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. 2010 Jun 10:11:313.
doi: 10.1186/1471-2105-11-313.

Functional classification of proteins based on projection of amino acid sequences: application for prediction of protein kinase substrates

Boris Sobolev  1 , Dmitry Filimonov, Alexey Lagunin, Alexey Zakharov, Olga Koborova, Alexander Kel, Vladimir Poroikov
Affiliations

Functional classification of proteins based on projection of amino acid sequences: application for prediction of protein kinase substrates

Boris Sobolev et al. BMC Bioinformatics. .

Abstract

Background: The knowledge about proteins with specific interaction capacity to the protein partners is very important for the modeling of cell signaling networks. However, the experimentally-derived data are sufficiently not complete for the reconstruction of signaling pathways. This problem can be solved by the network enrichment with predicted protein interactions. The previously published in silico method PAAS was applied for prediction of interactions between protein kinases and their substrates.

Results: We used the method for recognition of the protein classes defined by the interaction with the same protein partners. 1021 protein kinase substrates classified by 45 kinases were extracted from the Phospho.ELM database and used as a training set. The reasonable accuracy of prediction calculated by leave-one-out cross validation procedure was observed in the majority of kinase-specificity classes. The random multiple splitting of the studied set onto the test and training set had also led to satisfactory results. The kinase substrate specificity for 186 proteins extracted from TRANSPATH database was predicted by PAAS method. Several kinase-substrate interactions described in this database were correctly predicted. Using the previously developed ExPlain system for the reconstruction of signal transduction pathways, we showed that addition of the newly predicted interactions enabled us to find the possible path between signal trigger, TNF-alpha, and its target genes in the cell.

Conclusions: It was shown that the predictions of protein kinase substrates by PAAS were suitable for the enrichment of signaling pathway networks and identification of the novel signaling pathways. The on-line version of PAAS for prediction of protein kinase substrates is freely available at http://www.ibmc.msk.ru/PAAS/.

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Figures

Figure 1
Figure 1
Local similarity estimation. The diagonal corresponds to the shift value h providing the best match between the region of sequence Q and sequence D. Amn is the summarized similarity of superposed areas of sequences Q and D terminated at qm and dn+h, respectively. Thus, the score Ri = Aih - Ai-F, h, presents the highest similarity score being found for the selected region of sequence Q. Finally, the similarity score Si takes the maximal values from Ri+j scores.
Figure 2
Figure 2
Intersection of the kinase substrate classes.
Figure 3
Figure 3
Signal transduction cascade from TNF ligands to transcription factors reconstructed by ExPlain™ system. TNF ligand is depicted as orange triangle. Transcription factors (TFs, diamonds) are identified by promoter analysis of up-regulated genes upon TNF-alpha stimulation of HUVEC cell line. Dashed arrows represent the novel predicted kinase-substrate interactions helping to connect TNF ligands with TFs through cascades of phosphorylation events. All other arrows represent signal transduction interactions known in TRANSPATH®. The up-regulated molecules are red. The down-regulated molecules are green. Two underlined TFs can be reached from TNF ligands in less than 6 steps with the help of the novel kinase-substrate interactions only.
Figure 4
Figure 4
Binding sites for MEF-2A and STAT6 transcription factors. These binding sites are closely situated in promoters of three highly up-regulated genes upon TNF-alpha treatment. TF sites are found with ExPlain™ and position weight matrices (PWMs) from TRANSFAC® database. Sites are shown as arrows above the sequences of promoters. The names of PWMs are shown together with the obtained site score (shown in the brackets).
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