. Finally, a total of 56 molecules satisfied the drug-likeproperties and were subjected to molecular docking studies.
Molecular dockingTo refine the retrieved hit compounds and to eliminate thefalse positives, the training set compounds and 56 drug-likehits were docked into the active site of Hck using the GOLDprogram. To evaluate the aptness of GOLD, the co-crystal wasdocked in the active site of Hck to examine the parametersto produce the most appropriate binding orientation. Thedocked pose revealed an acceptable RMSD value of 0.52 Åbetween the co-crystal and the predicted structure(Figure S1). The candidate compounds were therefore dockedusing the same parameters. GOLD fitness score differentiatesmolecules based on their interacting ability. GOLD fitnessscore of most active/reference compound from the trainingset was taken as cutoff for the further screening of com-pounds. Chemscore was used as the rescoring function; itestimates the total free energy change that occurs upon lig-and binding, but it is not superior to Goldscore in predictingaffinities (49,50). The GOLD fitness score and Chemscore ofthe most active compound in the training set were 66.29 and�29.20, respectively (Table 4). Therefore, the compoundswere selected based on GOLD fitness score greater than 66.29, Chemscore lower than �29.20, and the ligand conforma-tions satisfying the necessary interactions in the active site.
Figure 5. Cost differences between Hypo1 and the 19 scrambled runs. A confidence level of 95% was used.
Table 4. Comparison of gold fitness score, chemscore and average bindingenergy of HCK and reference inhibitor/hit1/hit2/hit3 complex.
Finally, three hit compounds fulfilled the above criteria andalso mapped well to the pharmacophore features of Hypo 1(Figure 6) were characterized as final hits.
Molecular dynamics simulationsThe MD simulations have been performed over the final hit-Hck complexes to understand the coordination and dynamic
behavior with each other. The 20 ns MD simulations were per-formed by taking the best docked conformation of three hitsand a reference compound as the initial structure. All foursystems were subjected to the MD simulation. The proteinstability during the simulation was calculated by analyzingthe root mean square deviation (RMSD) of protein backboneatoms (Figure 7(A)) and potential energy (Figure 7(B)) of thesystem. The RMSD values were in between 1.2 Å and 1.7 Å
Figure 6. Mapping of hit compounds to Hypo1. (A) Hit 1, (B) Hit 2, (C) Hit 3. The RA and Hy-Ali features are displayed in orange and blue, respectively. (Color figureonline.)
Figure 7. The RMSD and potential energy graph for four complex systems. (A) The RMSD profile for the backbone atoms of HCK protein. (B) The potential energy ofthe system. These graphs were calculated during 20 ns MD simulations for each complex. Red, gray, cyan and magenta lines represent Inhibitor, Hit1, Hit2 and Hit3,respectively. (Color figure online.)
JOURNAL OF RECEPTORS AND SIGNAL TRANSDUCTION 9
throughout the simulations which shows that system are wellconverged. The average RMSD values obtained during 20 nssimulation were 1.57 Å, 1.52 Å, 1.46 Å and 1.58 Å for inhibitor,hit1, hit2, and hit3, respectively. The potential energy of thesystem was also stably maintained indicating that no abnor-mal behavior in the protein was observed during the simula-tion. The binding mode of the representative structures offour systems were analyzed from the last 4 ns trajectories.When all the representative structures were superimposed, itwas found that the binding patterns of hit compounds weresimilar to reference compound (Figure 8). Residues K273,E288, T316, M319 and D382 form a substrate binding pocketof Hck (35). These key residues were also found in the bind-ing of reference inhibitor and three hit compounds. In case ofreference compound, inhibitor (Figure 9(A), Table 5) formedhydrogen bonds with K273, E288, T316, and N369 of Hck.Furthermore, Inhibitor was stacked on K269 via cation-p inter-action. Inhibitor showed hydrophobic interactions with K249,L251, G252, V259, M261, F318, A320, G322, S323, L371 andA381. On the other hand, hit1 (Figure 9(B), Table 5) formedhydrogen bond interactions with K273, T316, M319, A320,N369 and hydrophobic interaction with L251, V259, E288,V301, T316, F318, K321, G322, A381, A368, N369, R366, D382.The benzene moiety of hit1 formed an r–p interaction withK273. Hit 2 (Figure 9(C), Table 5) formed hydrogen bondswith K273, E288, E317 and M319 as well as weak hydrogenbonds with T316 (3.09 Å) and D382 (3.16 Å). Hit 2 showedinteractions with hydrophobic pocket residues such as L251,V259, A271, F318, K321, G322, S323, F327, S330, L371 andA381. In hit3 binding, hydrogen bonds with K273, M319,A320 D382 and N369 were observed (Figure 9(D), Table 5).Hit3 showed hydrophobic interactions with V259, A271, E288,M292, I314, F318, N369, L371, A381 and F383. The benzenemoiety of hit3 was involved in cation-p and r-p interactionwith K273 and L251, respectively. Compiled together revealedthat, hit compounds bound to the active site by forming
hydrogen bond, hydrophobic, r–p and cation-p interactionsand the interacting residues are given in Table 5.
The number of intermolecular hydrogen bonds was alsoinspected between Hck and compounds throughout thesimulation (Figure 10). The average numbers of hydrogenbonds between the Hck and hit compounds were 3.9, 3.4 and1.4 for hit1, hit2 and hit3, respectively. Inhibitor maintainedalmost one hydrogen bond throughout the simulation. Thehit compounds showed comparatively more hydrogen bondsthan the reference compound. The Pubchem Structure anonline search tool was used to assess the novelty of hit com-pounds (51). These results confirmed that the hit compoundshave not been previously tested experimentally for the inhib-ition of Hck and can be recommended as potential new scaf-folds for designing potent and selective inhibitors of Hck. 2Dstructures of hit compounds are shown in Figure 11.
Analysis of the binding free energy of hck and referenceinhibitor/hit compounds
Docking studies followed by MD simulation offers the timedemanding step for binding free energy calculation (52,53).The DG values achieved from the sets of snapshot structuresproduced during the MD simulation, takes into account theligand conformation and the fluctuation of the protein in thecomplex, thus ensuring a proper adjustment of the ligand inthe binding site (52). Therefore, to validate binding modes ofHck with ligands, solvation energy, the electrostatic, and Vander Waals of interacting residues have been estimated byMM/PBSA method (44,45). The MM/PBSA calculation of Hck-ligand complexes using the hit 1, hit 2, hit 3 and referenceinhibitor as the ligands gave favorable DG values in the rangeof�30 to�140 kJ/mol which can be seen from Figure 12. Theaverage binding energy obtained for Hck-ligand complexeswere �67.6 kJ/mol (reference inhibitor), �89.3 kJ/mol (hit1),
Figure 8. The binding mode of the three hit compounds and reference inhibitor in the active site of HCK. All compounds in their representative structures weresuperimposed (left) and enlarged (right). The HCK protein is shown in gray color solid ribbon while the compounds are depicted by sticks. Green, gray, cyan and pinkstick represents Inhibitor, Hit1, Hit2 and Hit3, respectively.
10 R. BAVI ET AL.
�95.7 (hit2) and �86.1 (hit3) (Tables 4 and 6). In order to getfurther understanding into ligand-residue interactions, weemployed the MM/PBSA method to decompose the totalbinding free energy into ligand residue pairs (Figure 13).
The electrostatic interaction plays a key role in hydrogenbond formation. Particularly, Hck residues such as K273, T316,M319 and D382 have prominent electrostatic contributionand might be involved in the formation of several hydrogenbonds with ligands as shown in Figure 13. When the decom-position of the binding energy of the residues was compared,we found that the residues of Hck-hit complex showed muchmore electrostatic forces as compared to Hck-reference inhibi-tor complex (Figure 13). Thus, the total numbers of intermo-lecular hydrogen bonds are more for Hck-hit complex thanHck-reference inhibitor complex. In addition to electrostaticinteractions favorable van der Waals contributions are alsoassociated with these residues. These results suggest that Hckresidues interact with ligands. Nonetheless, other amino acids
Table 5. The molecular interactions between the compounds and HCK protein.
Compound Hydrogen bond (<3.0 Å) Hydrophobic interactionCation-p
Figure 9. The binding conformation and hydrogen bonding interactions of the three hit compounds and reference inhibitor in the active site of HCK. (A) Inhibitor:green, (B) Hit1: gray, (C) Hit2: cyan and (D) Hit3: pink. Hydrogen bond interactions between proteins and compounds are shown as black dotted line. Only polar hydro-gen atoms are shown for clarity.
Table 6. Calculated energy components, binding free energy (kJ/mol) of fourcomplex systems.
Energy components(kJ/mol) Inhibitor Hit1 Hit2 Hit3
Figure 10. The number of intermolecular hydrogen bonds between protein and compound during 20 ns MD simulations. Blue, brown, green and purple colors repre-sent Inhibitor Hit1, Hit2 and Hit3, respectively. (Color figure online.)
Figure 11. Two-dimensional structures of final hit compounds. Hit1 was obtained from NCI database while the remaining two hit compounds were identified fromAsinex database.
Figure 12. MM/PBSA estimated binding free energy of HCK and reference inhibitor/hit 1/hit 2/hit 3 complex throughout simulation time. Color coding; Inhibitor:green, Hit 1: gray, Hit 2: cyan and Hit 3: pink.
12 R. BAVI ET AL.
such as E288, E317, A320 and N369 show minor energeticcontributions. These results show that Hck has charged bind-ing pocket comprising of two Gln, one Lys, one Asn and oneAsp. These amino acid residues form strong ionic interactionswith ligands, thus, resulting in strong electrostatic potential inthe binding interface of Hck active site. The bound conform-ation of Hck and ligands shows that ligands get accommo-date in the active site of the enzyme through hydrogen bondand hydrophobic interactions.
Conclusions
Inhibition of Hck has emerged as a new promising target inthe field of cancer and AIDS since it influences several signal-ing pathways. In this study, we developed ligand basedpharmacophore models of Hck inhibitors using HypoGenalgorithm. Hypo1 was the best pharmacophore model whichshowed the best correlation coefficient (0.975), lowest RMSD(0.60), highest cost difference (49.31) and lowest total costvalue (91.14). Hypo1 was generated with three RA and oneHy-Ali features. Hypo1 was further validated using the exter-nal validation process and Fischer’s randomization method.The results of the external validation method exhibited agood correlation between the predicted and experimentalvalues having correlation coefficient of 0.96, showing goodpredictive ability of Hypo1. The statistical confidence forHypo1 was further confirmed by Fischer’s randomizationmethod. Hypo1 was used to search the databases such asChembridge, NCI, Asinex, and Maybridge and the resulting hitcompounds were further filtered by Lipinski’s rule of five,ADMET, and molecular docking to refine the retrieved hits.Based on the molecular interactions with the active site
amino acid residues, three hits were chosen as final hit com-pounds and were subjected to 20 ns MD simulations to refinetheir binding modes. The analyzed results suggested that thebinding mode of hit compounds was similar to the referencecompounds. The hit compounds bound to the active site resi-dues by forming hydrogen bond, hydrophobic, r–p and cat-ion-p interactions. Hence, we propose that the final hitcompounds as a virtual candidate for Hck inhibitors.
Disclosure statement
The authors declare no conflict of interest.
Funding
Dr. Rohit S. Bavi was financially supported by a postdoctoral fellowshipfrom BK21 PLUS program of Ministry of Education and Human ResourcesDevelopment, South Korea. This research was supported by Next-Generation BioGreen 21 Program from Rural Development Administration,Republic of Korea [Grant no. PJ01106202], and also supported by BasicScience Research Program through the National Research Foundation ofKorea (NRF) funded by the Ministry of Education [2014R1A1A2059773].
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