Estimating the total number of phosphoproteins and phosphorylation sites in eukaryotic proteomes.

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Phosphorylation is the most frequent post-translational modification made to proteins and may regulate protein activity as either a molecular digital switch or a rheostat. Despite the cornucopia of high-throughput (HTP) phosphoproteomic data in the last decade, it remains unclear how many proteins are phosphorylated and how many phosphorylation sites (p-sites) can exist in total within a eukaryotic proteome.

At present, values for these numbers remain in the realm of speculation. It has been suggested that the biological activity of between 1/3 and 2/3 of an organism’s proteome could be regulated by post-translational phosphorylation [1–4]. In the specific case of the human proteome, it has been proposed that 57,000, 500,000, 700,000, or even 1,000,000 p-sites may exist [5–8]. A deep phosphoproteome analysis on HeLa cells estimated that at least 75% of the proteome expressed in those cells can be phosphorylated, and this number may well rise to 90%, if phosphoproteomic experiments are performed at higher coverage [9].

In an effort to provide a reasonable and statistically defensible estimate based on current knowledge, we have mined over 1000 articles from the literature and gathered and filtered 187 publicly available HTP phosphoproteomic datasets from four well-studied species. By implementing two independent statistical methods - the Capture-Recapture method, and Curve-Fitting on the saturation curve of redundant phosphoproteins/p-sites vs non-redundant phosphoproteins/p-sites - we have obtained, for the first time, a reliable estimate of their total number for humans and three other model eukaryotes (mouse, Arabidopsis, and yeast).

Estimates were also adjusted for different levels of noise within the individual datasets and other confounding factors. We estimate that in total, 13,000, 11,000 and 3,000 phosphoproteins and 230,000, 156,000 and 40,000 p-sites exist in human, mouse and yeast, respectively, whereas estimates for Arabidopsis were not as reliable.

Conclusions: Most of the phosphoproteins have been discovered for human, mouse and yeast, while the dataset for Arabidopsis is still far from complete. The datasets for p-sites are not as close to saturation as those for phosphoproteins, Integration of the LTP data suggests that current HTP phosphoproteomics appears to be capable of capturing 70-95% of total phosphoproteins, but only 40-60% of total p-sites.

Figure 1. Estimation of the total number of phosphoproteins (1A, 1B) and p-sites (1C, 1D) for yeast, with the curve-fitting (assuming 1% noise) and Capture-Recapture methods, also correcting for 3 levels of noise (1%, 5%, 10%). In figures 1A and 1C, the x-axis is the cumulative number of redundant phosphoproteins/p-sites, whereas the y-axis is the cumulative number of non-redundant phosphoproteins/p-sites. The red curve is fitted for 1% noise. In figures 1B and 1D: Current is the total number of phosphoproteins/p-sites detected so far (by applying our filtering criteria). Current_3X is the total number of phosphoproteins/p-sites detected so far in at least 3 experiments. Rcapture is the estimation of maximum number of phosphoproteins/p-sites based on the Rcapture method (using the 15 largest datasets). Rcapture_HTP_vs_LTP is the estimation of maximum number of phosphoproteins/p-sites based on the Rcapture method, but this time using only two datasets, where one of them is the compendium of all HTP experiments and the second is the compendium of all LTP experiments from PhosphoGrid2. CF is the estimation of maximum number of phosphoproteins/p-sites based on the curve-fitting method of the saturation curve from all experiments. CF_3X is the estimation of maximum number of phosphoproteins/p-sites identified in at least 3 experiments, based on the curve-fitting method (in this case, a reasonable estimate was not possible). CF_best_start is the estimation of maximum number of phosphoproteins/p-sites based on the curve-fitting method of the saturation curve from all experiments, but this time, the largest experiment is used as first in the series. CF_best_end is the estimation of maximum number of phosphoproteins/p-sites based on the curve-fitting method of the saturation curve from all experiments, but this time, the largest experiment is used as last in the series. CF_half_exp is the estimation of maximum number of phosphoproteins/p-sites based on the curve-fitting method of the saturation curve from the first half experiments.

Figure 2. Estimation of the number of phosphoproteins (2A, 2B) and p-sites (2C, 2D) for human, with the Curve-Fitting (assuming 1% noise) and Capture-Recapture methods, also correcting for various levels of noise (1%, 5%, 10%). See legend of Figure 1 for explanations.

Figure 3. Estimation of the number of phosphoproteins (3A, 3B) and p-sites (3C, 3D) for mouse, with the Curve-Fitting (assuming 1% noise) and Capture-Recapture methods, also correcting for 3 levels of noise (1%, 5%, 10%). See legend of figure 1 for explanations. Estimates on figures 3B and 3D are obtained for a Vega annotated proteome of 16,000 protein-coding genes, where all estimates have been readjusted 25% upwards.

Figure 4. Estimation of the number of phosphoproteins (4A, 4B) and p-sites (4C, 4D) for Arabidopsis, with the Curve-Fitting (assuming 1% noise) and Capture-Recapture methods, also correcting for 3 levels of noise (1%, 5%, 10%). See legend of Figure 1 for explanations.