Prediction of antigenic epitopes on protein surfaces by consensus scoring.
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.


CONCLUSIONS
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.
Liang
Shide
S
School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, D-28759 Bremen, Germany.
Zheng
Dandan
D
Zhang
Chi
C
Zacharias
Martin
M
D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D000939:Epitopes / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods
D000939:Epitopes