Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1026995 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR26360400_1.fastq.gz | Fastqsanger.gz | |||
| SRR26360400_2.fastq.gz | Fastqsanger.gz | |||
| SRR26360401_1.fastq.gz | Fastqsanger.gz | |||
| SRR26360401_2.fastq.gz | Fastqsanger.gz | |||
| SRR26360402_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 32 |
Similar Datasets
Project description:Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state and phenotype. However, sifting through the millions of candidate variants in a personal genome or cancer genome, to identify only those variants that impact enhancer function, remains a major challenge. Prioritization of non-coding genome variation benefits from explainable AI to predict and interpret the impact of a mutation on gene regulation. Here we apply a specialized deep learning model to 10 phased melanoma genomes and identify functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression.
2021-03-03 | GSE159965 | GEO
Project description:The escalating crisis of multiresistant bacteria demands the rapid discovery of novel antibiotics that transcend the limitations imposed by the biased chemical space of current libraries. To address this challenge, we introduce an innovative deep learning-driven pipeline for de novo antibiotic design. This unique approach leverages a chemical language model, trained on a diverse chemical space encompassing drug-like molecules and natural products, coupled with transfer learning on diverse antibiotic scaffolds to efficiently generate structurally unprecedented antibiotic candidates. Through the use of predictive modeling and expert curation, we prioritized and synthesized the most promising and readily available candidates. Notably, our efforts culminated in a lead candidate demonstrating potent activity against methicillin-resistant Staphylococcus aureus. Iterative refinement through automated synthesis of 40 derivatives yielded a suite of active compounds, including 30 with activity against S. aureus and 17 against Escherichia coli. Among these, lead compound D8 exhibited remarkable submicromolar and single-digit micromolar potency against the aforementioned pathogens, respectively. Mechanistic investigations point to the generation of radical species as its primary mode of action. This work showcases the power of our innovative deep learning framework to significantly accelerate and expand the horizons of antibiotic drug discovery.
2025-10-06 | PXD066005 | Pride
Project description: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. The most potent and structurally novel compound without any obvious mechanistic liability was semapimod, an anti-inflammatory drug effective against stationary-phase E. coli and A. baumannii. Integrating microbiological assays, biochemical measurements, and single-cell microscopy, we show that semapimod selectively disrupts and permeabilizes the bacterial outer membrane by binding lipopolysaccharide. This work illustrates the value of harnessing non-traditional screening methods and deep learning models to identify non-toxic antibacterial compounds that are effective in infection-relevant contexts.
2023-08-21 | PXD044230 | Pride
Project description:Many archetypal and emerging classes of small-molecule therapeutics form covalent 38 protein adducts. In vivo, both the resulting conjugates and their off-target side conjugates have the potential to elicit antibodies, with implications for allergy and drug sequestration. Although β-lactam antibiotics are a drug class long associated with these immunological phenomena, the molecular underpinnings of off-target drug protein conjugation and consequent drug-specific immune responses remain incomplete. Here, using the classical β-lactam penicillin G (PenG) we have now probed the B and T cell determinants of drug-specific IgG responses to such conjugates in mice. Deep clonotyping reveals a dominant murine clonal antibody class encompassing phylogenetically-related IGHV1, IGHV5 and IGHV10 subgroup gene segments. Protein NMR and x-ray structural analyses reveal that these drive structurally convergent binding modes in adduct-specific antibody clones. Their common primary recognition mechanisms of the penicillin side-chain moiety (phenylacetamide in PenG)—regardless of CDRH3 length—limits cross-reactivity against other β-lactam antibiotics. This immunogenetics-guided discovery of the limited binding solutions available to antibodies against side products of an archetypal covalent inhibitor now suggests future potential strategies for the ‘germline-guided reverse engineering’ of such drugs away from unwanted immune responses.
2024-08-08 | PXD052026 | Pride
Project description:The authors use a large dataset (>30k) to train an explainable graph-based model to identify potential antibiotics with low cytotoxicity. The model uses a substructure-based approach to explore the chemical space. Using this method, they were able to screen 283 compounds and identify a candidate active against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci.
Model Type: Predictive machine learning model.
Model Relevance: The model predicts the probability of growth inhibition.
Model Encoded by: Sarima Chiorlu (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam
Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos18ie
2024-05-08 | MODEL2405080002 | BioModels
Project description:The authors use a large dataset (>30k) to train an explainable graph-based model to identify potential antibiotics with low cytotoxicity. The model uses a substructure-based approach to explore the chemical space. Using this method, they were able to screen 283 compounds and identify a candidate active against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci.
Model Type: Predictive machine learning model.
Model Relevance: Prediction of Human cytotoxicity endpoints.
Model Encoded by: Sarima Chiorlu (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam
Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos42ez
2024-05-13 | MODEL2405130002 | BioModels
Project description:A Deep Learning based Efficacy Prediction System for Drug Discovery
| PRJNA693487 | ENA
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.
2025-08-27 | GSE300731 | GEO
Project description:Genome-wide cis-decoding for expression designing in tomato using cistrome data and explainable deep learning
| PRJDB12795 | ENA