Application of SAR methods toward inhibition of bacterial peptidoglycan metabolizing enzymes

Abstract

Structure activity relationship (SAR) methods are applied for a study of inhibition of peptidoglycan metabolizing enzymes, which could represent new antibacterial targets. In this study, we exploit experimental data of inhibition of Mur A and Mur B enzymes for classification of large set of chemicals. Based on inhibitory potency of compounds and their structures from the literature, we developed classification models for new, potential inhibitors of Mur A and Mur B enzymes. The best model for Mur A has the following performance measures for the validation set: 0.85, 0.75, and 0.80, for sensitivity, specificity, and normalized Matthews correlation coefficient, respectively. The same measures of the best Mur B model are 0.94, 0.75, and 0.86. Such models could represent valuable computational tools for theoretic predictions of compounds' activities against specific targets. Additionally, application of such models, like any other computational tools, significantly reduces time and costs in the early phase of drug design.

Potential of MurA Enzyme and GBAP in Fsr Quorum Sensing System as Antibacterial Drugs Target: In vitro and In silico Study of Antibacterial Compounds from Myrmecodia pendans

“…It is in line with other research that reported a negative correlation between drug-likeness and activity of the compound. There are influencing structural properties contributing to whatever determine active or inactive a compound [ 44 ].…”