New molecular insights into the tyrosyl-tRNA synthase inhibitors: CoMFA, CoMSIA analyses and molecular docking studies

Abstract

Drug resistance caused by excessive and indiscriminate antibiotic usage has become a serious public health problem. The need of finding new antibacterial drugs is more urgent than ever before. Tyrosyl-tRNA synthase was proved to be a potent target in combating drug-resistant bacteria. In silico methodologies including molecular docking and 3D-QSAR were employed to investigate a series of newly reported tyrosyl-tRNA synthase inhibitors of furanone derivatives. Both internal and external cross-validation were conducted to obtain high predictive and satisfactory CoMFA model (q 2 = 0.611, r 2 pred = 0.933, r 2 m = 0.954) and CoMSIA model (q 2 = 0.546, r 2 pred = 0.959, r 2 m = 0.923). Docking results, which correspond with CoMFA/CoMSIA contour maps, gave the information for interactive mode exploration. Ten new molecules designed on the basis of QSAR and docking models have been predicted more potent than the most active compound 3-(4-hydroxyphenyl)-4-(2-morpholinoethoxy)furan-2(5H)-one (15) in the literatures. The results expand our understanding of furanones as inhibitors of tyrosyl-tRNA synthase and could be helpful in rationally designing of new analogs with more potent inhibitory activities.

Application of 3D-QSAR, Pharmacophore, and Molecular Docking in the Molecular Design of Diarylpyrimidine Derivatives as HIV-1 Nonnucleoside Reverse Transcriptase Inhibitors

“…The 3D-QSAR models were generated using the CoMFA and CoMSIA methods which could help us better understand visually the relationships between the structural features of DAPYs and their inhibitory activity [ 21 ]. The alignment quality has an important impact on the robustness and predictivity of the generated models [ 22 ]. The contour maps of the CoMFA and CoMSIA models were graphically presented using the field type of “Stdev*Coeff”.…”

Investigations of Structural Requirements for BRD4 Inhibitors through Ligand- and Structure-Based 3D QSAR Approaches

“…Similarly, an indices calculation was performed by the following equation 1. All the numerical calculations were executed in the same way as for the CoMFA analysis [ 31 , 33 , 34 ]. …”

On the use of electronegativity and electron affinity based pseudo‐molecular field descriptors in developing correlations for quantitative structure‐activity relationship modeling of drug activities

“…RMSE of cross-validation (RMSECV) was calculated using Ŷ train , Ŷ test , Y train, and Y test in Equation 6. Similarly, the coefficient of determination, R 2 cv , for cross-validation was calculated using the formula (Li et al, 2017;Roy et al, 2016) , was obtained as (Li et al, 2017;Roy et al, 2016), It may be clarified that training set (X train , Y train ) and test set (X test , Y test ) compounds were used to build the QSAR model (Equation 9), whereas, the validation set (X val , Y val ) compounds used for external validation of the model were not used in any of the steps involved in building the QSAR model.…”