{"doi":"10.1038/s41467-024-51933-2","title":"A Foundation Model Identifies Broad-Spectrum Antimicrobial Peptides against Drug-Resistant Bacterial Infection","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 show that three AMPs (T1-2, T1-5 and T2-10) significantly reduce resistance to S. aureus compared to ciprofloxacin and are effective against skin wound infection in a female wound mouse model infected with P. aeruginosa. In summary, deepAMP expedites discovery of effective, broad-spectrum AMPs against drug-resistant bacteria. New approaches to develop antimicrobial agents are urgently needed. In this study, the authors develop a peptide language-based deep generative model to design broad-spectrum antimicrobial peptides against drug-resistant bacteria and validate promising candidates in a wound mouse model.","journal":"Nature Communications","year":2024,"id":416720,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":105,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9521,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1201968,"name":"Xuanbai Ren","orcid":null,"position":1,"is_corresponding":false},{"id":1201284,"name":"Xiaoli Luo","orcid":"0009-0001-7301-4775","position":2,"is_corresponding":false},{"id":1201969,"name":"Zhuole Wang","orcid":null,"position":3,"is_corresponding":false},{"id":215981,"name":"Zhen‐lu Li","orcid":"0000-0003-2101-8237","position":4,"is_corresponding":false},{"id":537987,"name":"Xiaoyan Luo","orcid":"0009-0006-3859-9081","position":5,"is_corresponding":false},{"id":1201285,"name":"Jun Shen","orcid":"0000-0002-9403-7140","position":6,"is_corresponding":false},{"id":605787,"name":"Yun Li","orcid":"0000-0003-4489-1143","position":7,"is_corresponding":false},{"id":1201286,"name":"Dan Yuan","orcid":"0000-0003-2831-0388","position":8,"is_corresponding":false},{"id":70301,"name":"Ruth Nussinov","orcid":"0000-0002-8115-6415","position":9,"is_corresponding":false},{"id":231859,"name":"Xiangxiang Zeng","orcid":"0000-0003-1081-7658","position":10,"is_corresponding":false},{"id":641689,"name":"Junfeng Shi","orcid":"0000-0001-7705-8866","position":11,"is_corresponding":false},{"id":69831,"name":"Feixiong Cheng","orcid":"0000-0002-1736-2847","position":12,"is_corresponding":false},{"id":842167,"name":"Tingting Li","orcid":"0000-0002-2303-6394","position":0,"is_corresponding":true}],"reference_count":35,"raw_metadata":null,"created_at":"2026-07-19T01:56:44.873714Z","pmid":"39214978","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}