Prediction of antigenic epitopes on protein surfaces by consensus scoring

Shide Liang, Dandan Zheng, Chi Zhang, Martin Zacharias

Research output: Contribution to journalArticle

68 Citations (Scopus)

Abstract

Background: Prediction of antigenic epitopes on protein surfaces is important for vaccine design. Most existing epitope prediction methods focus on protein sequences to predict continuous epitopes linear in sequence. Only a few structure-based epitope prediction algorithms are available and they have not yet shown satisfying performance. Results: We present a new antigen Epitope Prediction method, which uses ConsEnsus Scoring (EPCES) from six different scoring functions - residue epitope propensity, conservation score, side-chain energy score, contact number, surface planarity score, and secondary structure composition. Applied to unbounded antigen structures from an independent test set, EPCES was able to predict antigenic eptitopes with 47.8% sensitivity, 69.5% specificity and an AUC value of 0.632. The performance of the method is statistically similar to other published methods. The AUC value of EPCES is slightly higher compared to the best results of existing algorithms by about 0.034. Conclusion: Our work shows consensus scoring of multiple features has a better performance than any single term. The successful prediction is also due to the new score of residue epitope propensity based on atomic solvent accessibility.

Original languageEnglish (US)
Article number1471
Number of pages1
JournalBMC bioinformatics
Volume10
DOIs
StatePublished - Sep 22 2009

Fingerprint

Epitopes
Scoring
Membrane Proteins
Proteins
Protein
Prediction
Antigens
Area Under Curve
Predict
Planarity
Vaccine
Protein Sequence
Secondary Structure
Test Set
Independent Set
Accessibility
Propensity Score
Vaccines
Specificity
Conservation

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

Prediction of antigenic epitopes on protein surfaces by consensus scoring. / Liang, Shide; Zheng, Dandan; Zhang, Chi; Zacharias, Martin.

In: BMC bioinformatics, Vol. 10, 1471, 22.09.2009.

Research output: Contribution to journalArticle

@article{38380dbe0f5748d794e7e896e8d48302,
title = "Prediction of antigenic epitopes on protein surfaces by consensus scoring",
abstract = "Background: Prediction of antigenic epitopes on protein surfaces is important for vaccine design. Most existing epitope prediction methods focus on protein sequences to predict continuous epitopes linear in sequence. Only a few structure-based epitope prediction algorithms are available and they have not yet shown satisfying performance. Results: We present a new antigen Epitope Prediction method, which uses ConsEnsus Scoring (EPCES) from six different scoring functions - residue epitope propensity, conservation score, side-chain energy score, contact number, surface planarity score, and secondary structure composition. Applied to unbounded antigen structures from an independent test set, EPCES was able to predict antigenic eptitopes with 47.8{\%} sensitivity, 69.5{\%} specificity and an AUC value of 0.632. The performance of the method is statistically similar to other published methods. The AUC value of EPCES is slightly higher compared to the best results of existing algorithms by about 0.034. Conclusion: Our work shows consensus scoring of multiple features has a better performance than any single term. The successful prediction is also due to the new score of residue epitope propensity based on atomic solvent accessibility.",
author = "Shide Liang and Dandan Zheng and Chi Zhang and Martin Zacharias",
year = "2009",
month = "9",
day = "22",
doi = "10.1186/1471-2105-10-302",
language = "English (US)",
volume = "10",
journal = "BMC Bioinformatics",
issn = "1471-2105",
publisher = "BioMed Central",

}

TY - JOUR

T1 - Prediction of antigenic epitopes on protein surfaces by consensus scoring

AU - Liang, Shide

AU - Zheng, Dandan

AU - Zhang, Chi

AU - Zacharias, Martin

PY - 2009/9/22

Y1 - 2009/9/22

N2 - Background: Prediction of antigenic epitopes on protein surfaces is important for vaccine design. Most existing epitope prediction methods focus on protein sequences to predict continuous epitopes linear in sequence. Only a few structure-based epitope prediction algorithms are available and they have not yet shown satisfying performance. Results: We present a new antigen Epitope Prediction method, which uses ConsEnsus Scoring (EPCES) from six different scoring functions - residue epitope propensity, conservation score, side-chain energy score, contact number, surface planarity score, and secondary structure composition. Applied to unbounded antigen structures from an independent test set, EPCES was able to predict antigenic eptitopes with 47.8% sensitivity, 69.5% specificity and an AUC value of 0.632. The performance of the method is statistically similar to other published methods. The AUC value of EPCES is slightly higher compared to the best results of existing algorithms by about 0.034. Conclusion: Our work shows consensus scoring of multiple features has a better performance than any single term. The successful prediction is also due to the new score of residue epitope propensity based on atomic solvent accessibility.

AB - Background: Prediction of antigenic epitopes on protein surfaces is important for vaccine design. Most existing epitope prediction methods focus on protein sequences to predict continuous epitopes linear in sequence. Only a few structure-based epitope prediction algorithms are available and they have not yet shown satisfying performance. Results: We present a new antigen Epitope Prediction method, which uses ConsEnsus Scoring (EPCES) from six different scoring functions - residue epitope propensity, conservation score, side-chain energy score, contact number, surface planarity score, and secondary structure composition. Applied to unbounded antigen structures from an independent test set, EPCES was able to predict antigenic eptitopes with 47.8% sensitivity, 69.5% specificity and an AUC value of 0.632. The performance of the method is statistically similar to other published methods. The AUC value of EPCES is slightly higher compared to the best results of existing algorithms by about 0.034. Conclusion: Our work shows consensus scoring of multiple features has a better performance than any single term. The successful prediction is also due to the new score of residue epitope propensity based on atomic solvent accessibility.

UR - http://www.scopus.com/inward/record.url?scp=70449408296&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70449408296&partnerID=8YFLogxK

U2 - 10.1186/1471-2105-10-302

DO - 10.1186/1471-2105-10-302

M3 - Article

VL - 10

JO - BMC Bioinformatics

JF - BMC Bioinformatics

SN - 1471-2105

M1 - 1471

ER -