Target-based de novo design of cyclic peptide binders

Abstract

Cyclic peptides have become a new focus in drug discovery due to their ability to bind challenging targets, including "undruggable" protein-protein interactions, with low toxicity. Despite their potential, general methods for de novo design of cyclic peptide ligands based on target protein structures remain limited. Here, we developed CYC_BUILDER, a reinforcement learning based fragment growing method for efficient assembly of peptide fragments and cyclization to generate diverse cyclic peptide binders for target proteins. CYC_BUILDER employs a Monte Carlo Tree Search (MCTS) framework to integrate seed fragment exploration, fragment fusion based peptide growth, structure optimization, evaluation and peptide cyclization. It supports peptide cyclization through both head-to-tail amide bond and disulfide bond formation. We first validated CYC_BUILDER on known protein-cyclic peptide complexes, demonstrating its ability to accurately re-generate cyclic peptide binders in terms of both sequences and binding poses. We then applied it to design cyclic peptide inhibitors for TNF, a key mediator in inflammation-related diseases. Among the nine experimentally tested designed peptides, four showed potent binding to TNF and inhibited its cellular activity. CYC_BUILDER provides an efficient tool for cyclic peptide drug design, offering significant potential for addressing challenging therapeutical targets.