Benchmarking the PEPOP methods for mimicking discontinuous epitopes.
Antigen-antibody interaction
Antigenicity
Benchmarking
Discontinuous B-cell epitope
Immunogenicity
Molecular mimicry
Peptide design
Protein surface
Protein-protein interactions (PPI)
Structural bioinformatics
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
30 Dec 2019
30 Dec 2019
Historique:
received:
04
02
2019
accepted:
04
11
2019
entrez:
1
1
2020
pubmed:
1
1
2020
medline:
4
3
2020
Statut:
epublish
Résumé
Computational methods provide approaches to identify epitopes in protein Ags to help characterizing potential biomarkers identified by high-throughput genomic or proteomic experiments. PEPOP version 1.0 was developed as an antigenic or immunogenic peptide prediction tool. We have now improved this tool by implementing 32 new methods (PEPOP version 2.0) to guide the choice of peptides that mimic discontinuous epitopes and thus potentially able to replace the cognate protein Ag in its interaction with an Ab. In the present work, we describe these new methods and the benchmarking of their performances. Benchmarking was carried out by comparing the peptides predicted by the different methods and the corresponding epitopes determined by X-ray crystallography in a dataset of 75 Ag-Ab complexes. The Sensitivity (Se) and Positive Predictive Value (PPV) parameters were used to assess the performance of these methods. The results were compared to that of peptides obtained either by chance or by using the SUPERFICIAL tool, the only available comparable method. The PEPOP methods were more efficient than, or as much as chance, and 33 of the 34 PEPOP methods performed better than SUPERFICIAL. Overall, "optimized" methods (tools that use the traveling salesman problem approach to design peptides) can predict peptides that best match true epitopes in most cases.
Sections du résumé
BACKGROUND
BACKGROUND
Computational methods provide approaches to identify epitopes in protein Ags to help characterizing potential biomarkers identified by high-throughput genomic or proteomic experiments. PEPOP version 1.0 was developed as an antigenic or immunogenic peptide prediction tool. We have now improved this tool by implementing 32 new methods (PEPOP version 2.0) to guide the choice of peptides that mimic discontinuous epitopes and thus potentially able to replace the cognate protein Ag in its interaction with an Ab. In the present work, we describe these new methods and the benchmarking of their performances.
RESULTS
RESULTS
Benchmarking was carried out by comparing the peptides predicted by the different methods and the corresponding epitopes determined by X-ray crystallography in a dataset of 75 Ag-Ab complexes. The Sensitivity (Se) and Positive Predictive Value (PPV) parameters were used to assess the performance of these methods. The results were compared to that of peptides obtained either by chance or by using the SUPERFICIAL tool, the only available comparable method.
CONCLUSION
CONCLUSIONS
The PEPOP methods were more efficient than, or as much as chance, and 33 of the 34 PEPOP methods performed better than SUPERFICIAL. Overall, "optimized" methods (tools that use the traveling salesman problem approach to design peptides) can predict peptides that best match true epitopes in most cases.
Identifiants
pubmed: 31888437
doi: 10.1186/s12859-019-3189-3
pii: 10.1186/s12859-019-3189-3
pmc: PMC6937815
doi:
Substances chimiques
Antigen-Antibody Complex
0
Epitopes
0
Peptides
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
738Subventions
Organisme : ANR
ID : ANR-11-LABX-0051
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