Immune epitope database analysis resource (IEDB-AR)

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Abstract

We present a new release of the immune epitope database analysis resource (IEDB-AR, http://tools.immuneepitope.org), a repository of web-based tools for the prediction and analysis of immune epitopes. New functionalities have been added to most of the previously implemented tools, and a total of eight new tools were added, including two B-cell epitope prediction tools, four T-cell epitope prediction tools and two analysis tools.

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Measuring the accuracy of diagnostic systems.

J Swets (1988)

Diagnostic systems of several kinds are used to distinguish between two classes of events, essentially "signals" and "noise". For them, analysis in terms of the "relative operating characteristic" of signal detection theory provides a precise and valid measure of diagnostic accuracy. It is the only measure available that is uninfluenced by decision biases and prior probabilities, and it places the performances of diverse systems on a common, easily interpreted scale. Representative values of this measure are reported here for systems in medical imaging, materials testing, weather forecasting, information retrieval, polygraph lie detection, and aptitude testing. Though the measure itself is sound, the values obtained from tests of diagnostic systems often require qualification because the test data on which they are based are of unsure quality. A common set of problems in testing is faced in all fields. How well these problems are handled, or can be handled in a given field, determines the degree of confidence that can be placed in a measured value of accuracy. Some fields fare much better than others.

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Reliable prediction of T-cell epitopes using neural networks with novel sequence representations.

Morten L. Nielsen, Claus Lundegaard, Peder Worning … (2003)

In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence-encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix-driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high-binding peptides. Finally, we use the method to predict T-cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design.

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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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Journal

Journal ID (nlm-ta): Nucleic Acids Res

Journal ID (iso-abbrev): Nucleic Acids Res

Journal ID (publisher-id): nar

Journal ID (hwp): nar

Title: Nucleic Acids Research

Publisher: Oxford University Press

ISSN (Print): 0305-1048

ISSN (Electronic): 1362-4962

Publication date (Print): 1 July 2008

Publication date (Electronic): 31 May 2008

Publication date PMC-release: 31 May 2008

Volume: 36

Issue: Web Server issue

Pages: W513-W518

Affiliations

¹Immune Epitope Database and Analysis Resource (IEDB-AR), La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA, ²Center for Biological Sequence Analysis, BioCentrum-DTU, Technical University of Denmark, DK-2800 Lyngby, Denmark and ³San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, USA

Author notes

*To whom correspondence should be addressed. +1 858 483 1922+1 858 752 6987 bpeters@ 123456liai.org

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

Article

Publisher ID: gkn254

DOI: 10.1093/nar/gkn254

PMC ID: 2447801

PubMed ID: 18515843

SO-VID: 445b46a0-d7a7-4fae-8030-2a3814ac11a2

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 31 January 2008

Date revision received : 14 April 2008

Date accepted : 20 April 2008

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Immune epitope database analysis resource (IEDB-AR)

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Genomic Prediction

Most cited references 23

Measuring the accuracy of diagnostic systems.

Reliable prediction of T-cell epitopes using neural networks with novel sequence representations.

Improved method for predicting linear B-cell epitopes

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