INTRODUCTION:

Tuberculosis (TB) is a prominent infectious disease caused by the organism, Mycobacterium tuberculosis (Mtb). Although it is a primordial disease it still continues to be the world’s largest threat to human lives. Nearly 10 million people suffer from this disease per year of which 1.5million die annually. Recently there has been a significant increase in the frequency of Multiple Drug Resistant TB (MDR-TB) and Extremely Drug Resistant TB (XDR-TB) cases primarily due to non-adhere and non-compliance to the drug therapy by the patient. Due to this there is an increased need and demand for research and development of newer and effective novel molecules to counteract this disease.^1,2

Decaprenylphosphoryl-ß-D-ribose-2'α-epimerase 1 (DprE1) a flavoprotein, is a crucial enzyme which plays an important role in cell wall biosynthesis.

The cell wall of Mtb is made of complex of peptidoglycan-mycolic acid and polysaccharides- arabinogalactan and lipoarabinomannan. DprE1 oxidises decaprenylphosphoryl-D-ribose to decaprenylphosphoryl-2-ketoribose which is precursor for Decaprenylphosporyl arabinose which in turn helps in the synthesis of the cell wall polysaccharides-arabinogalactan and lipoarabinomannan. Hence inhibition of the said enzyme will lead to ceasing of the synthesis of the cell-wall leading to inhibition of the growth of Mtb. This enzyme is a promising target for the anti TB drugs due to its valuable role in the cell wall bio-synthesis.^3,4,5

In this study a set of chemical compounds taken from the literature who shows DprE1 inhibitory activity in order to create and study 3D QSAR models and their structural attributes essential for anti TB activity.^6,7 This study provide insight for producing new derivatives of the DprE1 inhibitors with better therapeutic property and efficiency.

MATERIAL AND METHODS:

The study was conducted with the Schrodinger software. The chemical structures were drawn with the help of Marvin Sketch from ChemAxon. The ligand alignment and atom-based 3D QSAR models were generated with the help of Maestro version 11.4 of Schrodinger Inc. in a Linux Ubuntu 18.04.1 LTS operating system using an Intel Core processor (i3-4160) with 4GB RAM and Intel Haswell graphics card.^8,9,10

Molecular Dataset:

A set of 33 structures were selected from the literature having DprE1 inhibitory property in M. tuberculosis H37RA strain to generate 3D QSAR models. The wide diversity of the structural attributes of the ligands with respect to its indicated biological activity helped to make a sound basis for the generation of better QSAR models efficient in predicting activity. The MIC data of H37RA strain in µM was utilised after converting it to the logarithmic scale pMIC H37RA and was used as the depending variables for creating 3D QSAR models.^11,7,8,9 Refer Table 1.

Ligand Alignment:

This was done using flexible ligand alignment option provided by the maestro software. The data set structures were aligned in such a way that they superimpose one other and helps in studying and observing variations of the structural entities and their relations with one other. It is an important step in order to make precise and accurate 3D QSAR model. Fig1.

Figure 1. Flexible ligand alignment of the DprE1 inhibitors in the dataset

Building Atom based 3D QSAR model:

The building of QSAR models were carried out with the help of Atom Based QSAR provided in the Phase application, Maestro (Schrodinger). In this study using the selected structural dataset various atom based 3D QSAR models were generated. 79% of the dataset chosen as training set and 21% as test set to generate the QSAR model considering four Partial least square factors (PLS) to ensure minimum standard deviation along with better R²and Q²(>>0.7) which were taken as cross validated coefficient. This in turn helps in generating a better and more accurate Atom based 3D QSAR model.^7-14 Refer Table 1.

Table 1 Chemical Structure of DprE1 inhibitors with their experimental and predicted activity showing anti tubercular activity which are derived from the literature to generated Atom Based 3 D QSAR model.

Compound	Chemical Structure	MIC99 H37RA (µM)	pMIC H37RA	QSAR Set	Predicted Activity	Predicted error
55d		0.001	9.0000	Training	8.95357	-0.0464322
55c		0.002	8.6989	Test	8.63376	-0.0652103
77		0.004	8.3979	Training	7.98919	-0.408752
42		0.004	8.3979	Training	7.83424	-0.563701
87		0.008	8.0969	Test	7.7971	-0.299815
68		0.008	8.0969	Training	7.48115	-0.61576
62		0.013	7.8827	Training	7.45969	-0.423042
80		0.016	7.7959	Training	7.36415	-0.431733
76		0.016	7.7959	Training	7.83424	0.0383594
74		0.016	7.7959	Training	7.8557	0.059823
72		0.016	7.7959	Training	7.45969	-0.336193
70		0.016	7.7959	Test	7.48115	-0.31473
69		0.016	7.7959	Training	7.48115	-0.31473
67		0.016	7.7959	Training	7.45969	-0.336193
86		0.031	7.5086	Test	7.34753	-0.161106
85		0.031	7.5086	Training	7.89365	0.38501
82		0.031	7.5086	Training	7.30554	-0.203099
78		0.031	7.5086	Training	7.83424	0.325601
75		0.031	7.5086	Training	7.83424	0.325601
65		0.031	7.5086	Test	7.45969	-0.0489515
64		0.063	7.2001	Training	7.1223	-0.0783606
60		0.063	7.2001	Training	7.32128	0.120619
84		0.130	6.8861	Training	6.91971	0.0336496
81		0.130	6.8861	Training	7.36415	-0.47809
73		0.130	6.8861	Test	7.48115	0.595094
71		0.130	6.8861	Training	7.42254	0.536486
66		0.130	6.8861	Training	7.45969	0.57363
63		0.130	6.8861	Training	6.91993	0.0338712
61		0.130	6.8861	Training	6.82082	-0.0652351
55a		0.200	6.6989	Test	6.23654	-0.463725
83		0.310	6.5086	Training	7.45969	0.951048
55b		0.400	6.3979	Training	6.37333	-0.0246113
INH		0.800	6.0969	Training	6.08296	-0.0139486

Figure 2. Scatter plot for Atom Based 3D QSAR showing relation between activity observed and the predicted activity 6

RESULT AND DISCUSSION:

Atom Based 3D QSAR analysis:

Table 2 Statistical parameters of atom-based 3D QSAR model of DprE1 inhibitors

# Factors	R²	Q²
1	0.6067	0.3913
2	0.6437	0.6016
3	0.6648	0.6621
4	0.6715	0.7207

The atom-based 3D QSAR models were developed on the basis of their biological activity and their relation to its structural attributes. The experimental and predicted activity and errors showing anti tubercular activity as well as the training and test set selection of the dataset is displayed on Table 1. The graphical representation of the relation between Activity and predicted activity 4 of test and training set was show in Figure 2. The above model was selected due to its high q² (0.7207) and appropriate R²(0.6715). The atom based 3 D QSAR statistics for the selected model with 4 PLS factors are shown at Table 2. Due to its high cross validation coefficients this model was rendered to be precise and efficient.^15,16,17

3D QSAR visualisation:

The Atom type fractions parameters are shown in Table 3. The 3 D QSAR study was visualised in fields of Hydrophobic/non-polar interactions, Negative Ionic interactions, Positive ionic interactions, electron withdrawing and others factors such that the dark blue and red colour regions indicates the positive and negative effects on the activity.^{18, 19, 20} The visualisation of the QSAR model are shown in Figure3. The analysis was carried out using the ligand molecule 55d as the reference due the highest DprE1 inhibitory activity and 55b due the least inhibitory activity, 65 as the molecule showing the moderate inhibitory activity. This visualisation gives us the insight about the structural entities present in the ligand molecule and their relation to its biological activity also the effect of substitution of functional groups in the ligand molecule.

CONCLUSION:

The atom-based 3D QSAR model was designed and generated to obtain a precise and credible model for predicting the activity of the unknown derivatives showing DprE1 inhibitory activity also aiding in determining its minimum inhibitory concentration values. The above study suggests that the ligand molecule 55d possess high anti-tubercular activity with multiple prospects of modifying the structural framework to create new molecules with remarkable DprE1 inhibitory activity. This study gives a solid structural and therapeutical insight and knowledge for identifying and designing new molecules with DprE1 inhibitory activity in Mycobacterium tuberculosis.

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Received on 14.08.2020 Modified on 24.10.2020

Research J. Pharm. and Tech 2021; 14(11):5903-5910.

DOI: 10.52711/0974-360X.2021.01026

#Factors	Hydrophobic/non-polar	Negative Ionic	Positive ionic	Electron-withdrawing	Other
1	0.315441	0.099958	0.133566	0.149453	0.301582
2	0.349288	0.077727	0.096344	0.139483	0.337157
3	0.431402	0.063377	0.052868	0.133414	0.318938
4	0.432145	0.067110	0.062312	0.148515	0.289919