2025/03/01 by Ruole Chen, Yuhao You, Yanchao Liu +4 · 1 voice
Biochemistry, Genetics and Molecular Biology · Immunology and Microbiology · #Antimicrobial Peptides and Activities #Biochemical and Structural Characterization #Chemical Synthesis and Analysis
paper · pdf · doi:10.1111/1751-7915.70121
openalex publication_date 2025/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Antimicrobial peptides (AMPs) face stability and toxicity challenges in clinical use. Stapled modification enhances their stability and effectiveness, but its application in peptide design is rarely reported. This study built ten prediction models for stapled AMPs using deep and machine learning, tested their accuracy with an independent data set and wet lab experiments, and characterised stapled loop structures using structural, sequence and amino acid descriptors. AlphaFold improved stapled peptide structure prediction. The support vector machine model performed best, while two deep learning models achieved the highest accuracy of 1.0 on an external test set. Designed cysteine- and lysine-stapled peptides inhibited various bacteria with low concentrations and showed good serum stability and low haemolytic activity. This study highlights the potential of the deep learning method in peptide modification and design.