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Generation of Optimized Consensus Sequences for Hepatitis C Virus (Hcv) Envelope 2 Glycoprotein (E2) by a Modified Algorithm: Implication for a Pan-Genomic Hcv Vaccine Publisher



Mohabati R1 ; Rezaei R2 ; Mohajel N1 ; Ranjbar MM3 ; Samimirad K4 ; Azadmanesh K1 ; Roohvand F1
Authors
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Authors Affiliations
  1. 1. Department of Molecular Virology, Pasteur Institute of Iran, Tehran, Iran
  2. 2. School of Biology, College of Science, University of Tehran, Tehran, Iran
  3. 3. Department of FMD Vaccine Production, Razi Vaccine and Serum Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran
  4. 4. Department of Virology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

Source: Avicenna Journal of Medical Biotechnology Published:2024


Abstract

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 Pangenomic 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. © 2024, Avicenna Journal of Medical Biotechnology.
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