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. 2020 Mar;38(3):365-373.
doi: 10.1038/s41587-019-0344-3. Epub 2019 Dec 9.

The functional landscape of the human phosphoproteome

David Ochoa  1 , Andrew F Jarnuczak  2 , Cristina Viéitez  2   3 , Maja Gehre  3 , Margaret Soucheray  4   5 , André Mateus  3 , Askar A Kleefeldt  3 , Anthony Hill  3 , Luz Garcia-Alonso  2 , Frank Stein  3 , Nevan J Krogan  4   5 , Mikhail M Savitski  3 , Danielle L Swaney  4   5 , Juan A Vizcaíno  2 , Kyung-Min Noh  3 , Pedro Beltrao  6
Affiliations

The functional landscape of the human phosphoproteome

David Ochoa et al. Nat Biotechnol. 2020 Mar.

Abstract

Protein phosphorylation is a key post-translational modification regulating protein function in almost all cellular processes. Although tens of thousands of phosphorylation sites have been identified in human cells, approaches to determine the functional importance of each phosphosite are lacking. Here, we manually curated 112 datasets of phospho-enriched proteins, generated from 104 different human cell types or tissues. We re-analyzed the 6,801 proteomics experiments that passed our quality control criteria, creating a reference phosphoproteome containing 119,809 human phosphosites. To prioritize functional sites, we used machine learning to identify 59 features indicative of proteomic, structural, regulatory or evolutionary relevance and integrate them into a single functional score. Our approach identifies regulatory phosphosites across different molecular mechanisms, processes and diseases, and reveals genetic susceptibilities at a genomic scale. Several regulatory phosphosites were experimentally validated, including identifying a role in neuronal differentiation for phosphosites in SMARCC2, a member of the SWI/SNF chromatin-remodeling complex.

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Conflict of interest statement

Conflicts of interest

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1. Comprehensive catalog of in-vivo human phosphosites.
a) Number of phosphosites (Localisation probability > 0.5) binned by the number of peptide-spectrum matches coming from the re-analyzed human phospho-enriched datasets curated from PRIDE. b) Examples of broad or tissue-specific phosphosites with spectral count information. c) Phosphopeptide and unphosphorylated peptide MS/MS support for all human proteins binned using the consensus protein abundance from PaxDb d) Cumulative increase in the number of phosphosites for each added MS experiment by biological origin: all samples (blue), top 5 most common cell lines or tissues or remaining samples (grey). MS experiments were sorted by size. Inset - Accumulated instrument time and the total number of phosphosites identified per sample. e) Total number of unique identified phosphosites and MS/MS support in the combined PRIDE analysis and PSP.
Figure 2
Figure 2. A functional score for human phosphosites.
a) MAF1 phosphosites in the 65-80 region with feature annotations b) Feature discriminative power (AUCs) after repeated cross-validation between phosphosites of known and unknown function (AUC, red, green, yellow and blue bars). Discriminative power (AUCs) after integrating all features using different machine learning algorithms (AUC, grey bars). The contribution of each feature to the final model (point-ranges). c) Distribution of functional scores for all phosphosites (blue), with known regulatory roles (yellow) and associated with human diseases (green). Sample size (n) represents number of phosphosites. d) Functional score and regulatory function for phosphosites in different protein families.
Figure 3
Figure 3. Identification of functional sites regulating protein interaction and transcriptional activity.
a) Functional score for 10 phosphosites identified in RANBP1. b) Structural model of RANBP1 in complex with RAN (PDB:1k5g). c) MS binding quantification for RANBP1 interaction partners (RAN, RCC1, and NEMP1) pulling-down the control, the WT, or the S60E RANBP1 mutant. 3 biological replicates between wild type and mutant were compared using a two-sided t test and displayed when significant (p<0.05). Boxes represent Q1-Q3 with a centre in the median value. d) Functional score for 10 phosphosites identified in STAT1 with unknown (blue) known activating (yellow) and inhibitory (green) regulatory activity. e) Pearson correlation between the changes in phosphorylation levels of the known activation site of STAT1 (S727) with the changes in estimated STAT1 transcriptional activity across 74 tumor samples. Pointrange represents the binned median and confidence limits based on non-parametric bootstrap. f) Relationship between STAT1 phosphosite functional score and the Pearson correlation between the TF activity and the phosphorylation changes across 74 tumor samples. g) Fraction of phosphosites in TFs showing significantly correlated changes (Pearson’s correlation p<0.05) with the corresponding TF activity, stratified by their functional score. Data based on 323 transcription factor phosphosites obtained from TCGA and CPTAC consortia (see Methods).
Figure 4
Figure 4. Consequences of genetic variants for phosphosites with high functional scores.
a) Median (CI > 95%) minor allele frequency for variants sorted by phosphosite functional score and compared with synonymous and stop codon causing variants occurring at phosphosite positions. b) Mean functional score (CI > 95%) for phosphosites at positions with mutations found in patients and having benign, uncertain or pathogenic consequences. The S172P mutation in Tubulin, Beta 2B (TUBB2B) is highlighted as an example - see main text c) Fold ratio of mutations in phosphorylated positions reported in mutagenesis studies having gain/loss of function effects versus no effect and stratified by functional score. d) MS evidence for the phosphorylation of S151 and T153 in GAPDH and their structural context flanking a catalytic cysteine e) Position and functional score for all GAPDH phosphosites and alignment of two human phosphosites (S151 and T153) to the corresponding S. cerevisiae TDH3 (S149 and T151). Color gradient corresponds to supporting evidence of phosphorylation based on ancestral reconstruction f) Consensus growth curves and (g) mean and standard error of the area under the growth curve for wild type (WT), control, GAPDH knockout (KO) and S149 and T151 phospho-deficient mutants in the presence or absence of doxorubicin (75 μM). Every clone is present 4 times in each plate and the experiment repeated 3 times for a total of 12 replicates. h) TDH3 activity as mean and SE measured twice in 3 independent extracts obtained from control and mutant strains.
Figure 5
Figure 5. Smarcc2 S302A/S304A homozygous mutants show delayed neuronal differentiation.
a) Functional score for SMARCC2/BAF170 phosphosites. b) Design for CRISPR Knock-in mutagenesis of control (+/+), heterozygous (+/-) and homozygous (-/-) Smarcc2/Baf170 S302A/S304A mutation followed by expected neuronal differentiation timeline. c) PCA plot based on the normalized RNA-seq levels of 25,411 mouse genes obtained from of the 7 clonal lines at days 8 and 12. d) Normalized RNA-seq log counts of genes contained in signatures of Late ESC, neurons or transcriptionally regulated by REST as measured in 6 independent biological replicates presenting control (+/+), heterozygous (+/-) and homozygous (-/-) backgrounds. e) Merged immunofluorescence images of differentiated cells on day 12 stained with antibodies against neuronal microtubules Map2 (red) and Smarcc2/Baf170 (Green). Scale bar represents 25 μm.
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Comment in

  • Deciphering the human phosphoproteome.
    Franciosa G, Martinez-Val A, Olsen JV. Franciosa G, et al. Nat Biotechnol. 2020 Mar;38(3):285-286. doi: 10.1038/s41587-020-0441-3. Nat Biotechnol. 2020. PMID: 32055030 No abstract available.

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