Combining peptide recognition specificity and context information for the prediction of the 14‐3‐3‐mediated interactome in S. cerevisiae and H. sapiens

Large‐scale interaction studies contribute the largest fraction of protein interactions information in databases. However, co‐purification of non‐specific or indirect ligands, often results in data sets that are affected by a considerable number of false positives. For the fraction of interactions mediated by short linear peptides, we present here a combined experimental and computational strategy for ranking the reliability of the inferred partners. We apply this strategy to the family of 14‐3‐3 domains. We have first characterized the recognition specificity of this domain family, largely confirming the results of previous analyses, while revealing new features of the preferred sequence context of 14‐3‐3 phospho‐peptide partners. Notably, a proline next to the carboxy side of the phospho‐amino acid functions as a potent inhibitor of 14‐3‐3 binding. The position‐specific information about residue preference was encoded in a scoring matrix and two regular expressions. The integration of these three features in a single predictive model outperforms publicly available prediction tools. Next we have combined, by a naïve Bayesian approach, these “peptide features” with “protein features”, such as protein co‐expression and co‐localization. Our approach provides an orthogonal reliability assessment and maps with high confidence the 14‐3‐3 peptide target on the partner proteins.