It aims to study the complex protein–protein interactions and networks and allows a better understanding of immune responses and their role during normal, diseased and reconstitution states.
[3] After the recent advances in sequencing and proteomics technology, there have been many fold increase in generation of molecular and immunological data.
[36][37] Attempts are being made for the extraction of interesting and complex patterns from non-structured text documents in the immunological domain, such as categorization of allergen cross-reactivity information,[33] identification of cancer-associated gene variants and the classification of immune epitopes.
The assessment of protein allergenic potential focuses on three main aspects: (i) immunogenicity; (ii) cross-reactivity; and (iii) clinical symptoms.
The use of immunoinformatics tools can be useful to predict protein allergenicity and will become increasingly important in the screening of novel foods before their wide-scale release for human use.
Thus, there are major efforts under way to make reliable broad based allergy databases and combine these with well validated prediction tools in order to enable the identification of potential allergens in genetically modified drugs and foods.
National Institute of Allergy and Infectious Diseases (NIAID) has initiated an endeavor for systematic mapping of B and T cell epitopes of category A-C pathogens.
These pathogens include Bacillus anthracis (anthrax), Clostridium botulinum toxin (botulism), Variola major (smallpox), Francisella tularensis (tularemia), viral hemorrhagic fevers, Burkholderia pseudomallei, Staphylococcus enterotoxin B, yellow fever, influenza, rabies, Chikungunya virus etc.
Examples include a method for identification of vaccine targets from protein regions of conserved HLA binding[51] and computational assessment of cross-reactivity of broadly neutralizing antibodies against viral pathogens.
[52] These examples illustrate the power of immunoinformatics applications to help solve complex problems in public health.
Immunoinformatics could accelerate the discovery process dramatically and potentially shorten the time required for vaccine development.
It has been used to model T-cell-mediated suppression,[56] peripheral lymphocyte migration,[57] T-cell memory,[58] tolerance,[59] thymic function,[60] and antibody networks.
[61] Models are helpful to predicts dynamics of pathogen toxicity and T-cell memory in response to different stimuli.
For example, it was useful to examine the functional relationship between TAP peptide transport and HLA class I antigen presentation.
[62] TAP is a transmembrane protein responsible for the transport of antigenic peptides into the endoplasmic reticulum, where MHC class I molecules can bind them and presented to T cells.
[67][68][69] Other simulation tools use predicted cancer peptides to forecast immune specific anticancer responses that is dependent on the specified HLA.