ROC curve by varying threshold values for PHSDB dataset for the four kinases PKA, PKC, MAPK and CK2.

<p>ROC curve by varying threshold values for PHSDB dataset for the four kinases PKA, PKC, MAPK and CK2.</p

Structural pattern of eight different features for the nonpromoter sequences taken from EMBL and EID database.

<p>The sequences used here are predicted as true promoters by grammars. The structural profiles are plotted with average value of window size 3 nt.</p

A Grammar Inference Approach for Predicting Kinase Specific Phosphorylation Sites

<div><p>Kinase mediated phosphorylation site detection is the key mechanism of post translational mechanism that plays an important role in regulating various cellular processes and phenotypes. Many diseases, like cancer are related with the signaling defects which are associated with protein phosphorylation. Characterizing the protein kinases and their substrates enhances our ability to understand the mechanism of protein phosphorylation and extends our knowledge of signaling network; thereby helping us to treat such diseases. Experimental methods for predicting phosphorylation sites are labour intensive and expensive. Also, manifold increase of protein sequences in the databanks over the years necessitates the improvement of high speed and accurate computational methods for predicting phosphorylation sites in protein sequences. Till date, a number of computational methods have been proposed by various researchers in predicting phosphorylation sites, but there remains much scope of improvement. In this communication, we present a simple and novel method based on Grammatical Inference (GI) approach to automate the prediction of kinase specific phosphorylation sites. In this regard, we have used a popular GI algorithm Alergia to infer Deterministic Stochastic Finite State Automata (DSFA) which equally represents the regular grammar corresponding to the phosphorylation sites. Extensive experiments on several datasets generated by us reveal that, our inferred grammar successfully predicts phosphorylation sites in a kinase specific manner. It performs significantly better when compared with the other existing phosphorylation site prediction methods. We have also compared our inferred DSFA with two other GI inference algorithms. The DSFA generated by our method performs superior which indicates that our method is robust and has a potential for predicting the phosphorylation sites in a kinase specific manner.</p></div

Production rules of grammar G1 generation the promoter region of a human DNA sequence.

<p>Production rules of grammar G1 generation the promoter region of a human DNA sequence.</p

Genome-wide promoter prediction performance comparison of different promoterprediction programs with maximum allowable distance of 500 bp from annotated TSS.

<p>Genome-wide promoter prediction performance comparison of different promoterprediction programs with maximum allowable distance of 500 bp from annotated TSS.</p

Performance of grammar rule along with eight structural features of DNA on two datasets.

<p>Basestaking energy gives the best F-measure for all the dataset.</p

Genome wide precision and recall comparison with other methods.

<p><b>a</b>) Comparison of genome wide precision value at different maximum allowed distances from annotated TSS (1000 bp, 500 bp and 200 bp). <b>b</b>) Comparison of genome wide Recall value at different maximum allowed distances from annotated TSS (1000 bp, 500 bp and 200 bp).</p

Syntactic elements or the nonterminal elements for human promoter region.

<p>G→ Gap i.e., parts of DNA sequences that are not of our interest or their significance is still not known. Here a gap in the grammar rule defines some length of bases flanked by two subsequence of interest. In the grammar rules, the symbol G (any) is used to specify a gap of indefinite length whereas when gap length is known to be within a range, gap (#Lower Limit, Upper Limit#) is preferred.</p

A Prefix Tree Automaton (PTA) generated over Five protein sequences: i) RRKSACP, ii) RRKSIPK, iii) RRPSCAL, iv) RRTSCLI, v) RPKSPSK.

<p>A Prefix Tree Automaton (PTA) generated over Five protein sequences: i) RRKSACP, ii) RRKSIPK, iii) RRPSCAL, iv) RRTSCLI, v) RPKSPSK.</p

Performance comparison of six methods along with our proposed method in terms of precision, recall, accuracy, F-measure for the four types of kinases: (a)PKA, (b)PKC, (c)MAPK and (d)CK2.

<p>Performance comparison of six methods along with our proposed method in terms of precision, recall, accuracy, F-measure for the four types of kinases: (a)PKA, (b)PKC, (c)MAPK and (d)CK2.</p