<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>15(1)</volume><submitter>Li T</submitter><pubmed_abstract>Development of potent and broad-spectrum antimicrobial peptides (AMPs) could help overcome the antimicrobial resistance crisis. We develop a peptide language-based deep generative framework (deepAMP) for identifying potent, broad-spectrum AMPs. Using deepAMP to reduce antimicrobial resistance and enhance the membrane-disrupting abilities of AMPs, we identify, synthesize, and experimentally test 18 T1-AMP (Tier 1) and 11 T2-AMP (Tier 2) candidates in a two-round design and by employing cross-optimization-validation. More than 90% of the designed AMPs show a better inhibition than penetratin in both Gram-positive (i.e., S. aureus) and Gram-negative bacteria (i.e., K. pneumoniae and P. aeruginosa). T2-9 shows the strongest antibacterial activity, comparable to FDA-approved antibiotics. We sho</pubmed_abstract><journal>Nature communications</journal><pagination>7538</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11364768</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>A Foundation Model Identifies Broad-Spectrum Antimicrobial Peptides against Drug-Resistant Bacterial Infection.</pubmed_title><pmcid>PMC11364768</pmcid><pubmed_authors>Ren X</pubmed_authors><pubmed_authors>Yuan D</pubmed_authors><pubmed_authors>Li Y</pubmed_authors><pubmed_authors>Li Z</pubmed_authors><pubmed_authors>Shen J</pubmed_authors><pubmed_authors>Cheng F</pubmed_authors><pubmed_authors>Li T</pubmed_authors><pubmed_authors>Zeng X</pubmed_authors><pubmed_authors>Luo X</pubmed_authors><pubmed_authors>Shi J</pubmed_authors><pubmed_authors>Wang Z</pubmed_authors><pubmed_authors>Nussinov R</pubmed_authors></additional><is_claimable>false</is_claimable><name>A Foundation Model Identifies Broad-Spectrum Antimicrobial Peptides against Drug-Resistant Bacterial Infection.</name><description>Development of potent and broad-spectrum antimicrobial peptides (AMPs) could help overcome the antimicrobial resistance crisis. We develop a peptide language-based deep generative framework (deepAMP) for identifying potent, broad-spectrum AMPs. Using deepAMP to reduce antimicrobial resistance and enhance the membrane-disrupting abilities of AMPs, we identify, synthesize, and experimentally test 18 T1-AMP (Tier 1) and 11 T2-AMP (Tier 2) candidates in a two-round design and by employing cross-optimization-validation. More than 90% of the designed AMPs show a better inhibition than penetratin in both Gram-positive (i.e., S. aureus) and Gram-negative bacteria (i.e., K. pneumoniae and P. aeruginosa). T2-9 shows the strongest antibacterial activity, comparable to FDA-approved antibiotics. We sho</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Aug</publication><modification>2026-06-01T16:15:17.474Z</modification><creation>2025-04-06T13:44:57.69Z</creation></dates><accession>S-EPMC11364768</accession><cross_references><pubmed>39214978</pubmed><doi>10.1038/s41467-024-51933-2</doi></cross_references></HashMap>