Rapid prediction of key residues for foldability by machine learning model enables the design of highly functional libraries with hyperstable constrained peptide scaffolds
Fig 10
Comparison of sequence and structure diversity between our method and ProteinMPNN.
A. Sequence logo plot from ten unique sequences generated by our model based on the EETI-II scaffold. B. Predicted 3D structures of the ten sequences from our model, displaying some conformation variability. C. Sequence logo plot from ten unique sequences generated by ProteinMPNN based on the EETI-II scaffold. D. Predicted 3D structures of the ten sequences from ProteinMPNN, showing limited conformation variability.