Artificial neural network modeling of peptide mobility and peptide mapping in capillary zone electrophoresis

Peptide mapping of proteins by capillary electromigration methods

This review article provides a wide overview of important developments and applications of capillary electromigration methods in the area of peptide mapping of proteins in the period 1997-mid-2022, including review articles on this topic. It deals with all major aspects of peptide mapping by capillary electromigration methods: i) precleavage sample preparation involving purification, preconcentration, denaturation, reduction and alkylation of protein(s) to be analyzed, ii) generation of peptide fragments by off-line or on-line enzymatic and/or chemical cleavage of protein(s), iii) postcleavage preparation of the generated peptide mixture for capillary electromigration separation, iv) separation of the complex peptide mixtures by one-, two- and multidimensional capillary electromigration methods coupled with mass spectrometry detection, and v) a large application of peptide mapping for variable purposes, such as qualitative analysis of monoclonal antibodies and other protein biopharmaceuticals, monitoring of posttranslational modifications, determination of primary structure and investigation of function of proteins in biochemical and clinical research, characterization of proteins of variable origin as well as for protein and peptide identification in proteomic and peptidomic studies.

Prediction of metabolite identity from accurate mass, migration time prediction and isotopic pattern information in CE-TOFMS data

CE-TOFMS is a powerful method for profiling charged metabolites. However, the limited availability of metabolite standards hinders the process of identifying compounds from detected features in CE-TOFMS data sets. To overcome this problem, we developed a method to identify unknown peaks based on the predicted migration time (t(m)) and accurate m/z values. We developed a predictive model using 375 standard cationic metabolites and support vector regression. The model yielded good correlations between the predicted and measured t(m) (R=0.952 and 0.905 using complete and cross-validation data sets, respectively). Using the trained model, we subsequently predicted the t(m) for 2938 metabolites available from the public databases and assigned tentative identities to noise-filtered features in human urine samples. While 38.9% of the peaks were assigned metabolite names by matching with the standard library alone, the proportion increased to 52.2%. The proposed methodology increases the value of metabolomic data sets obtained from CE-TOFMS profiling.

Application of artificial neural networks in the prediction of product distribution in electrophoretically mediated microanalysis

The successful application of artificial neural networks toward the prediction of product distribution in electrophoretically mediated microanalysis is presented. To illustrate this concept, we examined the factors and levels required for optimization of reaction conditions for the conversion of nicotinamide adenine dinucleotide to nicotinamide adenine dinucleotide, reduced form by glucose-6-phosphate dehydrogenase in the conversion of glucose-6-phosphate to 6-phosphogluconate. A full factorial experimental design examining the factors voltage, enzyme concentration, and mixing time of reaction was utilized as input-output data sources for suitable artificial neural networks training for prediction purposes. This approach proved successful in predicting optimal values in a reduced number of experiments. Model validation addressing the extent of reaction and product ratios were subsequently determined experimentally in replicate analyses, with results shown to be in good agreement (<10% discrepancy difference) with predicted data.

Hydration, charge, size, and shape characteristics of peptides from their CZE analyses

A CZE model is presented for peptide characterization on the basis of well-established physicochemical equations. The effective mobility is used as basic data in the model to estimate relevant peptide properties such as, for instance, hydration, net and total electrical charge numbers, hydrodynamic size and shape, particle average orientation, and pH-microenvironment from the charge regulation phenomenon. Therefore 102 experimental effective mobilities of different peptides are studied and discussed in relation to previous work. An equation for the estimation of peptide hydration as a function of ionizing, polar, and non-polar amino acid residues is included in the model. It is also shown that the shape-orientation factor of peptides may be either lower or higher than one, and its value depends on a complex interplay among total charge number, molar mass, hydration, and amino acid sequence.

Recent developments in CE and CEC of peptides

The article brings a comprehensive survey of recent developments and applications of high-performance capillary electromigration methods, zone electrophoresis, ITP, IEF, affinity electrophoresis, EKC, and electrochromatography, to analysis, preparation, and physicochemical characterization of peptides. New approaches to the theoretical description and experimental verification of electromigration behavior of peptides and to methodology of their separations, such as sample preparation, adsorption suppression, and detection, are presented. Novel developments in individual CE and CEC modes are shown and several types of their applications to peptide analysis are presented: conventional qualitative and quantitative analysis, purity control, determination in biomatrices, monitoring of chemical and enzymatical reactions and physical changes, amino acid and sequence analysis, and peptide mapping of proteins. Some examples of micropreparative peptide separations are given and capabilities of CE and CEC techniques to provide important physicochemical characteristics of peptides are demonstrated.

Neural networks in analytical chemistry

This chapter covers a part of the spectrum of neural-network uses in analytical chemistry. Different architectures of neural networks are described briefly. The chapter focuses on the development of three-layer artificial neural network for modeling the anti-HIV activity of the HETP derivatives and activity parameters (pIC50) of heparanase inhibitors. The use of a genetic algorithm-kernel partial least squares algorithm combined with an artificial neural network (GA-KPLS-ANN) is described for predicting the activities of a series of aromatic sulfonamides. The retention behavior of terpenes and volatile organic compounds and predicting the response surface of different detection systems are presented as typical applications of ANNs in chromatographic area. The use of ANNs is explored in electrophoresis with emphasizes on its application on peptide mapping. Simulation of the electropherogram of glucagons and horse cytochrome C is described as peptide models. This chapter also focuses on discussing the role of ANNs in the simulation of mass and 13C-NMR spectra for noncyclic alkenes and alkanes and lignin and xanthones, respectively.

Modeling the electrophoresis of peptides and proteins: improvements in the "bead method" to include ion relaxation and "finite size effects"

A bead model methodology developed in our lab (Xin et al. J. Phys. Chem. B 2006, 110, 1038) and applicable to modeling the free solution electrophoretic mobility of peptides and proteins is generalized in two significant ways. First, an approximate account is taken of the relaxation effect, which makes the methodology applicable to more highly charged peptides and proteins than was previously possible. Second, a more accurate account is taken of the finite size of the beads making up the model structure. This improvement makes the method applicable at higher salt concentrations and/or to models consisting of larger sized subunits. The relaxation effect is accounted for by correcting "unrelaxed" mobilities on the basis of model size and average electrostatic surface, or zeta potential. Correction factors are estimated using those of spheres with the same hydrodynamic radius and zeta potential as the model structure. The correction factors of spheres are readily determined. The more general methodology is first applied to two sets of peptides (74 different peptides total) varying in size from 2 to 42 amino acids. The sets also cover a wide range of net charges. It is shown that accounting for finite bead size results in a small change in model mobilities under the conditions of the experiments (35 mM monovalent salt). The correction for ion relaxation, however, can be significant for highly charged peptides and improves agreement between model and experimental mobilities. Our correction procedure is also tested by examining the electrophoretic mobility of a particular protein "charge ladder" (Carbeck et al. J. Am. Chem. Soc. 1999, 121, 10,671), where the protein charge is varied over a wide range yet the conformation remains essentially constant. In summary, the effects of ion relaxation can be significant if the absolute electrophoretic mobility of a peptide exceeds approximately 0.20 cm2/(kV s).

Exploring the evaluation of net charge, hydrodynamic size and shape of peptides through experimental electrophoretic mobilities obtained from CZE

This work explores the validity of simple CZE models to analyze the electrophoretic mobilities of 102 peptides reported in literature. These models are based mainly on fundamental physicochemical theories providing analytical expressions amenable to relatively simple numerical analysis. Thus, the Linderstrøm-Lang capillary electrophoresis model (LLCEM) and its perturbed version (PLLCEM), proposed and applied previously to the CZE of globular proteins, are adapted and used here for peptides. Also the effects of pK-shifts on net charge, hydration and hydrodynamic size and shape of peptides are analyzed and discussed. Emphasis is placed on the fact that these parameters are physically coupled, and thus a variation in the net charge may produce an appreciable change in the hydrodynamic size of peptides. Within the framework of CZE, peptides may be assumed as having a hydrodynamic volume associated with either spherical or spheroidal particles. The effects on peptide net charge and hydrodynamic size, of electrostatic interaction between a pair of charged groups in the chain and electrical permitivitty around the peptide domain are studied. The predictions of the PLLCEM and LLCEM are in good agreement with results reported previously in the literature. Several limitations concerning these models and some needs for further research are also described.

Analysis ofSalvia officinalis plant extracts by capillary electrophoresis

Salvia officinalis (commonly called Sage) and similar plants contain many compounds of pharmaceutical interest and are used as a tea or in various pharmaceutical products. In this work, the use of CE for analysis of aqueous or ethanolic extracts from various Salvia plants has been studied. Especially, several buffers like borate, phosphate, acetate, etc., were examined under different concentrations, pH, separation voltage, injection time, and other parameters to find the optimal separation conditions. The optimization was also performed using experimental design and artificial neural networks. The optimal conditions were: separation voltage +20 kV, 40 mM buffer borate, pH 9.2, injection time 5 s, and UV detection at 280 nm. A new CE method has been developed, validated, and applied to analyze samples of S. officinalis from various countries.