Peptide News Digest

Research News

156 stories across all digests

Research coverage runs from preclinical mechanism papers to AI-driven peptide discovery. Most of what shows up here lives in Nature, Cell, Science, JAMA, and the abstracts from AACR, ESCMID, AAN, and ESCMID Global.

A few threads keep recurring. Macrocyclic and bicyclic peptides keep getting better at hitting "undruggable" targets — KRAS, beta-catenin, intracellular protein–protein interactions. Antimicrobial peptides have moved from theory to clinical candidates against carbapenem-resistant organisms and biofilms. Cancer peptide vaccines (ELI-002, autogene cevumeran, EVX-01) are producing real survival data. AI design tools — protein language models, transformer architectures, de novo platforms — are starting to generate hits that humans wouldn't.

If you want the lab side without the press releases, this is the right surface. The stories below name the lab, the journal, and the result.

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Cleveland Clinic Study: Most Patients Avoid Dramatic Weight Regain After Stopping GLP-1 Drugs

A Cleveland Clinic analysis of 7,938 adults who discontinued semaglutide or tirzepatide found that real-world outcomes are better than clinical trials suggested. Obesity patients regained just 0.5% of body weight on average after one year, and 45% either maintained or continued losing weight by restarting treatment, switching medications, or adopting lifestyle changes.

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NeurologyLive Review: GLP-1 Drugs Explored for Parkinson's, MS, and Sleep Disorders

A comprehensive NeurologyLive review examines evidence for repositioning GLP-1 drugs across neurological conditions. Exenatide and lixisenatide show motor benefits in Parkinson's disease, while GLP-1 agonists reduced intracranial pressure and migraine days in idiopathic intracranial hypertension. The semaglutide EVOKE trials in Alzheimer's failed clinically despite modest biomarker improvements.

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AI Generative Models Discover Novel Antimicrobial Peptides Against Drug-Resistant Bacteria

A study published in Nature Microbiology used a generative AI approach to discover antimicrobial peptides effective against multidrug-resistant bacteria. The deep learning pipeline screened millions of candidate sequences, achieving a 94.4% success rate in lab validation, with two candidates showing exceptional efficacy and low resistance potential.