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A grammar inference approach for predicting kinase specific phosphorylation sites.

Datta S, Mukhopadhyay S - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biophysics, Molecular Biology and Bioinformatics and Distributed Information Centre for Bioinformatics, University of Calcutta, Kolkata, West Bengal, India.

ABSTRACT
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.

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Performance of the Alergia algorithm in comparison to the other two grammar inference methods (RPNI and Genetic algorithm) in terms of precision, recall, accuracy and F-measure for kinases: (a)PKA, (b)PKC, (c)MAPK and (d)CK2.
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pone.0122294.g006: Performance of the Alergia algorithm in comparison to the other two grammar inference methods (RPNI and Genetic algorithm) in terms of precision, recall, accuracy and F-measure for kinases: (a)PKA, (b)PKC, (c)MAPK and (d)CK2.

Mentions: The comparison results of these three grammar inference methods in terms of precision, recall, accuracy and F-measure for all the kinases are shown in Fig 6A–6D According to the Fig 6, we obtain that the Alergia algorithm performs best in terms of all these parameters for all the kinases among the three GI algorithms. RPNI and genetic algorithm performs almost equally well although genetic algorithm performs slightly better than RPNI for predicting the true positives among total positives, i.e., yield a better recall value for MAPK and CK2 kinase. For all the other parameters and kinases, RPNI performs superior to genetic algorithm.


A grammar inference approach for predicting kinase specific phosphorylation sites.

Datta S, Mukhopadhyay S - PLoS ONE (2015)

Performance of the Alergia algorithm in comparison to the other two grammar inference methods (RPNI and Genetic algorithm) in terms of precision, recall, accuracy and F-measure for kinases: (a)PKA, (b)PKC, (c)MAPK and (d)CK2.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4401752&req=5

pone.0122294.g006: Performance of the Alergia algorithm in comparison to the other two grammar inference methods (RPNI and Genetic algorithm) in terms of precision, recall, accuracy and F-measure for kinases: (a)PKA, (b)PKC, (c)MAPK and (d)CK2.
Mentions: The comparison results of these three grammar inference methods in terms of precision, recall, accuracy and F-measure for all the kinases are shown in Fig 6A–6D According to the Fig 6, we obtain that the Alergia algorithm performs best in terms of all these parameters for all the kinases among the three GI algorithms. RPNI and genetic algorithm performs almost equally well although genetic algorithm performs slightly better than RPNI for predicting the true positives among total positives, i.e., yield a better recall value for MAPK and CK2 kinase. For all the other parameters and kinases, RPNI performs superior to genetic algorithm.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biophysics, Molecular Biology and Bioinformatics and Distributed Information Centre for Bioinformatics, University of Calcutta, Kolkata, West Bengal, India.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus