Enterprise AI Analysis
AntiPan: a genome-informed in silico pipeline for advancing subunit vaccine discovery against Staphylococcus aureus
This study introduces AntiPan, an enhanced in silico pipeline for identifying high-potential protein antigens for subunit vaccine design. AntiPan integrates five modules: pan-genome analysis, reverse vaccinology filters, protein assessment, immunoinformatics, and Toll-like receptors binding evaluation, while accounting for genomic diversity and immune evasion mechanisms. Using the genome of S. aureus isolated in Egypt, AntiPan identified 29 protective antigen candidates (PACs) implicated in host invasion, nutrient acquisition, and immune evasion. Ten PACs were shortlisted for future experimental validation, including IsdC, EbpS, SspB, EssA, TagH, SirA, EsxA, AmiA, HlgC, and HlgB. Molecular docking demonstrated that IsdC, AmiA, and TagH bind strongly and complementarily to the TLR1/TLR2 and TLR4/MD2 complexes, making them top vaccine candidates. Molecular dynamics simulations and MHC-epitope docking results further confirmed the immunogenicity potential of the top-ranked PACs. AntiPan is a command-line tool that provides an accessible, reproducible, and scalable platform for discovering bacterial vaccine targets. It applies to multidrug-resistant pathogens and is publicly available at: https://github.com/ComputationaBiologyLab/AntiPan.
Key Enterprise Impact Metrics
Our AI-driven analysis quantifies the direct impact this research can have on your operations and strategic goals.
0
Protective Antigen Candidates (PACs) Identified
0
Top-Ranked PACs for Validation
0
Global MHC-I Population Coverage (%)
0
Global MHC-II Population Coverage (%)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
This research details an advanced in silico pipeline for identifying high-potential protein antigens for subunit vaccine design against multidrug-resistant Staphylococcus aureus, integrating pan-genome analysis, reverse vaccinology, protein assessment, immunoinformatics, and TLR binding evaluation. It highlights a multi-antigen approach to overcome previous vaccine failures.
29 PACs
Identified Protective Antigen Candidates
AntiPan successfully identified 29 high-potential protective antigen candidates (PACs) for subunit vaccine design against Staphylococcus aureus, demonstrating a robust initial screening.
Enterprise Process Flow
Pan-genome Analysis
→
Reverse Vaccinology Filters
→
Protein Assessment
→
Immunoinformatics
→
TLR Binding Evaluation
→
Candidate PACs Identification
The AntiPan pipeline integrates several advanced modules for systematic antigen discovery, ensuring comprehensive evaluation from genomic diversity to immune interactions.
| Feature | AntiPan | Typical RV Pipeline |
|---|---|---|
| Pan-genome Analysis |
|
|
| Immune Evasion Mechanisms |
|
|
| Toxicity & Allergenicity Screening |
|
|
| Epitope Prediction Tools |
|
|
| Immune Response Focus |
|
|
| Target Pathogens |
|
|
AntiPan offers significant enhancements over traditional reverse vaccinology pipelines by incorporating advanced screening and immune interaction analyses.
Case Study: Staphylococcus aureus Vaccine Development
The Staphylococcus aureus genome analysis identified 29 protective antigen candidates (PACs), with 10 shortlisted for experimental validation. Key candidates like IsdC, AmiA, and TagH showed strong and complementary binding to TLR1/TLR2 and TLR4/MD2 complexes, crucial for innate immune activation. Molecular dynamics simulations further confirmed the stability and immunogenicity of these top PACs, providing a robust foundation for next-generation subunit vaccines against this multidrug-resistant pathogen. This targeted approach, focusing on local Egyptian isolates, addresses regional epidemiological needs while laying groundwork for global applicability.
Advanced ROI Calculator
Estimate the potential return on investment for integrating these AI-driven insights into your enterprise.
Estimated Annual Savings
$0
Hours Reclaimed Annually
0
Implementation Roadmap
A phased approach to integrate the findings into your enterprise, ensuring a smooth transition and measurable impact.
Phase 1: Genomic Data Integration & Pan-genome Analysis
Integrate existing and new S. aureus genomic data from diverse clinical settings. Perform pan-genome analysis to identify core, soft-core, shell, and cloud genes, establishing the foundational dataset for antigen discovery.
Phase 2: Reverse Vaccinology Filtering & Protein Assessment
Apply a multi-stage filtering process based on subcellular localization, virulence factors, homology to human/microbiota, transmembrane helices, molecular weight, antigenicity, and allergenicity. Conduct toxicity and essentiality assessments for shortlisted proteins.
Phase 3: Immunoinformatics & Epitope Prediction
Perform comprehensive B-cell and T-cell epitope mapping, including MHC-I and MHC-II binding predictions. Identify consensus and overlapping epitopes, and assess population coverage for broad immune response.
Phase 4: TLR Binding Evaluation & Candidate Prioritization
Conduct molecular docking simulations to evaluate the binding affinity of top PACs to human Toll-like Receptors (TLR1/TLR2 and TLR4/MD-2). Prioritize candidates based on immunogenicity and immune activation potential for experimental validation.
Phase 5: Experimental Validation & Multi-antigen Vaccine Design
Validate top-ranked PACs through recombinant protein expression, in vitro immunogenicity assays, and in vivo challenge models. Utilize validated PACs to design multi-antigen subunit vaccine constructs for broad-spectrum protection.
Ready to Advance Your Vaccine Discovery Pipeline?
Leverage AntiPan's cutting-edge in silico capabilities to accelerate your research into multidrug-resistant pathogens. Book a session to explore how our AI-driven insights can transform your approach.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat