3D-QSAR Studies of 1,2,4-Oxadiazole Derivatives as Sortase A Inhibitors

Abstract

Sortase A (SrtA) is an enzyme that catalyzes the attachment of proteins to the cell wall of Gram-positive bacterial membrane, preventing the spread of pathogenic bacterial strains. Here, one class of oxadiazole compounds was distinguished as an efficient inhibitor of SrtA via the “S. aureus Sortase A” substrate-based virtual screening. The current study on 3D-QSAR was done by utilizing preparation of the structure in the Schrödinger software suite and an assessment of 120 derivatives with the crystal structure of 1,2,4-oxadiazole which was extracted from the PDB data bank. The docking operation of the best compound in terms of pMIC ( pMIC = 2.77 ) was done to determine the drug likeliness and binding form of 1,2,4-oxadiazole derivatives as antibiotics in the active site. Using the kNN-MFA way, seven models of 3D-QSAR were created and amongst them, and one model was selected as the best. The chosen model based on q 2 (pred_ r 2 ) and R 2 values related to the sixth factor of PLS illustrates better and more acceptable external and internal predictions. Values of crossvalidation (pred_ r 2 ), validation ( q 2 ), and F were observed 0.5479, 0.6319, and 179.0, respectively, for a test group including 24 molecules and the training group including 96 molecules. The external reliability outcomes showed that the acceptable and the selective 3D-QSAR model had a high predictive potential ( R 2 = 0.9235 ) which was confirmed by the Y -randomization test. Besides, the model applicability domain was described successfully to validate the estimation of the model.

RETRACTED: In-silico modelling studies of 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase inhibitors via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

“…To increase the docking accuracy, the co-crystalized ligand was initially re-docked to analyze the ligand-active pocket interactions, and the RMSD scores were calculated between the docked poses and the co-crystalized ligand. The MOE program was adjusted to the triangle matcher method based on the London dG scoring function for placement selection and induced fit with GBVI/WSA dG scoring function for refinement before the docking starts ( Shakour et al., 2021 ). The database of ligands in MDB file docked (zanamivir and the thiazolinones) were then imported, and the docking simulation was executed for screening based on their binding scores.…”

Design of New Second‐Generation TRK Inhibitors Targeting Tropomyosin Receptor Kinases by Using Molecular Docking, Molecular Dynamics Simulations and ADMET Properties

“…With the TRK inhibitory activity of indolin‐2‐one small molecule pairs as the dependent variable and the field energy value as the independent variable, the model was created using PLS. The leave‐one‐out cross‐validation method was applied to obtain the cross‐validation correlation coefficient (q 2 ) and the optimal number of components (N) [25] . Subsequently, non‐cross‐validation was performed based on N to derive the correlation coefficient r 2 , standard estimation error (SEE), and F‐test value.…”

“…The leave-one-out cross-validation method was applied to obtain the cross-validation correlation coefficient (q 2 ) and the optimal number of components (N). [25] Subsequently, non-crossvalidation was performed based on N to derive the correlation coefficient r 2 , standard estimation error (SEE), and F-test value. Models with q 2 > 0.3 are considered to be over 95 % reliable, with a probability of less than 5 %.…”