Modeling the adaptive immune system: predictions and simulations

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

PAProC (http://www.paproc.de) is a prediction method for cleavages by human as well as wild type and mutant yeast proteasomes. The influences of different amino acids at different positions are determined by using a stochastic hillclimbing algorithm (Kuttler et al., 2000) based on the experimentally in vitro verified cleavage and non-cleavage sites (Nussbaum et al., 2001) . Both the FragPredict and PAProC methods make use of the limited in vitro proteasomal digest data available. FragPredict is a linear method, and it may not capture the non-linear features of the specificity of the proteasome. The NetChop (Kesmir et al., 2002) method tries to address these two issues. The prediction system is a multilayered ANN and uses naturally processed MHC class I ligands to predict proteasomal cleavage. Since some of these ligands are generated by the immunoproteasome, and some by the constitutive proteasome, such a method should predict the combined specificity of both forms of proteasomes. In 2003, NetChop-2.0 were evaluated to be the best-performing predictor on an independent evaluation set (Saxova´et al., 2003) . Pcleavage is another web accessible proteasomal cleavage predictor, which is SVM based and have a published performance comparable to NetChop-2.0 (Bhasin and Raghava, 2005) . An update of the NetChop method [NetChop-3.0, Nielsen et al. (2005) ] consists of a combination of several ANNs, each trained using a different sequence-encoding scheme of the data. NetChop 3.0 has an increase in the prediction sensitivity as compared to NetChop 2.0, without lowering the specificity, and is thus probably the current best predictor of proteasomal cleavage. Tenzer et al. (2004) have published a weight matrix based method for prediction of both constitutive-and immunoproteasomal cleavage specificity. Both matrices are trained on in vitro digest data.