<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Randall JR</submitter><funding>NIAID NIH HHS</funding><pagination>eade0008</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9833666</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>9(2)</volume><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.</pubmed_abstract><journal>Science advances</journal><pubmed_title>Designing and identifying β-hairpin peptide macrocycles with antibiotic potential.</pubmed_title><pmcid>PMC9833666</pmcid><funding_grant_id>R01 AI148419</funding_grant_id><funding_grant_id>R21 AI159203</funding_grant_id><funding_grant_id>R01 AI125337</funding_grant_id><pubmed_authors>Slater SL</pubmed_authors><pubmed_authors>Randall JR</pubmed_authors><pubmed_authors>Davidson G</pubmed_authors><pubmed_authors>Mavridou DAI</pubmed_authors><pubmed_authors>Davies BW</pubmed_authors><pubmed_authors>DuPai CD</pubmed_authors><pubmed_authors>Cole TJ</pubmed_authors><pubmed_authors>Wilke CO</pubmed_authors><pubmed_authors>Groover KE</pubmed_authors></additional><is_claimable>false</is_claimable><name>Designing and identifying β-hairpin peptide macrocycles with antibiotic potential.</name><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.</description><dates><release>2023-01-01T00:00:00Z</release><publication>2023 Jan</publication><modification>2024-12-04T00:10:40.29Z</modification><creation>2024-12-04T00:10:40.29Z</creation></dates><accession>S-EPMC9833666</accession><cross_references><pubmed>36630516</pubmed><doi>10.1126/sciadv.ade0008</doi></cross_references></HashMap>