Avicenna J Med Biotech

arij002

Avicenna Journal of Medical Biotechnology

2008-2835 2008-4625

Avicenna Research Institute

ajmb60594

Generation of Optimized Consensus Sequences for Hepatitis C virus (HCV) Envelope 2 Gly-coprotein (E2) by a Modified Algorithm: Implication for a Pan-genomic HCV Vaccine

Mohabati

Reyhaneh

Rezaei

Reza

Mohajel

Nasir

Ranjbar

Mohammad Mehdi

Samimi-Rad

Katayoun

Azadmanesh

Kayhan

Department of Immunology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran

Roohvand

Farzin

Nanobiotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran

16 4 268 278 24 4 2024 8 7 2024

Background: Despite the success of "direct-acting antivirals" in treating Hepatitis C Virus (HCV) infection, invention of a preventive HCV vaccine is crucial for global elimination of the virus. Recent data indicated the importance of the induction of Pan-genomic neutralizing Antibodies (PnAbs) against heterogenic HCV Envelope 2(E2), the cellular receptor binding antigen, by any HCV vaccine candidate. To overcome HCVE2 heterogeneity, "generation of consensus HCVE2 sequences" is proposed. However, Consensus Sequence (CS) generating algorithms such as "Threshold" and "Majority" have certain limitations including "Threshold-rigidity" which leads to induction of undefined residues and insensitivity of the "Majority" towards the "evolutionary cost of residual substitutions". Methods: Herein, first a modification to the "Majority" algorithm was introduced by incorporating BLOSUM matrices. Secondly, the HCVE2 sequences generated by the "Fitness" algorithm (using 1698 sequences from genotypes 1, 2, and 3) was compared with those generated by the "Majority" and "Threshold" algorithms using several in silico tools. Results: Results indicated that only "Fitness" provided completely defined, gapless HCVE2s for all genotypes/subtypes, while considered the evolutionary cost of amino acid replacements (main "Majority/Threshold" limitations) by substitution of several residues within the generated consensuses. Moreover, "Fitness-generated HCVE2 CSs" were superior for antigenic/immunogenic characteristics as an antigen, while their positions within the phylogenetic trees were still preserved. Conclusion: "Fitness" algorithm is capable of generating superior/optimum HCVE2 CSs for inclusion in a pan-genomic HCV vaccine and can be similarly used in CS generation for other highly variable antigens from other heterogenic pathogens.