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PEPOP: Computational design of immunogenic peptides

BACKGROUND: Most methods available to predict protein epitopes are sequence based. There is a need for methods using 3D information for prediction of discontinuous epitopes and derived immunogenic peptides. RESULTS: PEPOP uses the 3D coordinates of a protein both to predict clusters of surface acces...

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Autores principales: Moreau, Violaine, Fleury, Cécile, Piquer, Dominique, Nguyen, Christophe, Novali, Nicolas, Villard, Sylvie, Laune, Daniel, Granier, Claude, Molina, Franck
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2262870/
https://www.ncbi.nlm.nih.gov/pubmed/18234071
http://dx.doi.org/10.1186/1471-2105-9-71
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author Moreau, Violaine
Fleury, Cécile
Piquer, Dominique
Nguyen, Christophe
Novali, Nicolas
Villard, Sylvie
Laune, Daniel
Granier, Claude
Molina, Franck
author_facet Moreau, Violaine
Fleury, Cécile
Piquer, Dominique
Nguyen, Christophe
Novali, Nicolas
Villard, Sylvie
Laune, Daniel
Granier, Claude
Molina, Franck
author_sort Moreau, Violaine
collection PubMed
description BACKGROUND: Most methods available to predict protein epitopes are sequence based. There is a need for methods using 3D information for prediction of discontinuous epitopes and derived immunogenic peptides. RESULTS: PEPOP uses the 3D coordinates of a protein both to predict clusters of surface accessible segments that might correspond to epitopes and to design peptides to be used to raise antibodies that target the cognate antigen at specific sites. To verify the ability of PEPOP to identify epitopes, 13 crystallographically defined epitopes were compared with PEPOP clusters: specificity ranged from 0.75 to 1.00, sensitivity from 0.33 to 1.00, and the positive predictive value from 0.19 to 0.89. Comparison of these results with those obtained with two other prediction algorithms showed comparable specificity and slightly better sensitivity and PPV. To prove the capacity of PEPOP to predict immunogenic peptides that induce protein cross-reactive antibodies, several peptides were designed from the 3D structure of model antigens (IA-2, TPO, and IL8) and chemically synthesized. The reactivity of the resulting anti-peptides antibodies with the cognate antigens was measured. In 80% of the cases (four out of five peptides), the flanking protein sequence process (sequence-based) of PEPOP successfully proposed peptides that elicited antibodies cross-reacting with the parent proteins. Polyclonal antibodies raised against peptides designed from amino acids which are spatially close in the protein, but separated in the sequence, could also be obtained, although they were much less reactive. The capacity of PEPOP to design immunogenic peptides that induce antibodies suitable for a sandwich capture assay was also demonstrated. CONCLUSION: PEPOP has the potential to guide experimentalists that want to localize an epitope or design immunogenic peptides for raising antibodies which target proteins at specific sites. More successful predictions of immunogenic peptides were obtained when a peptide was continuous as compared with peptides corresponding to discontinuous epitopes. PEPOP is available for use at .
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spelling pubmed-22628702008-03-05 PEPOP: Computational design of immunogenic peptides Moreau, Violaine Fleury, Cécile Piquer, Dominique Nguyen, Christophe Novali, Nicolas Villard, Sylvie Laune, Daniel Granier, Claude Molina, Franck BMC Bioinformatics Research Article BACKGROUND: Most methods available to predict protein epitopes are sequence based. There is a need for methods using 3D information for prediction of discontinuous epitopes and derived immunogenic peptides. RESULTS: PEPOP uses the 3D coordinates of a protein both to predict clusters of surface accessible segments that might correspond to epitopes and to design peptides to be used to raise antibodies that target the cognate antigen at specific sites. To verify the ability of PEPOP to identify epitopes, 13 crystallographically defined epitopes were compared with PEPOP clusters: specificity ranged from 0.75 to 1.00, sensitivity from 0.33 to 1.00, and the positive predictive value from 0.19 to 0.89. Comparison of these results with those obtained with two other prediction algorithms showed comparable specificity and slightly better sensitivity and PPV. To prove the capacity of PEPOP to predict immunogenic peptides that induce protein cross-reactive antibodies, several peptides were designed from the 3D structure of model antigens (IA-2, TPO, and IL8) and chemically synthesized. The reactivity of the resulting anti-peptides antibodies with the cognate antigens was measured. In 80% of the cases (four out of five peptides), the flanking protein sequence process (sequence-based) of PEPOP successfully proposed peptides that elicited antibodies cross-reacting with the parent proteins. Polyclonal antibodies raised against peptides designed from amino acids which are spatially close in the protein, but separated in the sequence, could also be obtained, although they were much less reactive. The capacity of PEPOP to design immunogenic peptides that induce antibodies suitable for a sandwich capture assay was also demonstrated. CONCLUSION: PEPOP has the potential to guide experimentalists that want to localize an epitope or design immunogenic peptides for raising antibodies which target proteins at specific sites. More successful predictions of immunogenic peptides were obtained when a peptide was continuous as compared with peptides corresponding to discontinuous epitopes. PEPOP is available for use at . BioMed Central 2008-01-30 /pmc/articles/PMC2262870/ /pubmed/18234071 http://dx.doi.org/10.1186/1471-2105-9-71 Text en Copyright © 2008 Moreau et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 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.
spellingShingle Research Article
Moreau, Violaine
Fleury, Cécile
Piquer, Dominique
Nguyen, Christophe
Novali, Nicolas
Villard, Sylvie
Laune, Daniel
Granier, Claude
Molina, Franck
PEPOP: Computational design of immunogenic peptides
title PEPOP: Computational design of immunogenic peptides
title_full PEPOP: Computational design of immunogenic peptides
title_fullStr PEPOP: Computational design of immunogenic peptides
title_full_unstemmed PEPOP: Computational design of immunogenic peptides
title_short PEPOP: Computational design of immunogenic peptides
title_sort pepop: computational design of immunogenic peptides
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2262870/
https://www.ncbi.nlm.nih.gov/pubmed/18234071
http://dx.doi.org/10.1186/1471-2105-9-71
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