Literature DB >> 16224960

Comparing monolithic and microparticular capillary columns for the separation and analysis of peptide mixtures by liquid chromatography-mass spectrometry.

Hansjörg Toll1, Reiner Wintringer, Ulrike Schweiger-Hufnagel, Christian G Huber.   

Abstract

A mixture of ten proteins was trypsinized and injected onto poly-(styrene-divinylben-zene) monolithic columns (60 x 0.20 or 0.10 mm ID) and a column packed with C18 silica particles (75 x 0.075 mm ID), respectively. The columns were eluted at 200-2000 nL/min with gradients of ACN in 0.050% TFA. Eluting peptides were detected by ESI-MS/MS and subsequently identified by database searching. The 100 microm ID monolithic column showed the highest cumulative Mowse scores based on the highest ion scores for the peptides and the largest number of identified peptides. It is shown that the number of identified peptides strongly depends on the dynamic range within the peptide mixture. In consequence, all proteins were identified in a mixture of relatively balanced analyte amounts (12.5-80 fmol) whereas only peptides for six out of ten proteins were found in a sample of high-dynamic range (0.65-270 fmol). The 100 microm monolithic column showed the highest reproducibility for peptide identifications in three consecutive runs. Depending on sample amount, 57-72% of the identified peptides were detectable in each of the three runs of triplicate analyses. The results demonstrate the high suitability of 100 microm monolithic columns for high-resolution peptide separations in proteomic research.

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Year:  2005        PMID: 16224960     DOI: 10.1002/jssc.200500155

Source DB:  PubMed          Journal:  J Sep Sci        ISSN: 1615-9306            Impact factor:   3.645


  3 in total

1.  Potential of poly(styrene-co-divinylbenzene) monolithic columns for the LC-MS analysis of protein digests.

Authors:  Michiel H M van de Meent; Sebastiaan Eeltink; Gerhardus J de Jong
Journal:  Anal Bioanal Chem       Date:  2010-12-24       Impact factor: 4.142

Review 2.  Less common applications of monoliths. III. Gas chromatography.

Authors:  Frantisek Svec; Alexander A Kurganov
Journal:  J Chromatogr A       Date:  2007-07-13       Impact factor: 4.759

3.  Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics.

Authors:  Nico Pfeifer; Andreas Leinenbach; Christian G Huber; Oliver Kohlbacher
Journal:  BMC Bioinformatics       Date:  2007-11-30       Impact factor: 3.169

  3 in total

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