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Structure-Based Development of New and Potent Inhibitors of PIM Kinases: A Computational Study.

Byline: Abdul Wadood, Huma Khan, Mehreen Ghufran, Hammad Hassan, Sulaiman Shams, Ajmal Khan, Syed Sikander Azam and Reaz Uddin

Summary: The PIM kinases are a family of serine/threonine kinases that catalyze the ATP dependent phosphorylation. These kinases were identified to be overexpressed in a variety of malignancies and tumors and hence can be taken a therapeutic drug target. Pharmacophore-based virtual screening, were employed to identify lead compounds for PIM kinases. A seven featured pharmacophore model was developed on the basis of tricyclic benzothienopyrimidinone, a triple inhibitor of PIM kinases. The validated pharmacophore model was used to screen ZINC database. As a result, 1028 compounds were mapped on the pharmacophore model. These initial hits, were subjected to filtering via Lipinski's rule of five to predict drug like molecules. 1028 drug like hits were selected for evaluation using docking simulation.

Finally, 22 hits having different structural properties and binding modes with all the three PIM kinases were selected as lead candidates for triple inhibitors. These candidates having different scaffolds have a strong likelihood to act as further starting points in the development of new and potential PIM kinases inhibitors.

Keywords: PIM kinases, Cancer, Homology modeling, Virtual screening, Molecular docking.

Introduction

PIM kinases named for the genomic site, Proviral Integration site of Moloney Murine leukemia virus. This family of serine/threonine kinases was first recognized in a retroviral insertional mutagenesis study [1, 2]. These proto-oncogenes involved in cell survival, cell proliferation, protein translation and prevent apoptosis so contribute to tumorgenesis. PIM kinases produces its oncogenic activity via the regulation of, cell cycle progression, cap-dependent protein translation, MYC transcriptional activity and through survival signaling by BAD protein phosphorylation, which mediate the release of Bcl-X(L) an anti-apoptotic protein. PIM kinases are involved in Hematological malignancies and malignancies of epithelial origin (solid tumors). The family of PIM kinases comprises of three members, PIM-1, PIM-2 and PIM-3, that are genes of single copy found on X chromosome (PIM-2), on chromosome 17 (PIM-1), and on the chromosome 15 (PIM-3).

Firstly, high level of PIM-1 kinase was discovered in lymphoma tumours and leukaemia [3]. More recently, the increased level of PIM-1 kinase was found in tumors, alongwith prostate and pancreatic cancer, colorectal, liver, squamous cell and gastric carcino ma [4], liposarcoma [5] and bladder cancer [6]. The common occurrence of hormone refractory prostate cancer is due to chemoresistance of PIM-1 kinase [7]. PIM-2 kinases have been studied in variety of lymphomas, prostate and in liver cancer. PIM-3 kinases have been detected in malignant lesions of pancreas and liver. These kinases are somewhat different in their tissue distribution [8] but highly homologous at the amino acid level [9]. PIM kinases catalyze the phosphorylation process by transferring the phosphoryl group from ATP to OH group of substrate proteins.

All PIM kinases share common ATP binding site, the difference in their kinetic function was observed. This may be due to variation in the active site residues and conformations which finally affect the phosphorylation machinery.

A homology model provides rational opportunity to give 3D model in the absence of experimentally three dimensional structures [10, 11]. Structure based molecular mechanism and mechanism investigation is the applications of homology modeling. In the drug discovery process, high-throughput virtual screening (HTVS) is becoming complementary to high-throughput screening, to produce novel and possible drug like compounds [12]. Pharmacophore modeling and virtual screening are proficient approaches for the identification of novel and potent. In the present work pharmacophore modeling was used to predict the significant pharmacophoric features vital for the PIM kinases inhibitors. The ZINC database of small compounds was screen on the basis of developed pharmacophore model. Further, data reduction was carried out by the prediction of drug-likeness, docking simulation, binding energy and binding affinity calculations [13].

Finally, twenty-two new and potent lead compounds were predicted as triple inhibitors of PIM kinases.

Experimental

Structure-Based Pharmacophore Model Generation and Validation

Pharmacophore is an ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target and to block its biological response [14]. The common feature pharmacophore model was generated by using experimentally known triple inhibitor tricyclic benzothienopyrimidinone. This data was obtained from the literature, as it is more potent inhibitor for all three PIM kinases [15]. Pharmacophore building tool implemented in MOE (Molecular Operating Environment) was used for the generation and visualization of 3D pharmacophore.

To evaluate the quality of a pharmacophore model it must be validated [16]. The model is validated by two methods. 1) A test database of 50 identified inhibitors of PIM kinases was used for validation of generated model. The test database containing active and non-active/ least active compounds. The compounds of test database were screened on the seven-featured pharmacophore and their mapping modes were analyzed. 2) The presence of important chemical features of pharmacophore which interact with important amino acids in the active pocket of the corresponding receptor protein was also use for the validation of pharmacophore model [17].

Database Screening

As 3D query in in-silico screening, the validated model of pharmacophore was used to identify hits of different chemical natures. By using MOE software, against ZINC database the pharmacophore based virtual screening was carried out [18]. From the screening, 1028 structurally diverse hits were recovered presenting a better fit to the generated pharmacophore model. The properties of each hit ligand have been studied for Lipinski's rule of five to know that the retrieved hits have druglike properties.

Molecular Docking

The complexed X-ray structures of PIM-1 and PIM-2 kinases were obtained from protein data bank with PDB ID 3JYA and 2IWI respectively.

The 3D structure for PIM-3 was not available yet, so, the 3D structure of PIM-3 kinase was predicted by homology modeling. All the PIM kinases structures were protonated and energy minimized individually in the MMFF94x force field at a gradient value of 0.05 by enabling all bonded and non-bonded interactions. Individual dockings were performed for each PIM kinase to find out the binding modes and affinity variations between PIM kinases and the hit compounds.

All the retrieved hits were docked into the binding site of PIM kinases for further refinement of hit compounds. Docking protocol implemented in MOE as a docking program was used for molecular docking study [19]. The Proxy triangle methodology was applied [20]. The docked conformers are ranked by alpha HB scoring function, which is a linear combination of two terms, i.e. the hydrogen bonding effects and the geometric fit of the ligand to the binding site and finally both terms are summed over all ligand atoms. The conformations were refined and rescored in the same force filed to remove the duplicate conformations. At the end of docking process, the conformations with least docking score were chosen in each docking to study the binding orientations of hit compounds among all three PIM kinases.

150 top ranked compounds were selected on the basis of docking score for each PIM kinase to evaluate further. The ligands were ranked by the scores from the Generalized-Born Volume Integral/Weighted Surface Area (GBVI/WSA) and binding free energy calculation in the S field which is the score of the last stage. GBVI/WSA estimates the free energy of ligand from a certain pose. For all the scoring functions, more favorable poses are indicated by the lower score [13]. LigPlot implemented in MOE was use for the observation of resulted binding interactions between proteins and these hits.

Docking Validation

Initially the docking protocol is validated by comparing the position, conformation and orientation of a ligand, which is obtained from the docking with that one determined experimentally by X-ray crystallography [17]. Properly redocking the crystallographically detected inhibitor is a minimum requirement to determine whether the program is applicable to this system or not. To validate the docking protocol, the ligands from the complexed structures of PIM-1 and PIM-2 were extracted and redocked in the crystal structure of PIM-1 kinase (PDB code 3JYA) and PIM-2 kinase (PDB code 2IWI) bound with the inhibitor molecule. After redocking the top conformation of ligands were predicted by MOE and were very close to the crystal structure-bound conformations. The RMSD between the docked pose and its bound conformation in the crystal structure of PIM-1 and PIM-2 was <two, representing that MOE is able to reproduce correct pose.

Binding Affinity and Binding Energy Calculations

Generalized Born / Volume integral (GB / VI) implicit solvent method implemented in MOE was used to identify binding affinities of the hits with PIM kinases [21]. Generalized Born interaction energy is the non-bonded interaction energy between the ligand and receptor molecule that includes coulomb electrostatic interaction, Vander Waals, and implicit solvent interaction energies [13]. Although the strain energies of receptor molecule and ligands were not taken into account. The active pocket (binding site) of receptors were kept flexible to interact with ligand but were subjected to tether restraints that discourage gross movement. The ligand atoms were kept flexible to move in the binding pocket. In each case before calculating binding affinity, the energy minimization of binding pocket in PIM kinase-ligand complex was carried out.

Results and Discussion

Homology Modeling

Homology model 3D structure of PIM-3 was generated, using MOE software (Fig. 1 a). The 3D structure of human PIM1 protein (PDB code 4K0Y) was taken as template for homology model generation, which shows highest sequence identity of 76% with the query protein sequence. The model was validated by RAMPAGE and ERRAT servers. The Ramachandran plot generated by RAMPAGE showed that 81.5% in favored region, 12.1% of residues in allowed regions, and 6.4% residues were found in the disallowed regions. ERRAT showed overall quality factor 95%. Both of these results indicate that the generated PIM-3 model is valid with good stereo chemical quality. To find out the conformational variations, the template structures and PIM-3 model were superposed and it was found that they indicated the close structural identity with their RMSD values 0.62 Ao (Fig. 1 b). The validated model used further in this study.

Development and Validation of Structure-Based Pharmacophore Model

A common pharmacophore model based on tricyclic benzothienopyrimidinone was generated using MOE. A seven features pharmacophore model having two hydrogen bond acceptors (Acc), one hydrogen bond donor (Don), three hydrophobic (Hyd/HydA), and on aromatic atom (Aro) was developed using default parameters (Fig. 2 a).

The developed pharmacophore model was validated by using a test database having 50 known inhibitors (30 active and 20 inactive/least active inhibitors) [15, 22, 23]. The test database was screened on the developed pharmacophore model. Interestingly, all the 30 active compounds were predicted as active compounds whereas 20 inactive compounds were shown to be inactive by the developed pharmacophore model. The obtained results reflect the accuracy of our generated pharmacophore model.

The overlap of tricyclic benzothienopyrimidinone on pharmacophore model and binding mode this compound clearly showed that the two ACC, one Don, one Aro and three Hyd/HydA features of model have produced many vital interactions with key residues of PIM-1 (Lys67, Glu89, Glu121, Arg122, Asp186) [15], PIM-2 (Leu38, Phe43, Arg118, Pro119, Asp182) [24] and PIM-3 (Leu94, Trp112, Leu132, Arg134, Ala157, Arg159) (Fig. 3).

Screening of Database

The validated model of pharmacophore was used to screen ZINC database to identify compounds having similar structural features. 1028 structurally diversed hits were retrieved as a result of screening from ZINC database. To predict the drugability of initial hits, these hits were filtered on Lipinski's rule of five (Table-1). This rule describes that drug like compounds should have logP value <5, molecular weight <500 Da, hydrogen bond donors <5 and hydrogen bond acceptors <10, otherwise they have poor absorption or permeation [25]. As a results of this filtering, 912 hits were predicted as drug like molecules. These molecules were docked in the active sites of all the three PIM kinases for further assessment.

Table-1: ZINC database ID and drug like properties of final hit compounds from virtual screening.

S.No###ZINC ID###Drug like properties

###1###ZINC02096781###MW. 442.875 g/mol, LogP. 2.217, Don. 1, Acc. 5.

###2###ZINC02100736###MW. 492.935 g/mol, LogP. 3.525, Don. 1, Acc. 4.

###3###ZINC02104415###MW. 428.848 g/mol, LogP. 1.914, Don. 2, Acc. 3.

###4###ZINC02119768###MW. 428.848 g/mol, LogP. 1.914, Don. 2, Acc. 5.

###5###ZINC02157639###MW. 466.897 g/mol, LogP. 3.263, Don. 1, Acc. 4.

###6###ZINC02157642###MW. 466.897 g/mol, LogP. 3.263, Don. 1, Acc. 4.

###7###ZINC02202562###MW. 425.513 g/mol, LogP.-0.655, Don. 2, Acc. 5.

###8###ZINC04550634###MW. 338.371 g/mol, LogP. 2.445, Don. 3, Acc. 6.

###9###ZINC06457665###MW. 393.467 g/mol, LogP. 2.849, Don. 2, Acc. 5.

10###ZINC06503577###MW. 377.468 g/mol, LogP. 3.148, Don. 2, Acc. 4.

11###ZINC08877206###MW. 482.940 g/mol, LogP. 3.004, Don. 1, Acc. 5

12###ZINC12644860###MW. 241.318 g/mol, LogP. 1.377, Don. 2, Acc. 2.

13###ZINC13535465###MW. 255.345 g/mol, LogP. 1.767, Don. 2, Acc. 2.

14###ZINC15786050###MW. 351.430 g/mol, LogP. 2.636, Don. 3, Acc. 3.

15###ZINC35291524###MW. 423.314 g/mol, LogP. 4.328, Don. 2, Acc. 3.

16###ZINC35311133###MW. 427.430 g/mol, LogP. 5.287, Don. 3, Acc. 3.

17###ZINC38223335###MW. 427.912 g/mol, LogP. 3.502, Don. 2, Acc. 5.

18###ZINC38223339###MW. 462.357 g/mol, LogP. 4.155, Don. 2, Acc. 5.

19###ZINC49508855###MW. 442.608 g/mol, LogP. 0.406, Don. 3, Acc. 3.

20###ZINC57330053###MW. 271.344 g/mol, LogP. 0.138, Don. 2, Acc. 2.

21###ZINC65376271###MW. 301.370 g/mol, LogP. 1.450, Don. 2, Acc. 4.

22###ZINC65416635###MW. 383.516 g/mol, LogP. 1,728, Don. 3, Acc. 2.

23*###Reference###MW. 415.335 g/mol, LogP. 3.243, Don. 2, Acc. 2

Docking Simulation

Docking of all the predicted drug like hits were docked into the binding sites of all the three PIM kinases using MOE. Before docking the docking protocol was validated by docking the co-crystalized ligands of PIM-1 and PIM-2 kinases. The RMSDs of the co-crystalized and docked conformations were in the acceptable range, 0.3706 Adeg for PIM-1 and 0.5091 Adeg for PIM-2 respectively (Fig. 4 a, b). These re-docking results showed that our docking protocol is reliable and can be used for the docking of other compounds. The entire drug like hits was docked into the active sites of all three PIM kinases using the same docking protocol.

Based on docking scores top-ranked 150 compounds were selected for visual inspection to explore the interaction of these compounds with key residues of PIM-1, PIM-2 and PIM-3 kinases for further evaluation. Those compounds that showed interaction with significant active site residues of PIM-1, PIM-2 and PIM-3 kinase were selected as promising hits compounds. As a results, 32 compounds were predicted as promising hits compounds. These 32 compounds were further subjected to binding energy and binding affinity calculation to predict lead candidates for the inhibition of all the three PIM-kinases.

Binding Affinity and Binding Energy Prediction

For the identification of most potential lead candidates, binding affinities for the 32 promising hits were calculated using MOE. Energy minimization for each ligand complex was performed before calculation of binding affinity. The selection criteria for potential lead candidates were, compounds having binding energy and binding affinity good are equal to that calculated for the cocrystalized ligands, interaction with the important active site residues. On the basis this criteria, 22 out of 32 compounds fulfilled these requirement (Table-2). The mapping on pharmacophore model, binding interaction, binding affinity and binding energy prediction showed that these computationally identified lead candidates could be act as potent and novel PIM kinases inhibitors (Table-3).

Binding Modes of Finally Selected Compounds

In the docking simulation it was observed, that all the predicted lead candidates showed interactions with the key amino acids of each PIM kinases. Here some compounds were discussed which have good docking score and good binding affinities. For example, the docking conformation of compound 8, showed good binding interactions with the PIM-1, PIM-2 and PIM-3 residues of the binding sites (Table-3). From the docking conformation, it was also observed that the nitrogen atom of a pyrazole ring and oxygen atom OH groups of resorcinol moiety of compound 8 made interactions with Lys67, Glu124 and Val126 residues of PIM-1. In case of PIM-2, Phe43 and Glue83 establishes two hydrogen bonds with the OH groups of resorcinol moiety and Arg118 residue made two interactions with pyrimidine and pyrazole ring of compound 8.

PIM-3 kinase binding site residues, Leu132 and Ala157 made hydrogen bonds with hydroxyl group of resorcinol moiety, Asp134 made a hydrogen bond with the second OH group of same resorcinol moiety and Arg159 made arene-cation interaction with pyrimidine ring of compound 8 (Fig. 5 a, b, c).

In case of Compound 18, which showed docking scores -7.934, -15.7768 and -11.1358 for PIM-1, PIM-2 and PIM-3 respectively and gives good interactions with each PIM kinase binding site residue (Table-3). From the top-ranked docked conformation in case of PIM-1, it was observed that OH group of propanol and nitrogen atom and triazole ring of compound 18 forms interactions with the Phe49, Glu89 and Asp186 residues of binding site. In case of PIM-2 compound 18, the nitrogen atom of triazole forms arene-cation interaction with Phe43 and the oxygen atom of methoxy moiety forms hydrogen bond with Lys40 residue. While in case of PIM-3 compound 18, the OH group of propanol moiety interact with Leu94, the NH group of triazolo show hydrogen binding with Asp131 and oxygen atom of methoxy moiety interact with Ala157(Fig. 6 a, b, c).

Table-2: Docking scores, Binding energies and Binding affinities of the final hits.

###PIM1###PIM2###PIM3

S.No###Docking###Binding energy###Binding###Docking Binding energy Binding affinity Docking Binding energy Binding affinity

###affinity

###score###Kcal/mol###Kcal/mol###score###Kcal/mol###Kcal/mol###score###Kcal/mol###Kcal/mol

###1###-13.361###-26.699###-11.029###-13.867###-26.046###-11.265###-9.2553###-22.603###-10.392

###2###-13.342###-21.506###-12.632###-12.9866###-28.884###-10.325###-9.8492###-23.332###-10.135

###3###-17.577###-20.401###-12.092###-13.8576###-32.044###-12.318###-9.5492###-16.382###-9.608

###4###-14.415###-21.143###-12.882###-13.2804###-25.294###-11.167###-10.4542###-21.18###-12.014

###5###-14.298###-22.787###-11.787###-14.3985###-28.655###-11.499###-9.0074###-17.423###-10.035

###6###-13.883###-29.14###-12.103###-12.8773###-30.88###-10.966###-9.7738###-22.47###-9.867

###7###-14.828###-14.938###-13.377###-13.7442###-20.349###-10.993###-8.9343###-20.127###-9.546

###8###-17.662###-16.055###-13.845###-14.6682###-24.954###-11.412###-10.5936###-5.631###-10.779

###9###-14.095###-28.114###-13.253###-13.0532###-26.745###-11.098###-8.9039###-21.905###-9.525

###10 -14.197###-24.792###-12.365###-12.7483###-26.284###-11.909###-8.6644###-26.878###-11.004

###11 -13.794###-27.778###-12.448###-12.9276###-17.46###-10.023###-9.966###-19.671###-9.855

###12###-13.69###-8.925###-10.117###-13.1861###-17.669###-10.392###-8.685###-12.5###-10.686

###13###-13.4###-12.211###-10.815###-13.7519###-30.767###-13.037###-8.8597###-18.287###-10.241

###14 -12.996###-31.58###-9.995###-13.901###-35.061###-13.369###-9.4139###-19.074###-9.682

###15###-13.14###-14.997###-11.682###-13.583###-22.944###-11.386###-8.6657###-17.137###-9.029

###16 -13.964###-24.355###-11.527###-12.9497###-22.315###-10.881###-9.2222###-20.298###-9.422

###17 -14.309###-14.938###-13.377###-12.8736###-20. 49###-10.993###-8.8111###-20.127###-9.546

###18 -17.934###-27.808###-14.182###-15.7768###-26.083###-10.393###-11.1358###-23.148###-10.828

###19 -13.961###-27.399###-14.114###-14.0124###-22.797###-10.616###-9.6928###-22.232###-10.399

###20 -14.801###-12.996###-12.489###-14.7091###-30.414###-12.674###-8.7284###-15.216###-10.25

###21 -13.857###-20.97###-10.681###-13.0847###-21.81###-11.196###-8.7772###-14.034###-10.544

###22 -13.106###-25.161###-10.909###-14.0603###-22.622###-11.714###-8.7851###-15.892###-10.539

###23*###-9.798###-27.325###-9.639###-12.078###-28.08###-11.686###-6.309###-18.157###-9.245

Table-3: The two dimensional structures of the 22 final retrieved hit compounds.

###1)###ZINC02096781

###2)###ZINC02100736

###ZINC02104415###3)###ZINC02119768

###4)###ZINC02157639###5)###ZINC02157642

###6)###ZINC02202562

###7)###ZINC04550634

###8)###ZINC06457665###9)###ZINC06503577

###11) ZINC12644860

###10)###ZINC08877206

###13) ZINC15786050

###12)###ZINC13535465

###15)###ZINC35311133

###14)###ZINC35291524

###17)###ZINC38223339

###16)###ZINC38223335

###18)###ZINC49508855###19)###ZINC57330053

###20)###ZINC65376271###21)###ZINC65416635

Similar results were observed for other finally selected hit compounds for example the docking conformations of these compounds also showed interactions with the important active site residues of the enzyme these compounds are the derivatives of acetyl-acetamide, quinoline, pyrimidine and imidazole containing compounds and from the literature it was confirmed that these derivatives have many pharmacological functions. Compound 1-6, 11 and 22 were found acetylacetamide derivatives. Whereas compounds 12, 13,15 and 16 are quinolone-carbonitrile derivatives and compounds 7-10 and 17-20 are pyrimidine,triazole and piperazin derivatives. While compound 14 and 21 are imidazole and methoxybenzene derivatives.

Amide and pyrimidine compounds are useful in treatment of Pim-mediated diseases and other maladies, such as treatment of hematological malignancies and of solid tumors, for example prostate cancer, and head and neck cancer. Whereas, a variety of diseased conditions can be treated using quinolone and imidazole derivatives.

Conclusion

A number of different classes inhibitors have been reported for PIM kinases. However, these molecules only inhibit either PIM-1or PIM-2 kinase potently. As all the three PIM kinases have been shown to be involved in tumorigenesis and various types of cancer, therefore, it is necessary to simultaneously, inhibit all the three PIM kinases to achieve optimal cancer therapy. In-silico approach was used in above study to find novel, potent triple PIM kinases inhibitors. As results of this study, structurally different twenty-two compounds selected as final hits. These candidate compounds having unique scaffolds have a strong likelihood to act as further starting points in the development of novel and potent triple inhibitors for all three PIM kinases.

Acknowledgment

We thanked Muhammad Riaz and Syed Babar Jamal (Department of Biochemistry, Abdul Wali Khan University Mardan, Pakistan) for their support during manuscript preparation.

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Publication:Journal of the Chemical Society of Pakistan
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Date:Feb 28, 2017
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