%0 Journal Article
%T Prediction of antigenic epitopes on protein surfaces by consensus scoring
%A Shide Liang
%A Dandan Zheng
%A Chi Zhang
%A Martin Zacharias
%J BMC Bioinformatics
%D 2009
%I BioMed Central
%R 10.1186/1471-2105-10-302
%X 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.Realistic prediction of protein surface regions that are preferentially recognized by antibodies (antigenic epitopes) can help in the design of vaccine components and immuno-diagnostic reagents. Antigenic epitopes are classified as continuous or discontinues epitopes. If the residues involved in an epitope are contiguous in the polypeptide chain, this epitope is called a continuous epitope or a linear epitope. On the other hand, a discontinuous or non-linear epitope is composed of residues that are not necessarily continuous in the polypeptide sequence but have spatial proximity on the surface of a protein structure. A significant fraction of epitopes are discontinuous in the sense that antibody binding is not fully determined by a linear peptide segment but also influenced by adjacent surface regions [1].However, the majority of available epitope prediction methods focus on continuous epitopes due to the convenience of the investigation in which the amino acid sequence of a protein is taken as the input. Such prediction methods are based upon the amino acid properties including hydrophilicity [2,3], so
%U http://www.biomedcentral.com/1471-2105/10/302