Back

Zirconium(IV)-IMAC for phosphopeptide enrichment in phosphoproteomics

Arribas Diez, I.; Govender, I.; Naicker, P.; Stoychev, S.; Jordaan, J.; Jensen, O. N.

2020-04-13 biochemistry
10.1101/2020.04.13.038810 bioRxiv
Show abstract

Phosphopeptide enrichment is an essential step in large-scale, quantitative phosphoproteomics studies by mass spectrometry. Several phosphopeptide affinity enrichment techniques exist, such as Immobilized Metal ion Affinity Chromatography (IMAC) and Metal Oxide Affinity Chromatography (MOAC). We compared Zirconium (IV) IMAC (Zr-IMAC) magnetic microparticles to more commonly used Titanium (IV) IMAC (Ti-IMAC) and TiO2 magnetic microparticles for phosphopeptide enrichment from simple and complex protein samples prior phosphopeptide sequencing and characterization by mass spectrometry (LC-MS/MS). We optimized sample-loading conditions to increase phosphopeptide recovery for Zr-IMAC, Ti-IMAC and TiO2 based workflows. The performance of Zr-IMAC was enhanced by 19-22% to recover up to 5173 phosphopeptides from 200 {micro}g of protein extract from HepG2/C3A cells, making Zr-IMAC the preferred method for phosphopeptide enrichment in this study. Ti-IMAC and TiO2 performance were also optimized to improve phosphopeptide numbers by 28% and 35%, respectively. Furthermore, Zr-IMAC based phosphoproteomics in the magnetic microsphere format identified 23% more phosphopeptides than HPLC-based Fe(III)-IMAC for same sample amount (200 {micro}g), thereby adding 37% more uniquely identified phosphopeptides. We conclude that Zr-IMAC improves phosphoproteome coverage and recommend that this affinity enrichment method should be more widely used in biological and biomedical studies of cell signalling and in the search for disease-biomarkers.

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.