Effect of background electrolyte on the estimation of protein hydrodynamic radius and net charge through capillary zone electrophoresis

Abstract

Two physicochemical models are proposed for the estimation of both hydrodynamic radius and net charge of a protein when the capillary zone electrophoretic mobility at a given protocol, the set of pK of charged amino acids, and basic data from Protein Data Bank are available. These models also provide a rationale to interpret appropriately the effects of solvent properties on protein hydrodynamic radius and net charge. To illustrate the numerical predictions of these models, experimental data of electrophoretic mobility available in the literature for well-defined protocols are used. Five proteins are considered: lysozyme, staphylococcal nuclease, human carbonic anhydrase, bovine carbonic anhydrase, and human serum albumin. Numerical predictions of protein net charges through these models compare well with the results reported in the literature, including those found asymptotically through protein charge ladder techniques. Model calculations indicate that the hydrodynamic radius is sensitive to changes of the protein net charge and hence it cannot be assumed constant in general. Also, several limitations associated with models for estimating protein net charge and hydrodynamic radius from protein structure, amino acid sequence, and experimental electrophoretic mobility are provided and discussed. These conclusions also show clear requirements for further research.

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

“…Their experimental effective mobilities are reconsidered and discussed in relation to a previous work [15] where these proteins were studied as spherical hydrodynamic particles. Also it should be pointed out here that we have analyzed the characterization of proteins, peptides and amino acids through the model designated ''Perturbed Linderstrøm-Lang CE Model'' (PLLCEM) where an equation for the estimation of hydration was also included.…”

“…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. This basic problem has associated the charge regulation phenomenon, also designated proton-binding cooperativity, which is always present around electrophoretically migrating particles [15][16][17][18][19][20][21][22][23][24]. This phenomenon is relevant to estimate the protein pK-shifts of the i-ionizing group designated DpK i , the pH near molecule pH Ã , and the pH near the i-ionizing group indicated as pH i , for i 5 1yN c .…”

“…Here N c is the number of ionizing groups in the analyte by accounting also different positions that each one of them occupies in the macromolecular chain, both as amino acid residual and terminal amino and carboxylic groups. Thus, from one pH another, one expects to find deviations of the values taken by acid dissociation constants of ionizing groups in proteins and peptides, and hence the estimation of pK-shifts are required [15,[22][23][24][25][26][27][28][29][30]. In particular, the evaluation and prediction of pK-shifts of ionizing groups of amino acids residues in proteins have gained much attention in order to understand better complex biological systems.…”

“…The model is solved numerically by studying four proteins, for which CZEeffective mobilities and full protocol data are available in [15]. Also, emphasis is placed on the mathematical strategy to solve the resulting well-posed problem, by identifying the hydration number, estimated at a given pH, and the associated particle shape, through a consistent and convergent computational procedure presented in [15,17,22]. Then Section 3 analyses and discusses numerical results of the PLLCEM providing relevant physicochemical properties of the four proteins studied here.…”

Warum sind Proteine geladen? Netzwerke aus Ladungs‐Ladungs‐Wechselwirkungen in Proteinen, analysiert über Ladungsleitern und Kapillarelektrophorese

“…[134,147] [153] hat einen Wert von 1.0-1.5 und erweitert die Anwendbarkeit des linearisierten Poisson-Boltzmann-Gleichung für ein kugelförmiges Objekt unter Annahme eines niedrigen mittleren absoluten Oberflächenpotentials (j y s j < 25 mV; diese Annahme ist als Debye-Hückel-Näherung bekannt [154] ) und gleichmäßiger Ladungsverteilung. Die Gleichungen (21) [156][157][158] So betrachteten Yoon und Kim [156] das Protein nicht als Kugel, sondern als ein Ellipsoid mit einheitlichem Oberflächenpotential; elektrostatisch wird es noch nach der Debye-Hückel-Näherung behandelt, und Ionenrelaxation und Polarisation bleiben unbeachtet. Wird dieses Modell mit dem experimentell ermittelten Proteindiffusionsvermögen und mit einer Abschätzung der Ellipsoidform aus kristallographischen Daten parametrisiert, dann sagt es experimentelle Mobilitäten für Ladungsleitern gut vorher, [159] wenn die absolute Nettoladung des Proteins niedrig und das mittlere absolute Oberflächenpotential kleiner als ca.…”