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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.

2023-02-01 | GSE214305 | GEO

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.

2023-12-07 | MSV000093598 | MassIVE

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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Machine learning guided signal enrichment for ultrasensitive plasma tumor burden monitoring

Project description:Development of a novel machine learning guided ctDNA detection platform for use in liquid biopsy detection and therapeutic monitoring of solid tumors in several clinical contexts. Included are WGS alignments from our study.

| EGAS00001007306 | EGA

Merging full-spectrum and fragment ion intensity predictions from deep learning for high quality spectral libraries

Project description:In this work, we present a deep learning based full-spectrum prediction method and demonstrate the merits of using full-spectrum predicted approaches for library searching. Our proposed model provides flexibility to accommodate post-translational modifications and fills the current gap for long peptide predictions.

2023-06-28 | PXD040721 | Pride

Liu2023 - Acinetobacter baumannii growth inhibition prediction with Deep Learning

Project description:This model is a Chemprop neural network trained with a growth inhibition dataset. Authors screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. They discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii. Model Type: Predictive machine learning model. Model Relevance: The model predicts the probability of growth inhibition of the bacteria A. Baumannii. Model Encoded by: Miquel Duran-frigola (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos3804

2024-05-28 | MODEL2405080001 | BioModels

Narrow- and Broad-Spectrum Antibiotic Use among U.S. Children.

Project description:ObjectivesTo provide updated estimates of narrow- and broad-spectrum antibiotic use among U.S. children.Data sourcesLinked nationally representative data from the 2004-2010 Medical Expenditure Panel Survey Household Component and the 2000 Decennial Census.Study designRelationships between individual-, family-, and community-level characteristics and the use of antibiotics overall and in the treatment of respiratory tract infections (RTIs) are examined using multinomial choice models.Principal findingsMore than one quarter (27.3 percent) of children used at least one antibiotic each year with 12.8 percent using broad-spectrum and 18.5 percent using narrow-spectrum antibiotics. Among children with use, more than two-thirds (68.6 percent) used antibiotics to treat RTIs. Multivariate models revealed many differences across groups in antibiotic use, overall and in the treatment of RTIs. Differential use was associated with a broad range of factors related to need (e.g., age, health status), resources (e.g., insurance status, parental income, and education), race-ethnicity, and Census region.ConclusionsDespite encouraging reports regarding the declining use of antibiotics, large differences in use associated with resources, race-ethnicity, and Census regions suggest a need for further improvement in the judicious and appropriate prescribing of antibiotics for U.S. children.

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Reduction of excessive Kcnn2 activity ameliorates learning disability in the mouse model of Fetal Alcohol Spectrum Disorders

Project description:Learning disabilities are hallmarks of congenital conditions caused by prenatal exposure to harmful agents. This is particularly true for patients suffering from Fetal Alcohol Spectrum Disorders (FASD) who exhibit a wide range of cognitive deficiencies including impaired motor skill development. While these effects have been well characterized, the molecular effects that bring about these behavioral consequences remain to be determined. We have previously found that the acute molecular responses to alcohol in the embryonic brain are stochastic, varying among neural progenitor cells. However, the pathophysiological consequences stemming from these heterogeneous responses remain unknown. Here we show that acute responses to alcohol in progenitor cells lead to altered gene expression in their descendant neurons at the single-cell level. Among the altered genes, we found that an increase of the calcium-activated potassium channel Kcnn2 in subset neurons in the motor cortex correlates with motor skill learning deficits in the mouse model of FASD. We further show that postnatal blocking of Kcnn2 improves these learning deficits. These results propose Kcnn2 blockers as a novel intervention for learning disabilities in FASD and possibly for other neurocognitive conditions.

2021-08-30 | GSE141714 | GEO

Metagenomics-Guided Pathogen Discovery in Travelers' Diarrhea

Project description:Metagenomics-Guided Pathogen Discovery in Travelers' Diarrhea

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learning

Project description:learning

| PRJEB26278 | ENA