Molecular Docking and DFT Based QSAR Study on Oleanolic Acid Derivatives as Protein-Tyrosine Phosphatase 1B Inhibitors

Protein-tyrosine phosphatase 1B (PTP1B) is an attractive target for the treatment of type 2 diabetes. Oleanolic acid and its derivatives were found to be potent PTP1B inhibitors. In this study, we have performed QSAR studies followed by molecular docking. The docking study shows that most of the ligands can form hydrogen bonds with ARG24 and/or ARG254. Two quantitative structure activity relationships models have been constructed using different descriptors and the significance of these models is judged on the basis of correlation, Fischer F test, and quality factor (Q). It is believed that this study is helpful in the design of potent PTP1B inhibitors.


Introduction
Protein-tyrosine phosphatase 1B (PTP1B) is an attractive target for the treatment of type 2 diabetes and is found in a wide variety of human tissues [1,2]. The removal of the phosphoryl group from phosphotyrosine residue (s) in protein substrates by Protein-tyrosine phosphatases (PTPs) and the reverse action by protein tyrosine kinases is a common mechanism for the control of biological pathways [2][3][4].
PTP1B is the prototypical intracellular PTPs serves as a key negative regulator of insulin signaling pathway [5] and is over expressed in human breast cancer [6]. Knock-out studies suggest that the lack of PTP1B would result in increased insulin sensitivity and suppression of weight gain in mice [7].
Oleanane type triterpenes possess exciting pharmacological properties, including the anti-inflammatory, hypolipidemic, antioxidant, antidiabetic, microbicid and antiatherosclerotic actions [8][9][10]. They interfere in the neuro degenerative disorders and in the development of different types of cancer (Martín et al. 2010). Inhibition of PTP1B by oleanolic acid improves insulin sensitivity and stimulates glucose uptake [11]. Molecular docking studies indicate that triterpenes bind in the aryl phosphate binding site not in the catalytic site [12,13].
In this study, we have performed QSAR study followed by molecular docking with a series of oleanolic acid derivatives to explore the important properties of potent and selective PTP1B inhibitors.

Statistical methods
Multiple linear regression (MLR) analysis was used to build up QSAR models. Different combinations of parameters were tried to develop these models. On these selected parameters correlation analysis was done and intercorrelated parameters were eliminated. Statistical qualities of MLR equations were judged by parameters like correlation coefficient (R), square of the correlation coefficient (R 2 ), cross validated coefficient (R 2 cv ), standard deviation of the regression (S), Fischer statistics (F) and quality factor (Q). MLR program written by ourselves in Fortran-77 is used [14][15][16][17][18].
The -COOH group at C-17 forms two hydrogen bonds with ARG24 (1.885 Å) and ARG254 (1.901 Å). Substitution of -COOH group by -CONH 2 and -COOMe results ligands 5 and 7 have lower biological activities. This is due to the fact that ligand 1 has higher -EB compared to ligands 5 and 7.Again the -CONH 2 and -COOMe groups in ligands 5 (Figure 1b) and 7 (Figure 1c) do not make any hydrogen bond interaction with the enzyme.
The biological activity increases with increasing the carbon chain length at C-17 in ligands 2, 3, 4, 6 and 8. Except ligand 3, binding energy decreases with increasing chain size but their lipophilic efficiency increases. Again compound 8 has lower value of ∆Egap compared to the compounds 2, 3, 4 and 6 which suggest that complex formed between enzyme and ligand 8 (Figure 1d) is more stable than other. Compound 9 is an isomer of 11 though the biological activity of 9 is lower than 11. This is due to the ligand 9 has lower -EB than ligand 11 (Figure 1e).
For the compounds in the high bioactive range, such as compounds 11 to 26 (IC 50 <1 µM), there exists hydrogen bond (s) between amide backbone (especially with ARG24 and/or ARG254) and -(CH 2 ) 4 CONHCH (R 2 ) COOH group. Ligands 29, 30 and 31 are obtained from compound 1 by the substitution at the C-3 position and have greater biological activity. The biological activity of compound 29 (Figure 1f) is greater than 30 and 31 due to higher lipophilic efficiency.
The data set of 35 compounds was divided into two groups. The training sets constitute 28 compounds (1,2,3,4,5, 6,9,11,12,13 Among the generated QSAR models; two models were finally selected. Model summary of two best models are given below:         A plot between the predicted and the experimental pIC 50 for the training set by model 2.    A plot between the predicted and the experimental pIC 50 for the training set by model 1.

Figure 2
By using model number 1 and 2 the theoretical pIC 50 values of 28 training compounds are given in Table 3 together with experimental pIC 50 . Using the model number 1 and 2, we calculated the theoretical pIC 50 of the test set which appeared in Table 4. Statistical significance of these two models (model 1 and 2) were further supported by a plot of predicted pIC 50 vs. experimental pIC 50 (Figures 2 and 3) of training set inhibitors and give an idea about how fit model was trained and how well it predict the activity of the test set compounds (Figures 4 and 5).
Model 1 revealed that solvent accessible surface area (SASA), HOMO energy (EH) and LUMO energy (EL) were contributed positively to the model where binding energy (EB) and dipole moment (µ) were contributed negatively to the model. Solvent accessible surface area (SASA), molar refractivity (MR), and partition coefficient (logP) were contributed positively where molar volume (MV) was contributed negatively to the model 2.

Conclusion
In conclusion, this QSAR study has shown that binding energy (EB), HOMO energy (EH), LUMO energy (EL), dipole moment (µ), molar refractivity (MR), molar volume (MV), solvent accessible surface area (SASA) and partition coefficient (logP) are the important parameters for determining the activity of oleanolic acid derivatives. Model 1 and model 2 are the best equation for predicting the inhibitory activity of Protein-tyrosine phosphatase 1B and these QSAR models may be used in prediction of activity of designed compound. The docking study shows that the important interacting amino acids present in the active site are TYR20, GLN21, ARG24, ALA27, SER28, TYR46, ASP48, VAL49, ASP181, PHE182, ALA217, ILE219, ARG254, MET258, GLY259, GLN262, THR263. Most of the ligands can form hydrogen bonds with ARG24 and/or ARG254. Binding energies and partion coefficient (logP) play an important role for predicting the activity of the inhibitors.