Prediction of proteasome cleavage motifs by neural networks

Abstract

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 www.cbs.dtu.dk/services/NetChop/.

An integrative approach to CTL epitope prediction: A combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions

“…NetChop Cterm has been trained on natural MHC class I ligands, whereas NetChop 20S is trained on in vitro cleavage data. It has previously been shown that NetChop 20S is less accurate in predicting the C-terminal ends of naturally occurring MHC class I ligands than NetChop C-term 2.0 [17]. Similar results were obtained in a new implementation of NetChop (NetChop C-term 3.0 and NetChop 20S-3.0), where a superior performance was obtained using a novel network training strategy and sequence encoding scheme [18].…”

“…On a very small dataset of only five epitopes from HIV Nef, Kesmir et al [17] showed that combining predictions of proteasomal cleavage with measured TAP and MHC class I binding affinity correlates well with the observed number of MHC class I ligands presented on the cell. In another study, Peters et al [16] improved identification of epitopes by combining predictions of binding affinities to the HLA-A*0201 allele with predictions of TAP transport efficiency.…”

“…20S is trained on constitutive proteasome in vitro digests of yeast enolase and bovine-casein. For details on the dataset construction and ANN training see [17].…”

“…It is estimated that only 1 out of 200 peptides will bind a given MHC class I allele with sufficient strength to elicit a CTL response [1]. However, it has previously been shown that the proteasomal cleavage and the TAP transport efficiency show also some degree of specificity [9,10].…”

“…Several methods have also been developed that aim at predicting the proteasomal cleavage pattern of proteins. One such method is NetChop, which can be found in various versions, trained on different types of data [17]. In this work, we focus on NetChop C-term and NetChop 20S.…”