HCV genotyping and rational computational designing of an immunogenic multiepitope vaccine against genotype 3a

Pakistan currently holds the second-highest prevalence rate of Hepatitis C virus (HCV) globally. It makes it crucial to continuously monitor the circulating genotypes in the population, especially among the people who inject drugs (PWIDs), as they pose a significant risk of spreading new genotypes in the population. To address this issue, we identified the circulating HCV genotypes among PWIDs and non-PWIDs through Next Generation Sequencing (NGS). Additionally, a multi-epitope vaccine was designed through an immunoinformatic approach using NGS and Sanger sequencing results. The study indicated genotype 3a as the most prevalent genotype among the 61 HCV cases tested through NGS, followed by genotype 1a. The non-allergic and highly antigenic epitopes from both MHC Class-I and Class-II epitopes were retreived from non-structural proteins. Furthermore, B-cell epitopes were retrieved from the E2 protein. The selected epitopes showed 88.26% population coverage rate. Based on large conformational simulation analysis from NMSims, four best constructs suitable for vaccine design were further evaluated for their binding energies through all-atom molecular dynamics simulations and the MMGBSA. One of the constructs showed a low binding energy value with MHC, indicating its potential as a vaccine candidate. However, further experimental work is required to determine its efficacy and safety profile. This research emphasizes the promise of combining multiepitope vaccine design advanced computational methods to accelerate and improve vaccine development thereby filling a crucial gap in the fight against rising antibiotic resistance.