Toward More Selective Antibiotic Inhibitors: A Structural View of the Complexed Binding Pocket of E. coli Peptide Deformylase.

Peptide deformylase (PDF) is involved in bacterial protein maturation processes. Originating from the interest in a new antibiotic, tremendous effort was put into the refinement of PDF inhibitors (PDFIs) and their selectivity. We obtained a full NMR backbone assignment the emergent additional protein backbone resonances of ecPDF 1-147 in complex with 2-(5-bromo-1H-indol-3-yl)-N-hydroxyacetamide (2), a potential new structural scaffold for more selective PDFIs. We also determined the complex crystal structures of E. coli PDF (ecPDF fl) and 2. Our structure suggests an alternative ligand conformation within the protein, a possible starting point for further selectivity optimization. The orientation of the second ligand conformation in the crystal structure points toward a small region of the S1' pocket, which differs between bacterial PDFs and human PDF. Moreover, we analyzed the binding mode of 2 via NMR TITAN line shape analysis, revealing an induced fit mechanism.

Structural Insights into Antibacterial Payload Release from Gold Nanoparticles Bound to E. coli Peptide Deformylase.

The lack of new antibiotics and the rapidly rising number of pathogens resistant to antibiotics pose a serious problem to mankind. In bacteria, the cell membrane provides the first line of defence to antibiotics by preventing them from reaching their molecular target. To overcome this entrance barrier, it has been suggested[1] that small Gold-Nanoparticles (AuNP) could possibly function as drug delivery systems for antibiotic ligands. Using actinonin-based ligands, we provide here proof-of-principle of AuNP functionalisation, the capability to bind and inhibit the target protein of the ligand, and the possibility to selectively release the antimicrobial payload. To this end, we successfully synthesised AuNP coated with thio-functionalised actinonin and a derivative. Interactions between 15N-enriched His-peptide deformylase 1-147 from E. coli (His-ecPDF 1-147) and compound-coated AuNP were investigated via 2D 1H-15N-HSQC NMR spectra proving the direct binding to His-ecPDF 1-147. More importantly by adding dithiothreitol (DTT), we show that the derivative is successfully released from AuNPs while still bound to His-ecPDF 1-147. Our findings indicate that AuNP-conjugated ligands can address and bind intracellular target proteins. The system introduced here presents a new delivery platform for antibiotics and allows for the easy optimisation of ligand coated AuNPs.

Prediction of human drug-induced liver injury (DILI) in relation to oral doses and blood concentrations.

Drug-induced liver injury (DILI) cannot be accurately predicted by animal models. In addition, currently available in vitro methods do not allow for the estimation of hepatotoxic doses or the determination of an acceptable daily intake (ADI). To overcome this limitation, an in vitro/in silico method was established that predicts the risk of human DILI in relation to oral doses and blood concentrations. This method can be used to estimate DILI risk if the maximal blood concentration (Cmax) of the test compound is known. Moreover, an ADI can be estimated even for compounds without information on blood concentrations. To systematically optimize the in vitro system, two novel test performance metrics were introduced, the toxicity separation index (TSI) which quantifies how well a test differentiates between hepatotoxic and non-hepatotoxic compounds, and the toxicity estimation index (TEI) which measures how well hepatotoxic blood concentrations in vivo can be estimated. In vitro test performance was optimized for a training set of 28 compounds, based on TSI and TEI, demonstrating that (1) concentrations where cytotoxicity first becomes evident in vitro (EC10) yielded better metrics than higher toxicity thresholds (EC50); (2) compound incubation for 48 h was better than 24 h, with no further improvement of TSI after 7 days incubation; (3) metrics were moderately improved by adding gene expression to the test battery; (4) evaluation of pharmacokinetic parameters demonstrated that total blood compound concentrations and the 95%-population-based percentile of Cmax were best suited to estimate human toxicity. With a support vector machine-based classifier, using EC10 and Cmax as variables, the cross-validated sensitivity, specificity and accuracy for hepatotoxicity prediction were 100, 88 and 93%, respectively. Concentrations in the culture medium allowed extrapolation to blood concentrations in vivo that are associated with a specific probability of hepatotoxicity and the corresponding oral doses were obtained by reverse modeling. Application of this in vitro/in silico method to the rat hepatotoxicant pulegone resulted in an ADI that was similar to values previously established based on animal experiments. In conclusion, the proposed method links oral doses and blood concentrations of test compounds to the probability of hepatotoxicity.