Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii.

Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug resistance. Discovering new antibiotics against A. baumannii has proven challenging through conventional screening approaches. Fortunately, machine learning methods allow for the rapid exploration of chemical space, increasing the probability of discovering new antibacterial molecules. Here we screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. We trained a neural network with this growth inhibition dataset and performed in silico predictions for structurally new molecules with activity against A. baumannii. Through this approach, we discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii. Further investigations revealed that abaucin perturbs lipoprotein trafficking through a mechanism involving LolE. Moreover, abaucin could control an A. baumannii infection in a mouse wound model. This work highlights the utility of machine learning in antibiotic discovery and describes a promising lead with targeted activity against a challenging Gram-negative pathogen.

Genetic and Chemical Screening Reveals Targets and Compounds to Potentiate Gram-Positive Antibiotics against Gram-Negative Bacteria.

Gram-negative bacteria are intrinsically resistant to a plethora of antibiotics that effectively inhibit the growth of Gram-positive bacteria. The intrinsic resistance of Gram-negative bacteria to classes of antibiotics, including rifamycins, aminocoumarins, macrolides, glycopeptides, and oxazolidinones, has largely been attributed to their lack of accumulation within cells due to poor permeability across the outer membrane, susceptibility to efflux pumps, or a combination of these factors. Due to the difficulty in discovering antibiotics that can bypass these barriers, finding targets and compounds that increase the activity of these ineffective antibiotics against Gram-negative bacteria has the potential to expand the antibiotic spectrum. In this study, we investigated the genetic determinants for resistance to rifampicin, novobiocin, erythromycin, vancomycin, and linezolid to determine potential targets of antibiotic-potentiating compounds. We subsequently performed a high-throughput screen of ∼50,000 diverse, synthetic compounds to uncover molecules that potentiate the activity of at least one of the five Gram-positive-targeting antibiotics. This led to the discovery of two membrane active compounds capable of potentiating linezolid and an inhibitor of lipid A biosynthesis capable of potentiating rifampicin and vancomycin. Furthermore, we characterized the ability of known inhibitors of lipid A biosynthesis to potentiate the activity of rifampicin against Gram-negative pathogens.

Aldehyde to Ketone Homologation Enabled by Improved Access to Thioalkyl Phosphonium Salts.

Phosphines were previously unusable as Pummerer-type nucleophiles due to competing redox chemistry with sulfoxides. Here we circumvent this problem to achieve a formal phosphine Pummerer reaction that offers thioalkyl phosphonium salts that, in turn, give rise to diverse vinyl sulfides via Wittig olefinations. Thirty vinyl sulfides are thus prepared from (alkylthioalkyl)triphenyl phosphonium salts and aldehydes. The hydrolysis of these vinyl sulfides offers an efficient and versatile two-step one-carbon homologation of aldehydes to ketones.