Machine learning-driven discovery of highly selective antifungal peptides containing non-canonical β-amino acids.

ABSTRACT: Antimicrobial peptides (AMPs) are promising compounds for the treatment and prevention of multidrug-resistant infections because of their ability to directly disrupt microbial membranes, a mechanism that is less likely to lead to resistance compared to antibiotics. Unfortunately, natural AMPs are prone to proteolytic cleavage in vivo and have relatively low selectivity for microbial versus human cells, motivating the development of synthetic peptidomimetics of AMPs with improved peptide stability, activity, and selectivity. However, a lack of understanding of structure-activity relationships for peptidomimetics constrains development to rational design or experimental predictors, both of which are cost and time prohibitive, especially when the design space of possible sequenc

SUBMITTER: Chang DH 

PROVIDER: S-EPMC11867109 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature