Back

Global impact of phosphorylation on protein endurance

Wu, C.; Ba, Q.; Li, W.; Salovska, B.; Hou, P.; Mueller, T.; Rosenberger, G.; Gao, E.; Di, Y.; Liu, Y.

2020-03-13 systems biology
10.1101/2020.03.12.989467 bioRxiv
Show abstract

Post-translational modifications such as phosphorylation can have profound effects on the physicochemical and biological properties of proteins. However, high-throughput and systematic approaches have not yet been developed to assess the effects of specific modification types and sites on protein lifetime, which represents a key parameter for understanding signaling rewiring and drug development. Here we describe a proteomic method, DeltaSILAC, to quantify the impact of site-specific phosphorylation on the endurance of thousands of proteins in live cells. Being configured on the reproducible data-independent acquisition mass spectrometry (DIA-MS), the pulse labeling approach using stable isotope-labeled amino acids in cells (SILAC), together with a novel peptide-level matching strategy, this multiplexed assay revealed the global delaying effect of phosphorylation on protein turnover in growing cancer cells. Further, we identified local sequence and structural features in proximity to the phosphorylated sites that could be associated with protein endurance alterations. We found that phosphorylated sites accelerating protein turnover are functionally selected for cell fitness and evolutionarily conserved. DeltaSILAC provides a generalizable approach for prioritizing the effects of phosphorylation sites on protein lifetime in the context of cell signaling and disease biology, which is highly complementary to existing methods. Finally, DeltaSILAC is widely applicable to diverse post-translational modification types and different cell systems.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.