Rapid prediction of key residues for foldability by machine learning model enables the design of highly functional libraries with hyperstable constrained peptide scaffolds.

ABSTRACT: Peptides are an emerging modality for developing therapeutics that can either agonize or antagonize cellular pathways associated with disease, yet peptides often suffer from poor chemical and physical stability, which limits their potential. However, naturally occurring disulfide-constrained peptides (DCPs) and de novo designed Hyperstable Constrained Peptides (HCPs) exhibiting highly stable and drug-like scaffolds, making them attractive therapeutic modalities. Previously, we established a robust platform for discovering peptide therapeutics by utilizing multiple DCPs as scaffolds. However, we realized that those libraries could be further improved by considering the foldability of peptide scaffolds for library design. We hypothesized that specific sequence patterns within the peptide sca

SUBMITTER: Cai F 

PROVIDER: S-EPMC11611271 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature