In Silico affinity profiling differentiates consensus vaccine sequences where genetic and structural similarity fail: a case study on HCV E2 genotypic and pan-genotypic antigens

The World Health Organization’s goal to eradicate hepatitis C by 2030 remains unattainable without an effective vaccine. Consensus generation combined with layering has the potential to introduce vaccines against highly variable viruses like HCV. However, this approach yields numerous candidates that require in vivo testing. Profiling the affinity of these candidates to monoclonal antibodies via in silico methods can help reduce the number of candidates. In this study, we evaluated the differentiating power of in silico affinity profiling on two previously reported genotypic consensus sequences (NOTC1 and NOTC2) and two newly generated pan-genotypic sequences (L1-CON and L2-CON), each derived using distinct consensus generation algorithms. Structural models of these sequences were generated via homology modelling and simulated within a canonical ensemble. The resulting average structures were then used as inputs for antigen–antibody docking studies against all reported antibody structures known to bind to each epitope in the HCV E2 protein. Docking results for epitope II revealed lower binding energies for NOTC1 compared to NOTC2 ( p = 0279) and J-2a ( p = 0.0132) consistent with their reported in vivo performance. This discrimination was not reproduced in genetic distance from the common ancestor of Genotype 1 and also structural similarity between protein models and crystalographed antigen. Notably, neither sequence nor structural similarity correlated with the docking outcomes, highlighting the predictive value of the proposed affinity profiling pipeline as an initial assessment tool for vaccine candidates.