title: Prediction of B-cell epitopes using evolutionary information and propensity scales.
abstract: BACKGROUND
  Development of computational tools that can accurately predict presence and location of B-cell epitopes on pathogenic proteins has a valuable application to the field of vaccinology. Because of the highly variable yet enigmatic nature of B-cell epitopes, their prediction presents a great challenge to computational immunologists.
  
  
  METHODS
  We propose a method, BEEPro (B-cell epitope prediction by evolutionary information and propensity scales), which adapts a linear averaging scheme on 16 properties using a support vector machine model to predict both linear and conformational B-cell epitopes. These 16 properties include position specific scoring matrix (PSSM), an amino acid ratio scale, and a set of 14 physicochemical scales obtained via a feature selection process. Finally, a three-way data split procedure is used during the validation process to prevent over-estimation of prediction performance and avoid bias in our experiment results.
  
  
  RESULTS
  In our experiment, first we use a non-redundant linear B-cell epitope dataset curated by Sollner et al. for feature selection and parameter optimization. Evaluated by a three-way data split procedure, BEEPro achieves significant improvement with the area under the receiver operating curve (AUC) = 0.9987, accuracy = 99.29%, mathew's correlation coefficient (MCC) = 0.9281, sensitivity = 0.9604, specificity = 0.9946, positive predictive value (PPV) = 0.9042 for the Sollner dataset. In addition, the same parameters are used to evaluate performance on other independent linear B-cell epitope test datasets, BEEPro attains an AUC which ranges from 0.9874 to 0.9950 and an accuracy which ranges from 93.73% to 97.31%. Moreover, five-fold cross-validation on one benchmark conformational B-cell epitope dataset yields an accuracy of 92.14% and AUC of 0.9066.
  
  
  CONCLUSIONS
  Compared with other current models, our method achieves a significant improvement with respect to AUC, accuracy, MCC, sensitivity, specificity, and PPV. Thus, we have shown that an appropriate combination of evolutionary information and propensity scales with a support vector machine model can significantly enhance the prediction performance of both linear and conformational B-cell epitopes.
authors.affiliation: School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
authors.forename: Scott Yi-Heng
authors.initials: SY
authors.lastname: Lin
authors.forename: Cheng-Wei
authors.initials: CW
authors.lastname: Cheng
authors.forename: Emily Chia-Yu
authors.initials: EC
authors.lastname: Su
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D019540:Area Under Curve; D019295:Computational Biology / Q000379:methods*; D018985:Epitopes, B-Lymphocyte / Q000737:chemistry*; D019143:Evolution, Molecular; D056510:Position-Specific Scoring Matrices; D057216:Propensity Score; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
chemical_list: D000596:Amino Acids; D018985:Epitopes, B-Lymphocyte