Mapping the GALNT1 substrate landscape with versatile proteomics tools
Saei, A. A.; Lundstrom, S. L.; Lyu, H.; Gharibi, H.; Lu, W.; Fang, P.; Zhang, X.; Meng, Z.; Wang, J.; Gaetani, M.; Vegvari, A.; Gygi, S. P.; Zubarev, R. A.
Show abstract
O-GalNAc type glycosylation is a common post-translational modification (PTM) of proteins catalyzed by polypeptide GalNAc transferases, but the substrate specificity of these transferases is poorly understood. Here we develop a strategy based on integral thermal proteome solubility profiling to identify and prioritize the protein substrates of polypeptide N-acetylgalactosaminyltransferase 1 (GALNT1). Combined with glycoprotein enrichment followed by HCD and soft EThcD gas-phase fragmentation technique, we uncover hundreds of novel GALNT1 substrates in two model human cell lines. GALNT1-mediated O-glycosylation is more common on Thr than Ser residues, with a strong preference for Pro at positions +3 and +4 in respect to O-glycosylation. These results implicate GALNT1 in potentially regulating proteins in several diverse pathways, including some unexpected processes, such as TCA cycle and DNA transcription. This study depicts a roadmap for identification of functional substrates for glycosyltransferases, facilitating fundamental insight into the role of glycosylation in homeostasis and disease.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Towards middle-up analysis of polyclonal antibodies: subclass-specific N-glycosylation profiling of murine immunoglobulin G (IgG) by means of HPLC-MS 94%
- Comprehensive cell surface proteomics defines markers of classical, intermediate and non-classical monocytes 93%
- A Proteomic Platform to Identify Off-Target Proteins Associated with Therapeutic Modalities that Induce Protein Degradation or Gene Silencing 93%
"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.