AI Methods for Antimicrobial Peptides: Progress and Challenges.

Antimicrobial peptides (AMPs) are promising candidates to combat multidrug-resistant pathogens. However, the high cost of extensive wet-lab screening has made AI methods for identifying and designing AMPs increasingly important, with machine learning (ML) techniques playing a crucial role. AI approaches have recently revolutionised this field by accelerating the discovery of new peptides with anti-infective activity, particularly in preclinical mouse models.

Exploiting the fitness cost of metallo-β-lactamase expression can overcome antibiotic resistance in bacterial pathogens.

Carbapenems are last-resort antibiotics for treating bacterial infections. The widespread acquisition of metallo-β-lactamases, such as VIM-2, contributes to the emergence of carbapenem-resistant pathogens, and currently, no metallo-β-lactamase inhibitors are available in the clinic. Here we show that bacteria expressing VIM-2 have impaired growth in zinc-deprived environments, including human serum and murine infection models.

Machine learning in preclinical drug discovery.

Drug-discovery and drug-development endeavors are laborious, costly and time consuming. These programs can take upward of 12 years and cost US $2.5 billion, with a failure rate of more than 90%.

Discovery of a structural class of antibiotics with explainable deep learning.

The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis. Deep learning approaches have aided in exploring chemical spaces; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics.

Discovery of antibiotics that selectively kill metabolically dormant bacteria.

There is a need to discover and develop non-toxic antibiotics that are effective against metabolically dormant bacteria, which underlie chronic infections and promote antibiotic resistance. Traditional antibiotic discovery has historically favored compounds effective against actively metabolizing cells, a property that is not predictive of efficacy in metabolically inactive contexts. Here, we combine a stationary-phase screening method with deep learning-powered virtual screens and toxicity filtering to discover compounds with lethality against metabolically dormant bacteria and favorable toxicity profiles.

Applications of machine learning in microbial natural product drug discovery.

Introduction: Natural products (NPs) are a desirable source of new therapeutics due to their structural diversity and evolutionarily optimized bioactivities. NPs and their derivatives account for roughly 70% of approved pharmaceuticals. However, the rate at which novel NPs are discovered has decreased.

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.

Antibiotic discovery in the artificial intelligence era.

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.

Reactive metabolic byproducts contribute to antibiotic lethality under anaerobic conditions.

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.

A brief guide to machine learning for antibiotic discovery.

Rising antibiotic resistance and an alarmingly lean antibiotic pipeline require the adoption of novel approaches to rapidly discover new structural and functional classes of antibiotics. Excitingly, algorithmic approaches to antibiotic discovery are sufficiently advanced to meaningfully influence the antibiotic discovery process. Indeed, once trained on high-quality datasets, contemporary machine-learning and deep-learning models can be used to perform predictions for new antibiotics across vast chemical spaces, orders of magnitude more rapidly than compounds can be screened in the laboratory.

Deep learning identifies synergistic drug combinations for treating COVID-19.

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.

Cytoplasmic condensation induced by membrane damage is associated with antibiotic lethality.

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.

Clinically relevant mutations in core metabolic genes confer antibiotic resistance.

Although metabolism plays an active role in antibiotic lethality, antibiotic resistance is generally associated with drug target modification, enzymatic inactivation, and/or transport rather than metabolic processes. Evolution experiments of rely on growth-dependent selection, which may provide a limited view of the antibiotic resistance landscape. We sequenced and analyzed adapted to representative antibiotics at increasingly heightened metabolic states.

Eradicating Bacterial Persisters with Combinations of Strongly and Weakly Metabolism-Dependent Antibiotics.

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.

A Deep Learning Approach to Antibiotic Discovery.

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.

Bacterial metabolic state more accurately predicts antibiotic lethality than growth rate.

Growth rate and metabolic state of bacteria have been separately shown to affect antibiotic efficacy. However, the two are interrelated as bacterial growth inherently imposes a metabolic burden; thus, determining individual contributions from each is challenging. Indeed, faster growth is often correlated with increased antibiotic efficacy; however, the concurrent role of metabolism in that relationship has not been well characterized.

Bacterial Metabolism and Antibiotic Efficacy.

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.

A multiplexable assay for screening antibiotic lethality against drug-tolerant bacteria.

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.

Our Evolving Understanding of the Mechanism of Quinolones.

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.

Overcoming mcr-1 mediated colistin resistance with colistin in combination with other antibiotics.

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.

Bicarbonate Alters Bacterial Susceptibility to Antibiotics by Targeting the Proton Motive Force.

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.

Bacteria Getting into Shape: Genetic Determinants of Morphology.

Perturbation of cellular processes is a prevailing approach to understanding biology. To better understand the complicated biology that defines bacterial shape, a sensitive, high-content platform was developed to detect multiple morphological defect phenotypes using microscopy. We examined morphological phenotypes across the K-12 deletion (Keio) collection at the mid-exponential growth phase, revealing 111 deletions perturbing shape.

Pentamidine sensitizes Gram-negative pathogens to antibiotics and overcomes acquired colistin resistance.

The increasing use of polymyxins 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 lipopolysaccharide.

A cell-based approach to characterize antimicrobial compounds through kinetic dose response.

The rapid spread of antibiotic resistance has created a pressing need for the development of novel drug screening platforms. Herein, we report on the use of cell-based kinetic dose response curves for small molecule characterization in antibiotic discovery efforts. Kinetically monitoring bacterial growth at sub-inhibitory concentrations of antimicrobial small molecules generates unique dose response profiles.