The Research of New Inhibitors of Bacterial Methionine Aminopeptidase by Structure Based Virtual Screening Approach of ZINC DATABASE and In Vitro Validation

BACKGROUND

The great emergence of multi-resistant bacterial strains and the low renewal of antibiotics molecules are leading human and veterinary medicine to certain therapeutic impasses. Therefore, there is an urgent need to find new therapeutic alternatives including new molecules in the current treatments of infectious diseases. Methionine aminopeptidase (MetAP) is a promising target for developing new antibiotics because it is essential for bacterial survival.

OBJECTIVE

To screen for potential MetAP inhibitors by in silico virtual screening of the ZINC database and evaluate the best potential lead molecules by in vitro studies.

METHODS

We have considered 200,000 compounds from the ZINC database for virtual screening with FlexX software to identify potential inhibitors against bacterial MetAP. Nine chemical compounds of the top hits predicted were purchased and evaluated in vitro. The antimicrobial activity of each inhibitor of MetAP was tested by the disc-diffusion assay against one Gram-positive (Staphylococcus aureus) and two Gram-negative (Escherichia coli & Pseudomonas aeruginosa) bacteria. Among the studied compounds, compounds ZINC04785369 and ZINC03307916 showed promising antibacterial activity. To further characterize their efficacy, the minimum inhibitory concentration was determined for each compound by the microdilution method which showed significant results.

RESULTS

These results suggest compounds ZINC04785369 and ZINC03307916 as promising molecules for developing MetAP inhibitors.

CONCLUSION

Furthermore, they could therefore serve as lead molecules for further chemical modifications to obtain clinically useful antibacterial agents.

Binding site characteristics in structure-based virtual screening: evaluation of current docking tools

Two new docking programs FRED (OpenEye Scientific Software) and Glide (Schrödinger, Inc.) in combination with various scoring functions implemented in these programs have been evaluated against a variety of seven protein targets (cyclooxygenase-2, estrogen receptor, p38 MAP kinase, gyrase B, thrombin, gelatinase A, neuraminidase) in order to assess their accuracy in virtual screening. Sets of known inhibitors were added to and ranked relative to a random library of drug-like compounds. Performance was compared in terms of enrichment factors and CPU time consumption. Results and specific features of the two new tools are discussed and compared to previously published results using FlexX (Tripos, Inc.) as a docking engine. In addition, general criteria for the selection of docking algorithms and scoring functions based on binding-site characteristics of specific protein targets are proposed. Figure Enrichment factors obtained with FlexX, Glide and FRED docking engines in combination with different scoring functions for seven selected targets with highly variable binding sites