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As the global burden of antibiotic resistance continues to grow, creative approaches to antibiotic discovery are needed to accelerate the development of novel medicines. A rapidly progressing computational revolution-artificial intelligence-offers an optimistic path forward due to its ability to alleviate bottlenecks in the antibiotic discovery pipeline. In this review, we discuss how advancements in artificial intelligence are reinvigorating the adoption of past antibiotic discovery models-namely natural product exploration and small molecule screening. We then explore the application of contemporary machine learning approaches to emerging areas of antibiotic discovery, including antibacterial systems biology, drug combination development, antimicrobial peptide discovery, and mechanism of action prediction. Lastly, we propose a call to action for open access of high-quality screening datasets and interdisciplinary collaboration to accelerate the rate at which machine learning models can be trained and new antibiotic drugs can be developed.

Bactericidal antibiotics kill bacteria by perturbing various cellular targets and processes. Disruption of the primary antibiotic-binding partner induces a cascade of molecular events, leading to overproduction of reactive metabolic by-products. It remains unclear, however, how these molecular events contribute to bacterial cell death. Here, we take a single-cell physical biology approach to probe antibiotic function. We show that aminoglycosides and fluoroquinolones induce cytoplasmic condensation through membrane damage and subsequent outflow of cytoplasmic contents as part of their lethality. A quantitative model of membrane damage and cytoplasmic leakage indicates that a small number of nanometer-scale membrane defects in a single bacterium can give rise to the cellular-scale phenotype of cytoplasmic condensation. Furthermore, cytoplasmic condensation is associated with the accumulation of reactive metabolic by-products and lipid peroxidation, and pretreatment of cells with the antioxidant glutathione attenuates cytoplasmic condensation and cell death. Our work expands our understanding of the downstream molecular events that are associated with antibiotic lethality, revealing cytoplasmic condensation as a phenotypic feature of antibiotic-induced bacterial cell death., The detailed mechanisms of action of bactericidal antibiotics remain unclear. Here, Wong et al. show that these antibiotics induce cytoplasmic condensation through membrane damage and outflow of cytoplasmic contents, as well as accumulation of reactive metabolic by-products and lipid peroxidation, as part of their lethality.

Significance COVID-19 has caused more than 2.5 million deaths worldwide. It is imperative that we develop therapies that can mitigate the effect of the disease. While searching for individual drugs for this purpose has been met with difficulties, synergistic drug combinations offer a promising alternative. However, the lack of high-quality training data pertaining to drug combinations makes it challenging to use existing machine learning methods for effective novel combination prediction tasks. Our proposed approach addresses this challenge by leveraging additional readily available data, such as drug−target interactions, thus enabling an effective in silico search for synergistic combinations against SARS-CoV-2., Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxicity. Recent approaches have applied deep learning to identify synergistic drug combinations for diseases with vast preexisting datasets, but these are not applicable to new diseases with limited combination data, such as COVID-19. Given that drug synergy often occurs through inhibition of discrete biological targets, here we propose a neural network architecture that jointly learns drug−target interaction and drug−drug synergy. The model consists of two parts: a drug−target interaction module and a target−disease association module. This design enables the model to utilize drug−target interaction data and single-agent antiviral activity data, in addition to available drug−drug combination datasets, which may be small in nature. By incorporating additional biological information, our model performs significantly better in synergy prediction accuracy than previous methods with limited drug combination training data. We empirically validated our model predictions and discovered two drug combinations, remdesivir and reserpine as well as remdesivir and IQ-1S, which display strong antiviral SARS-CoV-2 synergy in vitro. Our approach, which was applied here to address the urgent threat of COVID-19, can be readily extended to other diseases for which a dearth of chemical−chemical combination data exists.

Understanding how bactericidal antibiotics kill bacteria remains an open question. Previous work has proposed that primary drug-target corruption leads to increased energetic demands, resulting in the generation of reactive metabolic byproducts (RMBs), particularly reactive oxygen species, that contribute to antibiotic-induced cell death. Studies have challenged this hypothesis by pointing to antibiotic lethality under anaerobic conditions. Here, we show that treatment of Escherichia coli with bactericidal antibiotics under anaerobic conditions leads to changes in the intracellular concentrations of central carbon metabolites, as well as the production of RMBs, particularly reactive electrophilic species (RES). We show that antibiotic treatment results in DNA double-strand breaks and membrane damage and demonstrate that antibiotic lethality under anaerobic conditions can be decreased by RMB scavengers, which reduce RES accumulation and mitigate associated macromolecular damage. This work indicates that RMBs, generated in response to antibiotic-induced energetic demands, contribute in part to antibiotic lethality under anaerobic conditions.

Summary Antibiotics target energy-consuming processes. As such, perturbations to bacterial metabolic homeostasis are significant consequences of treatment. Here, we describe three postulates that collectively define antibiotic efficacy in the context of bacterial metabolism: (1) antibiotics alter the metabolic state of bacteria, which contributes to the resulting death or stasis; (2) the metabolic state of bacteria influences their susceptibility to antibiotics; and (3) antibiotic efficacy can be enhanced by altering the metabolic state of bacteria. Altogether, we aim to emphasize the close relationship between bacterial metabolism and antibiotic efficacy as well as propose areas of exploration to develop novel antibiotics that optimally exploit bacterial metabolic networks., The metabolic state of bacteria significantly contributes to the efficacy of antibiotics. In this Perspective, Stokes et al. highlight the close relationship between bacterial cell metabolism and antibiotic efficacy, leveraging prior observations to describe areas for further exploration, with the goal of developing next-generation antibiotics that can optimally exploit the complex metabolic networks of bacteria.

The vast majority of bactericidal antibiotics display poor efficacy against bacterial persisters, cells that are in a metabolically repressed state. Molecules that retain their bactericidal functions against such bacteria often display toxicity to human cells, which limits treatment options for infections caused by persisters. Here, we leverage insight into metabolism-dependent bactericidal antibiotic efficacy to design antibiotic combinations that sterilize both metabolically active and persister cells, while minimizing the antibiotic concentrations required. These rationally designed antibiotic combinations have the potential to improve treatments for chronic and recurrent infections.

Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub—halicin—that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules. A trained deep neural network predicts antibiotic activity in molecules that are structurally different from known antibiotics, among which Halicin exhibits efficacy against broad-spectrum bacterial infections in mice., Defence Threat Reduction Agency (Grant HDTRA1-15- 1-0051)

The antibacterial properties of sodium bicarbonate have been known for years, yet the molecular understanding of its mechanism of action is still lacking. Utilizing chemical-chemical combinations, we first explored the effect of bicarbonate on the activity of conventional antibiotics to infer on the mechanism. Remarkably, the activity of 8 classes of antibiotics differed in the presence of this ubiquitous buffer. These interactions and a study of mechanism of action revealed that, at physiological concentrations, bicarbonate is a selective dissipater of the pH gradient of the proton motive force across the cytoplasmic membrane of both Gram-negative and Gram-positive bacteria. Further, while components that make up innate immunity have been extensively studied, a link to bicarbonate, the dominant buffer in the extracellular fluid, has never been made. Here, we also explored the effects of bicarbonate on components of innate immunity. Although the immune response and the buffering system have distinct functions in the body, we posit there is interplay between these, as the antimicrobial properties of several components of innate immunity were enhanced by a physiological concentration of bicarbonate. Our findings implicate bicarbonate as an overlooked potentiator of host immunity in the defense against pathogens. Overall, the unique mechanism of action of bicarbonate has far-reaching and predictable effects on the activity of innate immune components and antibiotics. We conclude that bicarbonate has remarkable power as an antibiotic adjuvant and suggest that there is great potential to exploit this activity in the discovery and development of new antibacterial drugs by leveraging testing paradigms that better reflect the physiological concentration of bicarbonate.

The many roads to resistance Antibiotic resistance arising from mutation is common among pathogenic bacteria. However, this process is not well understood, and most of the mutations that have been identified to confer resistance do so by modification of the intracellular target or enzymes that can disable the antibacterial compound within the cell. Screening for the evolution of resistance at different temperatures, Lopatkin et al. found that mutations that affect microbial metabolism can result in antibiotic resistance (see the Perspective by Zampieri). These mutations targeted central carbon and energy metabolism and revealed novel resistance mutations in core metabolic genes, expanding the known means by which pathogenic microbes can evolve resistance. Science , this issue p. eaba0862 ; see also p. 783

Antibiotic screens typically rely on growth inhibition to characterize compound bioactivity-an approach that cannot be used to assess the bactericidal activity of antibiotics against bacteria in drug-tolerant states. To address this limitation, we developed a multiplexed assay that uses metabolism-sensitive staining to report on the killing of antibiotic-tolerant bacteria. This method can be used with diverse bacterial species and applied to genome-scale investigations to identify therapeutic targets against tolerant pathogens.

Growth rate and metabolic state of bacteria have been separately shown to affect antibiotic efficacy1–3. However, the two are interrelated as bacterial growth inherently imposes a metabolic burden4; thus, determining individual contributions from each is challenging5,6. Indeed, faster growth is often correlated with increased antibiotic efficacy7,8; however, the concurrent role of metabolism in that relationship has not been well characterized. As a result, a clear understanding of the interdependence between growth and metabolism, and their implications for antibiotic efficacy, are lacking9. Here, we measured growth and metabolism in parallel across a broad range of coupled and uncoupled conditions to determine their relative contribution to antibiotic lethality. We show that when growth and metabolism are uncoupled, antibiotic lethality uniformly depends on the bacterial metabolic state at the time of treatment, rather than growth rate. We further reveal a critical metabolic threshold below which antibiotic lethality is negligible. These findings were general for a wide range of conditions, including nine representative bactericidal drugs and a diverse range of Gram-positive and Gram-negative species (Escherichia coli, Acinetobacter baumannii and Staphylococcus aureus). This study provides a cohesive metabolic-dependent basis for antibiotic-mediated cell death, with implications for current treatment strategies and future drug development. Metabolic state and ATP levels are better predictors of antibiotic lethality across diverse bacterial species than growth rate.

A poor understanding of the mechanisms by which antibiotics traverse the outer membrane remains a considerable obstacle to the development of novel Gram-negative antibiotics. Herein, we demonstrate that the Gram-negative bacterium Escherichia coli becomes susceptible to the narrow-spectrum antibiotic vancomycin during growth at low temperatures. Heterologous expression of an Enterococcus vanHBX vancomycin resistance cluster in E. coli confirmed that the mechanism of action was through inhibition of peptidoglycan biosynthesis. To understand the nature of vancomycin permeability, we screened for strains of E. coli that displayed resistance to vancomycin at low temperature. Surprisingly, we observed that mutations in outer membrane biosynthesis suppressed vancomycin activity. Subsequent chemical analysis of lipopolysaccharide from vancomycin-sensitive and -resistant strains confirmed that suppression was correlated with truncations in the core oligosaccharide of lipopolysaccharide. These unexpected observations challenge the current understanding of outer membrane permeability, and provide new chemical insights into the susceptibility of E. coli to glycopeptide antibiotics.

The maintenance of DNA supercoiling is essential for the proper regulation of a plethora of biological processes. As a consequence of this mode of regulation, ahead of the replication fork, DNA replication machinery is prone to introducing supercoiled regions into the DNA double helix. Resolution of DNA supercoiling is essential to maintain DNA replication rates that are amenable to life. This resolution is handled by evolutionarily conserved enzymes known as topoisomerases. The activity of topoisomerases is essential, and therefore constitutes a prime candidate for targeting by antibiotics. In this review, we present hallmark investigations describing the mode of action of quinolones, one of the antibacterial classes targeting the function of topoisomerases in bacteria. By chronologically analyzing data gathered on the mode of action of this imperative antibiotic class, we highlight the necessity to look beyond primary drug-target interactions towards thoroughly understanding the mechanism of quinolones at the level of the cell. Keywords: antibiotics; quinolones; topoisomerases; DNA replication; DNA supercoiling

Plasmid-borne colistin resistance mediated by mcr-1 may contribute to the dissemination of pan-resistant Gram-negative bacteria. Here, we show that mcr-1 confers resistance to colistin-induced lysis and bacterial cell death, but provides minimal protection from the ability of colistin to disrupt the Gram-negative outer membrane. Indeed, for colistin-resistant strains of Enterobacteriaceae expressing plasmid-borne mcr-1, clinically relevant concentrations of colistin potentiate the action of antibiotics that, by themselves, are not active against Gram-negative bacteria. The result is that several antibiotics, in combination with colistin, display growth-inhibition at levels below their corresponding clinical breakpoints. Furthermore, colistin and clarithromycin combination therapy displays efficacy against mcr-1-positive Klebsiella pneumoniae in murine thigh and bacteremia infection models at clinically relevant doses. Altogether, these data suggest that the use of colistin in combination with antibiotics that are typically active against Gram-positive bacteria poses a viable therapeutic alternative for highly drug-resistant Gram-negative pathogens expressing mcr-1., The plasmid-borne mcr-1 gene confers resistance to the antibiotic colistin. Here, MacNair et al. show that mcr-1 positive bacteria are however susceptible to colistin-mediated disruption of the outer membrane, and can be killed in vitro and in vivo by combining colistin with other antibiotics.

The increasing use of polymyxins1 in addition to the dissemination of plasmid-borne colistin resistance threatens to cause a serious breach in our last line of defence against multidrug-resistant Gram-negative pathogens, and heralds the emergence of truly pan-resistant infections. Colistin resistance often arises through covalent modification of lipid A with cationic residues such as phosphoethanolamine-as is mediated by Mcr-1 (ref. 2)-which reduce the affinity of polymyxins for lipopolysaccharide3. Thus, new strategies are needed to address the rapidly diminishing number of treatment options for Gram-negative infections4. The difficulty in eradicating Gram-negative bacteria is largely due to their highly impermeable outer membrane, which serves as a barrier to many otherwise effective antibiotics5. Here, we describe an unconventional screening platform designed to enrich for non-lethal, outer-membrane-active compounds with potential as adjuvants for conventional antibiotics. This approach identified the antiprotozoal drug pentamidine6 as an effective perturbant of the Gram-negative outer membrane through its interaction with lipopolysaccharide. Pentamidine displayed synergy with antibiotics typically restricted to Gram-positive bacteria, yielding effective drug combinations with activity against a wide range of Gram-negative pathogens in vitro, and against systemic Acinetobacter baumannii infections in mice. Notably, the adjuvant activity of pentamidine persisted in polymyxin-resistant bacteria in vitro and in vivo. Overall, pentamidine and its structural analogues represent unexploited molecules for the treatment of Gram-negative infections, particularly those having acquired polymyxin resistance determinants.

Small-molecule inhibitors of protein biosynthesis have been instrumental in the dissection of the complexities of ribosome structure and function. Ribosome biogenesis, on the other hand, is a complex and largely enigmatic process for which there is a paucity of chemical probes. Indeed, ribosome biogenesis has been studied almost exclusively using genetic and biochemical approaches without the benefit of small-molecule inhibitors of this process. Here, we provide a perspective on the promise of chemical inhibitors of ribosome assembly for future research. We explore key obstacles that complicate the interpretation of studies aimed at perturbing ribosome biogenesis in vivo using genetic methods, and we argue that chemical inhibitors are especially powerful because they can be used to induce perturbations in a manner that obviates these difficulties. Thus, in combination with leading-edge biochemical and structural methods, chemical probes offer unique advantages toward elucidating the molecular events that define the assembly of ribosomes.

The development of antibacterial compounds that perturb novel processes is an imperative in the challenge presented by widespread antibiotic resistance. While many antibiotics target the ribosome, molecules that inhibit ribosome assembly have yet to be used in this manner. Here we show that a novel inhibitor of ribosome biogenesis, lamotrigine, is capable of rescuing Caenorhabditis elegans from an established Salmonella infection, revealing that ribosome biogenesis is a promising target for the development of new antibiotics.

While small molecule inhibitors of the bacterial ribosome have been instrumental in understanding protein translation, no such probes exist to study ribosome biogenesis. We screened a diverse chemical collection that included previously approved drugs for compounds that induced cold sensitive growth inhibition in the model bacterium Escherichia coli. Among the most cold sensitive was lamotrigine, an anticonvulsant drug. Lamotrigine treatment resulted in the rapid accumulation of immature 30S and 50S ribosomal subunits at 15°C. Importantly, this was not the result of translation inhibition, as lamotrigine was incapable of perturbing protein synthesis in vivo or in vitro. Spontaneous suppressor mutations blocking lamotrigine activity mapped solely to the poorly characterized domain II of translation initiation factor IF2 and prevented the binding of lamotrigine to IF2 in vitro. This work establishes lamotrigine as a widely available chemical probe of bacterial ribosome biogenesis and suggests a role for E. coli IF2 in ribosome assembly. DOI: http://dx.doi.org/10.7554/eLife.03574.001, eLife digest Inside cells, molecular machines called ribosomes make proteins from instructions that are provided by genes. The ribosomes themselves are made up of about 50 proteins and three RNA molecules that need to be assembled like a 3-D jigsaw. In bacteria, a group of proteins called ribosome biogenesis factors help to assemble these pieces correctly. To study how a biological process works, scientists often look at what happens when a component is missing or not working properly. However, this approach cannot be used to study how ribosomes are made because stopping protein production entirely will kill the cell. Another approach is to use chemicals to temporarily stop or slow down a biological process, but researchers are yet to find a chemical that can do this for ribosome assembly. To address this problem, Stokes et al. ‘screened’ 30,000 chemicals in an effort to find one or more that could affect ribosome assembly in bacteria. The screen revealed that a drug called lamotrigine—which is used to treat epilepsy and other conditions in humans—could stop the assembly of ribosomes, but did not affect the production of proteins by completed ribosomes. The experiments also suggest that initiation factor 2, a protein that is involved in the production of other proteins, may also have a role in ribosome assembly. Another recent study found that the equivalent of initiation factor 2 in yeast acts as a quality control checkpoint during ribosome assembly, so the bacterial version may also perform a similar role. It is also be possible that lamotrigine might be used to help develop a novel mechanistic class of antibiotics. DOI: http://dx.doi.org/10.7554/eLife.03574.002