Generation of Optimized Consensus Sequences for Hepatitis C virus (HCV) Envelope 2 Glycoprotein (E2) by a Modified...

Area of Science:

• Virology
• Immunology
• Bioinformatics

Background:

• Preventive Hepatitis C Virus (HCV) vaccine development is crucial for global elimination, despite direct-acting antiviral successes.
• Induction of Pangenomic neutralizing Antibodies (PnAbs) against the heterogeneous HCV Envelope 2 (E2) protein is vital for vaccine efficacy.
• Existing consensus sequence algorithms (Threshold, Majority) have limitations, including undefined residues and insensitivity to evolutionary costs.

Purpose of the Study:

• To introduce a modified algorithm for generating consensus sequences of the HCV E2 protein.
• To compare the efficacy of the new "Fitness" algorithm against existing "Majority" and "Threshold" algorithms for HCV E2 consensus sequence generation.

Main Methods:

• A modified "Majority" algorithm incorporating BLOSUM matrices was developed.
• The "Fitness" algorithm was used to generate HCV E2 consensus sequences from 1698 sequences across genotypes 1, 2, and 3.
• In silico tools were employed to compare sequences generated by "Fitness", "Majority", and "Threshold" algorithms.

Main Results:

• The "Fitness" algorithm produced completely defined, gapless HCV E2 consensus sequences for all genotypes/subtypes.
• "Fitness" considered the evolutionary cost of amino acid substitutions, a key limitation of "Majority" and "Threshold" algorithms.
• Generated "Fitness" consensus sequences exhibited superior antigenic and immunogenic properties while maintaining phylogenetic positions.

Conclusions:

• The "Fitness" algorithm effectively generates superior consensus sequences for HCV E2, suitable for a pan-genomic HCV vaccine.
• This algorithm can be applied to generate consensus sequences for other highly variable antigens from diverse pathogens.