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PostMod: sequence based prediction of kinase-specific phosphorylation sites with indirect relationship

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10 pages
Post-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. Results Here, 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. Conclusion Our 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.
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BMC Bioinformatics
Research PostMod: sequence based prediction of kinasespecific phosphorylation sites with indirect relationship 1 22 Inkyung Jung, Akihisa Matsuyama, Minoru Yoshida 1,3 and Dongsup Kim*
BioMedCentral
Open Access
1 2 Addresses: Departmentof Bio and Brain Engineering, KAIST, Daejeon 305701, S. Korea,Chemical Genetics Laboratory, RIKEN, Wako, 3 Saitama 3510198, Japan andKAIST Institute for BioCentury, KAIST, Daejeon 305701, S. Korea Email: Inkyung Jung  snowdrop83@gmail.com; Akihisa Matsuyama  akihisa@riken.jp; Minoru Yoshida  yoshidam@riken.jp; Dongsup Kim*  kds@kiast.ac.kr *Corresponding author
fromThe Eighth Asia Pacific Bioinformatics Conference (APBC 2010) Bangalore, India 1821 January 2010
Published: 18 January 2010 BMC Bioinformatics2010,11(Suppl 1):S10
doi: 10.1186/1471210511S1S10
This article is available from: http://www.biomedcentral.com/14712105/11/S1/S10 ©2010 Jung et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract Background:Post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. Results:Here, 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. Conclusion:Our 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.
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