A large number of crystal structures are available on human active and inactive CDK₂ in complex with small ligands which bind deeply within ATP site. However, protein complex with triazolopyrimidine inhibitor having the PDB code 2C6O and 2.1Å resolution was retrieved from the RCSB Protein Data Bank (PDB) to be used as a structural template [14].

Occasionally, the protein was prepared using general purpose protocol (Discovery Studio-Biovia 2016) by removing water molecules in all the system. Adding bond orders and formal charges to the hetero groups of the protein along with addition of hydrogens using charmM forcefield and finally minimizing energy using adopted basis Newton-Raphson algorithm (NR).

The advantage of pharmacophore-based virtual screening in the drug discovery process is that most of the compounds with low probability to be active can be excluded early from further studies [31]. However, there are multiple pharmacophore-based virtual screening algorithms with the same principle: As an input, a small molecule database and a pharmacophore model are given, then each of

the molecules conformations is fitted to the model. As result, a hit list, fitting compounds, are given as output [10].

Enamine Kinase-Hinge-Region-Directed-library which consists of 18,020 compounds was screened using Ligand Pharmacophore Mapping protocol and screen library protocol in order to search novel compounds which have the generated pharmacophore. Besides, retrieved compounds were filtered by applying Lipinski’s rule of five (i.e., a molecule with a molecular mass less than 500 Da, no more than 5 hydrogen bond donors, no more than 10 hydrogen bond acceptors, and an octanol-water partition coefficient log P not greater than 5), and further sorted on the basis of fit value.

The selected compounds from Enamine library were docked into the active site of the previously prepared CDK₂ structure (2C60) with CDOCKER protocol implemented in Biovia Discovery Studio 2016. CDOCKER uses a CHARMm-based molecular dynamics (MD) scheme to dock ligands into a rigid receptor binding site. First, Random ligand conformations are generated using high-temperature MD. Second, the conformations are translated into the binding site. Third, Candidate poses are created using random rigid-body rotations followed by simulated annealing. A final minimization is then used to refine the ligand pose [32].

Moreover, the performance of the docking method on CDK inhibitors had been evaluated before it was carried out. The validation was achieved by measuring the RMSD between the orientation of the ligand existing in the crystal complex (2C6O) and its orientation found by our study. The RMSD value must be less than 2Å. However, CDOCKER interaction energy predicts the binding energies of the protein with retrieved hits [33]. The proximity between the receptor and hit was studied in terms of the electrostatic, hydrogen bond interactions and hydrophobic interaction.

A set of ten pharmacophore models was generated based on a training set containing 16 structurally diverse compounds (Table 2).

In the present work, we will discuss the first pharmacophore model (Hypothesis 1) which is basically composed of three features: a hydrogen bond acceptor, and two aromatic ring features (Figure 4).

The first pharmacophore model was considered the best model for so many reasons. For instance, in terms of hypothesis significance, what really matters is the magnitude of the difference between the cost of any returned hypothesis and the cost of the null hypothesis. By the way, cost analysis shows a null cost value of 118.527 and Hypo1 cost value of 73.896. So, the cost difference for it was 44.631. A cost difference value above 40 implies that the pharmacophore model correlates the estimated and experimental activity values between (75% - 90%).

Besides, total cost value should be away from the null cost and close to the fixed cost. However, among the total cost values of ten pharmacophore models, Hypo1 scored the closest value to the fixed cost value (63.187) than other models. Therefore, Hypo1 could be considered as a good model. In addition to the cost analysis of the models, hypothesis 1 was further assessed based on many factors. First, the correlation value of the first pharmacophore model was 0.906. Second, estimated activities of all ligands, even the lowest active one, were within one order of magnitude of the actual activity (Table 3). Hence, it is considered as an acceptable prediction of activity. The predictive ability of Hypo 1 on training set compounds is shown in Figure 5.

Third, the alignment of the training set ligands to the pharmacophore was taken into consideration in terms of its ability to explain the activity of the ligands. For instance, active ligands should map more features than inactive ligands. In other words, a ligand is inactive because it does not have an important feature, or the feature is present but cannot be oriented correctly in space. Occasionally, a general guideline for evaluating 3D QSAR pharmacophores is that the two most active ligands should map to all features of the pharmacophore.

Table 3 summarizes the training set aligned to the first pharmacophore model and illustrates that the three most active ligands have mapped to all features of the pharmacophore. Figure 6 illustrates the most active compound (2R3Q) mapped to the features of hypo1.

Forth, the result of Fischer’s randomization test confirmed the statistical confidence of Hypo1 and indicated that none of the randomly generated pharmacophore models obtained from this validation method scored better statistical values than Hypo1.

Finally, a test set of 35 ligands with known biological activity was used in order to determine the best models by assessing their ability to estimate the activity of a test set. Consequently, 24 compounds were mapped to the proposed pharmacophore (Table 4), the other 11 compounds which have not been mapped had activities over 1000 nm. Figure 7 represents the most active ligand of test set (2A0C) mapped to hypo1.

Thus, the hypothesis 1 is considered to be a good model to estimate the activity of new compounds. Moreover, heat map plot maps test set ligands to the set of generated pharmacophores by 3D QSAR. It represents fit values in a two-dimensional color map. As they are shown in (Figure 8), blue rectangles

which simulate low fit values are less in hypo 1 than the other nine hypotheses, which indicates beside the other statistical validation indicators that virtual screening can be carried out basing on hypo 1.

There is no doubt that pharmacophore models are excellent tools for scaffold hopping and identifying structurally diverse compounds as hits by using them as a filter in virtual screening. Eventually, the validated pharmacophore model (hypothesis1) has been used as 3Dimensional structural query to screen Enamine Kinase-Hinge-Region-Directed-Library of 18,020 compounds which has been also gone under Lipiniski filtration in order to evaluate their druglikeness and determine if they have chemical and physical properties that would make it a likely orally active drug in humans.

However, when a compound from the database will be considered as a hit, two criterions are employed, it needs to match to all of the pharmacophore features and to have high fit value. In other words, since the Fit Value is based on the actual distances of the conformation from the pharmacophore feature centers, the better fit gives a higher score and directly describes how well the compound fits to the model. As a result, 69 hits were retrieved according to the Fit values over 5.8 and to the Lipiniski filtration results.

The crystal structure of CDK₂ from the protein data bank (PDB ID: 2C6O) was selected and prepared for docking studies. Besides, the performance of the docking method on CDK inhibitors was evaluated by re-docking crystal ligand with less than 2 RMSD value. 69 hits with the highest fit value (over 5.8) were docked into the active site of CDK₂ using CDocker. As a result, compound 5909 (Figure 9(a)), compound 701 (Figure 9(b)) and compound 8397 (Figure 9(c)) showed very good binding modes with -CDocker interaction energy of 54.519, 52.665 and 48.94, respectively.

However, compound 5909 formed many significant interactions with the key binding amino acid residues of CDK₂ (Figure 10(a)). For instance, N atom

(which functions as a Hydrogen bond acceptor) in pyrimidine ring showed a hydrogen bond reaction with LEU83 (particularly with NH), as well as H atom (HBA) which is located between the two Nitrogens of pyrimidine ring interacted with the carbonyl group (HBD) of GLU81 by a hydrogen bond reaction. H atom in the side chain formed a hydrogen bond with ASP145 which is located in ribose region of ATP and which also interacted with the centroid of Pi ring of imidazole by a Pi-anion interaction. Besides, a Pi-cation interaction was formed between the electrons of a delocalized Pi system of imidazole in the side chain of the molecule and the positively charged Nitrogen in LYS33 side chain (in triphosphate region of ATP). Finally, pyrmidine ring formed a hydrophobic Pi-alkyl interaction bond with each of VAL18, ALA31 and ILE10. Meanwhile, Il10 also interacted with each of thiophene ring and phenyl ring by a Pi-alkyl bond.

701 is also one of the compounds which were marked as hits (Figure 10(b)). Occasionally, S atom in thiophen ring showed a hydrogen bond reaction with NH of LEU83. Another hydrogen bond was formed between the side chain and ASP145. Furthermore, there were hydrophobic Pi-alkyl interaction bonds between pyrimidine ring and each of VAL18 and ALA31, and between each of thiophene rings, and phenyl ring with ILE10.

The third hit in our study was compound 8397 (Figure 10(c)): Each of N atom (which functions as an HBD) in pyridine ring and NH (HBA) in pyrazole ring showed a hydrogen bond reaction with LEU83 (with NH and carbonyl group, respectively). Thiadiazole ring in the side chain of the compound interacted with Lys33 by an electrostatic Pi-cation bond. Besides, hydrophobic Pi-alkyl interaction bonds were formed by pyridine ring with each of VAL18, ALA31 and ILE10, pyrazole ring with IL10, and methyl group which is substituted on pyridine with PHE80. Moreover, substituted methyl groups on pyridine and pyrazole showed hydrophobic alkyl bonds with each of ALA31 and IL10, respectively.

All in All, binding modes and cdocker interaction energy values of compounds 5909, 701 and 8397 suggest them as good leads in order to design novel CDK₂ inhibitors.