Application of fragment based virtual screening towards inhibition of bacterial N-acetyglucosaminidase$.

A structure-based approach is applied for the development of inhibitors of bacterial N-acetyglucosaminidase (autolysin). Autolysins are enzymes involved in the degradation of peptidoglycan and therefore participate in bacterial cell growth and different lysis phenomena. Several studies indicate that by the inhibition of autolysins, and consequently of bacterial cell division, antibacterial activity can be obtained, thus paving the road to a novel group of therapeutics against human pathogens. As crystal structures of the autolysin E (AtlE)-ligand complexes were obtained in our laboratories, fragment-based virtual screening was the method of choice for the initial studies. Fragment libraries from various databases were merged to increase the number of compounds for the virtual screening. Twenty-four commercially available virtual hits were selected and subjected to quantitative analysis of binding interactions using the surface plasmon resonance technique. Twelve fragments showed fragment-AtlE interactions. For F1, the top hit of the virtual screening, a KD of 228 µM was determined, while other fragments displayed non-stoichiometric binding. Blind docking of potential binders uncovers three possible allosteric sites. Ligands of N-acetyglucosaminidase identified in our study represent valuable information for the further development of AtlE inhibitors, which could in future represent antibacterial agents acting by a novel mode of action.

Comparison of in silico tools for binding site prediction applied for structure-based design of autolysin inhibitors.

Autolysin E (AtlE) is a bacteriolytic enzyme which plays an important role in division and growth of bacterial cells and therefore represents a promising potential drug target. Its 3D structure has been recently elucidated. We used in silico prediction tools to study substrate or ligand (inhibitor) binding regions of AtlE. We applied several freely available tools and a commercial tool for binding site identification and compared results of the prediction. Calculation time, number of predictions and output data provided by specific software vary according to the different approaches utilized by specific method categories. Despite different approaches, binding sites in similar locations on the protein were predicted. Specific amino acid residues that form these binding sites were predicted as binding residues. The predicted residues, especially those with predicted highest conservation score, could theoretically have catalytic and binding properties. According to our results, we assume that E138, which has the highest conservation score, is the catalytic residue and F161, G162 and Y224, which are also highly conserved, are responsible for substrate binding. Ligands developed with binding specificity towards these residues could inhibit the catalysis and binding of the substrate of AtlE. The molecules with inhibitory potency could therefore represent potential new antibacterial agents.