Computational immunology

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In academia, computational immunology is a field of science that encompasses high-throughput genomic and bioinformatics approaches to immunology. The field's main aim is to convert immunological data into computational problems, solve these problems using mathematical and computational approaches and then convert these results into immunologically meaningful interpretations.


The immune system is a complex system of the human body and understanding it is one of the most challenging topics in biology. Immunology research is important for understanding the mechanisms underlying the defense of human body and to develop drugs for immunological diseases and maintain health. Recent findings in genomic and proteomic technologies have transformed the immunology research drastically. Sequencing of the human and other model organism genomes has produced increasingly large volumes of data relevant to immunology research and at the same time huge amounts of functional and clinical data are being reported in the scientific literature and stored in clinical records. Recent advances in bioinformatics or computational biology were helpful to understand and organize these large scale data and gave rise to new area that is called Computational immunology or immunoinformatics.

Computational immunology is a branch of bioinformatics and it is based on similar concepts and tools, such as sequence alignment and protein structure prediction tools. Immunomics is a discipline like genomics and proteomics, it is a science, which specifically combines Immunology with computer science, mathematics, chemistry, and biochemistry for large-scale analysis of immune system functions. 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. Computational immunology is a part of immunomics, which is focused on analyzing large scale experimental data.[1][2]


Computational immunology began over 90 years ago with the theoretic modeling of malaria epidemiology. At that time, the emphasis was on the use of mathematics to guide the study of disease transmission. Since then, the field has expanded to cover all other aspects of immune system processes and diseases.[3]

Immunological database[edit]

After the recent advances in sequencing and proteomics technology, there have been many fold increase in generation of molecular and immunological data; the data are so diverse that they can be categorized in different databases according to their use in the research. Until now there are total 31 different immunological databases noted in the Nucleic Acids Research (NAR) Database Collection, which are given in the following table, together with some more immune related databases;[4] the information given in the table is taken from the database descriptions in NAR Database Collection.

Database Description
ALPSbase Autoimmune lymphoproliferative syndrome database
AntigenDB Sequence, structure, and other data on pathogen antigens.[5]
AntiJen Quantitative binding data for peptides and proteins of immunological interest.[6]
BCIpep This database stores information of all experimentally determined B-cell epitopes of antigenic proteins. This is a curated database where detailed information about the epitopes are collected and compiled from published literature and existing databases, it covers a wide range of pathogenic organisms like virus, bacteria, protozoa and fungi. Each entry in database provides full information about a B-cell epitope that includes amino acid sequences, source of the antigenic protein, immunogenicity, model organism and antibody generation/neutralization test.[7]
dbMHC dbMHC provides access to HLA sequences, tools to support genetic testing of HLA loci, HLA allele and haplotype frequencies of over 90 populations worldwide, as well as clinical datasets on hematopoietic stem cell transplantation, and insulin dependent diabetes mellitus (IDDM), Rheumatoid Arthritis (RA), Narcolepsy and Spondyloarthropathy. For more information go to this link [permanent dead link]
DIGIT Database of ImmunoGlobulin sequences and Integrated Tools.[8]
FIMM FIMM is an integrated database of functional molecular immunology that focuses on the T-cell response to disease-specific antigens. FIMM provides fully referenced information integrated with data retrieval and sequence analysis tools on HLA, peptides, T-cell epitopes, antigens, diseases and constitutes one backbone of future computational immunology research. Antigen protein data have been enriched with more than 27,000 sequences derived from the non-redundant SwissProt-TREMBL-TREMBL_NEW (SPTR) database of antigens similar or related FIMM antigens across various species to facilitate a comprehensive analysis of conserved or variable T-cell epitopes.[9]
GPX-Macrophage Expression Atlas The GPX Macrophage Expression Atlas (GPX-MEA) is an online resource for expression based studies of a range of macrophage cell types following treatment with pathogens and immune modulators. GPX Macrophage Expression Atlas (GPX-MEA) follows the MIAME standard and includes an objective quality score with each experiment, it places special emphasis on rigorously capturing the experimental design and enables the statistical analysis of expression data from different micro-array experiments. This is the first example of a focussed macrophage gene expression database that allows efficient identification of transcriptional patterns, which provide novel insights into biology of this cell system.[10]
HaptenDB It is a comprehensive database of hapten molecules. This is a curated database where information is collected and compiled from published literature and web resources. Presently database has more than 1700 entries where each entry provides comprehensive detail about a hapten molecule that includes: i) nature of the hapten; ii) methods of anti- hapten antibody production; iii) information about carrier protein; iv) coupling method; v) assay method (used for characterization) and vi) specificities of antibodies; the Haptendb covers wide array of haptens ranging from antibiotics of biomedical importance to pesticides. This database will be very useful for studying the serological reactions and production of antibodies.[11]
HPTAA HPTAA is a database of potential tumor-associated antigens that uses expression data from various expression platforms, including carefully chosen publicly available microarray expression data, GEO SAGE data and Unigene expression data.[12]
IEDB-3D Structural data within the Immune Epitope Database.[13]
IL2Rgbase X-linked severe combined immunodeficiency mutations.[14]
IMGT IMGT is an integrated knowledge resource specialized in IG, TR, MHC, IG superfamily, MHC superfamily and related proteins of the immune system of human and other vertebrate species. IMGTW comprises 6 databases, 15 on-line tools for sequence, gene and 3D structure analysis, and more than 10,000 pages of resources Web. Data standardization, based on IMGT-ONTOLOGY, has been approved by WHO/IUIS.[15]
IMGT_GENE-DB IMGT/GENE-DB is the IMGT® comprehensive genome database for immunoglobulins (IG) and T cell receptors (TR) genes from human and mouse, and, in development, from other vertebrate species (e.g. rat). IMGT/GENE-DB is part of IMGT®, the international ImMunoGeneTics information system®, the high-quality integrated knowledge resource specialized in IG, TR, major histocompatibility complex (MHC) of human and other vertebrate species, and related proteins of the immune system (RPI) that belong to the immunoglobulin superfamily (IgSF) and to the MHC superfamily (MhcSF).[16]
IMGT/HLA There are currently over 1600 officially recognised HLA alleles and these sequences are made available to the scientific community through the IMGT/HLA database. In 1998, the IMGT/HLA database was publicly released. Since this time, the database has grown and is the primary source of information for the study of sequences of the human major histocompatibility complex; the initial release of the database contained allele reports, alignment tools, submission tools as well as detailed descriptions of the source cells. The database is updated quarterly with all the new and confirmatory sequences submitted to the WHO Nomenclature Committee and on average an additional 75 new and confirmatory sequences are included in each quarterly release; the IMGT/HLA database provides a centralized resource for everybody interested, either centrally or peripherally, in the HLA system.[17]
IMGT/LIGM-DB IMGT/LIGM-DB is the IMGT® comprehensive database of immunoglobulin (IG) and T cell receptor (TR) nucleotide sequences, from human and other vertebrate species, with translation for fully annotated sequences, created in 1989 by LIGM, Montpellier, France, on the Web since July 1995. IMGT/LIGM-DB is the first and the largest database of IMGT®, the international ImMunoGeneTics information system® , the high-quality integrated knowledge resource specialized in IG, TR, major histocompatibility complex (MHC) of human and other vertebrate species, and related proteins of the immune system (RPI) that belong to the immunoglobulin superfamily (IgSF) and to the MHC superfamily (MhcSF). IMGT/LIGM-DB sequence data are identified by the EMBL/GenBank/DDBJ accession number; the unique source of data for IMGT/LIGM-DB is EMBL which shares data with GenBank and DDBJ.[18]
Interferon Stimulated Gene Database Interferons (IFN) are a family of multifunctional cytokines that activate transcription of a subset of genes. The gene products induced by IFN are responsible for the antiviral, antiproliferative and immunomodulatory properties of this cytokine. In order to obtain a more comprehensive understanding of the genes regulated by IFNs we have used different microarray formats to identify over 400 interferon stimulated genes (ISG). To facilitate the dissemination of this data we have compiled a database comprising the ISGs assigned into functional categories; the database is fully searchable and contains links to sequence and Unigene information. The database and the array data are accessible via the World Wide Web at ( ). We intend to add published ISG-sequences and those discovered by further transcript profiling to the database to eventually compile a complete list of ISGs.
IPD-ESTDAB The Immuno Polymorphism Database (IPD) is a set of specialist databases related to the study of polymorphic genes in the immune system. IPD-ESTDAB is a database of immunologically characterised melanoma cell lines; the database works in conjunction with the European Searchable Tumour Cell Line Database (ESTDAB) cell bank, which is housed in TÜbingen, Germany and provides immunologically characterised tumour cells.[19][20]
IPD-HPA - Human Platelet Antigens Human platelet antigens are alloantigens expressed only on platelets, specifically on platelet membrane glycoproteins. These platelet-specific antigens are immunogenic and can result in pathological reactions to transfusion therapy; the IPD-HPA section contains nomenclature information and additional background material about Human platelet antigen. The different genes in the HPA system have not been sequenced to the same level as some of the other projects and so currently only single nucleotide polymorphisms (SNP) are used to determine alleles; this information is presented in a grid of SNP for each gene The IPD and HPA nomenclature committee hope to expand this to provide full sequence alignments when possible.[19][20]
IPD-KIR - Killer-cell Immunoglobulin-like Receptors The Killer-cell Immunoglobulin-like Receptors (KIR) are members of the immunoglobulin super family (IgSF) formerly called Killer-cell Inhibitory Receptors. KIRs have been shown to be highly polymorphic both at the allelic and haplotypic levels, they are composed of two or three Ig-domains, a transmembrane region and cytoplasmic tail, which can in turn be short (activatory) or long (inhibitory). The Leukocyte Receptor Complex (LRC), which encodes KIR genes, has been shown to be polymorphic, polygenic and complex in a manner similar to the MHC; the IPD-KIR Sequence Database contains the most up to date nomenclature and sequence alignments.[19][20]
IPD-MHC The MHC sequences of many different species have been reported, along with different nomenclature systems used in the naming and identification of new genes and alleles in each species. The sequences of the major histocompatibility complex from number of different species are highly conserved between species. By bringing the work of different nomenclature committees and the sequences of different species together it is hoped to provide a central resource that will facilitate further research on the MHC of each species and on their comparison; the first release of the IPD-MHC database involved the work of groups specialising in non-human primates, canines (DLA) and felines (FLA) and incorporated all data previously available in the IMGT/MHC database. This release included data from five species of ape, sixteen species of new world monkey, seventeen species of old world monkey, as well as data on different canines and felines. Since the first release, sequences from cattle (BoLA), swine (SLA), and rats (RT1) have been added and the work to include MHC sequences from chickens, horses (ELA) is still going on.[19][20]
MHCBN MHCBN is a comprehensive database comprising over 23000 peptides sequences, whose binding affinity with MHC or TAP molecules has been assayed experimentally. It is a curated database where entries are compiled from published literature and public databases; each entry of the database provides full information like (sequence, its MHC or TAP binding specificity, source protein) about peptide whose binding affinity (IC50) and T cell activity is experimentally determined. MHCBN has number of web-based tools for the analysis and retrieval of information. All database entries are hyperlinked to major databases like SWISS-PROT, PDB, IMGT/HLA-DB, PubMed and OMIM to provide the information beyond the scope of MHCBN. Current version of MHCBN contains 1053 entries of TAP binding peptides; the information about the diseases associated with various MHC alleles is also included in this version.[21]
MHCPEP This database contains list of MHC-binding peptides.[22]
MPID-T2 MPID-T2 ( is a highly curated database for sequence-structure-function information on MHC-peptide interactions. It contains all structures of major histocompatibility complex proteins (MHC) containing bound peptides, with emphasis on the structural characterization of these complexes. Database entries have been grouped into fully referenced redundant and non-redundant categories; the MHC-peptide interactions have been presented in terms of a set of sequence and structural parameters representative of molecular recognition. MPID will facilitate the development of algorithms to predict whether a query peptide sequence will bind to a specific MHC allele. MPID data has been sorted primarily on the basis of MHC Class, followed by organism (MHC source), next by allele type and finally by the length of peptide in the binding groove (peptide residues within 5 Å of the MHC). Data on inter-molecular hydrogen bonds, gap volume and gap index available in MPID are pre-computed and the interface area due to complex formation is calculated based on accessible surface area calculations; the available MHC-peptide databases have addressed sequence information as well as binding (or the lack thereof) of peptide sequences.[23]
MUGEN Mouse Database Murine models of immune processes and immunological diseases.[24]
Protegen Protective antigen database and analysis system.[25]
SuperHapten SuperHapten is a manually curated hapten database integrating information from literature and web resources. The current version of the database compiles 2D/3D structures, physicochemical properties and references for about 7,500 haptens and 25,000 synonyms; the commercial availability is documented for about 6,300 haptens and 450 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.[26]
The Immune Epitope Database (IEDB) The Immune Epitope Database (IEDB,, provides a catalog of experimentally characterized B and T cell epitopes, as well as data on MHC binding and MHC ligand elution experiments. 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. Over the course of four years, the data from 180,978 experiments were curated manually from the literature, covering about 99% of all publicly available information on peptide epitopes mapped in infectious agents (excluding HIV) and 93% of those mapped in allergens.[27]
TmaDB To analyse TMA output a relational database (known as TmaDB) has been developed to collate all aspects of information relating to TMAs. 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.[28]
VBASE2 VBASE2 is an integrative database of germ-line V genes from the immunoglobulin loci of human and mouse. It presents V gene sequences from the EMBL database and Ensembl together with the corresponding links to the source data; the VBASE2 dataset is generated in an automatic process based on a BLAST search of V genes against EMBL and the Ensembl dataset. The BLAST hits are evaluated with the DNAPLOT program, which allows immunoglobulin sequence alignment and comparison, RSS recognition and analysis of the V(D)J-rearrangements; 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. All VBASE2 sequences are compared with the datasets from the VBASE-, IMGT- and KABAT-databases (latest published versions), and the respective references are provided in each VBASE2 sequence entry; the VBASE2 database can be accessed by either a text based query form or by a sequence alignment with the DNAPLOT program. A DAS-server shows the VBASE2 dataset within the Ensembl Genome Browser and links to the database.[29]
Epitome Epitome is a database of all known antigenic residues and the antibodies that interact with them, including a detailed description of the residues involved in the interaction and their sequence/structure environments. 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.[30]
ImmGen The Immunological Genome consortium database includes expression profiles for more than 250 mouse immune cell types, and several data browsers to study the dataset.[31]

Online resources for allergy information are also available on Such data is valuable for investigation of cross-reactivity between known allergens and analysis of potential allergenicity in proteins; the Structural Database of Allergen Proteins (SDAP) stores information of allergenic proteins. The Food Allergy Research and Resource Program (FARRP) Protein Allergen-Online Database contains sequences of known and putative allergens derived from scientific literature and public databases. Allergome emphasizes the annotation of allergens that result in an IgE-mediated disease.


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.[35][36] 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,[32] identification of cancer-associated gene variants and the classification of immune epitopes.

Immunoinformatics is using the basic bioinformatics tools such as ClustalW,[37] BLAST,[38] and TreeView, as well as specialized immunoinformatics tools, such as EpiMatrix,[39][40] IMGT/V-QUEST for IG and TR sequence analysis, IMGT/ Collier-de-Perles and IMGT/StructuralQuery[41] for IG variable domain structure analysis.[42] Methods that rely on sequence comparison are diverse and have been applied to analyze HLA sequence conservation, help verify the origins of human immunodeficiency virus (HIV) sequences, and construct homology models for the analysis of hepatitis B virus polymerase resistance to lamivudine and emtricitabine.

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;[43] the experimental data is very much important to design and justify the models to predict various molecular targets. Computational immunology tools is the game between experimental data and mathematically designed computational tools.



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.[44] Immunogenicity is due to responses of an IgE antibody-producing B cell and/or of a T cell to a particular allergen. 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. According to the Codex alimentarius,[45] a protein is potentially allergenic if it possesses an identity of ≥6 contiguous amino acids or ≥35% sequence similarity over an 80 amino acid window with a known allergen. 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.[48]

Much has been done in understanding immune responses to various pathogens by integrating genomics and proteomics with bioinformatics strategies. Many exciting developments in large-scale screening of pathogens are currently taking place. 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. Rule-based systems have been reported for the automated extraction and curation of influenza A records.[49]

This development would lead to the development of an algorithm which would help to identify the conserved regions of pathogen sequences and in turn would be useful for vaccine development; 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;[51] 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. 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.[59] Models are helpful to predicts dynamics of pathogen toxicity and T-cell memory in response to different stimuli. There are also several models which are helpful in understanding the nature of specificity in immune network and immunogenicity.

For example, it was useful to examine the functional relationship between TAP peptide transport and HLA class I antigen presentation.[60] 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; as TAP does not bind all peptides equally, TAP-binding affinity could influence the ability of a particular peptide to gain access to the MHC class I pathway. Artificial neural network (ANN), a computer model was used to study peptide binding to human TAP and its relationship with MHC class I binding; the affinity of HLA-binding peptides for TAP was found to differ according to the HLA supertype concerned using this method. 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.[60]

There exist also methods which integrate peptide prediction tools with computer simulations that can provide detailed information on the immune response dynamics specific to the given pathogen's peptides .[61]

Cancer Informatics[edit]

Cancer is the result of somatic mutations which provide cancer cells with a selective growth advantage. 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.[62] Cancer immunology has been given so much importance that the data related to it is growing rapidly. 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.[63][64] Immunoinformatics have been useful in increasing success of tumour vaccination. Recently, pioneering works have been conducted to analyse the host immune system dynamics in response to artificial immunity induced by vaccination strategies.[65][66][67] Other simulation tools use predicted cancer peptides to forecast immune specific anticancer responses that is dependent on the specified HLA;[36] these resources are likely to grow significantly in the near future and immunoinformatics will be a major growth area in this domain.

See also[edit]


  1. ^ Tong JC, Ren EC (July 2009). "Immunoinformatics: current trends and future directions". Drug Discov. Today. 14 (13–14): 684–9. doi:10.1016/j.drudis.2009.04.001. PMID 19379830.
  2. ^ Korber B, LaBute M, Yusim K (June 2006). "Immunoinformatics comes of age". PLoS Comput. Biol. 2 (6): e71. doi:10.1371/journal.pcbi.0020071. PMC 1484584. PMID 16846250.
  3. ^ Ross, R. (1 February 1916). "An application of the theory of probabilities to the study of a priori pathometry. Part I" (PDF). Proceedings of the Royal Society A. 92 (638): 204–230. doi:10.1098/rspa.1916.0007.
  4. ^ Oxford Journals | Life Sciences | Nucleic Acids Research | Database Summary Paper Categories
  5. ^ Ansari HR, Flower DR, Raghava GP (January 2010). "AntigenDB: an immunoinformatics database of pathogen antigens". Nucleic Acids Res. 38 (Database issue): D847–53. doi:10.1093/nar/gkp830. PMC 2808902. PMID 19820110.
  6. ^ Toseland CP, Clayton DJ, McSparron H, et al. (October 2005). "AntiJen: a quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical, and cellular data". Immunome Res. 1 (1): 4. doi:10.1186/1745-7580-1-4. PMC 1289288. PMID 16305757.
  7. ^ Saha S, Bhasin M, Raghava GP (2005). "Bcipep: a database of B-cell epitopes". BMC Genomics. 6 (1): 79. doi:10.1186/1471-2164-6-79. PMC 1173103. PMID 15921533.
  8. ^ Chailyan A, Tramontano A, Marcatili P (January 2012). "A database of immunoglobulins with integrated tools: DIGIT". Nucleic Acids Res. 40 (Database issue): D1230–4. doi:10.1093/nar/gkr806. PMC 3245095. PMID 22080506.
  9. ^ Schönbach C, Koh JL, Flower DR, Wong L, Brusic V (January 2002). "FIMM, a database of functional molecular immunology: update 2002". Nucleic Acids Res. 30 (1): 226–9. doi:10.1093/nar/30.1.226. PMC 99079. PMID 11752300.
  10. ^ Grimes GR, Moodie S, Beattie JS, et al. (2005). "GPX-Macrophage Expression Atlas: a database for expression profiles of macrophages challenged with a variety of pro-inflammatory, anti-inflammatory, benign and pathogen insults". BMC Genomics. 6: 178. doi:10.1186/1471-2164-6-178. PMC 1351201. PMID 16343346.
  11. ^ Singh MK, Srivastava S, Raghava GP, Varshney GC (January 2006). "HaptenDB: a comprehensive database of haptens, carrier proteins and anti-hapten antibodies". Bioinformatics. 22 (2): 253–5. doi:10.1093/bioinformatics/bti692. PMID 16443637.
  12. ^ Wang X, Zhao H, Xu Q, et al. (January 2006). "HPtaa database-potential target genes for clinical diagnosis and immunotherapy of human carcinoma". Nucleic Acids Res. 34 (Database issue): D607–12. doi:10.1093/nar/gkj082. PMC 1347445. PMID 16381942.
  13. ^ Ponomarenko J, Papangelopoulos N, Zajonc DM, Peters B, Sette A, Bourne PE (January 2011). "IEDB-3D: structural data within the immune epitope database". Nucleic Acids Res. 39 (Database issue): D1164–70. doi:10.1093/nar/gkq888. PMC 3013771. PMID 21030437.
  14. ^ Puck JM (November 1996). "IL2RGbase: a database of gamma c-chain defects causing human X-SCID". Immunol. Today. 17 (11): 507–11. doi:10.1016/0167-5699(96)30062-5. PMID 8961626.
  15. ^ Lefranc MP (January 2001). "IMGT, the international ImMunoGeneTics database". Nucleic Acids Res. 29 (1): 207–9. doi:10.1093/nar/29.1.207. PMC 29797. PMID 11125093.
  16. ^ Giudicelli V, Chaume D, Lefranc MP (January 2005). "IMGT/GENE-DB: a comprehensive database for human and mouse immunoglobulin and T cell receptor genes". Nucleic Acids Res. 33 (Database issue): D256–61. doi:10.1093/nar/gki010. PMC 539964. PMID 15608191.
  17. ^ Robinson J, Malik A, Parham P, Bodmer JG, Marsh SG (March 2000). "IMGT/HLA database—a sequence database for the human major histocompatibility complex". Tissue Antigens. 55 (3): 280–7. doi:10.1034/j.1399-0039.2000.550314.x. PMC 29780. PMID 10777106.
  18. ^ Giudicelli V, Duroux P, Ginestoux C, et al. (January 2006). "IMGT/LIGM-DB, the IMGT comprehensive database of immunoglobulin and T cell receptor nucleotide sequences". Nucleic Acids Res. 34 (Database issue): D781–4. doi:10.1093/nar/gkj088. PMC 1347451. PMID 16381979.
  19. ^ a b c d Robinson J, Mistry K, McWilliam H, Lopez R, Marsh SG (January 2010). "IPD—the Immuno Polymorphism Database". Nucleic Acids Res. 38 (Database issue): D863–9. doi:10.1093/nar/gkp879. PMC 2808958. PMID 19875415.
  20. ^ a b c d Robinson J, Waller MJ, Fail SC, Marsh SG (December 2006). "The IMGT/HLA and IPD databases". Hum. Mutat. 27 (12): 1192–9. doi:10.1002/humu.20406. PMID 16944494.
  21. ^ Bhasin M, Singh H, Raghava GP (March 2003). "MHCBN: a comprehensive database of MHC binding and non-binding peptides". Bioinformatics. 19 (5): 665–6. doi:10.1093/bioinformatics/btg055. PMID 12651731.
  22. ^ Brusic V, Rudy G, Harrison LC (September 1994). "MHCPEP: a database of MHC-binding peptides". Nucleic Acids Research. 22 (17): 3663–5. doi:10.1093/nar/22.17.3663. PMC 308338. PMID 7937075.
  23. ^ Khan JM, Cheruku HR, Tong JC, Ranganathan S (April 2011). "MPID-T2: a database for sequence-structure-function analyses of pMHC and TR/pMHC structures". Bioinformatics. 27 (8): 1192–3. doi:10.1093/bioinformatics/btr104. PMID 21349870.
  24. ^ Aidinis V, Chandras C, Manoloukos M, et al. (January 2008). "MUGEN mouse database; animal models of human immunological diseases". Nucleic Acids Res. 36 (Database issue): D1048–54. doi:10.1093/nar/gkm838. PMC 2238830. PMID 17932065.
  25. ^ Yang B, Sayers S, Xiang Z, He Y (January 2011). "Protegen: a web-based protective antigen database and analysis system". Nucleic Acids Res. 39 (Database issue): D1073–8. doi:10.1093/nar/gkq944. PMC 3013795. PMID 20959289.
  26. ^ Günther S, Hempel D, Dunkel M, Rother K, Preissner R (January 2007). "SuperHapten: a comprehensive database for small immunogenic compounds". Nucleic Acids Res. 35 (Database issue): D906–10. doi:10.1093/nar/gkl849. PMC 1669746. PMID 17090587.
  27. ^ Sette, A. et al. The immune epitope database and analysis resource. Pattern Recognition in Bioinformatics, Proceedings 4146, 126-132 (2006).
  28. ^ Sharma-Oates A, Quirke P, Westhead DR (2005). "TmaDB: a repository for tissue microarray data". BMC Bioinformatics. 6: 218. doi:10.1186/1471-2105-6-218. PMC 1215475. PMID 16137321.
  29. ^ Retter I, Althaus HH, Münch R, Müller W (January 2005). "VBASE2, an integrative V gene database". Nucleic Acids Res. 33 (Database issue): D671–4. doi:10.1093/nar/gki088. PMC 540042. PMID 15608286.
  30. ^ Schlessinger A, Ofran Y, Yachdav G, Rost B (January 2006). "Epitome: database of structure-inferred antigenic epitopes". Nucleic Acids Res. 34 (Database issue): D777–80. doi:10.1093/nar/gkj053. PMC 1347416. PMID 16381978.
  31. ^ Jojic V; Shay T; Sylvia K; Zuk O; Sun X; Kang J; Regev A; Koller D; Immunological Genome Project Consortium (June 2013). "Identification of transcriptional regulators in the mouse immune system". Nature Immunology. 14 (6): 633–643. doi:10.1038/ni.2587. PMC 3690947. PMID 23624555.
  32. ^ a b Miotto O, Tan TW, Brusic V (2005). "Supporting the curation of biological databases with reusable text mining". Genome Inform. 16 (2): 32–44. PMID 16901087.
  33. ^ McDonald R, Scott Winters R, Ankuda CK, et al. (September 2006). "An automated procedure to identify biomedical articles that contain cancer-associated gene variants". Hum. Mutat. (Submitted manuscript). 27 (9): 957–64. doi:10.1002/humu.20363. PMID 16865690.
  34. ^ Wang P, Morgan AA, Zhang Q, Sette A, Peters B (2007). "Automating document classification for the Immune Epitope Database". BMC Bioinformatics. 8: 269. doi:10.1186/1471-2105-8-269. PMC 1965490. PMID 17655769.
  35. ^ Palladini A, Nicoletti G, Pappalardo F, Murgo A, Grosso V, Stivani V, Ianzano ML, Antognoli A, Croci S, Landuzzi L, De Giovanni C, Nanni P, Motta S, Lollini PL (October 2010). "In silico modeling and in vivo efficacy of cancer-preventive vaccinations". Cancer Research. 70 (20): 7756–63. doi:10.1158/0008-5472.CAN-10-0701. PMID 20924100.
  36. ^ a b Woelke A-L, von Eichborn J, Murgueitio M S, Worth C L, Castiglione F, Preissner R. (2011). "Development of Immune-Specific Interaction Potentials and Their Application in the Multi-Agent-System VaccImm". PLoS ONE. 6 (8): e23257. doi:10.1371/journal.pone.0023257. PMC 3157361. PMID 21858048.CS1 maint: Uses authors parameter (link)
  37. ^ Thompson JD, Higgins DG, Gibson TJ (November 1994). "CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice". Nucleic Acids Res. 22 (22): 4673–80. doi:10.1093/nar/22.22.4673. PMC 308517. PMID 7984417.
  38. ^ Altschul SF, Madden TL, Schäffer AA, et al. (September 1997). "Gapped BLAST and PSI-BLAST: a new generation of protein database search programs". Nucleic Acids Res. 25 (17): 3389–402. doi:10.1093/nar/25.17.3389. PMC 146917. PMID 9254694.
  39. ^ Elfaki, ME (24 Aug 2012). "Immunogenicity and immune modulatory effects of in silico predicted L. donovani candidate peptide vaccines". Human Vaccines & Immunotherapeutics. 8 (12): 1769–74. doi:10.4161/hv.21881. PMC 3656064. PMID 22922767.
  40. ^ De Groot, AS; et al. (March 2005). "HIV vaccine development by computer assisted design: the GAIA vaccine". Vaccine. 23 (17–18): 2136–48. doi:10.1016/j.vaccine.2005.01.097. PMID 15755584.
  41. ^ Kaas, Q. & Lefranc, M. IMGT Colliers de Perles: Standardized sequence-structure representations of the IgSF and MheSF superfamily domains. Current Bioinformatics 2, 21-30 (2007).
  42. ^ Brochet X, Lefranc MP, Giudicelli V (July 2008). "IMGT/V-QUEST: the highly customized and integrated system for IG and TR standardized V-J and V-D-J sequence analysis". Nucleic Acids Res. 36 (Web Server issue): W503–8. doi:10.1093/nar/gkn316. PMC 2447746. PMID 18503082.
  43. ^ Montañez R, Navas-Delgado I, Medina MA, Aldana-Montes JF, Sánchez-Jiménez F (December 2006). "Information integration of protein-protein interactions as essential tools for immunomics". Cell. Immunol. 244 (2): 84–6. doi:10.1016/j.cellimm.2006.12.008. PMID 17442285.
  44. ^ Oehlschlager S, Reece P, Brown A, et al. (December 2001). "Food allergy—towards predictive testing for novel foods". Food Addit Contam. 18 (12): 1099–107. doi:10.1080/02652030110050131. PMID 11761121.
  45. ^ CODEX Alimentarius: Home
  46. ^ Li KB, Issac P, Krishnan A (November 2004). "Predicting allergenic proteins using wavelet transform". Bioinformatics. 20 (16): 2572–8. doi:10.1093/bioinformatics/bth286. PMID 15117757.
  47. ^ van Noort SP, Nunes MC, Weedall GD, Hviid L, Gomes MG (2010). "Immune selection and within-host competition can structure the repertoire of variant surface antigens in Plasmodium falciparum—a mathematical model". PLoS ONE. 5 (3): e9778. doi:10.1371/journal.pone.0009778. PMC 2842302. PMID 20339540.
  48. ^ Chan MS, Isham VS (August 1998). "A stochastic model of schistosomiasis immuno-epidemiology". Math Biosci. 151 (2): 179–98. doi:10.1016/S0025-5564(98)10014-7. PMID 9711049.
  49. ^ Miotto O, Tan TW, Brusic V (2008). "Rule-based knowledge aggregation for large-scale protein sequence analysis of influenza A viruses". BMC Bioinformatics. 9 (Suppl 1): S7. doi:10.1186/1471-2105-9-S1-S7. PMC 2259408. PMID 18315860.
  50. ^ Olsen LR, Simon C, Kudahl UJ, Bagger FO, Winther O, Reinherz EL, Zhang GL, Brusic V (2015). "A computational method for identification of vaccine targets from protein regions of conserved human leukocyte antigen binding". BMC Medical Genomics. 8(Suppl 4) (S1): S1. doi:10.1186/1755-8794-8-S4-S1. PMC 4682376. PMID 26679766.
  51. ^ Sun J, Kudahl UJ, Simon C, Cao Z, Reinherz EL, Brusic V (2014). "Large-Scale Analysis of B-Cell Epitopes on Influenza Virus Hemagglutinin – Implications for Cross-Reactivity of Neutralizing Antibodies". Frontiers in Immunology. 5 (38): 38. doi:10.3389/fimmu.2014.00038. PMC 3916768. PMID 24570677.
  52. ^ Ali M, Pandey RK, Khatoon N, Narula A, Mishra A, Prajapati VK (2017). "Exploring dengue genome to construct a multi-epitope based subunit vaccine by utilizing immunoinformatics approach to battle against dengue infection". Scientific Reports. 7 (1): 9232. doi:10.1038/s41598-017-09199-w. PMC 5569093. PMID 28835708.
  53. ^ Khatoon N, Pandey RK, Prajapati VK (2017). "Exploring Leishmania secretory proteins to design B and T cell multi-epitope subunit vaccine using immunoinformatics approach". Scientific Reports. 7 (1): 8285. doi:10.1038/s41598-017-08842-w. PMC 5557753. PMID 28811600.
  54. ^ León K, Peréz R, Lage A, Carneiro J (November 2000). "Modelling T-cell-mediated suppression dependent on interactions in multicellular conjugates". J. Theor. Biol. 207 (2): 231–54. doi:10.1006/jtbi.2000.2169. PMID 11034831.
  55. ^ Srikusalanukul W, De Bruyne F, McCullagh P (June 2000). "Modelling of peripheral lymphocyte migration: system identification approach". Immunol. Cell Biol. 78 (3): 288–93. doi:10.1046/j.1440-1711.2000.00907.x. PMID 10849118.
  56. ^ Jacob J, Baltimore D (June 1999). "Modelling T-cell memory by genetic marking of memory T cells in vivo". Nature. 399 (6736): 593–7. doi:10.1038/21208. PMID 10376601.
  57. ^ Dolezal J, Hraba T (1988). "A contribution to mathematical modelling of immunological tolerance". Arch. Immunol. Ther. Exp. (Warsz.). 36 (1): 23–30. PMID 3266071.
  58. ^ Mehr R, Segel L, Sharp A, Globerson A (October 1994). "Colonization of the thymus by T cell progenitors: models for cell-cell interactions". J. Theor. Biol. 170 (3): 247–57. doi:10.1006/jtbi.1994.1185. PMID 7996854.
  59. ^ Faro J, Carneiro J, Velasco S (February 1997). "Further studies on the problem of immune network modelling". J. Theor. Biol. 184 (4): 405–21. doi:10.1006/jtbi.1996.0252. PMID 9082072.
  60. ^ a b Brusic V, van Endert P, Zeleznikow J, Daniel S, Hammer J, Petrovsky N (1999). "A neural network model approach to the study of human TAP transporter". In Silico Biol. (Gedrukt). 1 (2): 109–21. PMID 11471244.
  61. ^ Rapin N, Lund O, Bernaschi M, Castiglione F. (2010). "Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system". PLoS ONE. 5 (4): e9862. doi:10.1371/journal.pone.0009862. PMC 2855701. PMID 20419125.CS1 maint: Uses authors parameter (link)
  62. ^ Kaminker JS, Zhang Y, Watanabe C, Zhang Z (July 2007). "CanPredict: a computational tool for predicting cancer-associated missense mutations". Nucleic Acids Res. 35 (Web Server issue): W595–8. doi:10.1093/nar/gkm405. PMC 1933186. PMID 17537827.
  63. ^ Jonsson PF, Bates PA (September 2006). "Global topological features of cancer proteins in the human interactome". Bioinformatics. 22 (18): 2291–7. doi:10.1093/bioinformatics/btl390. PMC 1865486. PMID 16844706.
  64. ^ Sun J, Zhao Z (2010). "A comparative study of cancer proteins in the human protein-protein interaction network". BMC Genomics. 11 (Suppl 3): S5. doi:10.1186/1471-2164-11-S3-S5. PMC 2999350. PMID 21143787.[permanent dead link]
  65. ^ Palladini A, Nicoletti G, Pappalardo F, Murgo A, Grosso V, Ianzano ML, Antognoli A, Croci S, Landuzzi L, De Giovanni C, Nanni P, Motta S, Lollini PL (2010). "In silico modeling and in vivo efficacy of cancer-preventive vaccinations". Cancer Research. 70 (20): 7755–63. doi:10.1158/0008-5472.CAN-10-0701. PMID 20924100.
  66. ^ Pappalardo F, Forero IM, Pennisi M, Palazon A, Melero I, Motta S (2011). "Modeling induced immune system response against B16-melanoma". PLoS ONE. 6 (10): e26523. doi:10.1371/journal.pone.0026523. PMC 3197530. PMID 22028894.
  67. ^ Pappalardo F, Pennisi M, Ricupito A, Topputo F, Bellone M (2014). "Induction of T cell memory by a dendritic cell vaccine: a computational model". Bioinformatics. 30 (13): 1884–91. doi:10.1093/bioinformatics/btu059. PMID 24603984.

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