Welcome to the official documentation for igpred, a computational method developed for predicting B-cell epitopes that induce specific classes of antibodies, such as IgG, IgE, or IgA. While traditional methods focus on general B-cell response, AbCpred provides functional insights into the type of humoral immunity a peptide will trigger, which is crucial for vaccine design and allergy research.
Web Server: http://crdd.osdd.net/raghava/abcpred/(https://webs.iiitd.edu.in/raghava/igpred)
Gupta, S., Ansari, H. R., Gautam, A., & Raghava, G. P. S. (2013). Identification of B-cell epitopes in an antigen for inducing specific class of antibodies. Biology Direct, 8, 27. https://doi.org/10.1186/1745-6150-8-27
Zonedo:-(https://doi.org/10.5281/zenodo.20097037)
The immune system generates different classes of antibodies (isotypes) to perform various effector functions. AbCpred is the first method designed to understand the relationship between the primary sequence of B-cell epitopes and the specific class of antibody they induce.
The models were trained on a high-quality dataset derived from the Immune Epitope Database (IEDB):
- IgG-specific epitopes: 11,981 entries.
- IgE-specific epitopes: 2,341 entries.
- IgA-specific epitopes: 403 entries.
- Non-B-cell epitopes: 22,835 entries.
- Class-Specific Prediction: Predicts whether a peptide sequence is a B-cell epitope and identifies its antibody-inducing class (IgG, IgE, or IgA).
- Amino Acid Analysis: Utilizes observations that certain residues are preferred in different epitope classes (e.g., Cys in IgE epitopes; Pro and Gly in IgA epitopes).
- Hybrid Models: Employs Support Vector Machines (SVM) based on amino acid composition and dipeptide composition for high-accuracy classification.
- IgG vs. Non-B-cell: Achieved a maximum accuracy of 74.59% using amino acid composition.
- IgE vs. Non-B-cell: Achieved a maximum accuracy of 83.21%.
- IgA vs. Non-B-cell: Achieved a maximum accuracy of 80.85%.
AbCpred leverages various sequence-based features to model the class-specific nature of B-cell epitopes.
- Machine Learning: Developed using SVM-light with various kernels (Linear, Polynomial, RBF, and Sigmoid) and validated through 5-fold cross-validation.
- Feature Extraction: Includes amino acid composition, dipeptide composition, and binary profile patterns.
- Analysis Tools: Provides insights into residue preference and positional conservation at the N and C terminals of epitopes.
- Vaccine Development: Designing epitopes that selectively induce protective antibody classes like IgG or IgA.
- Allergy Research: Identifying IgE-inducing epitopes to understand and manage allergic reactions.
- Immunology: Understanding the fundamental sequence-level determinants of antibody isotype switching.
Prof. Gajendra P. S. Raghava Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India.
Email: raghava@imtech.res.in
This resource is open-access and distributed under the terms of the Creative Commons Attribution License, permitting unrestricted use and distribution provided the original work is properly credited.