PostMod: sequence based prediction of kinase-specific phosphorylation sites with indirect relationship
<p>Abstract</p> <p>Background</p> <p>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. E...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2010-01-01
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| Series: | BMC Bioinformatics |
| _version_ | 1819033727743819776 |
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| author | Yoshida Minoru Matsuyama Akihisa Jung Inkyung Kim Dongsup |
| author_facet | Yoshida Minoru Matsuyama Akihisa Jung Inkyung Kim Dongsup |
| author_sort | Yoshida Minoru |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p> |
| first_indexed | 2024-12-21T07:22:26Z |
| format | Article |
| id | doaj.art-b7e53b747e634cedbbc1eddccb549973 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-21T07:22:26Z |
| publishDate | 2010-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-b7e53b747e634cedbbc1eddccb5499732022-12-21T19:11:45ZengBMCBMC Bioinformatics1471-21052010-01-0111Suppl 1S1010.1186/1471-2105-11-S1-S10PostMod: sequence based prediction of kinase-specific phosphorylation sites with indirect relationshipYoshida MinoruMatsuyama AkihisaJung InkyungKim Dongsup<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p> |
| spellingShingle | Yoshida Minoru Matsuyama Akihisa Jung Inkyung Kim Dongsup PostMod: sequence based prediction of kinase-specific phosphorylation sites with indirect relationship BMC Bioinformatics |
| title | PostMod: sequence based prediction of kinase-specific phosphorylation sites with indirect relationship |
| title_full | PostMod: sequence based prediction of kinase-specific phosphorylation sites with indirect relationship |
| title_fullStr | PostMod: sequence based prediction of kinase-specific phosphorylation sites with indirect relationship |
| title_full_unstemmed | PostMod: sequence based prediction of kinase-specific phosphorylation sites with indirect relationship |
| title_short | PostMod: sequence based prediction of kinase-specific phosphorylation sites with indirect relationship |
| title_sort | postmod sequence based prediction of kinase specific phosphorylation sites with indirect relationship |
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