Modeling the adaptive immune system: predictions and simulations

Lundegaard, Claus; Lund, Ole; Kesmir, Can; Brunak, Søren; Nielsen, Morten

An extensive study of linear B-cell epitope prediction methods was published by Blythe and Flower (2005) . To test how well peaks in single amino acid scale propensity profiles are (significantly) associated with known linear epitope locations, 484 amino acid propensities from the AAindex database (http://www.genome.ad.jp) (Kawashima and Kanehisa, 2000) were used. As test set they used 50 epitope-mapped proteins defined by polyclonal antibodies, which were the best non-redundant test set available. Blythe and Flower (2005) found, however, that even the predictions based on the most accurate amino acid scales were only marginally better than random, suggesting that more sophisticated approaches is needed to predict the linear epitopes. BepiPred , an algorithm that combines scores from the Parker hydrophilicity scale (Parker et al., 1986 ) and a PSSM trained on linear epitopes, shows a small, but significant, increase in AUC over earlier scale-based methods. The sequence parametrizer algorithm (Sollner, 2006; Sollner and Mayer, 2006) , along with its associated machine-learning methods uses the common single amino acid propensity scales, but also incorporates neighborhood parameters reflecting the probability that a given stretch of amino acids exists within a predefined proximity of a specific amino acid residue. Training and testing on epitope sequences pulled from a high-quality proprietary database, as well as several publicly accessible databases, yields a degree of accuracy that is greatly increased over single-parameter methods.