High-throughput functional characterization of protein phosphorylation sites in yeast

Abstract

Phosphorylation is a critical post-translational modification involved in the regulation of almost all cellular processes. However, less than 5% of thousands of recently discovered phosphosites have been functionally annotated. Here, we devised a chemical genetic approach to study the functional relevance of phosphosites in S. cerevisiae. We generated 474 yeast strains with mutations in specific phosphosites that were screened for fitness in 102 conditions, along with a gene deletion library. Of these phosphosites, 42% exhibited growth phenotypes, suggesting that these are more likely functional. We inferred their function based on the similarity of their growth profiles with that of gene deletions, and validated a subset by thermal proteome profiling and lipidomics. A high fraction exhibited phenotypes not seen in the corresponding gene deletion suggestive of a gain-of-function effect. For phosphosites conserved in humans, the severity of the yeast phenotypes is indicative of their human functional relevance. This high-throughput approach allows for functionally characterizing individual phosphosites at scale.

The impact of genomic variation on protein phosphorylation states and regulatory networks

“…We also detected 1,595 phQTLs affecting 1,266 phosphopeptides (60%) and 466 phResQTLs affecting 389 phospho‐residuals (44%) (Dataset [Link] , [Link] ). The physiological importance (growth effects) of 76 of the phosphopeptides that we quantified had previously been analyzed using phospho‐deficient mutants (Viéitez et al , 2022 ). Based on that data, we estimated that at least 39 of these phosphopeptides were functionally relevant.…”

“…A previous study by Viéitez and colleagues has investigated the fitness effects of a selection of phosphosites in a range of growth conditions (Viéitez et al , 2022 ). They did this by changing the phospho‐residue to an alanine each for 474 specific phosphosites, resulting in 474 strains, each carrying a different mutation.…”

Leveraging Protein Dynamics to Identify Functional Phosphorylation Sites using Deep Learning Models

“…Although both phosphorylation sites have not been directly validated, our predictions are supported by several lines of experimental evidence. Through a literature search, S106 and Y137 have been identified in the allosteric site . The side-chain hydroxyl group of Y137 interacts with the allosteric compound, with the main chain carbonyl group coordinating the regulatory Ca 2+ . , In addition, the phosphorylation at Y137 can result in increased RAF interaction and enhanced downstream signaling, through an allosteric alteration of the KRAS conformation and effector binding …”

Phosphoproteomics reveals rewiring of the insulin signaling network and multi-nodal defects in insulin resistance

“…In line with this view, we employed a pipeline for identifying context-specific kinase substrates that identified hundreds of putative GSK3 substrates in adipocytes, and these data subsequently facilitated the detection of GSK3 dysregulation whereas existing annotations could not. Beyond mapping upstream regulators of phosphosites, recent computational 64 and biochemical 65 approaches have made progress in estimating whether a given phosphosite will have a downstream regulatory role, though they are yet to tailor predictions to different experimental contexts. Sustained efforts to map signaling topology and function will reveal how the signaling alterations we identified in insulin resistance are imbedded within and shape the insulin signaling network.…”