Computational Development of a GluN1 Synthetic Peptide Mimetic for Neutralization of Autoantibodies in Anti-NMDAR Autoimmune Encephalitis

Abstract Purpose/Objective This study aimed to design and computationally evaluate a synthetic GluN1-mimetic peptide as a decoy to bind and neutralize pathogenic autoantibodies in anti-NMDA receptor (NMDAR) encephalitis, a severe autoimmune neurological disorder affecting approximately 1.5 per million individuals annually. Methods Key GluN1 epitope residues (351–390 of the amino-terminal domain) were identified from crystallographic evidence and patient-derived antibody binding studies. Multiple peptide variants were rationally designed to mimic the antibody-binding interface. AlphaFold2 was used to predict peptide structures. Rigid-body docking simulations were conducted with HADDOCK 2.4 to model peptide–antibody complexes, and binding affinities were quantified using PRODIGY. A scrambled peptide control was included to establish docking specificity. Results The top-performing peptide demonstrated favorable predicted binding (ΔG = −21.5 kcal/mol, Kd = 1.7 × 10⁻¹⁶ M) with an average pLDDT score of 90%, a buried surface area of 3,255.5 Ų, and 18 intermolecular hydrogen bonds. Relative to the scrambled control (ΔG = −8.3 kcal/mol), the designed peptide showed substantially stronger predicted binding. Conclusion/Implications These results support the validity of an epitope-mimicry design strategy and establish a scalable computational framework for prioritizing peptide decoy candidates applicable to other antibody-mediated autoimmune disorders. Experimental validation remains necessary to confirm real-world efficacy.