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Updated: Jun 25, 2026

Sequence-specific Labeling of Nucleic Acids and Proteins with Methyltransferases and Cofactor Analogues
Published on: November 22, 2014
Metal-coded affinity tag labeling: a demonstration of analytical robustness and suitability for biological
Robert Ahrends1, Stefan Pieper, Boris Neumann
1Department of Chemistry, Humboldt-Universitaet zu Berlin, Brook-Taylor Strasse 2, 12489 Berlin, Germany.
Abstract:
Quantitative peptide and protein analysis is one of the most promising fields in modern life science. Besides stable isotope coded labeling, metal chelate complexes are an alternative tool for quantification. The development of metal-coded affinity tags (MeCAT) was aimed to provide a robust tool for the quantification of peptides and proteins by utilizing lanthanide-harboring metal tags. It was shown that MeCAT is suited for relative quantification of proteins via standard mass spectrometric methods. The approach of tagging biomolecules with MeCAT offers the unique advantage of absolute quantification via inductively coupled plasma mass spectrometry (ICPMS), a well-established technique for assessing concentrations down to low attomole ranges. This work investigates the compatibility of MeCAT labeling to analysis workflows such as nano liquid chromatography/electrospray ionization tandem mass spectrometry (nano-LC/ESI-MS(n)). Focus was given toward the separation behavior of labeled peptides and the dynamic range of detection and peptide charge distribution. Furthermore, the stability of MeCAT under harsh analytical conditions was investigated. With the application of the MeCAT technique to a standard analysis scheme in proteomics, such as the investigation of changes in an Escherichia coli proteome, we successfully addressed the suitability to utilize MeCAT on biological samples. Furthermore, we demonstrated that MeCAT complexes are stable under a variety of conditions and that by applying LC/ESI-MS it is possible to cover a dynamic range of 2 orders of magnitude down to the low femtomole range with an average standard deviation below 15%. Therefore, this technique is suitable to common proteomic workflows and enables relative as well as absolute differential peptide quantification.
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