Rapid prediction of key residues for foldability by machine learning model enables the design of highly functional libraries with hyperstable constrained peptide scaffolds
Fig 6
Re-evaluation of DCP libraries reveals design insights for improved hit rates.
A. ES score distribution across various DCP scaffolds with the originally randomized regions marked in green colored regions. B. Correlation between the “unmatched scores” and the hit rates for 7 previously reported DCP scaffolds; a higher degree of mismatch corresponds to lower hit rates, emphasizing the importance of model-guided design.