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

Proteomics. 2011 Jan;11(1):128-43. doi: 10.1002/pmic.201000030. Epub 2010 Dec 6.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • 14-3-3 Proteins / metabolism*
  • Computational Biology / methods*
  • Humans
  • Peptides / metabolism*
  • Phosphopeptides / metabolism
  • Protein Binding
  • Protein Interaction Mapping / methods*
  • Saccharomyces cerevisiae / metabolism*

Substances

  • 14-3-3 Proteins
  • Peptides
  • Phosphopeptides