Kinase peptide specificity: Improved determination and relevance to protein phosphorylation

Fujii et al. 10.1073/pnas.0401881101.

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Fig. 7. Determination of score and prediction rank for a potential phosphorylation site. (a) Method for scoring a phosphorylation site with a position-specific scoring matrix (PSSM) is illustrated for the PKC-d PSSM (Fig. 1b) and the sequence surrounding MARCKS S159 as the substrate. The method of Yaffe et al. (1) is used to assign both a raw score and a prediction ranking (expressed as percentile). The "total raw score" for the site is 7.9, which is the sum of the 14 individual scores from the PSSM (Fig. 1b) for the residues in the peptide from P–7 to P+6, such as a value of 1.2 for the K at P+2. (b) Conversion of raw score to prediction rank. A prediction rank, which is more useful, can be derived from the total raw score; this conversion is accomplished by comparing the total raw score for this S with scores calculated for the ≈1 million S and T residues in the 15,661 defined human genes in the National Center for Biotechnology Information Reference Sequence (Refseq) collection. Because a score of 7.9 corresponds to the top 0.02 percentile, the PKC-d PSSM correctly predicts that MARCKS S159 is an excellent PKC substrate.

1. Yaffe, M. B., Leparc, G. G., Lai, J., Obata, T., Volinia, S. & Cantley, L. C. (2001) Nat. Biotechnol. 19, 348-353.

Fig. 8. A panel of proteomic peptides used to compare predicted vs. measured phosphorylation of peptides. A panel of 75 proteomic peptides was synthesized. The dominant criteria for selection of the sequences for the peptides was computerized scanning of human protein sequences among National Center for Biotechnology Information reference sequences (http://ncbi.nlm.nih.gov/RefSeq) to identify sites with an abundance of positively charged residues in positions P–3 to P+3 relative to a potential P0 phosphorylation position (S or T) and with good diversity in the P–1 and P+1 positions. The selection of these 75 peptides is not biased by PSSM information because they have been chosen before the development of this method. Details on 11 particularly informative peptides are tabulated. In vitro phosphorylation of each of the peptides (at 10 m M) by PKC-d and PKC-z under standard conditions resulting in stoichiometry of phosphorylation £ 0.1. Results are expressed as relative phosphorylation, normalized to 100% for the best peptide. Four different predictions also were made for each peptide: its susceptibility to phosphorylation by PKC-d and PKC-z based on our method (positional scanning of oriented peptide library, PS-OPL) or the amino acid sequencing of phosphorylated oriented peptide library (ASP-OPL) method (Scansite, http://scansite.mit.edu). Phosphorylation above a cutoff of 10% was considered positive (shown with orange fill). Prediction ranks in the 1st percentile were considered positive (yellow fill), and ranks in the first 0.2 percentile were strong positives (red fill).

Fig. 9. Relevance of peptide specificity predictions to phosphorylation of intact proteins. (a) Tabulation of PKC-d prediction ranks for sites near the basic effector domain of MARCKS (see Fig. 6a). (b) Determination of prediction rank of reported PKC sites. One hundred twenty-four reported PKC phosphorylation sites were accumulated from the peer-reviewed literature; sites attributed to atypical PKCs were excluded from the analysis. All sites were chosen based on reported phosphorylation of human, mouse, rat, or bovine protein; for the current analysis, sequence of the orthologous human site was scored to determine the PKC-d prediction rank based on either our PS-OPL PSSM or the previously described ASP-OPL (1). Sites also were scored for match to the "consensus sequence" [RKQH][RKM]X[ST][FLY][RKHQ][AVFLRK] predicted for PKC-d by PREDIKIN (2). Results for some of the most informative peptides are tabulated in b (see Fig. 6 b and c) (3-8).

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