Literature DB >> 19136663

In silico analysis of phosphoproteome data suggests a rich-get-richer process of phosphosite accumulation over evolution.

Nozomu Yachie1, Rintaro Saito, Junichi Sugahara, Masaru Tomita, Yasushi Ishihama.   

Abstract

Recent phosphoproteome analyses using mass spectrometry-based technologies have provided new insights into the extensive presence of protein phosphorylation in various species and have raised the interesting question of how this protein modification was gained evolutionarily on such a large scale. We investigated this issue by using human and mouse phosphoproteome data. We initially found that phosphoproteins followed a power-law distribution with regard to their number of phosphosites: most of the proteins included only a few phosphosites, but some included dozens of phosphosites. The power-law distribution, unlike more commonly observed distributions such as normal and log-normal distributions, is considered by the field of complex systems science to be produced by a specific rich-get-richer process called preferential attachment growth. Therefore, we explored the factors that may have promoted the rich-get-richer process during phosphosite evolution. We conducted a bioinformatics analysis to evaluate the relationship of amino acid sequences of phosphoproteins with the positions of phosphosites and found an overconcentration of phosphosites in specific regions of protein surfaces and implications that in many phosphoproteins these clusters of phosphosites are activated simultaneously. Multiple phosphosites concentrated in limited spaces on phosphoprotein surfaces may therefore function biologically as cooperative modules that are resistant to selective pressures during phosphoprotein evolution. We therefore proposed a hypothetical model by which the modularization of multiple phosphosites has been resistant to natural selection and has driven the rich-get-richer process of the evolutionary growth of phosphosite numbers.

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Year:  2009        PMID: 19136663      PMCID: PMC2689765          DOI: 10.1074/mcp.M800466-MCP200

Source DB:  PubMed          Journal:  Mol Cell Proteomics        ISSN: 1535-9476            Impact factor:   5.911


  52 in total

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5.  Large-scale phosphorylation analysis of mouse liver.

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6.  Automated phosphoproteome analysis for cultured cancer cells by two-dimensional nanoLC-MS using a calcined titania/C18 biphasic column.

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Journal:  Anal Sci       Date:  2008-01       Impact factor: 2.081

7.  Stable isotope labeling of Arabidopsis thaliana cells and quantitative proteomics by mass spectrometry.

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8.  Analysis of phosphorylation sites on proteins from Saccharomyces cerevisiae by electron transfer dissociation (ETD) mass spectrometry.

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9.  Successive and selective release of phosphorylated peptides captured by hydroxy acid-modified metal oxide chromatography.

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10.  Preferential attachment in the evolution of metabolic networks.

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  18 in total

1.  Global identification and characterization of both O-GlcNAcylation and phosphorylation at the murine synapse.

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Journal:  Mol Cell Proteomics       Date:  2012-05-29       Impact factor: 5.911

2.  Phosphoproteomics of Arabidopsis Highly ABA-Induced1 identifies AT-Hook-Like10 phosphorylation required for stress growth regulation.

Authors:  Min May Wong; Govinal Badiger Bhaskara; Tuan-Nan Wen; Wen-Dar Lin; Thao Thi Nguyen; Geeng Loo Chong; Paul E Verslues
Journal:  Proc Natl Acad Sci U S A       Date:  2019-01-22       Impact factor: 11.205

3.  Crosstalk between signaling pathways provided by single and multiple protein phosphorylation sites.

Authors:  Hafumi Nishi; Emek Demir; Anna R Panchenko
Journal:  J Mol Biol       Date:  2014-11-09       Impact factor: 5.469

Review 4.  Human Protein Reference Database and Human Proteinpedia as resources for phosphoproteome analysis.

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Journal:  Mol Biosyst       Date:  2011-12-08

5.  Phosphorylation in protein-protein binding: effect on stability and function.

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Journal:  Structure       Date:  2011-12-07       Impact factor: 5.006

6.  Protein kinases phosphorylate long disordered regions in intrinsically disordered proteins.

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7.  Towards the systematic discovery of signal transduction networks using phosphorylation dynamics data.

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8.  Phosphoproteomics by mass spectrometry: insights, implications, applications and limitations.

Authors:  Viveka Mayya; David K Han
Journal:  Expert Rev Proteomics       Date:  2009-12       Impact factor: 3.940

9.  Cooperativity within proximal phosphorylation sites is revealed from large-scale proteomics data.

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Review 10.  Role of O-Linked N-Acetylglucosamine Protein Modification in Cellular (Patho)Physiology.

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Journal:  Physiol Rev       Date:  2020-07-30       Impact factor: 37.312

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