Cyclic peptides are promising therapeutic agents for challenging targets, especially protein-protein interactions. However, computationally designing cyclic peptide binders remains challenging. Here, we present CYCBUILDER, a reinforcement learning-based framework that assembles peptide fragments and performs efficient cyclization via head-to-tail amide or disulfide bonds, which uses a Monte Carlo Tree Search to guide fragment selection, peptide growth, and structure refinement. We show that CYCBUILDER was able to successfully regenerate native binding sequences and poses for known cyclic peptide-protein complexes. We have applied CYCBUILDER to generate cyclic peptide binders for TNFα and found that the design results outperformed those from AfCycDesign and Anchor Extension in binding energy, structural diversity, and efficiency. We experimentally tested the activity of nine designed peptides, and four of them demonstrated potent binding and cellular activity. CYCBUILDER offers a powerful tool for cyclic peptide discovery with broad applications in therapeutics and synthetic biology.
Wang et al. (Mon,) studied this question.