Copyright
©The Author(s) 2025.
World J Hepatol. Jul 27, 2025; 17(7): 107620
Published online Jul 27, 2025. doi: 10.4254/wjh.v17.i7.107620
Published online Jul 27, 2025. doi: 10.4254/wjh.v17.i7.107620
Table 1 Web servers available for predicting Cytotoxic T Lymphocyte, Helper T lymphocyte, and B-cell epitopes
| Web server | Prediction methods | Website link | Ref. |
| Cytotoxic T lymphocyte epitope | |||
| NetCTL 1.2 | Uses an ANN approach to combine MHC class I peptide binding, proteasomal C-terminal cleavage, and TAP transport efficiency | https://services.healthtech.dtu.dk/services/NetCTL-1.2/ | Larsen et al[69], 2007 |
| NetMHCpan 4.1 | Predicts peptide binding to MHC molecules based on quantitative binding affinity and eluted ligands identified by mass spectrometry, using an ANN approach | https://services.healthtech.dtu.dk/services/NetMHCpan-4.1/ | Reynisson et al[70], 2020 |
| CTLPred | Predicts CTL epitopes based on T cell epitope patterns, utilizing both ANN and SVM approaches | http://crdd.osdd.net/raghava/ctlpred/ | Bhasin and Raghava[71], 2004 |
| Helper T lymphocyte epitope | |||
| NetMHCIIpan 4.0 | Predicts peptide binding to MHC II molecules (HLA-DR, HLA-DQ, HLA-DP) based on binding affinity and eluted ligands identified by mass spectrometry, using an ANN approach | https://services.healthtech.dtu.dk/services/NetMHCIIpan-4.0/ | Reynisson et al[70], 2020 |
| ProPred | Predicts MHC Class II (HLA-DR) binding regions within antigen sequences using QM | http://crdd.osdd.net/raghava/propred/ | Singh and Raghava[72], 2001 |
| MARIA | Predicts the likelihood of antigen presentation from a specific gene related to HLA class II alleles, using peptide sequences from mass spectrometry, antigen gene expression levels, and protease cleavage patterns with an ANN approach | https://maria.stanford.edu/ | Chen et al[73], 2019 |
| Linear B Cell epitope | |||
| ABCpred | Uses an RNN approach that considers peptide length to predict B cell epitopes within antigen sequences | http://crdd.osdd.net/raghava/abcpred/ | Saha and Raghava[114], 2006 |
| Bepipred Linear Epitope Prediction 2.0 | Uses a random forest algorithm trained on annotated epitopes from antibody-antigen protein structures | https://services.healthtech.dtu.dk/services/BepiPred-2.0/ | Jespersen et al[115], 2017 |
| BCEPS | Predicts linear B cell epitopes using an SVM approach based on the tertiary structure of antibody-antigen complexes | http://imbio.med.ucm.es/bceps/ | Ras-Carmona et al[116], 2021 |
| SEMA | Applies a transfer learning approach using a pre-trained deep learning model to predict conformational B cell epitopes based on primary antigen sequences and tertiary structures | https://sema.airi.net/ | Shashkova et al[117], 2022 |
| LBtope | Uses SVM and Ibk approaches on a large dataset of experimentally validated B cell epitopes and non-epitopes to predict linear B cell epitopes | https://webs.iiitd.edu.in/raghava/lbtope/index.php | Singh et al[118], 2013 |
| Bcepred | Predicts B cell epitopes using physicochemical properties, such as hydrophilicity, flexibility, accessibility, polarity, exposed surface, and turns | http://crdd.osdd.net/raghava/bcepred/ | Saha and Raghava[77], 2004 |
| COBEpro | Uses an SVM to predict short peptide fragments within query antigen sequences, calculating an epitope propensity score for each residue | https://scratch.proteomics.ics.uci.edu/ | Sweredoski and Baldi[119], 2009 |
| CLBTope | Combines alignment-based and alignment-free machine learning methods to predict B cell epitopes, using epitope and non-epitope sequence composition | https://webs.iiitd.edu.in/raghava/clbtope/ | Kumar et al[120], 2024 |
Table 2 Web servers available for epitope selection
Table 3 Applications of immunoinformatics in chronic hepatitis B therapeutic vaccine design
| Targeted antigens | Data type | Data source | Immunoinformatics applications | Ref. |
| HBV Polymerase | Proteomics | NCBI | Consensus sequence formation, epitope prediction, epitope selection (based on immunogenicity, toxicity, and conservation), population coverage calculation, and molecular docking analysis | Zheng et al[96], 2017 |
| HBV polymerase; HBx | Proteomics | HBVdb | Epitope prediction and epitope selection (based on conservation and immunogenicity) | de Beijer et al[98], 2020 |
| HBV polymerase | Proteomics | NCBI | Consensus sequence formation, epitope prediction, epitope selection (antigenicity, allergenicity, toxicity), population coverage calculation, physicochemical property prediction of vaccine candidates, secondary and tertiary structure prediction, molecular docking analysis, and molecular dynamics simulation | Ahmed et al[100], 2021 |
| HBc; HBx | Proteomics | NCBI | Epitope prediction and epitope selection (conservation and autoimmunity) | Saeed et al[101], 2023 |
| LHBs | Proteomics | NCBI | Epitope prediction, physicochemical property prediction, vaccine candidate antigenicity and allergenicity analysis, molecular docking analysis, molecular dynamics simulation, and immune response simulation | Zhu et al[104], 2024 |
- Citation: Naully PG, Tan MI, El Khobar KE, Sukowati CHC, Giri-Rachman EA. Advancing therapeutic vaccines for chronic hepatitis B: Integrating reverse vaccinology and immunoinformatics. World J Hepatol 2025; 17(7): 107620
- URL: https://www.wjgnet.com/1948-5182/full/v17/i7/107620.htm
- DOI: https://dx.doi.org/10.4254/wjh.v17.i7.107620