SEPIa, a knowledge-driven algorithm for predicting conformational B-cell epitopes from the amino acid sequence.
BACKGROUND
The identification of immunogenic regions on the surface of antigens, which are able to be recognized by antibodies and to trigger an immune response, is a major challenge for the design of new and effective vaccines. The prediction of such regions through computational immunology techniques is a challenging goal, which will ultimately lead to a drastic limitation of the experimental tests required to validate their efficiency. However, current methods are far from being sufficiently reliable and/or applicable on a large scale.


RESULTS
We developed SEPIa, a B-cell epitope predictor from the protein sequence, which is sufficiently fast to be applicable on a large scale. The originality of SEPIa lies in the combination of two classifiers, a naïve Bayesian and a random forest classifier, through a voting algorithm that exploits the advantages of both. It is based on 13 sequence-based features, whose values in a 9-residue sequence window are compiled to predict the epitope/non-epitope state of the central residue. The features are related to the type of amino acid, its conservation in homologous proteins, and its tendency of being exposed to the solvent, soluble, flexible, and disordered. The highest signal is obtained from statistical amino acid preferences, but all 13 features contribute non-negligibly in the predictor. SEPIa's average prediction accuracy is limited, with an AUC score (area under the receiver operating characteristic curve) that reaches 0.65 both in 10-fold cross-validation and on an independent test set. It is nevertheless slightly higher than that of other methods evaluated on the same test set.


CONCLUSIONS
SEPIa was applied to a test protein whose epitopes are known, human β2 adrenergic G-protein-coupled receptor, with promising results. Although the actual AUC score is rather low, many of the predicted epitopes cluster together and overlap the experimental epitope region. The reasons underlying the limitations of SEPIa and of all other B-cell epitope predictors are discussed.
BioModeling, BioInformatics & BioProcesses (3BIO), Université Libre de Bruxelles (ULB), CP 165/61, 50 Roosevelt Ave, 1050, Brussels, Belgium.|Present address: Institute of Mechanical, Process & Energy Engineering, Heriot-Watt University, Edinburgh, EH14 4AS, UK.
Georgios A
GA
Dalkas
BioModeling, BioInformatics & BioProcesses (3BIO), Université Libre de Bruxelles (ULB), CP 165/61, 50 Roosevelt Ave, 1050, Brussels, Belgium. mrooman@ulb.ac.be.|Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, CP 263, Triumph Bld, 1050, Brussels, Belgium. mrooman@ulb.ac.be.
Marianne
M
Rooman
D000465:Algorithms; D000595:Amino Acid Sequence; D000936:Antigen-Antibody Complex; D019540:Area Under Curve; D018985:Epitopes, B-Lymphocyte / Q000737:chemistry / Q000276:immunology / Q000378:metabolism*; D006801:Humans; D008969:Molecular Sequence Data; D017433:Protein Structure, Secondary; D012372:ROC Curve; D018343:Receptors, Adrenergic, beta-2 / Q000737:chemistry / Q000378:metabolism
D000936:Antigen-Antibody Complex; D018985:Epitopes, B-Lymphocyte; D018343:Receptors, Adrenergic, beta-2
Antigen-antibody complexes; B-cell epitopes; Bioinformatics predictor; Immunoinformatics; Machine learning; Physicochemical properties; Statistical potentials; β2 adrenergic G-protein-coupled receptor