PXD067696-1

PXD067696 is an original dataset announced via ProteomeXchange.

Title	Deep learning-enabled discovery of antibiotics effective against Neisseria gonorrhoeae
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.
HostingRepository	PRIDE
AnnounceDate	2026-05-07
AnnouncementXML	Submission_2026-05-07_08:44:59.891.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Massimiliano Gaetani
SpeciesList	scientific name: Neisseria gonorrhoeae (strain ATCC 700825 / FA 1090); NCBI TaxID: NEWT:242231;
ModificationList	monohydroxylated residue; deamidated residue; iodoacetamide derivatized residue
Instrument	Orbitrap Exploris 480

Revision	Datetime	Status	ChangeLog Entry
0	2025-08-25 04:19:36	ID requested
⏵ 1	2026-05-07 08:45:00	announced

Dataset with its publication pending

submitter keyword: Deep learning, antibiotics, drug discovery, Infectious disease, Neisseria gonorrhoeae, PISA assay, Proteomics

Massimiliano Gaetani
contact affiliation	Chemical Proteomics, Division of Chemistry I, Department of Medical Biochemistry and Biophysiscs (MBB), Karolinska Institutet, Biomedicum, Solna, Sweden
contact email	massimiliano.gaetani@ki.se
lab head
Massimiliano Gaetani
contact affiliation	Karolinska Institutet
contact email	massimiliano.gaetani@ki.se
dataset submitter

Dataset FTP location NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2026/05/PXD067696

PRIDE project URI

• PRIDE
  1. PXD067696
     1. Label: PRIDE project
     2. Name: Deep learning-enabled discovery of antibiotics effective against Neisseria gonorrhoeae