Prediction of electrophoretic mobilities of peptides in capillary zone electrophoresis by quantitative structure-mobility relationships using the offord model and artificial neural networks

Abstract

The aim of this work was to explore the usefulness of empirical models and multivariate analysis techniques in predicting electrophoretic mobilities of small peptides in capillary zone electrophoresis (CZE). The data set consists of electrophoretic mobilities, measured at pH 2.5, for 125 peptides ranging in size between 2 and 14 amino acids. Among the existing empirical models, the Offord model (i.e., mu identical with Q/M(2/3)) gave the best correlation for the data set. A quantitative structure-mobility relationship (QSMR) was developed using the Offord's charge-over-mass term (Q/M(2/3)) as one descriptor combined with the corrected steric substituent constant (E(s, c)) and molar refractivity (MR) descriptors to account for the steric effects and bulkiness of the amino acid side chains. The multilinear regression (MLR) of the data set showed an improvement in the predictive ability of the model over the simple Offord's relationship. A 3-4-1 back propagation artificial neural networks (BP-ANN) model resulted in a significant improvement in the predictive ability of the QSMR over the MLR treatment, especially for peptides of higher charges that contain basic amino acids arginine, histidine, and lysine. The improved correlations by the BP-ANN analysis suggest the existence of nonlinear characteristic in the mobility-charge relationships.

Separation Technology as a Powerful Tool for Unfolding Molecular Complexity of Natural Organic Matter and Humic Substances

“…M is the molecular mass, and a and α are two constants determined experimentally which can vary between classes of chemical compounds (DNA, peptides, organic acids) (Jalali -Heravi et al, 2005 ).…”

Capillary electrophoresis of proteins 2003–2005

“…However, a more detailed comparison of the theoretical and experimental data indicated that the correlation is only weak [7,8]. Combination of the Offord model with a corrected steric substituent constant and molar refractivity descriptors improved, however, the predictivity of the model, especially for peptides containing basic amino acids [9]. Similarly, CZE of five model proteins resulted in the conclusion that a primitive continuum model is appropriate for predicting mobilities of proteins [10].…”

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

“…One is to extend the two-variable QSPR models with further parameters to increase the descriptive power of the models and enhance the operational range to all the different groups of peptides [6,7]. The other approach suggests the use of nonlinear modeling methods, such as artificial neural networks (ANNs) to develop more accurate and robust QSPR models [4,[8][9][10][11]. Actually, ANNs were effectively used to address the reported shortcomings of the semiempirical models.…”