The immune epitope database (IEDB) 3.0

By R. Vita, James A. Overton, J. Greenbaum, J. Ponomarenko, J. Clark, Jason R. Cantrell, D. Wheeler, Joseph L. Gabbard, D. Hix, Alessandro Sette and 1 more. Nucleic Acids Res., 2014

1,073 citations recorded by the source corpus.

The IEDB, www.iedb.org, contains information on immune epitopes—the molecular targets of adaptive immune responses—curated from the published literature and submitted by National Institutes of Health funded epitope discovery efforts. From 2004 to 2012 the IEDB curation of journal articles published since 1960 has caught up to the present day, with >95% of relevant published literature manually curated amounting to more than 15 000 journal articles and more than 704 000 experiments to date. The revised curation target since 2012 has been to make recent research findings quickly available in the IEDB and thereby ensure that it continues to be an up-to-date resource. Having gathered a comprehensive dataset in the IEDB, a complete redesign of the query and reporting interface has been performed in the IEDB 3.0 release to improve how end users can access this information in an intuitive and biologically accurate manner. We here present this most recent release of the IEDB and describe the user testing procedures as well as the use of external ontologies that have enabled it.

Subfields: Communication, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/2e5701b71ccf3352b30b584c2e48fdc307376385

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Citations in the corpus

The Immune Epitope Database and Analysis Resource in Epitope Discovery and Synthetic Vaccine Design (2017, background): “It also has data for macaque, chimpanzee, mouse, cattle, pig, and rat (13).”
VDJdb: a curated database of T-cell receptor sequences with known antigen specificity (2017, background): “org/, (3)) focuses on antigenic peptide epitopes without any linkage to cognate TCR sequences.”
Evaluation of machine learning methods to predict peptide binding to MHC Class I proteins (2017, methodology): “The Immune Epitope Database (IEDB) [31] provides a large public set of experimentally characterized peptides and peptide-MHC binding affinities.”
ArrayPitope: Automated Analysis of Amino Acid Substitutions for Peptide Microarray-Based Antibody Epitope Mapping (2017, background): “Two B cell epitopes for HSA have been mapped and are available within the IEDB [18].”
A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction (2020, background): “The Immune Epitope Database (IEDB) is the largest public resource for HLA ligands and T-cell epitopes [20].”
HLA class I binding prediction via convolutional neural networks (2017, methodology): “This dataset contained HLA class I binding data curated from four widely used, publicly available MHC datasets: IEDB [Vita et al ., 2015], An-tiJen [Toseland et al ., 2005], MHCBN [Lata et al ., The input is the peptide.”
Fundamentals and Methods for T- and B-Cell Epitope Prediction (2017, methodology): “These peptide sequences are generally available in specialized epitope databases such as IEDB [15], EPIMHC [16], Antijen [17, 18].”
RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning (2022, background): “Immune Epitope Database (82) Antibody and T cell epitopes”
Improved methods for predicting peptide binding affinity to MHC class II molecules (2018, methodology): “In this paper, we present updated versions of our binding affinity prediction methods, NetMHCII and NetMHCIIpan, trained on an extended data set of more than 100,000 quantitative peptide-binding measurements from IEDB (26), covering 36 HLA-DR, 27 HLA-DQ, 9 HLA-DP, as well as 8 mouse MHC-II molecules”
Gapped sequence alignment using artificial neural networks: application to the MHC class I system (2016, background): “Contact: mniel@cbs.dtu.dk Supplementary information: Supplementary data are available at Bioinformatics online.”
NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets (2016, methodology): “The MHC class I binding dataset was downloaded from the IEDB [16] (http://tools.immuneepitope.org/main/data-sets; dataset used for retraining the IEDB class I binding prediction tools).”
The Ontology for Biomedical Investigations (2016, background): “Fig 3 depicts how the list of T cell assay types used in the IEDB has been mapped to OBI.”
A Recurrent Mutation in Anaplastic Lymphoma Kinase with Distinct Neoepitope Conformations (2018, methodology): “The peptides generated by transgene are tested for binding against the inferred HLA haplotypes using the IEDB suite of MHC-I and MHC-II epitope predictors.3 Each 2n − 1-mer input peptide yields n calls for each allele in the HLA haplotype, for each n = (9, 10) for MHCI and n = 15 for MHC-II.”
A Review of Deep Learning Methods for Antibodies (2020, methodology): “The IEDB contains information on sequences, experimental data and in some cases structure data for 973,072 linear and discontinuous epitopes [53].”
MHCflurry: open-source class I MHC binding affinity prediction (2017, neutral): “[18] , 55,473 were not also present in the IEDB dataset.”
pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens (2016, methodology): “There are also existing software tools (IEDB [16], EpiBot [17], EpiToolKit [18]) that compile the results generated from individual epitope prediction algorithms to improve the prediction accuracy with consensus methods or a unified final ranking.”