The Immune Epitope Database 2.0

By R. Vita, Laura Zarebski, J. Greenbaum, Hussein Emami, I. Hoof, Nima Salimi, R. Damle, Alessandro Sette, Bjoern Peters. Nucleic Acids Res., 2009

672 citations recorded by the source corpus.

The Immune Epitope Database (IEDB, www.iedb.org) provides a catalog of experimentally characterized B and T cell epitopes, as well as data on Major Histocompatibility Complex (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, nonhuman primates, rodents, pigs, cats and all other tested species are included. Both positive and negative experimental results are captured. Over the course of 4 years, the data from 180 978 experiments were curated manually from the literature, which covers ∼99% of all publicly available information on peptide epitopes mapped in infectious agents (excluding HIV) and 93% of those mapped in allergens. In addition, data that would otherwise be unavailable to the public from 129 186 experiments were submitted directly by investigators. The curation of epitopes related to autoimmunity is expected to be completed by the end of 2010. The database can be queried by epitope structure, source organism, MHC restriction, assay type or host organism, among other criteria. The database structure, as well as its querying, browsing and reporting interfaces, was completely redesigned for the IEDB 2.0 release, which became publicly available in early 2009.

Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9d79185d82c03c30778f3635bfbdcf605330f41b

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Citations in the corpus

SVMTriP: A Method to Predict Antigenic Epitopes Using Support Vector Machine to Integrate Tri-Peptide Similarity and Propensity (2012, methodology): “In addition, based on the epitope databases such as IEDB [9], Bcipep [10], and FIMM [11], there are also some methods using machine learning approaches, such as Hidden Markov Model (HMM) [12], Artificial Neural Network (ANN) [13], and Support Vector Machine (SVM) [14,15], to locate linear epitopes, ”
The immune epitope database (IEDB) 3.0 (2014, methodology): “Details describing the curation process put in place and followed by the curation team, including quality controls for accuracy and consistency, have been discussed previously (2).”
A fast Peptide Match service for UniProt Knowledgebase (2013, background): “So far, to our knowledge, no other peptide match services provide all of the key characteristics in our new service, including search speed, support for any query peptide of 3 amino acids long, coverage of all UniProtKB sequences including isoforms (UniProt Consortium, 2013) and frequent update.”
Peptide binding predictions for HLA DR, DP and DQ molecules (2010, methodology): “This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Immune Epitope Database (IE”
Stability-mediated epistasis constrains the evolution of an influenza protein (2013, background): “We identified human CTL epitopes of ≤12 residues with at least 89% conservation in Aichi/1968 NP and a verified T-cell response from the Immune Epitope Database (Vita et al., 2010).”
Properties of MHC Class I Presented Peptides That Enhance Immunogenicity (2013, methodology): “These peptides were obtained from data sets from Assarsson et al [43], Kotturi et al. [44] and an unpublished data set on Coxiella Burnetti-derived peptides as well as from the IEDB [45].”
Exploring dengue genome to construct a multi-epitope based subunit vaccine by utilizing immunoinformatics approach to battle against dengue infection (2017, methodology): “Along with IC 50 value of peptides, a percentile rank is developed by comparing the IC 50 values of peptides against a set of random antigen from SWISSPROT database, compounds with least percentile rank show high affinity 28 .”
ViPR: an open bioinformatics database and analysis resource for virology research (2011, background): “org) (11), >2900 structures from the Protein Data Bank (PDB; www.”
Removing T-cell epitopes with computational protein design (2014, methodology): “The peptide-binding repertoire of many MHCII alleles has been extensively characterized (5), and a number of methods has been developed for predicting the affinity of novel peptides for a given MHCII (6).”