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Deep learning-guided discovery of a narrow-spectrum antibiotic against Acinetobacter baumannii

ABSTRACT: Deep learning-guided discovery of a narrow-spectrum antibiotic against Acinetobacter baumannii

PROVIDER: PRJNA884833 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR21728520_1.fastq.gz	Fastqsanger.gz
	SRR21728520_2.fastq.gz	Fastqsanger.gz
	SRR21728521_1.fastq.gz	Fastqsanger.gz
	SRR21728521_2.fastq.gz	Fastqsanger.gz
	SRR21728522_1.fastq.gz	Fastqsanger.gz

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Deep learning-guided discovery of a narrow-spectrum antibiotic against Acinetobacter baumannii

Project description:Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug-resistance due to its robust outer membrane and its ability to acquire and retain extracellular DNA. Moreover, it can survive for prolonged durations on surfaces and is resistant to desiccation. 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 chemical matter with antibacterial activity against this burdensome pathogen. Here, we screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. We trained a deep neural network with this growth inhibition dataset and performed predictions on the Drug Repurposing Hub for structurally novel molecules with activity against A. baumannii. Through this approach, we discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii, which could overcome intrinsic and acquired resistance mechanisms in clinical isolates. Further investigations revealed that abaucin perturbs lipoprotein trafficking through a mechanism involving LolE, a functionally conserved protein that contributes to shuttling lipoproteins from the inner membrane to the outer membrane. Moreover, abaucin was able to control an A. baumannii infection in a murine wound model. Together, this work highlights the utility of machine learning in discovering new antibiotics and describes a promising lead with narrow-spectrum activity against a challenging Gram-negative pathogen.

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Discovery and AI-guided mechanistic elucidation of a novel Enterobacteriaceae-specific antibiotic

Project description:Current clinical antibiotics are largely broad-spectrum agents that promote intestinal dysbiosis and colonisation of Enterobacteriaceae, which are often drug-resistant. Indeed, dysbiosis creates an ideal niche for adherent-invasive Escherichia coli (AIEC) in patients with inflammatory bowel disease (IBD). There is an urgent and unmet need for novel narrow-spectrum and microbiome-sparing antibiotics. Here, we screened >10,000 molecules for antibacterial activity against AIEC and discovered enterololin, an antibacterial compound with targeted activity against Enterobacteriaceae species. Molecular substructure- and deep learning-guided mechanism of action investigations revealed that enterololin perturbs lipoprotein trafficking through a mechanism involving the LolCDE complex. Moreover, enterololin can suppress an AIEC infection in mouse models, while largely preserving the overall microbiome composition. This work highlights the utility of deep learning methods for predicting molecular interactions, thereby accelerating mechanism of action elucidation of novel molecules, and identifies a promising Enterobacteriaceae-specific antibacterial candidate for further development to treat challenging infections in IBD patients.

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Data files for "A novel inhibitor of P. aeruginosa folate metabolism exploits metabolic differences for narrow-spectrum antibiotic targeting"

Project description:Raw MS files for the manuscript, "A novel inhibitor of P. aeruginosa folate metabolism exploits metabolic differences for narrow-spectrum antibiotic targeting", by Chain et al.

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A novel inhibitor of P. aeruginosa folate metabolism exploits metabolic differences for narrow-spectrum antibiotic targeting

Project description:A novel inhibitor of P. aeruginosa folate metabolism exploits metabolic differences for narrow-spectrum antibiotic targeting

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Project description:The importance of synthetic microbial communities in agriculture is increasingly being recognized, but methods for constructing targeted synthetic communities using existing microbial resources remain limited. In this study, six plant beneficial bacterial strains with distinct capacities for utilizing rhizosphere resources and functions of nitrogen fixation, phosphate solubilization, indoleacetic acid synthesis, and siderophore production were selected to construct stable and multifunctional communities for promoting plant growth. Metabolic modeling revealed a negative correlation between resource utilization width and community stability: that is, narrow-spectrum resource-utilizing strains enhanced metabolic interaction potential and reduced metabolic resource overlap, thereby increasing the stability of the community. Comprehensive validation, including large-scale simulations, metabolite supplementation, cross-feeding, and in situ experiments, consistently confirmed the central roles of the narrow-spectrum resource-utilizing strains, Cellulosimicrobium cellulans E and Pseudomonas stutzeri G, which formed metabolic interaction networks via the secretion of asparagine, vitamin B12, isoleucine, and their precursors or derivatives. Two synthetic multifunctional communities, SynCom4 and SynCom5, demonstrated high stability in the tomato rhizosphere and led to an increase in plant dry weight by over 80%. Our study elucidates the link between resource utilization width and community stability, providing a rational basis for the construction of stable, multifunctional microbial communities for specific habitats.

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2026-05-15 | GSE330487 | GEO

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Project description:Neisseria gonorrhoeae is a Gram-negative, sexually transmitted pathogen that poses a major public health threat due to rapidly increasing resistance to all recommended antibiotics. Addressing this crisis requires more efficient approaches to antibiotic discovery and the replenishment of the dwindling drug development pipeline. Here, we demonstrate that deep learning models can augment high-throughput screening to identify readily available molecules with narrow-spectrum activity against multidrug-resistant N. gonorrhoeae. We phenotypically tested 38,650 small molecules for growth inhibition and used these data to train a predictive graph neural network (GNN). Benchmarking against alternative architectures, including large language models, revealed that GNNs most effectively identified active, drug-like molecules that were structurally distinct from both the training set and known antibiotics. Applying the model to ~6 million compounds in silico, we prioritized 213 for experimental testing and found that 83 (38%) inhibited N. gonorrhoeae growth. Two compounds were structurally novel, potent against all tested multidrug-resistant strains, displayed favorable selectivity indices, and were rapidly bactericidal with low frequencies of resistance. Multi-omics analyses revealed that these compounds circumvent resistance by targeting previously unexploited pathways in N. gonorrhoeae. Our findings establish a paradigm for deep learning–enabled discovery of selective antibacterial agents and provide a promising path toward addressing the urgent threat of antimicrobial resistance in N. gonorrhoeae.

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