A small-molecule inhibitor of BamA impervious to efflux and the outer membrane permeability barrier.

The development of new antimicrobial drugs is a priority to combat the increasing spread of multidrug-resistant bacteria. This development is especially problematic in gram-negative bacteria due to the outer membrane (OM) permeability barrier and multidrug efflux pumps. Therefore, we screened for compounds that target essential, nonredundant, surface-exposed processes in gram-negative bacteria. We identified a compound, MRL-494, that inhibits assembly of OM proteins (OMPs) by the ß-barrel assembly machine (BAM complex). The BAM complex contains one essential surface-exposed protein, BamA. We constructed a bamA mutagenesis library, screened for resistance to MRL-494, and identified the mutation bamAE470K BamAE470K restores OMP biogenesis in the presence of MRL-494. The mutant protein has both altered conformation and activity, suggesting it could either inhibit MRL-494 binding or allow BamA to function in the presence of MRL-494. By cellular thermal shift assay (CETSA), we determined that MRL-494 stabilizes BamA and BamAE470K from thermally induced aggregation, indicating direct or proximal binding to both BamA and BamAE470K Thus, it is the altered activity of BamAE470K responsible for resistance to MRL-494. Strikingly, MRL-494 possesses a second mechanism of action that kills gram-positive organisms. In microbes lacking an OM, MRL-494 lethally disrupts the cytoplasmic membrane. We suggest that the compound cannot disrupt the cytoplasmic membrane of gram-negative bacteria because it cannot penetrate the OM. Instead, MRL-494 inhibits OMP biogenesis from outside the OM by targeting BamA. The identification of a small molecule that inhibits OMP biogenesis at the cell surface represents a distinct class of antibacterial agents.

High-Throughput Agonist Shift Assay Development for the Analysis of M1-Positive Allosteric Modulators.

Agonist shift assays feature cross-titrations of allosteric modulators and orthosteric ligands. Information generated in agonist shift assays can include a modulator's effect on the orthosteric agonist's potency (alpha) and efficacy (beta), as well as direct agonist activity of the allosteric ligand (tauB) and the intrinsic binding affinity of the modulator to the unoccupied receptor (KB). Because of the heavy resource demand and complex data handling, these allosteric parameters are determined infrequently during the course of a drug discovery program and on a relatively small subset of compounds. Automation of agonist shift assays enables this data-rich analysis to evaluate a larger number of compounds, offering the potential to differentiate compound classes earlier and prospectively prioritize based on desired molecular pharmacology. A high-throughput calcium-imaging agonist shift assay was pursued to determine the allosteric parameters of over 1000 positive allosteric modulator (PAM) molecules for the human muscarinic acetylcholine receptor 1 (M1). Control compounds were run repeatedly to demonstrate internal consistency. Comparisons between potency measurements and the allosteric parameter results demonstrate that these different types of measurements do not necessarily correlate, highlighting the importance of fully characterizing and understanding the allosteric properties of leads.

Prospective Assessment of Virtual Screening Heuristics Derived Using a Novel Fusion Score.

High-throughput screening (HTS) is a widespread method in early drug discovery for identifying promising chemical matter that modulates a target or phenotype of interest. Because HTS campaigns involve screening millions of compounds, it is often desirable to initiate screening with a subset of the full collection. Subsequently, virtual screening methods prioritize likely active compounds in the remaining collection in an iterative process. With this approach, orthogonal virtual screening methods are often applied, necessitating the prioritization of hits from different approaches. Here, we introduce a novel method of fusing these prioritizations and benchmark it prospectively on 17 screening campaigns using virtual screening methods in three descriptor spaces. We found that the fusion approach retrieves 15% to 65% more active chemical series than any single machine-learning method and that appropriately weighting contributions of similarity and machine-learning scoring techniques can increase enrichment by 1% to 19%. We also use fusion scoring to evaluate the tradeoff between screening more chemical matter initially in lieu of replicate samples to prevent false-positives and find that the former option leads to the retrieval of more active chemical series. These results represent guidelines that can increase the rate of identification of promising active compounds in future iterative screens.

Dynamic Compound-Dependent Acoustic Transfer to Investigate Inhibitor Reversibility.

Automated mechanism of action studies are introducing the need for tailored compound delivery, which can be challenging for standard compound management procedures. Jump dilution assays investigating inhibitor reversibility require compound delivery at specific volumes to assay specific concentrations of 10 × IC50 for each inhibitor. Creating custom-made source plates with unique compound concentrations to dispense a uniform single volume can be prohibitively slow. A broadly applicable tool that enables on-the fly dispensing of variable amounts of stock concentrations was developed using the Acoustic Transfer System (ATS). The Dynamic Transfer Modification Program (DTMP) is an integrated LabVIEW program used to automate customized volume transfers from each well based on compound identity within a given source plate. A jump dilution investigating the time-dependent inhibition of the enzyme dipeptidyl peptidase-4 (DPP4) with multiple inhibitors is described here to demonstrate the delivery of specific volumes of various compounds in a high-throughput manner. The ability to automate this process allows for the characterization of inhibitor reversibility earlier in the drug discovery process, resulting in better informed lead candidate selection.