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bioRxiv · 10.64898/2026.03.26.714496

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

Misra, P.·Movva, N. S. V.·Shah, R.
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Abstract

Purpose/ObjectiveThis 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. MethodsKey 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. ResultsThe top-performing peptide demonstrated favorable predicted binding ({Delta}G = -21.5 kcal/mol, Kd = 1.7 x 10-{superscript 1} M) with an average pLDDT score of 90%, a buried surface area of 3,255.5 [A]{superscript 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/ImplicationsThese 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.

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Misra, P., Movva, N. S. V., Shah, R.. 2026-03-30. Computational Development of a GluN1 Synthetic Peptide Mimetic for Neutralization of Autoantibodies in Anti-NMDAR Autoimmune Encephalitis. https://doi.org/10.64898/2026.03.26.714496

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