Effect of pH and ionic strength of running buffer on peptide behavior in capillary electrophoresis: Theoretical calculation and experimental evaluation

Cifuentes, Alejandro; Poppe, H.

doi:10.1002/elps.1150160185

Cited by 42 publications

(39 citation statements)

References 39 publications

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“…In particular, relevant reviews including CZE of proteins and peptides are available (see, for instance, [2][3][4][5] and citations therein). At present CZE models are useful in the physicochemical characterization and interpretation of the effective mobility data of peptides and proteins [6][7][8][9][10][11][12][13][14][15][16]. One model type of interest is that considering the ''inverse problem'' [17] where, for a given protocol involving wellspecified bulk pH, ionic strength I, temperature T, electrical permittivity e and viscosity Z of the BGE, the experimental effective mobility is provided as the basic data to evaluate analyte properties such as, for instance, hydration, effective electrical charge, hydrodynamic size and shape, and pH-microenvironment, among others.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the interplay among charge, hydration and shape of proteins through the modeling of their CZE mobility data

2009

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Electrophoretic mobility data of four proteins are analyzed and interpreted through a physicochemical CZE model, which provides estimates of quantities like equivalent hydrodynamic radius (size), effective charge number, shape orientation factor, hydration, actual pK values of ionizing groups, and pH near molecule, among others. Protein friction coefficients are simulated through the creeping flow theory of prolate spheroidal particles. The modeling of the effective electrophoretic mobility of proteins requires consideration of hydrodynamic size and shape coupled to hydration and effective charge. The model proposed predicts native protein hydration within the range of values obtained experimentally from other techniques. Therefore, this model provides consistently other physicochemical properties such as average friction and diffusion coefficients and packing fractal dimension. As the pH varies from native conditions to those that are denaturing the protein, hydration and packing fractal dimension change substantially. Needs for further research are also discussed and proposed.

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Section: Introductionmentioning

confidence: 99%

Analysis of the interplay among charge, hydration and shape of proteins through the modeling of their CZE mobility data

2009

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“…The chromatographic retention or electrophoretic migration time contains information on the physiochemical properties of the peptides, such as hydrophobicity in RPC and size and charge in standard capillary zone electrophoresis (CZE). A model of chromatographic retention [124][125][126][127][128] or electrophoretic migration [129][130][131][132][133][134][135][136][137][138] can be fitted to experimental data from known proteins. These models are then used to predict the retention time for candidate peptides in a database.…”

Section: Mass Spectrometry Peptide Mass Fingerprinting and Informationmentioning

confidence: 99%

“…In addition to the accurate mass measurement and distribution of peptides in the protein sequence, containing information on the non-random behaviour of trypsin, protein structure, post-translational modifications and database sequence errors, there is also information on the physiochemical properties of the individual peptides. This is primarily hydrophobicity in reversed-phase chromatography [124][125][126][127][128] and size and charge in capillary zone electrophoresis [129][130][131][132][133][134][135][136][137][138]. A predictor of retention time according to Eq.…”

Section: Figure 23 (Color Panel Opposite Side) Lc-fticr (A) and Ce-mentioning

confidence: 99%

Identification and Characterization of Peptides and Proteins Using Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

Palmblad

Bergquist

2003

Journal of Chromatography Library

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Mass spectrometry has in recent years been established as the standard method for protein identification and characterization in proteomics with excellent intrinsic sensitivity and specificity. Fourier transform ion cyclotron resonance is the mass spectrometric technique that provides the highest resolving power and mass accuracy, increasing the amount of information that can be obtained from complex samples. This thesis concerns how useful information on proteins of interest can be extracted from mass spectrometric data on different levels of protein structure and how to obtain this data experimentally. It was shown that it is possible to analyze complex mixtures of protein tryptic digests by direct infusion electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry and identify abundant proteins by peptide mass fingerprinting. Coupling on-line methods such as liquid chromatography and capillary electrophoresis increased the number of proteins that could be identified in human body fluids. Protein identification was also improved by novel statistical methods utilizing prediction of chromatographic behavior and the non-randomness of enzymatic digestion. To identify proteins by short sequence tags, electron capture dissociation was implemented, improved and finally coupled on-line to liquid chromatography for the first time. The combined techniques can be used to sequence large proteins de novo or to localize and characterize any labile post-translational modification. New computer algorithms for the automated analysis of isotope exchange mass spectra were developed to facilitate the study of protein structural dynamics. The non-covalent interaction between HIV-inhibitory peptides and the oligomerization of amyloid β-peptides were investigated, reporting several new findings with possible relevance for development of anti-HIV drug therapies and understanding of fundamental mechanisms in Alzheimer's disease.

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“…Therefore, optimization of the separation of any complex sample mixture is a laborintensive and time-consuming task, normally starting with preparing a set of background electrolyte systems of various pH values, concentrations and buffer components with the ultimate goal of obtaining baseline separation of all sample components. The possibility of predicting peptide mobilities in CZE based on information derived from their structural composition holds the promise to considerably reduce the tedious separation optimization effort [1]. Thus, the option to use algorithms or simulation tools to suggest the optimal buffer composition for CE separation of a given peptide mixture with softwaresuggested buffer candidates is appealing.…”

Section: Introductionmentioning

confidence: 99%

Two variable semi‐empirical and artificial neural‐network‐based modeling of peptide mobilities in CZE: The effect of temperature and organic modifier concentration

2009

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This work was focused on investigating the effects of two separation influencing parameters in CZE, namely temperature and organic additive concentration upon the electrophoretic migration properties of model tripeptides. Two variable semi-empirical (TVSE) models and back-propagation artificial neural networks (ANN) were applied to predict the electrophoretic mobilities of the tripeptides with non-polar, polar, positively charged, negatively charged and aromatic R group characteristics. Previously published work on the subject did not account for the effect of temperature and buffer organic modifier concentration on peptide mobility, in spite of the fact that both were considered to be influential factors in peptide analysis. In this work, a substantial data set was generated consisting of actual electrophoretic mobilities of the model tripeptides in 30 mM phosphate buffer at pH 7.5, at 20, 25, 30, 35 and 40 degrees C and at four different organic additive containing running buffers (0, 5, 10 and 15% MeOH) applying two electric field strengths (12 and 16 kV) to assess our mobility predicting models. Based on the Arrhenius plots of natural logarithm of mobility versus reciprocal absolute temperature of the various experimental setups, the corresponding activation energy values were derived and evaluated. Calculated mobilities by TVSE and back-propagation ANN models were compared with each other and to the experimental data, respectively. Neural network approaches were able to model the complex impact of both temperature and organic additive concentrations and resulted in considerably higher predictive power over the TVSE models, justifying that the effect of these two factors should not be neglected.

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Effect of pH and ionic strength of running buffer on peptide behavior in capillary electrophoresis: Theoretical calculation and experimental evaluation

Cited by 42 publications

References 39 publications

Analysis of the interplay among charge, hydration and shape of proteins through the modeling of their CZE mobility data

Analysis of the interplay among charge, hydration and shape of proteins through the modeling of their CZE mobility data

Identification and Characterization of Peptides and Proteins Using Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

Two variable semi‐empirical and artificial neural‐network‐based modeling of peptide mobilities in CZE: The effect of temperature and organic modifier concentration

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