{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Randall JR"],"funding":["NIAID NIH HHS"],"pagination":["eade0008"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9833666"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["9(2)"],"pubmed_abstract":["Peptide macrocycles are a rapidly emerging class of therapeutic, yet the design of their structure and activity remains challenging. This is especially true for those with β-hairpin structure due to weak folding properties and a propensity for aggregation. Here, we use proteomic analysis and common antimicrobial features to design a large peptide library with macrocyclic β-hairpin structure. Using an activity-driven high-throughput screen, we identify dozens of peptides killing bacteria through selective membrane disruption and analyze their biochemical features via machine learning. Active peptides contain a unique constrained structure and are highly enriched for cationic charge with arginine in their turn region. Our results provide a synthetic strategy for structured macrocyclic peptide design and discovery while also elucidating characteristics important for β-hairpin antimicrobial peptide activity."],"journal":["Science advances"],"pubmed_title":["Designing and identifying β-hairpin peptide macrocycles with antibiotic potential."],"pmcid":["PMC9833666"],"funding_grant_id":["R01 AI148419","R21 AI159203","R01 AI125337"],"pubmed_authors":["Slater SL","Randall JR","Davidson G","Mavridou DAI","Davies BW","DuPai CD","Cole TJ","Wilke CO","Groover KE"],"additional_accession":[]},"is_claimable":false,"name":"Designing and identifying β-hairpin peptide macrocycles with antibiotic potential.","description":"Peptide macrocycles are a rapidly emerging class of therapeutic, yet the design of their structure and activity remains challenging. This is especially true for those with β-hairpin structure due to weak folding properties and a propensity for aggregation. Here, we use proteomic analysis and common antimicrobial features to design a large peptide library with macrocyclic β-hairpin structure. Using an activity-driven high-throughput screen, we identify dozens of peptides killing bacteria through selective membrane disruption and analyze their biochemical features via machine learning. Active peptides contain a unique constrained structure and are highly enriched for cationic charge with arginine in their turn region. Our results provide a synthetic strategy for structured macrocyclic peptide design and discovery while also elucidating characteristics important for β-hairpin antimicrobial peptide activity.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jan","modification":"2024-12-04T00:10:40.29Z","creation":"2024-12-04T00:10:40.29Z"},"accession":"S-EPMC9833666","cross_references":{"pubmed":["36630516"],"doi":["10.1126/sciadv.ade0008"]}}