Prediction of antigenic epitopes on protein surfaces by consensus scoring
1 School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, D-28759 Bremen, Germany
2 Department of Radiation Oncology, Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, 23298, USA
3 The Center for Plant Science Innovation, School of Biological Sciences, University of Nebraska, Lincoln, NE, 68588, USA
4 Physics Department, Technical University Munich, James Franck Str. 1, D-85747 Garching, Germany
Citation and License
BMC Bioinformatics 2009, 10:302 doi:10.1186/1471-2105-10-302Published: 22 September 2009
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.
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.
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.