A Multi-Model Strategy for Optimizing Hepatitis B Virus preS1 Epitope Recognition by the Antibody HzKR127

Abstract

Functional cure of chronic hepatitis B virus (HBV) infection remains a significant challenge, making viral-entry-blocking antibodies a promising antiviral strategy. Here, we developed a structure-guided computational workflow for affinity-enhancing candidate mutations at the interface between the humanized neutralizing antibody HzKR127 and the HBV preS1 peptide epitope. Based on the crystal structure of the HzKR127–preS1 complex, we performed single-site saturation mutagenesis across the paratope, evaluating variants with a consensus effect score integrated from seven computational models. Benchmarking against published alanine scanning data showed that our consensus score effectively identified major-affinity-loss residues, achieving ROC AUC values of 0.81 and 0.84 for residue-level and site-level predictions, respectively. Mutational profiling revealed distinct asymmetric mutational responses, with broad intolerance on the preS1 side and localized favorable substitutions within antibody CDRs. Multilevel prioritization identified 26 antibody-side candidates, 17 of which showed improved HADDOCK refinement scores compared to the wild type. In particular, the H:D97W/F/Y substitutions presented the strongest structural rationale for enhancing improved interfacial packing through aromatic hydrophobic contacts with preS1 Phe10. These findings provide a prioritized list of candidates for experimental validation and a practical framework for the rational optimization of antibodies targeting functionally constrained viral epitopes.