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Abstract
Reference
Prediction of cleavage motifs of proteasome by neural networks.
C. Kesmir, A. Nussbaum, Hansjorg Schild, Vincent Detours and S. Brunak, manuscript submitted.
Abstract
We present a predictive method that can simulate an essential step in the
antigen presentation in higher vertebrates, namely the step involving the
degradation of polypeptides by proteasome into fragments which have the
potential to bind to MHC Class I molecules. Cleavage prediction algorithms
published so far were trained on data from in vitro digestion experiments
with constitutive proteasomes. As a result, they did not take into account the
characteristics of the structurally modified proteasomes -often called
immunoproteasomes- found in cells stimulated by gamma-interferon under
physiological conditions. Our algorithm has been trained not only on in
vitro data, but also on MHC Class I ligand data which better reflect
immunoproteasome functions. This feature, together with the use of neural
networks, a nonlinear classification technique, make the prediction of Class~I
ligand boundaries more accurate: 65% of the cleavage sites and 85% of the
non-cleavage sites are correctly determined. Moreover, we show that the neural
networks trained on the constitutive proteasome data learns a specificity that
differs from that of the networks trained on MHC Class I ligands (mainly
products of the immunoproteasome). The tools developed in this study in
combination with a predictor of MHC and TAP binding capacity should give a more
complete prediction of the generation and presentation of peptides on MHC
Class I molecules. Here we demonstrate that such an approach produces an
accurate prediction of the CTL the epitopes in HIV Nef.
CORRESPONDENCE
Can Kesmir,
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