From in silico to in vitro: discovery and validation of peptide modulators of IL-6/IL-6R signaling in rheumatoid arthritis.

BACKGROUND

Rheumatoid arthritis (RA) is a chronic autoimmune disease in which dysregulated interleukin-6 (IL-6) signaling through the IL-6 receptor (IL-6R) plays a central pathogenic role. Although monoclonal antibodies targeting this pathway are clinically effective, their use is limited by parenteral administration, high cost, and systemic immunosuppression. Peptide-based inhibitors represent a complementary strategy for modulating cytokine-receptor interactions, offering advantages in design flexibility and manufacturability. Recent advances in immunoinformatics and artificial intelligence (AI) facilitate the rational identification of peptide candidates with favorable safety profiles.

METHODS

We developed an integrated AI-assisted discovery pipeline incorporating immunoinformatics-based safety screening (toxicity, allergenicity, and antigenicity prediction), structural modeling, molecular docking with MM/GBSA rescoring, and 100-ns molecular dynamics (MD) simulations to identify IL-6R-targeting peptides. Seven candidate peptides (P01-P07) were prioritized based on predicted safety, binding energetics, and structural stability. Lead candidates were experimentally evaluated using competitive ELISA assays for IL-6/IL-6R binding and cell-based assays measuring IL-6-induced STAT3 phosphorylation.

RESULTS

Computational analyses consistently identified peptide P01 as the top-ranked candidate, exhibiting stable binding conformations, persistent hydrogen bonding at the IL-6R interface, low RMSD and RMSF values during MD simulations, and the most favorable MM/GBSA binding free energy. In vitro experiments confirmed that P01 competitively inhibited IL-6/IL-6R binding (IC50 ≈ 1.6 µM) and suppressed IL-6-induced STAT3 phosphorylation without detectable cytotoxicity. The strong concordance between computational predictions and experimental findings supports the robustness of the discovery workflow.

CONCLUSION

This study establishes a safety-focused, AI-driven peptide discovery framework integrating in silico prioritization with experimental validation. Peptide P01 represents a tractable early-stage IL-6R antagonist that provides a foundation for future structure-guided optimization and development as a complementary therapeutic modality for IL-6-driven inflammatory diseases.