Algorithmic Prediction of Proteasomal Cleavages

Proteasomes are the key proteases in cytosolic protein degradation. Some of their degradation products, peptides of 8-11 amino acids in length, are presented on the cell surface bound to major histocompatibility complex (MHC) class I molecules. These, in turn, can be recognized by T lymphozytes screening the body for virus-infected cells.

Prediction of proteasomal cleavage specificity would facilitate the search for CTL epitopes by narrowing down the number of potential peptides selected by MHC class I binding prediction.

Network-based model proteasomes are developed and trained by an evolutionary algorithm with the experimental cleavage data of yeast and human 20 S proteasomes with an array of affinities for a window of ten flanking amino acids.

The affinity parameters of the model, which decide for or against cleavage, correspond with the cleavage motifs determined experimentally. The model proteasomes reproduce and predict proteasomal cleavages, positions and quantitative cleavage strength, with high degree of accuracy.

The prediction algorithms can be used via an Internet site.