AI Article Synopsis

  • - The development of a vaccine for Hepatitis C Virus (HCV) is essential despite the effectiveness of existing treatments, particularly focusing on inducing Pangenomic neutralizing Antibodies (PnAbs) against the diverse HCV Envelope 2 protein.
  • - Current algorithms for creating Consensus Sequences (CS) face challenges such as rigidity and insensitivity to evolutionary changes, prompting researchers to modify the "Majority" algorithm with BLOSUM matrices and assess it against the "Fitness" algorithm.
  • - The "Fitness" algorithm outperformed others by producing well-defined HCVE2 sequences for all HCV genotypes, considering evolutionary factors and offering improved properties for vaccine development, suggesting its applicability for other variable pathogens as well. *

Article 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 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11589427PMC
http://dx.doi.org/10.18502/ajmb.v16i4.16743DOI Listing

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