Discovery of new MurF inhibitors via pharmacophore modeling and QSAR analysis followed by in-silico screening

The pharmacophoric space of streptococcal MurF was explored using a set of 39 known inhibitors. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and physicochemical descriptors that access self-consistent quantitative structure–activity relationship (QSAR) (r2=0.93,F=56.9,rLOO2=0.91,rPRESS2 against eight external test inhibitors = 0.75). Two orthogonal pharmacophores (of cross-correlation r2 = 0.26) emerged in the QSAR equation suggesting the existence of at least two distinct binding modes accessible to ligands within MurF binding pocket. The validity of the QSAR equation and the associated pharmacophore models was experimentally established by the identification of three promising new MurF inhibitors retrieved from the NCI database. Docking studies conducted on active hits supported the binding modes suggested by the pharmacophore/QSAR analysis.

In-silico-based discovery of new promising MurF inhibitors is reported.Figure optionsDownload as PowerPoint slide