In this study, the ligand-based pharmacophore, field-based 3D-QSAR model, and docking studies on pyrazoline derivatives were performed to discover the structural characteristics of antiamoebic activity. The code and activity of these inhibitors (experimental and predicted) are shown in Table 1.
A total of 5 different variant hypotheses were created at the end of the common pharmacophore identification treatment. A maximum of six features allowed to develop a hypothesis and a number of CPHs (Common Pharmacophore Hypothesis) was reported to be common to all molecules based on hydrogen bond donor /acceptor (D)/(A), hydrophobic (H), and aromatic ring (R). There were five hypotheses based on the combination DHHHR, two hypotheses based on HHHRR, two hypotheses based on AHHRR, and one hypothesis based on ADHHR. We have selected those of the pharmacophore models whose phase scores ranked in the top (Table II). The top model was found to be associated with the five-point hypothesis (Fig. 1), which consists of three hydrophobic (H), one aromatic rings (R), and one hydrogen bond donor(D). The actives ligands have the highest activity as shown in Fig. 2. The alignment of all active ligands with their pharmacophoric features is shown in Fig. 3. The score of all hypotheses was presented in Table 2.
Field-Based 3D-QSAR model
Using the field-based QSAR method (in Schrodinger software) to create a correlation model between the available activity values and the 3D properties of a set of aligned compounds. There is the ability to display the QSAR model in the workspace for qualitative evaluation, which would allow us to add or remove functional groups and use the model to predict the activities of other molecules. The 3D QSAR model was developed in the Schrodinger software phase module taking 48 compounds for training, while the model was validated using 12 compounds as a test set with six PLS factors. The PLS method shows good statistical values and predictions for the data set ( see Table 3). The statistical values of the QSAR method show a better regression coefficient of R2 (0.837), a large value of F (35.3) and P (1.11e-014), a small value of standard deviation (0.135), RMSE (0.17), and high values of Pearson-P (0.913) indicate a statistically significant regression model. The model was validated by the cross-validation correlation coefficient q2 = 0.766.
The graph of the biological activity obtained by comparison with the predicted biological activity of the training and test sets is shown in Fig. 4. The relative contributions of steric, electrostatic, hydrophobic, H-bond acceptor, and H-bond donor fields are 0.332, 0.182, 0.328, 0.118, and 0.052, respectively (see Table 4 for the best PLS). The effective validity of the model is indicated by its ability the predict biological activity for new molecules. A close analysis of different validity tests indicates that the model created by the Field-Based QSAR analysis is very good. The experimental and predicted activities for the training set and test set are given in Table I. From Table 4, it appears that the electrostatic, H-bond Acceptor, and H-bond Donor fields contribute less compared with steric and hydrophobic fields on the biological reactivity.
The field-based QSAR contours for steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor properties are shown in Fig. 5a to Fig. 5e, ,respectively. In the contour maps, each colored contour corresponds to specific properties. In the steric field, green contours show regions where voluminous groups increase biological function, while yellow contours show regions where voluminous groups decrease activity. In the electrostatic field, the red contours correspond to regions favored by positive electrostatics, but the blue contours indicate regions favored by negative electrostatics. The yellow contours relate to hydrophobically favored regions, while the white contours correspond to hydrophobically unfavored regions. The magenta and red contours correspond to favorable and unfavorable regions for hydrogen bond acceptors, respectively, while the purple and cyan contours represent favorable and unfavorable regions for hydrogen bond donor groups, respectively.
The field-based steric QSAR in Fig. 5a shows no presence of yellow contours on the molecule, revealing the absence of unfavorable regions. The presence of large green contours on the adamantine, pyrazoline, and benzene rings indicates a significant steric in these regions. Therefore, voluminous substituents in these regions increase the activity. The electrostatic contour plots of the field-based QSAR are shown in Fig. 5b. The blue contour denotes the area where positive groups are required for high activity, while the red area corresponds to a region favorable for negative groups. The hydrophobic contour plots from the field-based QSAR are shown in Fig. 5c. The white contours indicate regions of unfavorable hydrophobic interaction near the adamantine and pyrazoline rings. The yellow contours are located near the benzene ring, indicating that any voluminous groups at this position correspond to favorable hydrophobic regions. The hydrogen bond donor contour maps of the field-based QSAR are shown in Fig. 5d. The contour maps reveal the favorable (purple) and unfavorable (cyan) portions of the hydrogen bond donors. The purple contours are visualized near the adamantine ring attached to the nitrogen atom, indicating that the hydrogen bond donor functionalities in this area increase activity. The hydrogen bond acceptor fields of the field-based QSAR (Fig. 5e) are indicated by red and magenta contours, the red contours correspond to the parts where hydrogen bond acceptors are favorable, and the magenta contours indicate the parts where hydrogen bond acceptors on inhibitors are unfavorable for activity. In this position (red), any substituent containing an acceptor group increases activity.
Binding site examination was carried out using the SiteMap tool of Schrodinger software. SiteMap found five sites from the site score that comprises size, volume, amino acid exposure, enclosure, contact, hydrophobicity, hydrophilicity, and donor/acceptor ratio. The sites with a site score of 1 and above can be an ideal site for the ligand binding. The best level of potential receptor binding sites was identified using SiteMap. The best site had a score of 0.99 site score, 1.02 Dscore, 234.3 volume score, 3.74 hydrogen bond donor/acceptor score, 0.87 hydrophilic score, and 0.56 hydrophobic score. The active site residues were identified as PHE292, LEU291, VAL266, HIE264, LEU263, LYS261, MET260, LYS259, GLY258, LEU90, ASN89, ASN88, VAL87, PHE3, MET1, GLN2, VAL41, and GLN40, respectively. So active site 1 is most likely to do the docking process.
Docking studies were executed to know the intermolecular interaction of the ligand with the targeted enzyme by applying Glide module . The docking technique was performed to study the binding mode of active ligands on the receptor (5ZEF) and to obtain information for further optimization of the structure. Grid generation to determine the binding site on the receptor was performed via the receptor grid generation panel with default settings. Glide XP was used for docking purposes. To estimate the docking of protonated ligands, the docking score should be considered. Thus, in this work, a docking score was applied to compare the stability of the simulated complexes. The average docking score obtained for the potential inhibitors 14, 32, 33, 35, 36 and 55 were − 4.03, -3.538, -3.535, -5.176, -4.96 and − 4.34, respectively. Then, the inhibitor 35 / 5ZEF complex is more stable than inhibitors 14 / 5ZEF, inhibitors 32 / 5ZEF, inhibitors 33 / 5ZEF, inhibitors 36 / 5ZEF, and inhibitors 55 / 5ZEF complexes. Among these inhibitors studies, inhibitor 35 of them had a docking score higher in comparison with all active ligands. The results of docking analysis were described in Table 5 and Fig. 6.
Docking studies of the ligands 32 and 33 have shown that the nitrogen atom (NH) acts as a hydrogen bond donor and makes a hydrogen bond to the PHE292 for both structures. For ligands 35, the benzene ring (contain two fluorine atoms) of the ligand makes π-π interactions with PHE 3 residue. For ligands 36, the benzene ring (contain two fluorine atoms) of the ligand makes π-π interactions with PHE 3 residue, and the nitrogen atom (NH) acts as a hydrogen bond donor and makes a hydrogen bond to the LEU263. For ligands 14, the benzene ring (contain the CH3 group) of the ligand makes π-π interactions with PHE 3 residue and the O atom of the amide group makes a hydrogen bond acceptor to the HIE 264. For ligands 55, the benzene ring (contain a chlorine atom) of the ligand makes π-π interaction (π –cation) with LYS 261. Those overall interactions with PHE 3 could be the reason for the high docking score and hence, it promises to be potent and also selective for antiamoebic activity.
The ADMET (absorption, distribution, metabolism, elimination, toxicity) analytical properties of the compounds can be determined in-silico using the qikprop module of the Schrödinger Suite 2018. In this way, we evaluated the physiochemical descriptors and pharmaceutically relevant properties of the active ligands to analyze the drug properties (Tables 5a and 5b).
The computed number of likely metabolic reactions (# metab) is in the range of 1–6. All the active ligands showed good partition coefficient (QP log Po/w) values (4.7 to 5.77), which were critical for the absorption and distribution of drugs. The approximate number of hydrogen bonds that would be given by the solute to water molecules in an aqueous solution of the compounds is in the range of 0 to 1. The estimated number of hydrogen bonds that the solute would accept from water molecules in an aqueous solution of the compounds is in the range of 3 to 7. The number of violations of Lipinski's rule of five is in the range of 0 to 1. The compounds have 100% of % Human Oral Absorption. The estimated brain/blood partition coefficient (QPlogBB) is in the range of 0.713 to 0.865. Thus, almost all the properties of the active ligands are within the recommended values. From these results, we can say that the active ligands can be used in clinical trials due to good ADME properties (absorption, distribution, metabolism, and excretion). The details of the ADMET properties for active ligands are shown in Tables6a and 6b.