Improved method for predicting linear B-cell epitopes

BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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 methods tested. The server and data sets are publicly available at

Draft Genome Sequence of Type Strain Streptococcus gordonii ATCC 10558

Streptococcus gordonii ATCC 10558(T) was isolated from a patient with infective endocarditis in 1946 and announced as a type strain in 1989. Here, we report the 2,154,510-bp draft genome sequence of S. gordonii ATCC 10558(T). This sequence will contribute to knowledge about the pathogenesis of infective endocarditis