Computational Design of Next-Gen Peptide Biopesticides: Targeting the Nicotinic Acetylcholine Receptor in Rice Pests Class

Abstract

The brown planthopper (Nilaparvata lugens) threatens global rice production, and conventional pesticides face resistance and environmental challenges. We present a computational framework integrating AlphaFold3-predicted structure of the nicotinic acetylcholine receptor 3 subunit (nAChR-3) with multi-force-field molecular dynamics (MD) refinements to design peptide biopesticides. Crucially, we introduce "Dipeptide Probing", a high-throughput MD-based screening strategy employing 20 phenylalanine-containing dipeptides (F-X) to map dynamic binding sites. Unlike rigid docking or static free energy calculations, this approach captures transient interactions and cooperative binding phenomena, identifying Phe-Met (FM) as the top binder through hydrophobic contacts and multiple hydrogen bonding, while{pi} -stacking contributed minimally to complex stability, contradicting conventional paradigms. Additionally, MD simulations revealed an unexpected Aggregation-Induced Hydrophobicity Binding (AIHB) mechanism: FM dipeptides self-assemble via hydrogen bonds, orienting hydrophilic groups toward solvent and exposing hydrophobic surfaces to the target, thereby stabilizing complex formation to the helix bundle surface of nAChR-3. This cooperative behavior, undetectable by docking (e.g., AutoDock Vina failed to predict FM binding) or static energy methods, resolves limitations of reductionist approaches. Our work establishes "Dipeptide Probing" as a generalizable paradigm for dynamic binding-site mapping and underscores AIHBs potential to revolutionize peptide-based agrochemical design by leveraging emergent intra-/intermulti-molecule interactions.