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Immunological problems bother million people of different ages and races around the whole world. Many tools are used to investigate, understand, and predict infectious and autoimmune diseases based on antibody-protein interactions (1). Some immune responses can be ineffective or even dangerous to people (2), and the aim of immunoinformatics is to develop computational methods (3). Antibodies in human blood interact with antigens (epitopes) (1).
B-cell and T-cell epitopes should be identified in the immune system as a considerable factor in vaccine design and a possibility to replace a whole pathogen (3, 4). Despite the already achieved progress in the field of immunoinformatics, epitope prediction remains a challenge for many clinical and biomedical researchers. Computational algorithms include support vector machines (SVMs), Position Specific Scoring Matrices (PSSMs), Artificial Neural Networks (ANNs), epimatrix algorithm, and numerous quantitative matrices.
A variety of studies about immunogenicity and therapeutic interventions can be used as a theoretical background for the current project. The idea of removing T-cell epitopes with a SVM computational method was introduced (2). Unlike B-cell epitopes that can recognise native proteins and polysaccharide antigens and be evaluated via peptide scanning, T-cell epitopes recognise only some linear peptide antigens (4).
Antigen recognition by B-cells occurs through receptors and the creation of special secrete soluble forms (antibodies). Conformational epitope prediction (CEP) is the tool that generates surface antigen fragments and evaluates statistical characteristics (2). T-cell epitopes introduce the receptors that may detect antigens when they are displayed in their surface. Epimatrix and PSSMs are the tools to predict this receptor (4). The task is not only to choose the best option but to describe the characteristics of the frequently used techniques.
References
- Patro R, Norel R, Prill RJ, Saez-Rodriguez J, Lorenz P, Steinbeck F, et al. A computational method for designing diverse linear epitopes including citrullinated peptides with desired binding affinities to intravenous immunoglobulin. BMC Bioinformatics. 2016; 17(1): 155-168.
- King C, Garza EN, Mazor R, Linehan JL, Pastan I, Baker D. Removing t-cell epitopes with computational protein design. Proc Natl Acad Sci U S A. 2014; 111(23): 8577-8582.
- Backert L, Kohlbacher O. Immunoinformatics and epitope prediction in the age of genomic medicine. Genome Med. 2015; 7(1): 119-131.
- Soria-Guerra RE, Nieto-Gomez R, Govea-Alonso DO, Rosales-Mendoza S. An overview of bioinformatics tools for epitope prediction: implications on vaccine development. J Biomed Inform. 2015; 53: 405-414.
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