Random Hybrid Polypeptides Regulate Immune Signaling through Promiscuous and Dynamic Interaction Landscapes

We developed a high-throughput synthesis and screening workflow combined with machine-learning-assisted optimization to identify functional compositions for immunomodulation in random hybrid polypeptides (RHPs). Using a selenium-containing polypeptide platform, we constructed a nucleoside-functionalized RHP library incorporating nucleoside, cationic, and anionic interaction motifs. Machine-learning-assisted optimization of this library identified a lead RHP, termed IDP-IMP (intrinsically disordered protein-inspired immunomodulatory polypeptide), that suppresses CpG DNA-Toll-like receptor 9 (TLR9) signaling with limited cytotoxicity. Rather than acting as a conventional strong nucleic acid scavenger or TLR9 antagonist, IDP-IMP showed moderate binding/association with both CpG and TLR9 through different side chain compositions, leading to reduced intracellular CpG-TLR9 colocalization. Across the polymer library, stronger CpG binding did not necessarily correspond to greater inhibitory activity, indicating that CpG binding alone did not predict functional activity. In a collagen-induced arthritis rat model, IDP-IMP attenuated inflammation and joint pathology without detectable systemic toxicity. These findings demonstrate a high-throughput approach for identifying statistical polymer compositions perturbing CpG-TLR9 signaling. The results highlight the importance of IDP-like weak, multivalent, and dynamic interactions in immunomodulation.