IMMUNOINFORMATICS PDF

Road, Kolkata , India. Email: ni. Abstract Genome sequencing of humans and other organisms has led to the accumulation of huge amounts of data, which include immunologically relevant data. A large volume of clinical data has been deposited in several immunological databases and as a result immunoinformatics has emerged as an important field which acts as an intersection between experimental immunology and computational approaches. It not only helps in dealing with the huge amount of data but also plays a role in defining new hypotheses related to immune responses.

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SuperHapten SuperHapten is a manually curated hapten database integrating information from literature and web resources. The commercial availability is documented for about 6, haptens and related antibodies, enabling experimental approaches on cross-reactivity. The haptens are classified regarding their origin: pesticides, herbicides, insecticides, drugs, natural compounds, etc. Queries allow identification of haptens and associated antibodies according to functional class, carrier protein, chemical scaffold, composition or structural similarity.

The database represents the molecular structures recognized by adaptive immune receptors and the experimental contexts in which these molecules were determined to be immune epitopes. Epitopes recognized in humans, non-human primates, rodents, pigs, cats and all other tested species are included. Both positive and negative experimental results are captured. These data include the TMA construction protocol, experimental protocol and results from the various immunocytological and histochemical staining experiments including the scanned images for each of the TMA cores.

Furthermore, the database contains pathological information associated with each of the specimens on the TMA slide, the location of the various TMAs and the individual specimen blocks from which cores were taken in the laboratory and their current status. TmaDB has been designed to incorporate and extend many of the published common data elements and the XML format for TMA experiments and is therefore compatible with the TMA data exchange specifications developed by the Association for Pathology Informatics community.

It presents V gene sequences from the EMBL database and Ensembl together with the corresponding links to the source data.

As a result of the BLAST hit evaluation, the VBASE2 entries are classified into 3 different classes: class 1 holds sequences for which a genomic reference and a rearranged sequence is known. Class 2 contains sequences, which have not been found in a rearrangement, thus lacking evidence of functionality.

Class 3 contains sequences which have been found in different V D J rearrangements but lack a genomic reference. Each entry in the database describes one interaction between a residue on an antigenic protein and a residue on an antibody chain. Every interaction is described using the following parameters: PDB identifier, antigen chain ID PDB position of the antigenic residue, type of antigenic residue and its sequence environment, antigen residue secondary structure state, antigen residue solvent accessibility, antibody chain ID, type of antibody chain heavy or light , CDR number, PDB position of the antibody residue, and type of antibody residue and its sequence environment.

Additionally, interactions can be visualized using an interface to Jmol. Such data is valuable for investigation of cross-reactivity between known allergens and analysis of potential allergenicity in proteins. Allergome emphasizes the annotation of allergens that result in an IgE-mediated disease. Tools[ edit ] A variety of computational, mathematical and statistical methods are available and reported. These tools are helpful for collection, analysis, and interpretation of immunological data.

They include text mining, [32] information management, [33] [34] sequence analysis, analysis of molecular interactions, and mathematical models that enable advanced simulations of immune system and immunological processes.

Such as categorization of allergen cross-reactivity information, [32] identification of cancer-associated gene variants and the classification of immune epitopes. There are also some computational models which focus on protein—protein interactions and networks. There are also tools which are used for T and B cell epitope mapping, proteasomal cleavage site prediction, and TAP— peptide prediction.

Computational immunology tools is the game between experimental data and mathematically designed computational tools. Allergies[ edit ] Allergies , while a critical subject of immunology, also vary considerably among individuals and sometimes even among genetically similar individuals. The assessment of protein allergenic potential focuses on three main aspects: i immunogenicity; ii cross-reactivity; and iii clinical symptoms. Therefore, immunogenicity studies focus mainly on identifying recognition sites of B-cells and T-cells for allergens.

The three-dimensional structural properties of allergens control their allergenicity. 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.

Though the developments are on primary stage, the World Health organization and Food and Agriculture Organization have proposed guidelines for evaluating allergenicity of genetically modified foods. Though there are rules, their inherent limitations have started to become apparent and exceptions to the rules have been well reported [46] Infectious diseases and host responses[ edit ] In the study of infectious diseases and host responses, the mathematical and computer models are a great help.

These models were very useful in characterizing the behavior and spread of infectious disease, by understanding the dynamics of the pathogen in the host and the mechanisms of host factors which aid pathogen persistence. Examples include Plasmodium falciparum [47] and nematode infection in ruminants. Many exciting developments in large-scale screening of pathogens are currently taking place. 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.

Rule-based systems have been reported for the automated extraction and curation of influenza A records. This would be helpful in limiting the spread of infectious disease. Examples include a method for identification of vaccine targets from protein regions of conserved HLA binding [50] and computational assessment of cross-reactivity of broadly neutralizing antibodies against viral pathogens.

Immunoinformatics could accelerate the discovery process dramatically and potentially shorten the time required for vaccine development. Immunoinformatics tools have been used to design the vaccine against Dengue virus [52] and Leishmania [53] Immune system function[ edit ] Using this technology it is possible to know the model behind immune system.

It has been used to model T-cell-mediated suppression, [54] peripheral lymphocyte migration, [55] T-cell memory, [56] tolerance, [57] thymic function, [58] and antibody networks. There are also several models which are helpful in understanding the nature of specificity in immune network and immunogenicity. This research could have important implications for the design of peptide based immuno-therapeutic drugs and vaccines.

It shows the power of the modeling approach to understand complex immune interactions. Recently it has been very important to determine the novel mutations. Genomics and proteomics techniques are used worldwide to identify mutations related to each specific cancer and their treatments.

Computational tools are used to predict growth and surface antigens on cancerous cells. There are publications explaining a targeted approach for assessing mutations and cancer risk. Algorithm CanPredict was used to indicate how closely a specific gene resembles known cancer-causing genes. Protein—protein interaction networks provide valuable information on tumorigenesis in humans.

Cancer proteins exhibit a network topology that is different from normal proteins in the human interactome. Recently, pioneering works have been conducted to analyse the host immune system dynamics in response to artificial immunity induced by vaccination strategies.

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Computational immunology

SuperHapten SuperHapten is a manually curated hapten database integrating information from literature and web resources. The commercial availability is documented for about 6, haptens and related antibodies, enabling experimental approaches on cross-reactivity. The haptens are classified regarding their origin: pesticides, herbicides, insecticides, drugs, natural compounds, etc. Queries allow identification of haptens and associated antibodies according to functional class, carrier protein, chemical scaffold, composition or structural similarity. The database represents the molecular structures recognized by adaptive immune receptors and the experimental contexts in which these molecules were determined to be immune epitopes. Epitopes recognized in humans, non-human primates, rodents, pigs, cats and all other tested species are included. Both positive and negative experimental results are captured.

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Immunoinformatics

Methods Mol Biol. Immunoinformatics: a brief review. A large volume of data relevant to immunology research has accumulated due to sequencing of genomes of the human and other model organisms. At the same time, huge amounts of clinical and epidemiologic data are being deposited in various scientific literature and clinical records. This accumulation of the information is like a goldmine for researchers looking for mechanisms of immune function and disease pathogenesis. Thus the need to handle this rapidly growing immunological resource has given rise to the field known as immunoinformatics.

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