Retention prediction of phenylthiohydantoin amino acid derivatives in reversed-phase liquid chromatography

Summary The concept of retention prediction for the separation of phenylthiohydatoin-amino acid derivatives in isocratic reversed-phase liquid chromatography is described. A novel retention-solubility parameter, R, is defined, for the retention prediction strategy and the performance of this R value is evaluated by comparing measured and predicted retention data. Excellent agreement between these values were observed. It is concluded that the R value has a very high potential in describing the retention of phenylthiohydantopinamino acid derivatives withdifferent types of separation systems consisting of C-18, C-8 and phenethyl bonded stationary phases and various mobile phases.     

Retention prediction of o-phthalaldehyde amino acid derivatives in reversed-phase liquid chromatography

Summary Retention prediction of o-phthalaldehyde amino acid derivatives in reversed-phase liquid chromatography has been investigated. The retention of all derivatives could be predicted within about 10% relative error under the appropriate separation conditions in both isocratic and gradient-elution modes.     

Prediction of Peptide Retention in RP-LC

The retention of small peptides can be predicted by summing the hydrophobic contribution to retention of each amino acid of peptides. But the retention time of peptides larger than 10–15 residues are less than that predicted by summing the retention coefficients of each constitutive residue. A new prediction model, considering the effects of the peptide length and contact area of each amino acid with the stationary phase for larger peptides was proposed. The model was validated by 136 peptides identified by nano-flow 2-D-LC-ESI-MS-MS platform and other retention data observed from literature. The high degree of correlation between the observed and predicted retention time by using the new model is not only good evidence for the accuracy of our predictive method, but supports the supposition that the peptide length and contact area of each amino acid with stationary phase are important factors affecting peptide retention time for larger peptide. In addition it is found that the range of peptide length is wider, the accuracy of prediction is better. The ratio coefficient of surface area of non-polar, polar and charged amino residues contacting with the stationary phase were all calculated to be less than one. Revised: 30 June and 11 August 2005     

基于深度学习的保留时间预测方法的研究进展及应用

在基于液相色谱-质谱联用的蛋白质组学研究中,肽段的保留时间作为有效区分不同肽段的特征参数,可以根据肽段自身的序列等信息对其进行预测.使用预测得到的保留时间辅助质谱数据鉴定肽段序列可以提高鉴定的准确性,因此对保留时间预测的工作一直受到领域内的广泛关注.传统的保留时间预测方法通常是根据氨基酸序列计算肽段的理化性质,进而计算...     

A simple,fast, and accurate thermodynamic‐based approach for transfer and prediction of gas chromatography retention times between columns and instruments Part III: Retention time prediction on target column

This is the third part of a three‐part series of papers. In Part I, we presented a method for determining the actual effective geometry of a reference column as well as the thermodynamic‐based parameters of a set of probe compounds in an in‐house mixture. Part II introduced an approach for estimating the actual effective geometry of a target column by collecting retention data of the same mixture of probe compounds on the target column and using their thermodynamic parameters, acquired on the reference column, as a bridge between both systems. Part III, presented here, demonstrates the retention time transfer and prediction from the reference column to the target column using experimental data for a separate mixture of compounds. To predict the retention time of a new compound, we first estimate its thermodynamic‐based parameters on the reference column (using geometric parameters determined previously). The compound's retention time on a second column (of previously determined geometry) is then predicted. The models and the associated optimization algorithms were tested using simulated and experimental data. The accuracy of predicted retention times shows that the proposed approach is simple, fast, and accurate for retention time transfer and prediction between gas chromatography columns.     

Optimization of artificial neural network for retention modeling in high-performance liquid chromatography

An artificial neural network (ANN) model for the prediction of retention times in high-performance liquid chromatography (HPLC) was developed and optimized. A three-layer feed-forward ANN has been used to model retention behavior of nine phenols as a function of mobile phase composition (methanol-acetic acid mobile phase). The number of hidden layer nodes, number of iteration steps and the number of experimental data points used for training set were optimized. By using a relatively small amount of experimental data (25 experimental data points in the training set), a very accurate prediction of the retention (percentage normalized differences between the predicted and the experimental data less than 0.6%) was obtained. It was shown that the prediction ability of ANN model linearly decreased with the reduction of number of experiments for the training data set. The results obtained demonstrate that ANN offers a straightforward way for retention modeling in isocratic HPLC separation of a complex mixture of compounds widely different in pK_a and log K_ow values.     

Computer-assisted retention prediction system in reversed-phase HPLC for O-ethyl O-aryl N-isopropyl phosphoramidothioates

A computer-assisted retention prediction system (RPS) of fifteen O-ethyl O-aryl N-isopropyl phosphoramidothioates ( 1 ) in reversed-phase HPLC was investigated. The system is based on the use of four physicochemical parameters (hydrophobicity II, electric effect σ, field effect F and steric effect E_s) which is closely related to the retention mechanism in reversed-phase HPLC. The system was evaluated by comparing the measured retention data with the predicted ones. The predicted values were consistent with the measured values within a relative error of 11.5%.     

Prediction of protein retention times in gradient hydrophobic interaction chromatographic systems

A two-step methodology has been developed for the prediction of protein retention time in linear-gradient HIC systems. Isocratic retention parameters were determined from ln(k')-salt concentration plots for a number of commercially available proteins with a range of properties. Quantitative structure property relationship (QSPR) models based on a support vector machine (SVM) approach were generated for predicting isocratic retention parameters for proteins not included in the model generation. The predicted parameters were then used to calculate protein gradient retention times and the results indicate that this approach is well suited for predicting experimental gradient retention data. The approach presented in this paper may have implications for HIC methods development at both the bench and process scales.     

Error analysis and performance of different retention models in the transference of data from/to isocratic/gradient elution

The transferability of retention data among isocratic and gradient RPLC elution modes is studied. For this purpose, 16 beta-blockers were chromatographed under both isocratic and gradient elution with acetonitrile-water mobile phases. Taking into account the elution mode where the experimental data come from, and the mode where the retention should be predicted, the following combinations are possible: isocratic predictions from (i) isocratic or (ii) gradient experimental designs; and gradient predictions from (iii) isocratic or (iv) gradient data. Each of these possibilities was checked using three retention models that relate the logarithm of the retention factor: (a) linearly and (b) quadratically with the volume fraction of organic solvent, and (c) linearly with a normalised mobile phase polarity parameter. The study was carried out under two different perspectives: a straightforward examination of the prediction errors and the analysis of the uncertainties derived from the variance-covariance matrix of the fitted models. The best combinations of prediction mode and model were: (i)-(b), (ii)-(c), (iii)-(b), and (iv)-(a) or (c).     

Predicting retention data by target factor analysis and multiple regression analysis

Target factor analysis is used to predict gas-chromatographic retention indices from a training-set data matrix for 13 solutes and 15 stationary phases. In the target-combination approach, sets of data vectors are target-tested in combination and the resulting coefficients for the best model are used for prediction. Retention indices for 42 solutes and 24 stationary phases are predicted to better than 1% even with a three-factor model. In the target free-float approach, values for missing retention indices on target test vectors are predicted. Predictions from sets of target-test data selected by chemical intuition are compared to those obtained from sets of target-test data selected by using models from the combination step. The target-combination approach and multiple-regression approach are overall of similar utility for predicting new data.     

硝胺及其甲基衍生物的从头计算研究 3: 甲硝胺及其同位素衍生物的谐性力场和振动光谱

用TEXAS从头计算程序,取STO-4-21G基组,计算了甲硝胺的谐性力场和振动光谱.直接理论计算的谐性力场经由其他分子转移来的经验校正因子校正后,提供了甲硝胺振动基频的预测.预测值和甲硝胺分子在气相中的振动光谱实验值之间的平均偏差为31cm^-1.为了获得更合适的气相甲硝胺振动力场和预测它的同位素衍生物的振动光谱,我们优化了一组新的校正因子,使理论值和实验值的平均偏差减为8.9cm^-1.用这组校正因子得到的力场预测了三个同位素衍生物的振动光谱,其同位素位移的理论预测值和实验值符合良好.     

New uniform algorithm to predict reversed phase retention values under different gradient conditions

A new numerical emulation algorithm was established to calculate retention parameters in RP-HPLC with several retention times under different linear or nonlinear binary gradient elution conditions and further predict the retention time under any other binary gradient conditions. A program was written according to this algorithm and nine solutes were used to test the program. The prediction results were excellent. The maximum relative error of predicted retention time was less than 0.45%.     

Molecular Distance‐Edge Vector (μ) and Chromatographic Retention Index of Alkanes

A new method of quantitative structure‐retention relationship (QSRR) is proposed for estimating and predicting gas chromatographic retention indices of alkanes by using a novel molecular distance‐edge vector, called μ vector, containing 10 elements. The QSRR model (Ml), between the μ vector and chromatographic retention indices of 64 alkanes, was developed by using multiple linear regression (MLR) with the correlation coefficient being R = 0.9992 and the root mean square (RMS) error between the estimated and measured retention indices being RMS = 5.938. In order to explain the equation stability and prediction abilities of the M1 model, it is essential to perform a cross‐validation (CV) procedure. Satisfactory CV results have been obtained by using one external predicted sample every time with the average correlation coefficient being R = 0.9988 and average RMS = 7.128. If 21 compounds, about one third drawn from all 64 alkanes, construct an external prediction set and the 43 remaining construct an internal calibration set, the second QSRR model (M2) can be created by using calibration set data with statistics being R = 0.9993 and RMS = 5.796. The chromatographic retention indices of 21 compounds in the external testing set can be predicted by the M2 model and good prediction results are obtained with R = 0.9988 and RMS = 6.508.     

Comparative study on the thermodynamic retention models in HPLC

Summary A series of model substances with known solubility parameter were chromatographed and from the temperature dependence of the capacity factor, some of the thermodynamic parameters influencing solute retention were determined. A linear relationship was derived between the enthalpy density and the solute solubility parameter from which a graphical method was introduced for the determination of phase characteristics.Comparing the predicted and measured capacity factor values it has been found that the predicted values are very sensitive to the literature data selected for the computation; however, by using the van der Waals molecular volume in the calculation significantly lower deviation was found from the measured data. Two equations are given for the prediction of selectivity and as the mathematical criteria of the validity of the used thermodynamic models. The results show that the predicted selectivity values are similar to the measured data using given initial parameters in the computation. However, the unreliability of the literature data makes the application of the retention models difficult.Dedicated to Professor J. F. K. Huber on the occasion of his 60th birthday.     

Monte-carlo simulation of gas chromatographic separation for the prediction of retention times and peak half widths

Summary A new method for prediction of gas chromatographic retention times and peak half widths is based on the renewal theory. The only requirements are the heats of vaporization of the compounds to be separated and one calibration measurement. With this data, retention times and peak half widths can be predicted for isothermal as well as temperature-programmed gas chromatography. For the separation of non-polar substances on non-polar stationary phases the prediction error for retention times is approx. 1–2%. First simulations of polar molecules and polar stationary phases indicate that this method is also applicable in these cases but some extension will be required.     

Application of the Solvatic Model for Prediction of Retention in RP-LC for Multi-Step Gradient Profiles

Application of the solvatic retention model of reversed-phase liquid chromatography was studied to predict retention of phenylisothiocyanate derivatives of amino acids from structural formulae and stationary and mobile phase properties. The gradient elution mode with methanol and acetonitrile aqueous mobile phases was used. It was shown that practically acceptable prediction or retention time values can be achieved after the first approximation step when experimental data of one run are used. The zero approximation level predictions—from structural formulae, column and mobile phase properties can be used as a “first guess” method from which further optimization can begin.

     

Prediction of peptide retention time in reversed-phase high-performance liquid chromatography.

Peptide retention in reversed-phase chromatography depends mainly on the amino acid composition of peptides and can therefore be predicted by summing the relative hydrophobic contributions of each constitutive amino acid residue. The prediction is correct for small peptides but overestimates the retention times of peptides larger than 10-15 residues. A new prediction model is proposed in which the contribution to peptide retention of each amino acid residue is not a constant but a decreasing function of peptide length. From the retention times of 104 peptides, the parameters of decreasing functions were estimated by a non-linear multiple regression analysis. The contribution to peptide retention of charged, polar and non-polar residues appears to be differently affected by peptide length. The secondary structure of most peptides during reversed-phase high-performance liquid chromatography could be responsible for this. The high correlation between the predicted and observed retention times of peptides which were not used to establish the model indicates a good predictive accuracy of the new model.