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The medium was supplemented with IsoVitalex 1% v/V final (BD 211876). The bacteria were cultured at 37°C with 5% CO2 and grown to an OD600 of approximately 0.3. Both bacterial cultures and lysates were treated in four replicates with 10 µM of each antibiotic for 20 min at 37°C.  For each condition, 20 mL of bacterial culture was used (4 conditions × 4 replicates × 2 antibiotics). The bacteria cells were washed with 37°C PBS. For performing PISA assay in lysate, pelleted bacteria were resuspended in 2 mL lysis buffer (PBS + Halt protease inhibitor) in Eppendorf tubes. The bacteria were subjected to five freeze-thaw cycles in liquid nitrogen to extract proteins. Then the samples were centrifuged at 7000g for 5 min at RT to remove debris. The supernatant was then collected and treated with the antibiotic or DMSO for 20 min at 37°C. Each replicate was then aliquoted into 12-wells in a 96-well PCR plate (40 µL per well). The samples were then exposed to a temperature gradient from 46-70°C in the Biometra T-GRADIENT thermocycler for 3 min followed by an additional 3 min at RT and then snap frozen. The 12 samples for each replicate were then pooled into a single tube, transferred to polypropylene ultracentrifuge tubes and centrifuged at 100,000g for 20 min at 4°C. The supernatant was collected for proteomics analysis.  For performing PISA assay in living bacteria, pelleted bacteria were resuspended in 2 mL warm PBS and treated with antibiotic or DMSO for 20 min at 37°C. Each replicate was then aliquoted into 12-wells in a 96-well PCR plate and heated for 3 min, kept at RT for 3 min and then snap frozen. The samples were freeze thawed for 2 more cycles, and then ultracentrifuged like above.  Thirty µg protein from each sample was reduced with 10 mM DTT for 45 min, alkylated with 50 mM IAA in the dark for 45 min and subjected to methanol chloroform precipitation. Samples were resuspended in 20 mM EPPS buffer, 8 M urea, pH 8.0, and then diluted down to 1.6 M urea and digested with trypsin (1:60 trypsin:protein). Each digest was labeled using TMTpro 16-plex reagents (Thermo Fischer), and the samples were pooled and cleaned using a Sep-Pack C18 column (Waters).  NanoLC-MS/MS analysis. The pooled sample was fractionated off-line by capillary reversed phase chromatography at pH 10 into 96 fractions concatenated into 12 final fractions and dried. NanoLC-MS/MS analyses were performed on an Orbitrap Exploris 480 mass spectrometer (Thermo Fisher Scientific) equipped with an EASY-Spray source and Ultimate 3000 UPLC. Each fraction was speed-vacuum dried, then resuspended in 0.1% FA, 2% ACN buffer and analyzed by nanoLC-MS/MS, as previously described. Samples were desalted on a PepMap C18 trap column and separated on a 50 cm EASY-Spray C18 column (300 nL/min, 55 °C) using a binary solvent system (0.1% FA, 2% ACN in solvent A; 98% ACN, 0.1% FA in solvent B). Elution used a 3–26% B gradient over 97 min and 26–95% over 9 min, followed by a 5 min wash and re-equilibration. MS data were acquired in data-dependent mode with a 45 s dynamic exclusion, full MS scans (m/z 375–1500) at 120,000 resolution, followed by HCD MS/MS (33% NCE) on the top precursors (charge 2+–6+) detected at 45,000 resolution."],"repository":["Pride"],"quantification_method":[""],"modification":[""],"data_protocol":["Proteome Discoverer 3.2 software (Thermo Scientific) was utilized for the database search and quantification against the UniProt N. gonorrhoeae (UP000000535; 2106 entries) protein database, downloaded on 28 February 2025. Cysteine carbamidomethylation was set as a fixed modification, along with TMT-related modifications, and methionine oxidation as variable modifications. Enzyme specificity was defined as trypsin with a maximum of two missed cleavages. A 1% false discovery rate was employed as a filter at both the protein and peptide levels. Contaminants were removed, and proteins with missing values were eliminated. Only proteins with at least two unique peptides were included in the quantitative analysis. The quantified abundance of each protein in each sample (each TMT channel) was normalized to the total intensity of all proteins in that sample. The average ratio across replicates of each compound compared to the vehicle control was calculated, and the Log2 values of these ratios were determined. A two-tailed student’s t-test was employed to calculate the p-value."],"omics_type":["Proteomics"],"labhead":["Massimiliano Gaetani"],"instrument_platform":[""],"submission_type":["PARTIAL"],"labhead_affiliation":["Chemical Proteomics, Division of Chemistry I, Department of Medical Biochemistry and Biophysiscs (MBB), Karolinska Institutet, Biomedicum, Solna, Sweden"],"species":["Neisseria Gonorrhoeae (strain Atcc 700825 / Fa 1090)"],"publication":["Not available"],"submitter_mail":["massimiliano.gaetani@ki.se"],"submitter_affiliation":["Karolinska Institutet"],"submitter_country":["Sweden"],"additional_accession":[]},"is_claimable":false,"name":"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. 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