Misra, P.; Movva, N. S. V.; Shah, R.

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 ({Delta}G=-21.5 kcal/mol, Kd= 1.7 x 10-16) with an average pLDDT score of 90%, a buried surface area of 3,255.5 [A]2, and 18 intermolecular hydrogen bonds. Relative to the scrambled control ({Delta}G=-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. Keywords: Autoimmune Encephalitis, Antibodies, Proteins, Biochemistry