Applicability of linear and nonlinear retention‐time models for reversed‐phase liquid chromatography separations of small molecules, peptides, and intact proteins

We introduce a method for data inspection in liquid separations of peptides using amino acid retention coefficients and their relative change across experiments. Our method allows for the direct comparison between actual experimental conditions, regardless of sample content and without the use of internal standards. The modeling uses linear regression of peptide retention time as a function of amino acid composition. We demonstrate the pH dependency of the model in a control experiment where the pH of the mobile phase was changed in controlled way. We introduce a score to identify the false discovery rate on peptide spectrum match level that corresponds to the set of most robust models, i.e. to maximize the shared agreement between experiments. We demonstrate the method utility in reversed‐phase liquid chromatography using 24 datasets with minimal peptide overlap. We apply our method on datasets obtained from a public repository representing various separation designs, including one‐dimensional reversed‐phase liquid chromatography followed by tandem mass spectrometry, and two‐dimensional online strong cation exchange coupled to reversed‐phase liquid chromatography followed by tandem mass spectrometry, and highlight new insights. Our method provides a simple yet powerful way to inspect data quality, in particular for multidimensional separations, improving comparability of data at no additional experimental cost.

Section: Introductionmentioning

confidence: 99%

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

Mohammed

Palmblad

2018

“…Tyteca et al. [117] found a similar deviation from the LSS model for small molecules and peptides and found a better fit for the Q and the NK model. Next to smaller molecules, the retention data of proteins was also investigated, which led to the conclusion that, because of the very steep

\ln k

versus φ curves, nonlinear retention behavior in proteins could not be proven.…”

Section: Methodsmentioning

confidence: 80%

Recent applications of retention modelling in liquid chromatography

Uijl

Schoenmakers

Pirok

et al. 2020

Recent applications of retention modelling in liquid chromatography (2015–2020) are comprehensively reviewed. The fundamentals of the field, which date back much longer, are summarized. Retention modeling is used in retention‐mechanism studies, for determining physical parameters, such as lipophilicity, and for various more‐practical purposes, including method development and optimization, method transfer, and stationary‐phase characterization and comparison. The review focusses on the effects of mobile‐phase composition on retention, but other variables and novel models to describe their effects are also considered. The five most‐common models are addressed in detail, i.e. the log‐linear (linear‐solvent‐strength) model, the quadratic model, the log–log (adsorption) model, the mixed‐mode model, and the Neue–Kuss model. Isocratic and gradient‐elution methods are considered for determining model parameters and the evaluation and validation of fitted models is discussed. Strategies in which retention models are applied for developing and optimizing one‐ and two‐dimensional liquid chromatographic separations are discussed. The review culminates in some overall conclusions and several concrete recommendations.

“…. was applied :

w = 4 \cdot \frac{t_{0}}{\sqrt{N_{gradient}}} \cdot (1 + k_{elut}) \cdot f,

where N gradient represents the plate number corrected for the retention factor at the moment of elution ( k elution ) :

N_{gradient} = N_{observed} \cdot \frac{{((), 1 + k_{elution})}^{2}}{{((), 1 + k_{observed})}^{2}},

where N observed and k observed are plate number and retention factor values that can be obtained directly from a chromatogram recorded in gradient mode . The corresponding value for N gradient was determined from the scouting runs to be 1100.…”

Section: Resultsmentioning

confidence: 99%

“…The corresponding value for N gradient was determined from the scouting runs to be 1100. The correction factor ( f ) takes into account the non‐Gaussian peak profiles (assuming f = 1 and 2 for peaks 1 and 2, respectively) . Figure A shows the resulting resolution between peaks 1 and 2 as a function of gradient time.…”

Section: Resultsmentioning

confidence: 99%

Generic approach to the method development of intact protein separations using hydrophobic interaction chromatography

Tyteca

Vos

Tassi

et al. 2017

Self Cite

We describe a liquid chromatography method development approach for the separation of intact proteins using hydrophobic interaction chromatography. First, protein retention was determined as function of the salt concentration by isocratic measurements and modeled using linear regression. The error between measured and predicted retention factors was studied while varying gradient time (between 15 and 120 min) and gradient starting conditions, and ranged between 2 and 15%. To reduce the time needed to develop optimized gradient methods for hydrophobic interaction chromatography separations, retention-time estimations were also assessed based on two gradient scouting runs, resulting in significantly improved retention-time predictions (average error < 2.5%) when varying gradient time. When starting the scouting gradient at lower salt concentrations (stronger eluent), retention time prediction became inaccurate in contrast to predictions based on isocratic runs. Application of three scouting runs and a nonlinear model, incorporating the effects of gradient duration and mobile-phase composition at the start of the gradient, provides accurate results (improved fitting compared to the linear solvent-strength model) with an average error of 1.0% and maximum deviation of -8.3%. Finally, gradient scouting runs and retention-time modeling have been applied for the optimization of a critical-pair protein isoform separation encountered in a biotechnological sample.