SARS-CoV-2変異株に対する組み合わせペプチドミメティクスの計算論的洞察と影響

The SARS-CoV-2 receptor-binding domain (RBD) undergoes frequent mutations, weakening current therapeutics. Despite its importance in viral entry, most inhibitors lack resilience to antigenic drift, emphasizing the need for broad-spectrum agents that maintain binding across variants while preserving host specificity.

The authors employed computational mutagenesis to design h-ACE2-derived peptidomimetics incorporating nonstandard amino acids (NSAAs), targeting conserved RBD residues in SARS-CoV-2, Omicron, and JN.1. Approximately 10,000 NSAAs were screened at six hotspot positions, followed by molecular dynamics simulations and density functional theory (DFT) analyses to assess binding energy, electronic compatibility, and structural stability.

NSAA substitutions enhanced peptidomimetic-RBD affinity through non-canonical interactions: bromothiophenyl residues engaged in halogen bonding (distance: 3.1-3.4 Å), triazole rings formed π-stacking networks, and sulfonamide linkers stabilized hydrogen bonds (-104.5 to -113.1 kcal/mol binding free energy). Multi-mutated peptides retained native-like electrostatic profiles (<±2 kcal/mol deviation) while demonstrating structural stabilization of variant RBDs (RMSD ≤1.2 Å). Hydrogen bond profiling revealed triazole-mediated interactions as critical for cross-variant recognition, with 90% persistence across simulations.

NSAA-engineered peptidomimetics act as pan-variant RBD inhibitors, resisting antigenic drift while preserving host-binding specificity. This computational-quantum hybrid strategy offers a blueprint for resilient antiviral therapeutics against evolving pathogens.