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. 2012 Sep 19;367(1602):2574-83.
doi: 10.1098/rstb.2012.0010.

Protein kinases display minimal interpositional dependence on substrate sequence: potential implications for the evolution of signalling networks

Brian A Joughin  1 , Chengcheng Liu, Douglas A Lauffenburger, Christopher W V Hogue, Michael B Yaffe
Affiliations

Protein kinases display minimal interpositional dependence on substrate sequence: potential implications for the evolution of signalling networks

Brian A Joughin et al. Philos Trans R Soc Lond B Biol Sci. .

Abstract

Characterization of in vitro substrates of protein kinases by peptide library screening provides a wealth of information on the substrate specificity of kinases for amino acids at particular positions relative to the site of phosphorylation, but provides no information concerning interdependence among positions. High-throughput techniques have recently made it feasible to identify large numbers of in vivo kinase substrates. We used data from experiments on the kinases ATM/ATR and CDK1, and curated CK2 substrates to evaluate the prevalence of interactions between substrate positions within a motif and the utility of these interactions in predicting kinase substrates. Among these data, evidence of interpositional sequence dependencies is strikingly rare, and what dependency exists does little to aid in the prediction of novel kinase substrates. Significant increases in the ability of models to predict kinase-substrate specificity beyond position-independent models must come largely from inclusion of elements of biological and cellular context, rather than further analysis of substrate sequences alone. Our results suggest that, evolutionarily, kinase substrate fitness exists in a smooth energetic landscape. Taken with results from others indicating that phosphopeptide-binding domains do exhibit interpositional dependence, our data suggest that incorporation of new substrate molecules into phospho-signalling networks may be rate-limited by the evolution of suitability for binding by phosphopeptide-binding domains.

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Figures

Figure 1.
Figure 1.
A comparison of the ability of first- and second-order models to correctly identify true kinase substrates. A field of true kinase substrates withheld from training was hidden among mock substrates for each kinase. A field of 10% true and 90% mock substrates was scored using first- and second-order models, and the fraction of true substrates in the top 10% of highest-scoring sequences was counted. The procedure was repeated 1000 times. Plotted boxes span the 25th to 75th percentile of values, with the red line in the boxes marking the medians. Whiskers extend 1.5 times the distance between the 25th and 75th percentiles, and any points more distant from the median are explicitly plotted. Red- and blue-dashed lines at the values 10% and 100% represent the fraction of true substrates expected in the top 10% of scores expected if true and mock substrates were scored randomly, and the maximum possible fraction of true substrates in the top 10% of scores, respectively. (a) Mock substrates generated by shuffling true substrates to maintain the probability of each amino acid at each position while breaking interpositional dependencies. (b) Mock substrates chosen by randomly selecting sequences from the human proteome conforming to basic known elements of kinase specificity: ‘pS/pT-P’ for ATM/ATR, ‘pS/pT-P’ for CDK1/Cyclin B and ‘pS/pT-X-X-D/E’ for CK2. Because CK2 phosphorylates a number of true substrates that do not have +3 D/E, the CK2 models were trained and tested both with all substrate sequences and with only +3 D/E sequences included.
Figure 2.
Figure 2.
A comparison of the ability of first- and second-order models to correctly identify true kinase substrates, correcting for occurrence of amino acid pairs not present among training data. True kinase substrates were predicted as in figure 1, but rather than examining the top 10% of scored test sequences, a number of sequences for each random splitting of test and training data was examined equal to the least of: 10% of the tested sequences, or the number of tested sequences given a non-zero score under the first- or second-order model. (a) Mock substrates generated by position-wise shuffling of true substrates. (b) Mock substrates chosen by randomly selecting sequences from the human proteome conforming to basic known elements of kinase specificity.
Figure 3.
Figure 3.
A model of evolutionary fitness landscapes for substrates of kinases and phosphopeptide-binding domains. (a) Data presented in this paper indicate that kinase substrate fitness may be position-wise independent in the substrate amino acid sequence, and therefore favourable regions of the substrate fitness space may be accessed relatively easily by a chain of sequential single random mutations. (b) Data presented elsewhere [–23] indicate that phosphopeptide-binding domains express significant interpositional dependencies, indicating that favourable regions of ligand fitness may be separated by energetic barriers.
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