An algorithm for the prediction of proteasomal cleavages

Proteasomes, major proteolytic sites in eukaryotic cells, play an important part in major histocompatibility class I (MHC I) ligand generation and thu s in the regulation of specific immune responses. Their cleavage specificit y is of outstanding interest for this process. In order to generalize previously determined cleavage motifs of 20 S protea somes, we developed network-based model proteasomes trained by an evolution ary algorithm with experimental cleavage data of yeast and human 20 S prote asomes. A window of ten flanking amino acid residues proved sufficient for the model proteasomes to reproduce the experimental results with 98-100% ac curacy. Actual experimental data were reproduced significantly better than randomly selected cleavage sites, suggesting that our model proteasomes wer e able to extract rules inherent to proteasomal cleavage data. The affinity parameters of the model, which decide for or against cleavage, correspond with the cleavage motifs determined experimentally. The predictive power of the model was verified for unknown (to the program) test conditions: the p rediction of cleavage numbers in proteins and the generation of MHC I ligan ds from short peptides. In summary, our model proteasomes reproduce and predict proteasomal cleavag es with high degree of accuracy. They present a promising approach for pred icting proteasomal cleavage products in future attempts and, in combination with existing algorithms for MHC I ligand prediction, will be tested to im prove cytotoxic T lymphocyte epitope prediction. (C) 2000 Academic Press.