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

<p>Abstract</p> <p>Background</p> <p>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 <it>in silico </it>method PAAS was applied for prediction of interactions between protein kinases and their substrates.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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 <url>http://www.ibmc.msk.ru/PAAS/</url>.</p

Recognition of protein function using the local similarity

The functional annotation of amino acid sequences is one of the most important problems in bioinformatics. Different programs have been successfully applied for recognition of some functional classes; nevertheless, many functional groups still cannot be predicted with the required accuracy. We developed a new method for protein function recognition using the original approach of sequence description. Each sequence of the training set is compared with the query sequence, and the local similarity scores are calculated for the query sequence positions and used as input data for the original classifier. The method was tested using leave-one-out cross-validation for three data sets covering 58 enzyme classes. Two tested sets including noncrossing functional classes were recognized with high accuracy at various levels of classification hierarchy. The majority of these classes were predicted with 100% accuracy, showing a prediction ability comparable with the HMMer method and an accuracy superior to the SVM-Prot program. When the tested set was composed of intersected classes of ligand specificity, the prediction accuracy was less; however, the accuracy increased as the size of the predicted class expanded. The proposed method can be used for both predicting protein functional class and selecting the functionally significant sites in a sequence