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Antibody-protein interactions: benchmark datasets and prediction tools evaluation

BACKGROUND: The ability to predict antibody binding sites (aka antigenic determinants or B-cell epitopes) for a given protein is a precursor to new vaccine design and diagnostics. Among the various methods of B-cell epitope identification X-ray crystallography is one of the most reliable methods. Us...

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Autores principales: Ponomarenko, Julia V, Bourne, Philip E
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2174481/
https://www.ncbi.nlm.nih.gov/pubmed/17910770
http://dx.doi.org/10.1186/1472-6807-7-64
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author Ponomarenko, Julia V
Bourne, Philip E
author_facet Ponomarenko, Julia V
Bourne, Philip E
author_sort Ponomarenko, Julia V
collection PubMed
description BACKGROUND: The ability to predict antibody binding sites (aka antigenic determinants or B-cell epitopes) for a given protein is a precursor to new vaccine design and diagnostics. Among the various methods of B-cell epitope identification X-ray crystallography is one of the most reliable methods. Using these experimental data computational methods exist for B-cell epitope prediction. As the number of structures of antibody-protein complexes grows, further interest in prediction methods using 3D structure is anticipated. This work aims to establish a benchmark for 3D structure-based epitope prediction methods. RESULTS: Two B-cell epitope benchmark datasets inferred from the 3D structures of antibody-protein complexes were defined. The first is a dataset of 62 representative 3D structures of protein antigens with inferred structural epitopes. The second is a dataset of 82 structures of antibody-protein complexes containing different structural epitopes. Using these datasets, eight web-servers developed for antibody and protein binding sites prediction have been evaluated. In no method did performance exceed a 40% precision and 46% recall. The values of the area under the receiver operating characteristic curve for the evaluated methods were about 0.6 for ConSurf, DiscoTope, and PPI-PRED methods and above 0.65 but not exceeding 0.70 for protein-protein docking methods when the best of the top ten models for the bound docking were considered; the remaining methods performed close to random. The benchmark datasets are included as a supplement to this paper. CONCLUSION: It may be possible to improve epitope prediction methods through training on datasets which include only immune epitopes and through utilizing more features characterizing epitopes, for example, the evolutionary conservation score. Notwithstanding, overall poor performance may reflect the generality of antigenicity and hence the inability to decipher B-cell epitopes as an intrinsic feature of the protein. It is an open question as to whether ultimately discriminatory features can be found.
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spelling pubmed-21744812008-01-04 Antibody-protein interactions: benchmark datasets and prediction tools evaluation Ponomarenko, Julia V Bourne, Philip E BMC Struct Biol Research Article BACKGROUND: The ability to predict antibody binding sites (aka antigenic determinants or B-cell epitopes) for a given protein is a precursor to new vaccine design and diagnostics. Among the various methods of B-cell epitope identification X-ray crystallography is one of the most reliable methods. Using these experimental data computational methods exist for B-cell epitope prediction. As the number of structures of antibody-protein complexes grows, further interest in prediction methods using 3D structure is anticipated. This work aims to establish a benchmark for 3D structure-based epitope prediction methods. RESULTS: Two B-cell epitope benchmark datasets inferred from the 3D structures of antibody-protein complexes were defined. The first is a dataset of 62 representative 3D structures of protein antigens with inferred structural epitopes. The second is a dataset of 82 structures of antibody-protein complexes containing different structural epitopes. Using these datasets, eight web-servers developed for antibody and protein binding sites prediction have been evaluated. In no method did performance exceed a 40% precision and 46% recall. The values of the area under the receiver operating characteristic curve for the evaluated methods were about 0.6 for ConSurf, DiscoTope, and PPI-PRED methods and above 0.65 but not exceeding 0.70 for protein-protein docking methods when the best of the top ten models for the bound docking were considered; the remaining methods performed close to random. The benchmark datasets are included as a supplement to this paper. CONCLUSION: It may be possible to improve epitope prediction methods through training on datasets which include only immune epitopes and through utilizing more features characterizing epitopes, for example, the evolutionary conservation score. Notwithstanding, overall poor performance may reflect the generality of antigenicity and hence the inability to decipher B-cell epitopes as an intrinsic feature of the protein. It is an open question as to whether ultimately discriminatory features can be found. BioMed Central 2007-10-02 /pmc/articles/PMC2174481/ /pubmed/17910770 http://dx.doi.org/10.1186/1472-6807-7-64 Text en Copyright © 2007 Ponomarenko and Bourne.; 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
Ponomarenko, Julia V
Bourne, Philip E
Antibody-protein interactions: benchmark datasets and prediction tools evaluation
title Antibody-protein interactions: benchmark datasets and prediction tools evaluation
title_full Antibody-protein interactions: benchmark datasets and prediction tools evaluation
title_fullStr Antibody-protein interactions: benchmark datasets and prediction tools evaluation
title_full_unstemmed Antibody-protein interactions: benchmark datasets and prediction tools evaluation
title_short Antibody-protein interactions: benchmark datasets and prediction tools evaluation
title_sort antibody-protein interactions: benchmark datasets and prediction tools evaluation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2174481/
https://www.ncbi.nlm.nih.gov/pubmed/17910770
http://dx.doi.org/10.1186/1472-6807-7-64
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