in silico analysis of neutralizing antibody epitopes on the hepatitis c virus surface glycoproteins

Objective: despite of antiviral drugs and successful treatment, an effective vaccine against hepatitis c virus (hcv)infection is still required. recently, bioinformatic methods same as prediction algorithms, have greatly contributed tothe use of peptides in the design of immunogenic vaccines. therefore, finding more conserved sites on the surfaceglycoproteins (e1 and e2) of hcv, as major targets to design an effective vaccine against genetically different virusesin each genotype was the goal of the study.materials and methods:in this experimental study, 100 entire sequences of e1 and e2 were retrieved from the ncbiwebsite and analyzed in terms of mutations and critical sites by bioedit 7.7.9, mega x software. furthermore, hcv-1asamples were obtained from some infected people in iran, and reverse transcriptase-polymerase chain reaction (rtpcr)assay was optimized to amplify their e1 and e2 genes. moreover, all three-dimensional structures of e1 ande2 downloaded from the pdb database were analyzed by yasara. in the next step, three interest areas of humoralimmunity in the e2 glycoprotein were evaluated. osprey3.0 protein design software was performed to increase theaffinity to neutralizing antibodies in these areas.results:we found the effective in silico binding affinity of residues in three broadly neutralizing epitopes of e2glycoprotein. first, positions that have substitution capacity were detected in these epitopes. furthermore, residuesthat have high stability for substitution in these situations were indicated. then, the mutants with the strongest affinityto neutralize antibodies were predicted. i414m, t416s, i422v, i414m-t416s, and q412n-i414m-t416s substitutionstheoretically were exhibited as mutants with the best affinity binding.conclusion:using an innovative filtration strategy, the residues of e2 epitopes which have the best in silico bindingaffinity to neutralizing antibodies were exhibited and a distinct peptide library platform was designed.