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

Silong Zhai, Yahong Tan, Chengyun Zhang, Christopher John Hipolito, Lulu Song, Cheng Zhu, Youming Zhang, Hongliang Duan, Yizhen Yin

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)11187-11200
Number of pages14
JournalJournal of Medicinal Chemistry
Volume66
Issue number16
DOIs
Publication statusPublished - 24 Aug 2023
Externally publishedYes

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