Covariance-Based MD Simulation Analysis Pinpoints Nanobody Attraction and Repulsion Sites on SARS-CoV-2 Omicron Spike Protein

Abstract The heavily mutated receptor binding domain (RBD) of the SARS-CoV-2 Omicron Spike protein poses a challenge to the therapeutic efficacy of existing neutralizing antibodies and nanobodies. The molecular basis of their disrupted binding lies in the altered surface interactions between antibodies/nanobodies and the RBD. As such, we present a comprehensive all-atom molecular dynamics (MD) investigation of eleven distinct nanobodies (H11-H4, RE5D06, WNB2, MR17, Huo-H3, SB15, VHH-E, Ty1, NM1230, SB23, and SB45) bound to the Omicron spike RBD. Multi-microsecond all-atom MD simulations were combined with our recent practical covariance-based method analysis to map stabilizing vs. destabilizing interactions at the nanobody–RBD interfaces. This approach identified key residue contacts including hydrogen bonds, salt bridges, and hydrophobic interactions that stabilize each complex. Additionally, we identified charged repulsions and other unfavorable contacts introduced by Omicron mutations. Despite this diversity, certain RBD regions emerge as hotspots contacted by multiple nanobodies, while other interactions are unique to individual binders. Omicron-specific mutations are shown to disrupt or alter several nanobody contacts; in particular, our dynamic correlation analysis pinpoints cases of electrostatic clash (repulsive interactions) caused by residue substitutions in Omicron RBD. These destabilizing interactions correlate with reduced binding stability and help explain why some first-generation nanobodies lose efficacy against Omicron. Collectively, our results establish an integrated all atom MD and covariance analysis workflow that rapidly maps nanobody–RBD interfaces and quantifies how CDR sequence variations modulate binding energetics, insights that are critical for structure guided engineering. By pinpointing both stabilizing networks and mutation induced clash sites, the covariance method delivers a mechanistic blueprint for engineering next generation nanobodies capable of maintaining potency against ongoing SARS-CoV-2 evolution.