ReVac

ReVac identifies and prioritizes potential vaccine candidate proteins across bacterial pan-genomes by integrating multiple feature prediction programs and a curated scoring system.

Key Features:

• Integration of feature prediction programs: Aggregates outputs from diverse protein feature prediction tools rather than eliminating proteins based on single-tool results.
• Scoring system with curated controls: Ranks candidates using a scoring scheme based on curated positive and negative control potential vaccine candidates (PVCs).
• Pan-genome analysis: Surveys multiple genomes to evaluate conservation of proteins, DNA, and protein repeats across isolates.
• Orthologous clustering: Clusters conserved genes to distinguish core and dispensable genome components.
• Redundancy-based prediction: Combines multiple prediction tools and aggregated protein features to improve robustness of candidate rankings.
• Conservation-based prioritization: Prioritizes PVCs based on conservation status across genomes in conjunction with the feature-based scoring system.

Scientific Applications:

• PVC prioritization for bacterial subunit vaccine design: Provides ranked candidates for experimental testing based on aggregated feature scores and conservation.
• Pan-genome conservation assessment: Enables evaluation of protein, DNA, and repeat conservation across multiple pathogen isolates.
• Case studies: Applied to 69 Moraxella catarrhalis genomes and 270 non-typeable Haemophilus influenzae genomes, prioritizing 64 and 29 proteins as PVCs, respectively.

Methodology:

Integrates diverse feature prediction programs without pre-filtering proteins; computes a scoring scheme using curated positive and negative control PVC datasets; performs pan-genome analyses to assess conservation of proteins, DNA, and protein repeats; conducts orthologous clustering to separate core and dispensable genes; aggregates multiple prediction outputs to rank candidates.