Rapid prediction of key residues for foldability by machine learning model enables the design of highly functional libraries with hyperstable constrained peptide scaffolds
Fig 2
Sequences and enrichment scores of nine disulfide-rich peptides.
Generation of large datasets from YSD combined with alanine scanning libraries for eight HCP scaffolds and EETI-II scaffold. For each scaffold, both the measured and predicted ES scores were normalized such that the highest score within each scaffold is 1.