Summarizing the attempts in this work to improve epitope identification by combining different prediction steps, it has to be concluded that currently the only reliable strategy is to filter out those peptides exhibiting poor TAP transport scores, and use MHC-I binding affinity predictions to identify epitopes among the transportable peptides. This algorithm is implemented on the publicly available website www.mhc-pathway.net The website currently contains binding predictions for five different MHC-I alleles, which will be updated as more data becomes available. It is also planned to include more TAP scoring matrices describing its transport preference in different species.
The next step along this line is to include the proteasome, for which currently no prediction algorithms with sufficiently high reliability are available. Accurate prediction of proteasomal fragments would lead to a further improvement of TAP transport predictions which then - instead of considering all precursors up to length L as equally probable - can be restricted to those precursors actually generated. Eventually, this should also make the down-weighting of N-terminal residues in the TAP predictions obsolete, because there would be no uncertainty as to which precursors are generated, and co-evolution between peptide specificities of the proteasome, TAP and MHC-I would be included in the model. To establish a consistent database for proteasomal cleavage prediction, it is planned to apply the described novel evaluation protocol to a series of proteasomal digests with a large number of substrates and different types of proteasomes (e.g. constitutive / immuno proteasome, with and without the 11S and 19S regulators). The extracted cleavage probabilities can then be analyzed using the SMM framework established here for sequence based prediction of peptide affinities to MHC-I and TAP.
In principal, the SMM + pair coefficients algorithm can be applied to all problems that require the prediction of a property associated with a sequence. However, the approach is likely to be successful only when the assumption of independent additive contributions of each sequence positions to the property under investigation is a decent approximation. To test the SMM + pair coefficient approach on problems completely different from affinity experiments, it was applied to the identification of cis-prolines from their sequence environment (Lorenzen, et al., ) and the [page 98↓]prediction of contacts between residues of membrane helices and either residues of other helices or lipids in the membrane itself (Hildebrand, et al., ), both with positive preliminary results. The application, refinement and testing of the limits of this approach is another goal for the future.
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