Issue 43, 2016

Mesoporous metal oxide nanoparticles for selective enrichment of phosphopeptides from complex sample matrices

Abstract

Selective enrichment of phosphopeptides is a key factor for successful detection of protein phosphorylations. Here, three types of mesoporous nanoparticles, ZnSn(OH)6, P0.5–Ti-40 and P0.5–Ti-50, were evaluated for their efficiency in enriching phosphopeptides from β-casein and Arabidopsis thaliana leaf protein tryptic digests and compared with commercially available benchmarks. The three types of nanoparticles had different core metal ions and were synthesized under different reaction conditions, so they showed different characters in phosphopeptide enrichment. Various factors that may affect enrichment efficiency including the peptide to nanoparticle ratio and the composition and concentration of the loading buffer were optimized. Under optimal conditions, the three types of mesoporous nanoparticles were utilized to capture phosphopeptides from the Arabidopsis leaf protein digest. ZnSn(OH)6 can enrich 3557 phosphopeptides with 3826 phosphosites from 1611 phosphoproteins in a 95 min nano-LC/MS run. For P0.5–Ti-40, 1474 phosphoproteins with 3850 phosphopeptides and 4589 phosphosites were identified. For P0.5–Ti-50, 1449 phosphoproteins, 3454 phosphopeptides and 3872 phosphosites were enriched. The three types of mesoporous metal oxide nanoparticles showed excellent enrichment efficiency towards phosphopeptides compared with commercially available benchmarks (TiO2 beads and PolyMAC-Ti) and provided a low cost and efficient alternative for future protein phosphorylation studies.

Graphical abstract: Mesoporous metal oxide nanoparticles for selective enrichment of phosphopeptides from complex sample matrices

Supplementary files

Article information

Article type
Paper
Submitted
21 Jul 2016
Accepted
09 Oct 2016
First published
11 Oct 2016

Anal. Methods, 2016,8, 7747-7754

Mesoporous metal oxide nanoparticles for selective enrichment of phosphopeptides from complex sample matrices

M. Han and Z. Li, Anal. Methods, 2016, 8, 7747 DOI: 10.1039/C6AY02069K

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