In silico analysis of phosphoproteome data suggests a rich-get-richer process of phosphosite accumulation over evolution.
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.
Yachie
Nozomu
N
Institute for Advanced Biosciences, Keio University, 403-1, Daihoji, Tsuruoka, Yamagata 997-0017, Japan.
Saito
Rintaro
R
Sugahara
Junichi
J
Tomita
Masaru
M
Ishihama
Yasushi
Y
D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D016384:Consensus Sequence; D019143:Evolution, Molecular; D006801:Humans; D051379:Mice; D008954:Models, Biological; D008969:Molecular Sequence Data; D010750:Phosphoproteins / Q000032:analysis* / Q000737:chemistry; D010766:Phosphorylation; D010768:Phosphoserine / Q000378:metabolism; D020543:Proteome / Q000032:analysis*; D012984:Software
D010750:Phosphoproteins; D020543:Proteome; D010768:Phosphoserine