Abstract
BackgroundPost-translational modifications (PTMs) have a key role in regulating cell functions. Consequently, identification of PTM sites has a significant impact on understanding protein function and revealing cellular signal transductions. Especially, phosphorylation is a ubiquitous process with a large portion of proteins undergoing this modification. Experimental methods to identify phosphorylation sites are labor-intensive and of high-cost. With the exponentially growing protein sequence data, development of computational approaches to predict phosphorylation sites is highly desirable.ResultsHere, we present a simple and effective method to recognize phosphorylation sites by combining sequence patterns and evolutionary information and by applying a novel noise-reducing algorithm. We suggested that considering long-range region surrounding a phosphorylation site is important for recognizing phosphorylation peptides. Also, from compared results to AutoMotif in 36 different kinase families, new method outperforms AutoMotif. The mean accuracy, precision, and recall of our method are 0.93, 0.67, and 0.40, respectively, whereas those of AutoMotif with a polynomial kernel are 0.91, 0.47, and 0.17, respectively. Also our method shows better or comparable performance in four main kinase groups, CDK, CK2, PKA, and PKC compared to six existing predictors.ConclusionOur method is remarkable in that it is powerful and intuitive approach without need of a sophisticated training algorithm. Moreover, our method is generally applicable to other types of PTMs.
Highlights
Post-translational modifications (PTMs) have a key role in regulating cell functions
Post-translational modifications (PTMs) have important implication on the protein functions involved in signal transductions and many human diseases
When we tested our new method on 48 different kinase groups, the results indicated that the two innovative features of our present work, i.e., a new sequence similarity scoring method and the noise-reducing system, both contributed to the outstanding performance of the new method in recognizing phosphorylation sites correctly, showing better performance than AutoMotif which is one of the best-performing methods
Summary
Post-translational modifications (PTMs) have a key role in regulating cell functions. Post-translational modifications (PTMs) have important implication on the protein functions involved in signal transductions and many human diseases. Phosphorylation is one of the most ubiquitous of these processes with a reported 30 ~50% of eukaryotic proteins undergoing this modification. For this reason, identifying phosphorylation sites is important for understanding functional role of proteins and cell signalling networks. ELM [1,2], PhosphoSite [3], and PhosPhAt [4] Those techniques are time-consuming and high cost approaches. Due to such practical limitation, an efficient computational algorithm to recognize phosphorylation sites is highly desirable
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