Literature DB >> 20491621

A summary of computational resources for protein phosphorylation.

Yu Xue1, Xinjiao Gao, Jun Cao, Zexian Liu, Changjiang Jin, Longping Wen, Xuebiao Yao, Jian Ren.   

Abstract

Protein phosphorylation is the most ubiquitous post-translational modification (PTM), and plays important roles in most of biological processes. Identification of site-specific phosphorylated substrates is fundamental for understanding the molecular mechanisms of phosphorylation. Besides experimental approaches, prediction of potential candidates with computational methods has also attracted great attention for its convenience, fast-speed and low-cost. In this review, we present a comprehensive but brief summarization of computational resources of protein phosphorylation, including phosphorylation databases, prediction of non-specific or organism-specific phosphorylation sites, prediction of kinase-specific phosphorylation sites or phospho-binding motifs, and other tools. The latest compendium of computational resources for protein phosphorylation is available at: http://gps.biocuckoo.org/links.php.

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Year:  2010        PMID: 20491621     DOI: 10.2174/138920310791824138

Source DB:  PubMed          Journal:  Curr Protein Pept Sci        ISSN: 1389-2037            Impact factor:   3.272


  20 in total

1.  Systematic analysis of protein phosphorylation networks from phosphoproteomic data.

Authors:  Chunxia Song; Mingliang Ye; Zexian Liu; Han Cheng; Xinning Jiang; Guanghui Han; Zhou Songyang; Yexiong Tan; Hongyang Wang; Jian Ren; Yu Xue; Hanfa Zou
Journal:  Mol Cell Proteomics       Date:  2012-07-13       Impact factor: 5.911

2.  Systematic analysis of the phosphoproteome and kinase-substrate networks in the mouse testis.

Authors:  Lin Qi; Zexian Liu; Jing Wang; Yiqiang Cui; Yueshuai Guo; Tao Zhou; Zuomin Zhou; Xuejiang Guo; Yu Xue; Jiahao Sha
Journal:  Mol Cell Proteomics       Date:  2014-10-07       Impact factor: 5.911

3.  Computational methods and opportunities for phosphorylation network medicine.

Authors:  Yian Ann Chen; Steven A Eschrich
Journal:  Transl Cancer Res       Date:  2014-06-01       Impact factor: 1.241

4.  Probabilistic Prediction of Protein Phosphorylation Sites Using Classification Relevance Units Machines.

Authors:  Mark Menor; Kyungim Baek; Guylaine Poisson
Journal:  ACM SIGAPP Appl Comput Rev       Date:  2012-12-01

5.  Phospho.ELM: a database of phosphorylation sites--update 2011.

Authors:  Holger Dinkel; Claudia Chica; Allegra Via; Cathryn M Gould; Lars J Jensen; Toby J Gibson; Francesca Diella
Journal:  Nucleic Acids Res       Date:  2010-11-09       Impact factor: 16.971

6.  Deciphering the Arginine-binding preferences at the substrate-binding groove of Ser/Thr kinases by computational surface mapping.

Authors:  Avraham Ben-Shimon; Masha Y Niv
Journal:  PLoS Comput Biol       Date:  2011-11-17       Impact factor: 4.475

7.  Using multitask classification methods to investigate the kinase-specific phosphorylation sites.

Authors:  Shan Gao; Shuo Xu; Yaping Fang; Jianwen Fang
Journal:  Proteome Sci       Date:  2012-06-21       Impact factor: 2.480

8.  PSEA: Kinase-specific prediction and analysis of human phosphorylation substrates.

Authors:  Sheng-Bao Suo; Jian-Ding Qiu; Shao-Ping Shi; Xiang Chen; Ru-Ping Liang
Journal:  Sci Rep       Date:  2014-03-31       Impact factor: 4.379

9.  Identifying protein phosphorylation sites with kinase substrate specificity on human viruses.

Authors:  Neil Arvin Bretaña; Cheng-Tsung Lu; Chiu-Yun Chiang; Min-Gang Su; Kai-Yao Huang; Tzong-Yi Lee; Shun-Long Weng
Journal:  PLoS One       Date:  2012-07-23       Impact factor: 3.240

10.  Prediction of protein phosphorylation sites by using the composition of k-spaced amino acid pairs.

Authors:  Xiaowei Zhao; Wenyi Zhang; Xin Xu; Zhiqiang Ma; Minghao Yin
Journal:  PLoS One       Date:  2012-10-22       Impact factor: 3.240

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