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.
Highlights
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
Power-law Rule in Phosphoproteins—We analyzed the distribution of phosphoproteins in human and mouse with regard to their number of Ser(P) sites using phosphoprotein data obtained from the UniProt database
To investigate whether the power-law distributions were potentially affected by the characteristics of the phosphoprotein sequences, we examined the correlations between the numbers of Ser(P) sites and the lengths of the phosphoproteins in human and mouse
Summary
Phosphosite Data—We initially obtained the phosphosite data and phosphoproteome sequences from the UniProt database [22], which incorporates large scale data from many high quality phosphoproteomics studies. Characteristics of Phosphoprotein Sequences—To investigate whether the characteristics of phosphoproteins affect their distribution with regard to the number of phosphosites they contain, we observed the lengths and compositions of the amino acid sequences of phosphoproteins In both human and mouse, we calculated the Pearson’s correlation coefficients between the number of Ser(P) sites and protein lengths and between the number of Ser(P) sites and number of Ser residues. To estimate the concentration of multiple phosphosites on the protein surface, we adopted sliding window sizes of 40, 20, and 10 AA using human and mouse phosphoproteomics data, and we compared the probability distributions of all the possible distances between pairs of Ser(P) sites with those of the negative controls. Sequence Consensus around Phosphosites—We used the data set of Ser(P) sites obtained from UniProt to evaluate the sequence consensus of the amino acids surrounding the phosphosites within each phosphoprotein of human and mouse. This random procedure was repeated 1,000 times, and its mean and S.D. were computed
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