Dataset on insightful bio-evaluation of 2-(quinoline-4-yloxy)acetamide analogues as potential anti-Mycobacterium tuberculosis catalase-peroxidase agents via in silico mechanisms

The continuous havoc wrecked by tuberculosis among humans worldwide remains colossal. In this work, twenty-one (21) 2-(quinoline-4-yloxy)acetamide analogues were observed against Mycobacterium tuberculosis catalase-peroxidase (This enzyme shields bacteria from poisonous drug-like molecules) (PDB ID: 1sj2) using density functional theory method, QSAR study using material studio software and docking method via PyMol, AutoDock Tool, AutoDock Vina and Discovery studio 2017 as well as ADMET study via admetSAR2. Twelve descriptors were obtained from the optimized compounds which were used to develop valid QSAR model. More so, the binding affinity between 2-(quinoline-4-yloxy)acetamide analogues and Mycobacterium tuberculosis catalase-peroxidase (PDB ID: 1sj2) via docking method were reported. ADMET properties of some selected compounds were also examined.


a b s t r a c t
The continuous havoc wrecked by tuberculosis among humans worldwide remains colossal. In this work, twentyone (21) 2-(quinoline-4-yloxy)acetamide analogues were observed against Mycobacterium tuberculosis catalaseperoxidase (This enzyme shields bacteria from poisonous drug-like molecules) (PDB ID: 1sj2) using density functional theory method, QSAR study using material studio software and docking method via PyMol, AutoDock Tool, AutoDock Vina and Discovery studio 2017 as well as ADMET study Keywords: 2-(quinoline-4-yloxy)acetamide Tuberculosis QSAR DFT Docking ADMET via admetSAR2. Twelve descriptors were obtained from the optimized compounds which were used to develop valid QSAR model. More so, the binding affinity between 2-(quinoline-4-yloxy)acetamide analogues and Mycobacterium tuberculosis catalase-peroxidase (PDB ID: 1sj2) via docking method were reported. ADMET properties of some selected compounds were also examined.
© 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ) Specification Table   Subject Bioinformatics Specific subject area Drug Discovery Type of data

Value of the Data
• The calculated data (descriptors) from the optimized 2-(quinoline-4-yloxy)acetamide derivatives will help researchers to recognize descriptors which describe their inhibiting capacity. • The selected descriptors from the optimized 2-(quinoline-4-yloxy)acetamide derivatives will also assist researchers to develop reliable and valid QSAR model with effective cytotoxicity. • The calculated binding affinity will help scientists to locate 2-(quinoline-4-yloxy)acetamide based compound with utmost inhibiting ability against Mycobacterium tuberculosis catalaseperoxidase (PDB ID: 1sj2). • The proposed drug-like molecules will assist researchers to have access to library of molecules with better inhibiting ability than the standard drug used in this work. Table 1 showed 2D structures of 2-(quinoline-4-yloxy)acetamide derivatives experimentally synthesised by Borsoi et al. [1] which was further converted to 3D and optimized using quantum chemical method via 6-31G * as basis set. Table 1 3D structures of 2-(quinoline-4-yloxy)acetamide derivatives. As shown in Table S1, twelve descriptors were obtained from the optimized 2-(quinoline-4yloxy)acetamide derivatives and further screened for anti-tuberculosis activity. The descriptors obtained were highest occupied molecular orbital energy (E HOMO ), lowest unoccupied molecular orbital energy (E LUMO ), band gap, dipole moment, molecular weight, area, volume, ovality, lipophilicity (Log P), polarizability, hydrogen bond donor (HBD) and hydrogen bond acceptor (HBA) and the screened descriptors were also used for further analysis. Table 2 displays the developed quantitative structure-activity relationship (QSAR) model using selected descriptors obtained from the optimized 2-(quinoline-4-yloxy)acetamide derivatives via series of software (Dataset Division GUI 1.2 software [ 2 , 3 ] and material studio software [4] ). The descriptors involved in the developed QSAR model were E HOMO , Log P and HBA and the statistical factors considered for QSAR validation were adjusted squared correlation coefficient (Adj R 2 ) (0.92), cross validation correlation coefficient (C.VR 2 ) (0.89), P-Value ( P < 0.0 0 01) and F-Value (52.26). The predicted inhibition concentration (IC 50 ) using the developed model were displayed in Table 3 .

Data Description
The binding affinity obtained between the optimized 2-(quinoline-4-yloxy)acetamide derivatives and Mycobacterium tuberculosis catalase-peroxidase (PDB ID: 1sj2) [5] were reported in Table 4     tuberculosis catalase-peroxidase (PDB ID: 1sj2) and their calculated binding affinity were compared to calculated binding affinity for Isoniazid ( Table 4 ). The amino acid residues involved in the interaction between compound 11 as well as P1 and Mycobacterium tuberculosis catalaseperoxidase were displayed in Figs. 1 and 2 .  Table 5 shows the Lipinski rule of five for compounds with highest calculated binding affinity (Compound 11 and P1 (from the proposed compounds). The calculated factors considered for the Lipinski rule of five were molecular weight ≤ 500 amu, AlogP ≤ 5, H-bond acceptor ≤ 10, h-bond donor ≤ 5, rotatable bonds ≤5. Also, the selected compounds (Compound 11 and P1) were subjected to adsorption, distribution, metabolism, excretion and toxicity analysis (ADMET) using admetsar 2 server (S2).

Experimental Design, Materials and Methods
Twenty-one molecular compounds were optimized using density functional theory via Spartan 14 software [6] . In density functional theory method, three-parameter density functional which includes Becke's gradient exchange correction [7] and the Lee, Yang, Parr correlation functional. As reported by Semire et al., (2017) [8] , exactness of density functional theory (DFT) method is a function of the selected basis set; therefore, 6-31G * was used for optimization of the investigated drug-like molecules. The examined 2-(quinoline-4-yloxy)acetamide derivatives were: compounds investigated in this work were docked against Mycobacterium tuberculosis catalaseperoxidase (PDB ID: 1sj2) using series of software. The downloaded Mycobacterium tuberculosis catalase-peroxidase from protein data bank ( www.rcsb.org ) was subjected to PyMOL software so as to remove non-amino acid material before locating active site for docking calculation using autodock tool and autodock vina 1.1.2 respectively. The calculated grid box to identify the binding site for Mycobacterium tuberculosis catalase-peroxidase (PDB ID: 1sj2) was as follows: center (X = 39.493, Y = 5.682, Z = 43.68) and size (X = 24, Y = 32, Z = 116), the spacing was set to be 1.00 Å and the exhaustiveness was set at default (8) ( Fig. 3 ).

Ethics Statement
Not applicable.

Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article. of this work. Also, this research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Supplementary Materials
Supplementary material associated with this article can be found in the online version at doi: 10.1016/j.dib.2021.107441 .