Rapid prediction of key residues for foldability by machine learning model enables the design of highly functional libraries with hyperstable constrained peptide scaffolds
Fig 5
Validation of machine-learning-predicted non-touchable residues for folding using yeast surface display.
The libraries are constructed based on an HCP scaffold and a DCP scaffold, with PDB codes of 5JI4 (blue curve) and 3Q8J (green curve), respectively. Solid lines: positive libraries with the randomization on amenable residues; dashed lines: negative libraries with randomization on “non-touchable” residues.