B-cell epitopes are the sites of molecules that are recognized by antibodies
of the immune system. Knowledge of B-cell epitopes may be used in the design
of vaccines and diagnostics tests. It is therefore of interest to develop
improved methods for predicting B-cell epitopes. In this paper, we describe
an improved method for predicting linear B-cell epitopes.
In order to do this, three data sets of linear B-cell epitope annotated
proteins were constructed. A data set was collected from the literature,
another data set was extracted from the AntiJen database and a data sets
of epitopes in the proteins of HIV was collected from the Los Alamos HIV
database. An unbiased validation of the methods was made by testing on data
sets on which they were neither trained nor optimized on. We have measured
the performance in a non-parametric way by constructing ROC-curves.
The best single method for predicting linear B-cell epitopes is the hidden
Markov model. Combining the hidden Markov model with one of the best propensity
scale methods, we obtained the BepiPred method. When tested on the validation
data set this method performs significantly better than any of the other