Phosphatase specificity principles uncovered by MRBLE:Dephos and global substrate identification

Identification of phosphatases that dephosphorylate the co-chaperone BAG3

The co-chaperone BAG3 plays critical roles in maintaining cellular proteostasis. It associates with 14-3-3 proteins during the trafficking of aggregation-prone proteins and facilitates their degradation through chaperone-assisted selective autophagy in cooperation with small heat shock proteins. Although reversible phosphorylation regulates BAG3 function, the involved phosphatases remain unknown. Here, we used affinity purification mass spectrometry to identify phosphatases that target BAG3. Of the hits, we evaluated the involvement of protein phosphatase-1 (PP1) using chemical inhibitors and activators in in vitro and cellular approaches. Our results demonstrate that PP1 can dephosphorylate BAG3-pS136 in cells and counteract 14-3-3γ association with BAG3 at this motif. Furthermore, protein phosphatase-5 (PP5) co-enriched with proteostasis-related proteins, and it has the capacity to dephosphorylate a BAG3 phosphorylation-site cluster regulating the interaction of BAG3 with small heat shock proteins and BAG3-mediated protein degradation. Our findings provide new insights into the regulation of BAG3 by phosphatases. This paves the way for future research focused on the precise control of BAG3 function through its regulatory proteins, potentially holding therapeutic promise for diseases characterized by disrupted proteostasis.

GPSD: a hybrid learning framework for the prediction of phosphatase-specific dephosphorylation sites

Protein phosphorylation is dynamically and reversibly regulated by protein kinases and protein phosphatases, and plays an essential role in orchestrating a wide range of biological processes. Although a number of tools have been developed for predicting kinase-specific phosphorylation sites (p-sites), computational prediction of phosphatase-specific dephosphorylation sites remains to be a great challenge. In this study, we manually curated 4393 experimentally identified site-specific phosphatase-substrate relationships for 3463 dephosphorylation sites occurring on phosphoserine, phosphothreonine, and/or phosphotyrosine residues, from the literature and public databases. Then, we developed a hybrid learning framework, the group-based prediction system for the prediction of phosphatase-specific dephosphorylation sites (GPSD). For model training, we integrated 10 types of sequence features and utilized three types of machine learning methods, including penalized logistic regression, deep neural networks, and transformer neural networks. First, a pretrained model was constructed using 561 416 nonredundant p-sites and then fine-tuned to generate computational models for predicting general dephosphorylation sites. In addition, 103 individual phosphatase-specific predictors were constructed via transfer learning and meta-learning. For site prediction, one or multiple protein sequences in FASTA format could be inputted, and the prediction results will be shown together with additional annotations, such as protein-protein interactions, structural information, and disorder propensity. The online service of GPSD is freely available at https://gpsd.biocuckoo.cn/. We believe that GPSD can serve as a valuable tool for further analysis of dephosphorylation.

The expanding landscape of canonical and non-canonical protein phosphorylation

Protein phosphorylation is a crucial regulatory mechanism in cell signaling, acting as a molecular switch that modulates protein function. Catalyzed by protein kinases and reversed by phosphoprotein phosphatases, it is essential in both normal physiological and pathological states. Recent advances have uncovered a vast and intricate landscape of protein phosphorylation that include histidine phosphorylation and more unconventional events, such as pyrophosphorylation and polyphosphorylation. Many questions remain about the true size of the phosphoproteome and, more importantly, its site-specific functional relevance. The involvement of unconventional actors such as pseudokinases and pseudophosphatases adds further complexity to be resolved. This review explores recent discoveries and ongoing challenges, highlighting the need for continued research to fully elucidate the roles and regulation of protein phosphorylation.

A protein phosphatase 1 specific phosphatase targeting peptide (PhosTAP) to identify the PP1 phosphatome

Phosphoprotein phosphatases (PPPs) are the key serine/threonine phosphatases that regulate all essential signaling cascades. In particular, Protein Phosphatase 1 (PP1) dephosphorylates ~80% of all ser/thr phosphorylation sites. Here, we developed a phosphatase targeting peptide (PhosTAP) that binds all PP1 isoforms and does so with a stronger affinity than any other known PP1 regulator. This PhosTAP can be used as a PP1 recruitment tool for Phosphorylation Targeting Chimera (PhosTAC)-type recruitment in in vitro and cellular experiments, as well as in phosphoproteomics experiments to identify PP1-specific substrates and phosphosites. The latter is especially important to further our understanding of cellular signaling, as the identification of substrates and especially phosphosites that are targeted by specific phosphatases lags behind that of their kinase counterparts. Using PhosTAP-based proteomics, we show that, counter to our current understanding, many PP1 regulators are also substrates, that the number of residues between regulator PP1-binding and phosphosites vary significantly, and that PP1 counteracts the activities of mitotic kinases. Finally, we also found that Haspin kinase is a direct substrate of PP1 and that its PP1-dependent dephosphorylation modulates its activity during anaphase. Together, we show that PP1-specific PhosTAPs are a powerful tool for +studying PP1 activity in vitro and in cells.

Substrate recognition principles for the PP2A-B55 protein phosphatase

The PP2A-B55 phosphatase regulates a plethora of signaling pathways throughout eukaryotes. How PP2A-B55 selects its substrates presents a severe knowledge gap. By integrating AlphaFold modeling with comprehensive high-resolution mutational scanning, we show that α helices in substrates bind B55 through an evolutionary conserved mechanism. Despite a large diversity in sequence and composition, these α helices share key amino acid determinants that engage discrete hydrophobic and electrostatic patches. Using deep learning protein design, we generate a specific and potent competitive peptide inhibitor of PP2A-B55 substrate interactions. With this inhibitor, we uncover that PP2A-B55 regulates the nuclear exosome targeting (NEXT) complex by binding to an α-helical recruitment module in the RNA binding protein 7 (RBM7), a component of the NEXT complex. Collectively, our findings provide a framework for the understanding and interrogation of PP2A-B55 function in health and disease.

Phosphoproteomic investigation of targets of protein phosphatases in EGFR signaling

Receptor tyrosine kinases (RTKs) initiate cellular signaling pathways, which are regulated through a delicate balance of phosphorylation and dephosphorylation events. While many studies of RTKs have focused on downstream-activated kinases catalyzing the site-specific phosphorylation, few studies have focused on the phosphatases carrying out the dephosphorylation. In this study, we analyzed six protein phosphatase networks using chemical inhibitors in context of epidermal growth factor receptor (EGFR) signaling by mass spectrometry-based phosphoproteomics. Specifically, we focused on protein phosphatase 2C (PP2C), involved in attenuating p38-dependent signaling pathways in various cellular responses, and confirmed its effect in regulating p38 activity in EGFR signaling. Furthermore, utilizing a p38 inhibitor, we classified phosphosites whose phosphorylation status depends on PP2C inhibition into p38-dependent and p38-independent sites. This study provides a large-scale dataset of phosphatase-regulation of EGF-responsive phosphorylation sites, which serves as a useful resource to deepen our understanding of EGFR signaling.

Substrate recognition principles for the PP2A-B55 protein phosphatase

The PP2A-B55 phosphatase regulates a plethora of signaling pathways throughout eukaryotes. How PP2A-B55 selects its substrates presents a severe knowledge gap. By integrating AlphaFold modelling with comprehensive high resolution mutational scanning, we show that α-helices in substrates bind B55 through an evolutionary conserved mechanism. Despite a large diversity in sequence and composition, these α-helices share key amino acid determinants that engage discrete hydrophobic and electrostatic patches. Using deep learning protein design, we generate a specific and potent competitive peptide inhibitor of PP2A-B55 substrate interactions. With this inhibitor, we uncover that PP2A-B55 regulates the nuclear exosome targeting complex by binding to an α-helical recruitment module in RBM7. Collectively, our findings provide a framework for the understanding and interrogation of PP2A-B55 in health and disease.