MTBVeb: A Web-Based Platform for Designing Vaccines against Existing and Emerging Strains of Mycobacterium tuberculosis
MTBVeb is a web-based computational platform developed for designing vaccines against existing and emerging strains of Mycobacterium tuberculosis (Mtb).
The platform integrates:
- Comparative genomics
- Antigen prediction
- Epitope prediction
- Vaccine candidate identification
- Strain-specific analysis
MTBVeb assists researchers in designing:
- Strain-based vaccines
- Antigen-based vaccines
- Epitope-based vaccines
against drug-sensitive and drug-resistant strains of Mycobacterium tuberculosis.
A Web-Based Platform for Designing Vaccines against Existing and Emerging Strains of Mycobacterium tuberculosis
- Sandeep Kumar Dhanda
- Pooja Vir
- Deepak Singla
- Sudheer Gupta
- Shailesh Kumar
- Gajendra P. S. Raghava
PLoS ONE
11
4
e0153771
20 April 2016
Tuberculosis remains one of the deadliest infectious diseases worldwide.
Major challenges include:
- Drug-resistant tuberculosis strains
- Limited efficacy of BCG vaccine in adults
- Emerging multidrug-resistant strains
- Lack of effective universal vaccines
Modern technologies such as:
- Next Generation Sequencing (NGS)
- Immunoinformatics
- Epitope prediction
- Comparative genomics
can help accelerate vaccine development against emerging Mtb strains.
MTBVeb was developed to address these challenges using computational approaches.
The study aimed to:
- Develop a comprehensive vaccine design platform
- Analyze multiple Mtb strains
- Identify antigenic vaccine candidates
- Predict B-cell and T-cell epitopes
- Support vaccine design against newly sequenced strains
- Integrate genome analysis and epitope prediction tools
The study analyzed:
| Strain Category | Number |
|---|---|
| Tuberculoid strains | 23 |
| Vaccine strains | 5 |
| Non-tuberculoid strains | 30 |
| Total strains | 59 |
These included:
- Drug-sensitive strains
- Drug-resistant strains
- Clinical isolates
- Laboratory strains
- BCG vaccine variants
A total of:
- 178 vaccine candidates
- 166 unique proteins
were identified from literature and previous studies.
- 125 proteins
- 20 proteins
- 33 proteins
These proteins were analyzed across all strains using sequence similarity search.
The platform compared vaccine candidate proteins across different Mtb strains.
- Virulent and secretory proteins differentiate pathogenic and non-pathogenic strains
- RD proteins differentiate vaccine strains from pathogenic strains
- Many vaccine candidates are membrane or extracellular proteins
The study generated overlapping 9-mer peptides from vaccine candidate proteins.
| Description | Count |
|---|---|
| Unique 9-mer peptides | 103,522 |
| Immune Feature | Count |
|---|---|
| B-cell epitopes | 8,292 |
| MHC Class I binders | 46,484 |
| CTL epitopes | 34,922 |
| MHC Class II binders | 14,907 |
| Th1 epitopes | 6,242 |
| Th2 epitopes | 9,720 |
Experimentally validated epitopes were obtained from:
- Immune Epitope Database (IEDB)
| Epitope Type | Unique Peptides |
|---|---|
| B-cell epitopes | 659 |
| T-cell epitopes | 1,806 |
| MHC binders | 1,208 |
Most validated epitopes mapped to vaccine candidate proteins.
The platform integrated several prediction tools:
| Tool | Purpose |
|---|---|
| LBtope | B-cell epitope prediction |
| Propred | MHC Class II prediction |
| Propred1 | MHC Class I prediction |
| CTLpred | CTL epitope prediction |
| IFNepitope | IFN-gamma epitope prediction |
| IL4pred | IL-4 inducing peptide prediction |
The database organizes information into:
- Strains
- Antigens
- Epitopes
- MySQL
- PHP 5.2.9
- HTML
- JavaScript
- Apache HTTP Server 2.2
- Linux Operating System
Allows users to:
- Compare strains
- Analyze newly sequenced genomes
- Identify conserved regions
- Visualize genomes
Integrated genome browsers:
- JBrowse
- CGView
- Argo
Users can:
- Browse vaccine candidates
- Compare vaccine targets
- Perform similarity searches
- Map epitopes on antigens
Users can:
- Predict B-cell epitopes
- Predict T-cell epitopes
- Perform advanced epitope search
- Identify epitopes with desired immune responses
The study demonstrated that:
- Comparative genomics can assist vaccine design
- RD proteins help differentiate vaccine strains
- Immunoinformatics pipelines effectively identify vaccine epitopes
- Large-scale epitope prediction can support subunit vaccine design
- MTBVeb provides more integrated functionality than existing TB vaccine resources
Compared with other TB vaccine databases, MTBVeb provides:
- Strain-specific analysis
- Experimental epitope mapping
- User strain analysis
- Epitope prediction pipelines
- Vaccine strain comparison
MTBVeb can be used for:
- Tuberculosis vaccine development
- Drug-resistant strain analysis
- Epitope vaccine design
- Comparative genomics
- Immunoinformatics
- Antigen discovery
- Host-pathogen interaction studies
http://crdd.osdd.net/raghava/mtbveb/
Dhanda SK, Vir P, Singla D, Gupta S, Kumar S, Raghava GPS.
A Web-Based Platform for Designing Vaccines against Existing and Emerging Strains of Mycobacterium tuberculosis.
PLoS ONE. 2016;11(4):e0153771.
DOI: https://doi.org/10.1371/journal.pone.0153771
Email: raghava@iiitd.ac.in
Address:
Indraprastha Institute of Information Technology Delhi
This project/documentation is intended for academic and research purposes only.