Original method NetChop v. 2.0
Prediction of proteasome cleavage motifs by neural networks.
Kesmir C, Nussbaum AK, Schild H, Detours V, Brunak S.
Protein Eng., 15(4):287-96, 2002.
We present a predictive method that can simulate an essential step in
the antigen presentation in higher vertebrates, namely the step
involving the proteasomal degradation of polypeptides into fragments
which have the potential to bind to MHC Class I molecules. Proteasomal
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 reflect a
combination of immunoproteasome and constitutive proteasome
specificity. This feature, together with the use of neural networks, a
non-linear classification technique, make the prediction of MHC 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, i.e. the specificity of the immunoproteasome is
different than the constitutive proteasome. 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. The method is available at
Update to NetChop v. 3.0
The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage.
Nielsen M, Lundegaard C, Lund O, Kesmir C.
Immunogenetics.57(1-2): 33-41, 2005.
Cytotoxic T cells (CTLs) perceive the world through small peptides that
are eight to ten amino acids long. These peptides (epitopes) are
initially generated by the proteasome, a multi-subunit protease that is
responsible for the majority of intra-cellular protein degradation. The
proteasome generates the exact C-terminal of CTL epitopes, and the
N-terminal with a possible extension. CTL responses may diminish if the
epitopes are destroyed by the proteasomes. Therefore, the prediction of
the proteasome cleavage sites is important to identify potential
immunogenic regions in the proteomes of pathogenic microorganisms (or
humans). We have recently shown that NetChop, a neural network-based
prediction method, is the best method available at the moment to do
such predictions; however, its performance is still lower than desired.
Here, we use novel sequence encoding methods and show that the new
version of NetChop predicts approximately 10% more of the cleavage
sites correctly while lowering the number of false positives with close
to 15%. With this more reliable prediction tool, we study two important
questions concerning the function of the proteasome. First, we estimate
the N-terminal extension of epitopes after proteasomal cleavage and
find that the average extension is relatively short. However, more than
30% of the peptides have N-terminal extensions of three amino acids or
more, and thus, N-terminal trimming might play an important role in the
presentation of a substantial fraction of the epitopes. Second, we show
that good TAP ligands have an increased chance of being cleaved by the
proteasome, i.e., the specificity of TAP has evolved to fit the
specificity of the proteasome. This evolutionary relationship allows
for a more efficient antigen presentation.