Literature DB >> 19957962

Utility of retention prediction model for investigation of peptide separation selectivity in reversed-phase liquid chromatography: impact of concentration of trifluoroacetic acid, column temperature, gradient slope and type of stationary phase.

Martin Gilar1, Hongwei Xie, Aleksander Jaworski.   

Abstract

Peptide separation selectivity in reversed-phase liquid chromatography was investigated using a training set of 165 peptides with a total of 1698 amino acid residues. Gradient separation was performed at selected chromatographic conditions, varying column temperature, gradient slope, ion-pairing reagent concentration, and type of stationary phase. The retention times for each set of experiments were utilized to calculate the amino acid retention coefficients using a published prediction model (Rapid Commun. Mass Spectrom. 2007, 21, 2813-2821). The calculated retention coefficients reflect the contribution of each type of amino acid residue to peptide retention at a given chromatographic condition. For example, the concentration of ion-pairing reagent (trifluoroacetic acid) had the strongest impact on the retention of peptides containing an increasing number of basic (charged) amino acids, such as arginine, lysine, and histidine, while the retention coefficients of other amino acids are minimally affected. Increasing the separation temperature resulted in a moderate decrease of amino acid retention coefficients with the exception of isoleucine, leucine, valine, and proline. This finding suggests that these residues enhance peptide retention at elevated temperature. Amino acid residue retention coefficients were also helpful to understand the impact of sorbent pore size (130 vs 300 A) on peptide retention selectivity. In addition we investigated the selectivity differences of various reversed-phase chromatographic sorbents. The trends suggested by calculated retention coefficients were confirmed using a set of synthetic peptides with specifically designed sequences.

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Year:  2010        PMID: 19957962     DOI: 10.1021/ac901931c

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  7 in total

1.  Using iRT, a normalized retention time for more targeted measurement of peptides.

Authors:  Claudia Escher; Lukas Reiter; Brendan MacLean; Reto Ossola; Franz Herzog; John Chilton; Michael J MacCoss; Oliver Rinner
Journal:  Proteomics       Date:  2012-04       Impact factor: 3.984

2.  Proteomic analysis of enterotoxigenic Escherichia coli (ETEC) in neutral and alkaline conditions.

Authors:  Lucia Gonzales-Siles; Roger Karlsson; Diarmuid Kenny; Anders Karlsson; Åsa Sjöling
Journal:  BMC Microbiol       Date:  2017-01-07       Impact factor: 3.605

3.  Peptide barcoding for establishment of new types of genotype-phenotype linkages.

Authors:  Kana Miyamoto; Wataru Aoki; Yuta Ohtani; Natsuko Miura; Shunsuke Aburaya; Yusei Matsuzaki; Kaho Kajiwara; Yoshinori Kitagawa; Mitsuyoshi Ueda
Journal:  PLoS One       Date:  2019-04-23       Impact factor: 3.240

4.  A Comprehensive Study of Gradient Conditions for Deep Proteome Discovery in a Complex Protein Matrix.

Authors:  Xing Wei; Pei N Liu; Brian P Mooney; Thao Thi Nguyen; C Michael Greenlief
Journal:  Int J Mol Sci       Date:  2022-10-03       Impact factor: 6.208

5.  Nucleic acid separations using superficially porous silica particles.

Authors:  Elizabeth D Close; Alison O Nwokeoji; Dafydd Milton; Ken Cook; Darsha M Hindocha; Elliot C Hook; Helen Wood; Mark J Dickman
Journal:  J Chromatogr A       Date:  2016-02-23       Impact factor: 4.759

6.  Estimation of Nonlinear Adsorption Isotherms in Gradient Elution RP-LC of Peptides in the Presence of an Adsorbing Additive.

Authors:  Dennis Åsberg; Marek Leśko; Tomas Leek; Jörgen Samuelsson; Krzysztof Kaczmarski; Torgny Fornstedt
Journal:  Chromatographia       Date:  2017-03-28       Impact factor: 2.044

7.  Visualization and application of amino acid retention coefficients obtained from modeling of peptide retention.

Authors:  Yassene Mohammed; Magnus Palmblad
Journal:  J Sep Sci       Date:  2018-09-04       Impact factor: 3.645

  7 in total

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