PepScaf: Harnessing Machine Learning with In Vitro Selection toward De Novo Macrocyclic Peptides against IL-17C/IL-17RE Interaction

The combination of library-based screening and artificial
intelligence
(AI) has been accelerating the discovery and optimization of hit ligands.
However, the potential of AI to assist in de novo macrocyclic peptide
ligand discovery has yet to be fully explored. In this study, an integrated
AI framework called PepScaf was developed to extract the critical
scaffold relative to bioactivity based on a vast dataset from an initial
in vitro selection campaign against a model protein target, interleukin-17C
(IL-17C). Taking the generated scaffold, a focused macrocyclic peptide
library was rationally constructed to target IL-17C, yielding over
20 potent peptides that effectively inhibited IL-17C/IL-17RE interaction.
Notably, the top two peptides displayed exceptional potency with IC50 values of 1.4 nM. This approach presents a viable methodology
for more efficient macrocyclic peptide discovery, offering potential
time and cost savings. Additionally, this is also the first report
regarding the discovery of macrocyclic peptides against IL-17C/IL-17RE
interaction.