IEDB-AR: immune epitope database—analysis resource in 2019

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Abstract

The Immune Epitope Database Analysis Resource (IEDB-AR, http://tools.iedb.org/) is a companion website to the IEDB that provides computational tools focused on the prediction and analysis of B and T cell epitopes. All of the tools are freely available through the public website and many are also available through a REST API and/or a downloadable command-line tool. A virtual machine image of the entire site is also freely available for non-commercial use and contains most of the tools on the public site. Here, we describe the tools and functionalities that are available in the IEDB-AR, focusing on the 10 new tools that have been added since the last report in the 2012 NAR webserver edition. In addition, many of the tools that were already hosted on the site in 2012 have received updates to newest versions, including NetMHC, NetMHCpan, BepiPred and DiscoTope. Overall, this IEDB-AR update provides a substantial set of updated and novel features for epitope prediction and analysis.

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NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data.

Morten Nielsen, Vanessa Jurtz, Sinu Paul … (2017)

Cytotoxic T cells are of central importance in the immune system's response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC class I molecules. Peptide binding to MHC molecules is the single most selective step in the Ag-presentation pathway. Therefore, in the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has attracted widespread attention. In the past, predictors of peptide-MHC interactions have primarily been trained on binding affinity data. Recently, an increasing number of MHC-presented peptides identified by mass spectrometry have been reported containing information about peptide-processing steps in the presentation pathway and the length distribution of naturally presented peptides. In this article, we present NetMHCpan-4.0, a method trained on binding affinity and eluted ligand data leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an increase in predictive performance compared with state-of-the-art methods when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes.

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Gapped sequence alignment using artificial neural networks: application to the MHC class I system.

Massimo Andreatta, Morten A. Nielsen (2016)

Many biological processes are guided by receptor interactions with linear ligands of variable length. One such receptor is the MHC class I molecule. The length preferences vary depending on the MHC allele, but are generally limited to peptides of length 8-11 amino acids. On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment.

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Improved method for predicting linear B-cell epitopes

Jens Larsen, Ole Lund, Morten Bundgaard-Nielsen (2006)

Background B-cell epitopes are the sites of molecules that are recognized by antibodies of the immune system. Knowledge of B-cell epitopes may be used in the design of vaccines and diagnostics tests. It is therefore of interest to develop improved methods for predicting B-cell epitopes. In this paper, we describe an improved method for predicting linear B-cell epitopes. Results In order to do this, three data sets of linear B-cell epitope annotated proteins were constructed. A data set was collected from the literature, another data set was extracted from the AntiJen database and a data sets of epitopes in the proteins of HIV was collected from the Los Alamos HIV database. An unbiased validation of the methods was made by testing on data sets on which they were neither trained nor optimized on. We have measured the performance in a non-parametric way by constructing ROC-curves. Conclusion The best single method for predicting linear B-cell epitopes is the hidden Markov model. Combining the hidden Markov model with one of the best propensity scale methods, we obtained the BepiPred method. When tested on the validation data set this method performs significantly better than any of the other methods tested. The server and data sets are publicly available at .

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Author and article information

Journal

Journal ID (nlm-ta): Nucleic Acids Res

Journal ID (iso-abbrev): Nucleic Acids Res

Journal ID (publisher-id): nar

Title: Nucleic Acids Research

Publisher: Oxford University Press

ISSN (Print): 0305-1048

ISSN (Electronic): 1362-4962

Publication date (Print): 02 July 2019

Publication date (Electronic): 22 May 2019

Publication date PMC-release: 22 May 2019

Volume: 47

Issue: W1

Pages: W502-W506

Affiliations

[1 ]Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA

[2 ]Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark

[3 ]Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Argentina

[4 ]Department of Medicine, University of California, San Diego, CA 92122, USA

Author notes

To whom correspondence should be addressed. Tel: +1 858 752 6914; Fax:+1 858 752 6987; Email: bpeters@ 123456lji.org

The authors wish it to be known that, in their opinion, the first three authors should be regarded as Joint First Authors.

Author information

Sandeep Kumar Dhanda http://orcid.org/0000-0003-1381-7434

Swapnil Mahajan http://orcid.org/0000-0003-2136-1801

Massimo Andreatta http://orcid.org/0000-0002-8036-2647

Paolo Marcatili http://orcid.org/0000-0003-2615-5695

Article

Publisher ID: gkz452

DOI: 10.1093/nar/gkz452

PMC ID: 6602498

PubMed ID: 31114900

SO-VID: 958ba9e3-1a55-4ac7-b1b5-cde97651d4bd

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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

History

Date accepted : 10 May 2019

Date revision received : 01 May 2019

Date received : 12 February 2019

Page count

Pages: 5

Funding

Funded by: National Institutes of Health 10.13039/100000002

Award ID: HHSN272201200010C

Award ID: 75N93019C00001

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IEDB-AR: immune epitope database—analysis resource in 2019

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Genomic Prediction

Most cited references 17

NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data.

Gapped sequence alignment using artificial neural networks: application to the MHC class I system.

Improved method for predicting linear B-cell epitopes

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