Mobility modeling of peptides in capillary electrophoresis

Determination of physicochemical parameters of (bio)molecules and (bio)particles by capillary electromigration methods

The review provides an overview of recent developments and applications of capillary electromigration (CE) methods for the determination of important physicochemical parameters of various (bio)molecules and (bio)particles. These parameters include actual and limiting (absolute) ionic mobilities, effective electrophoretic mobilities, effective charges, isoelectric points, electrokinetic potentials, hydrodynamic radii, diffusion coefficients, relative molecular masses, acidity (ionization) constants, binding constants and stoichiometry of (bio)molecular complexes, changes of Gibbs free energy, enthalpy and entropy and rate constants of chemical reactions and interactions, retention factors and partition and distribution coefficients. For the determination of these parameters, the following CE methods are employed: zone electrophoresis in a free solution or in sieving media, isotachophoresis, isoelectric focusing, affinity electrophoresis, electrokinetic chromatography, and electrochromatography. In the individual sections, the procedures for the determination of the above parameters by the particular CE methods are described.

Enzyme-Assisted Extraction of Phenolics from Capparis spinosa Fruit: Modeling and Optimization of the Process by RSM and ANN

The current study intends to appraise the effect of enzyme complexes on the recovery of phenolics from Capparis spinosa fruit extract using the response surface methodology (RSM) and artificial neural networking (ANN). Enzymatic treatment of C. spinosa fruit extract was optimized under a set of conditions (enzyme concentration, pH, temperature, and time) against each enzyme formulation such as Kemzyme Plus Dry, Natuzyme, and Zympex-014. The extract yield observed for Kemzyme Plus Dry (42.00%) was noted to be higher than those for Zympex-014 (39.80%) and Natuzyme (38.50%). Based on the higher results, the values of Kemzyme Plus Dry-based extract were further employed in different parameters of RSM. The F-value (16.03) and p-values (<0.05) implied that the selected model is significant. Similarly, the higher values for the coefficient of determination (R ²) at 0.9740 and adjusted R ² (adj. R ²) at 0.9132 indicated that the model is significant in relation to given experimental parameters. ANN-predicted values were very close to the experimental values, which demonstrated the applicability of the ANN model. Antioxidant activities also exhibited profound results in terms of total phenolic content values (24.76 mg GAE/g), total flavonoid content values (24.56 mg CE/g), and the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay (IC₅₀) (5.12 mg/mL). Scanning electron microscopy revealed that after enzymatic hydrolysis, the cell walls were broken as compared with nonhydrolyzed materials. Five phenolics, namely, quercetin, m-coumaric acid, sinapic acid, kaempferol, and p-coumaric acid, were identified from C. spinosa extract by gas chromatography-mass spectrometry (GC/MS). The results of this study reveal that the proposed optimization techniques, using Kemzyme Plus Dry among others, had a positive effect on the recovery of phenolic bioactive compounds and thus increased the antioxidant potential of C. spinosa fruit extract.

Evaluation of Machine Learning Models for Proteoform Retention and Migration Time Prediction in Top-Down Mass Spectrometry

Reversed-phase liquid chromatography (RPLC) and capillary zone electrophoresis (CZE) are two primary proteoform separation methods in mass spectrometry (MS)-based top-down proteomics. Proteoform retention time (RT) prediction in RPLC and migration time (MT) prediction in CZE provide additional information for accurate proteoform identification and quantification. While existing methods are mainly focused on peptide RT and MT prediction in bottom-up MS, there is still a lack of methods for proteoform RT and MT prediction in top-down MS. We systematically evaluated eight machine learning models and a transfer learning method for proteoform RT prediction and five models and the transfer learning method for proteoform MT prediction. Experimental results showed that a gated recurrent unit (GRU)-based model with transfer learning achieved a high accuracy (R = 0.978) for proteoform RT prediction and that the GRU-based model and a fully connected neural network model obtained a high accuracy of R = 0.982 and 0.981 for proteoform MT prediction, respectively.

Capillary Zone Electrophoresis-Tandem Mass Spectrometry As an Alternative to Liquid Chromatography-Tandem Mass Spectrometry for Top-down Proteomics of Histones

Top-down proteomics (TDP) is an ideal approach for deciphering the histone code and it routinely employs reversed-phase liquid chromatography (RPLC)-tandem mass spectrometry (MS/MS). Because of the extreme complexity of histones regarding the number of proteoforms, new analytical tools with high-capacity separation and highly sensitive detection of proteoforms are required for TDP of histones. Here we present capillary zone electrophoresis (CZE)-MS/MS via the electro-kinetically pumped sheath-flow CE-MS interface for large-scale top-down delineation of histone proteoforms. CZE-MS/MS identified a comparable number of proteoforms to RPLC-MS/MS from a calf histone sample with more than 30-fold less sample consumption (75-ng vs. Three μg), indicating its substantially higher sensitivity. We identified about 400 histone proteoforms from the calf histone sample using two-dimensional size-exclusion chromatography (SEC)-CZE-MS/MS with less than 300-ng proteins consumed. We identified histone proteoforms carrying various tentative post-translational modifications (PTMs), for example, acetylation, methylation (mono-, di-, and tri-), phosphorylation, and succinylation. The electrophoretic mobility (μef) of unmodified histone proteoforms can be predicted accurately (R2 = 0.98) with an optimized semiempirical model based on our recent work. The results render CZE-MS/MS as a useful tool for deciphering the histone code in a proteoform-specific manner and on a global scale.

Predicting Electrophoretic Mobility of Proteoforms for Large-Scale Top-Down Proteomics

Large-scale top-down proteomics characterizes proteoforms in cells globally with high confidence and high throughput using reversed-phase liquid chromatography (RPLC)-tandem mass spectrometry (MS/MS) or capillary zone electrophoresis (CZE)-MS/MS. The false discovery rate (FDR) from the target-decoy database search is typically deployed to filter identified proteoforms to ensure high-confidence identifications (IDs). It has been demonstrated that the FDRs in top-down proteomics can be drastically underestimated. An alternative approach to the FDR can be useful for further evaluating the confidence of proteoform IDs after the database search. We argue that predicting retention/migration time of proteoforms from the RPLC/CZE separation accurately and comparing their predicted and experimental separation time could be a useful and practical approach. Based on our knowledge, there is still no report in the literature about predicting separation time of proteoforms using large top-down proteomics data sets. In this pilot study, for the first time, we evaluated various semiempirical models for predicting proteoforms' electrophoretic mobility (μef) using large-scale top-down proteomics data sets from CZE-MS/MS. We achieved a linear correlation between experimental and predicted μef of E. coli proteoforms (R2 = 0.98) with a simple semiempirical model, which utilizes the number of charges and molecular mass of each proteoform as the parameters. Our modeling data suggest that the complete unfolding of proteoforms during CZE separation benefits the prediction of their μef. Our results also indicate that N-terminal acetylation and phosphorylation both decrease the proteoforms' charge by roughly one charge unit.

Impact of metal ionic characteristics on adsorption potential of Ficus carica leaves using QSPR modeling

The present study describes Quantitative Structure Property Relationship (QSPR) modeling to relate metal ions characteristics with adsorption potential of Ficus carica leaves for 13 selected metal ions (Ca⁺², Cr⁺³, Co⁺², Cu⁺², Cd⁺², K⁺¹, Mg⁺², Mn⁺², Na⁺¹, Ni⁺², Pb⁺², Zn⁺², and Fe⁺²) to generate QSPR model. A set of 21 characteristic descriptors were selected and relationship of these metal characteristics with adsorptive behavior of metal ions was investigated. Stepwise Multiple Linear Regression (SMLR) analysis and Artificial Neural Network (ANN) were applied for descriptors selection and model generation. Langmuir and Freundlich isotherms were also applied on adsorption data to generate proper correlation for experimental findings. Model generated indicated covalent index as the most significant descriptor, which is responsible for more than 90% predictive adsorption (α = 0.05). Internal validation of model was performed by measuring [Formula: see text] (0.98). The results indicate that present model is a useful tool for prediction of adsorptive behavior of different metal ions based on their ionic characteristics.

Predicting Electrophoretic Mobility of Tryptic Peptides for High-Throughput CZE-MS Analysis

A multiparametric sequence-specific model for predicting peptide electrophoretic mobility has been developed using large-scale bottom-up proteomic CE-MS data (5% (∼0.8M) acetic acid as background electrolyte). Peptide charge (Z) and size (molecular mass, M) are the two major factors determining electrophoretic mobility, in complete agreement with previous studies. The extended size of the data set (>4000 peptides) permits access to many sequence-specific factors that impact peptide mobility. The presence of acidic residues Asp and Glu near the peptide N-terminus is by far the most prominent among them. The induction effect of the side chain of N-terminal Asp reduces the basicity of the N-terminal amino group and, as hence, its charge, by ∼0.27 units, lowering mobility. The correlation of the model (R² ∼ 0.995) indicates that the peptide separation process in CZE is relatively simple and can be predicted to a much higher precision than current RP-HPLC models. Similar to RP-HPLC prediction studies, we anticipate future developments that introduce peptide migration standards, collect larger data sets for modeling through the alignment of multiple CZE-MS acquisitions, and study of the behavior of peptides carrying post-translational modifications. The increased size of data sets will also permit investigation of the fine-scale effects of peptide secondary structure on peptide mobility. We observed that peptides with higher helical propensity tend to have higher than predicted electrophoretic mobility; the incorporation of these features into CZE migration models will require significantly larger data sets.

Separation of Peptides by Capillary Electrophoresis

Peptides are an important class of analytes in chemistry, biochemistry, food chemistry, as well as medical and pharmaceutical sciences including biomarker analysis in peptidomics and proteomics. As a high-resolution technique, capillary electrophoresis (CE) is well suited for the analysis of polar compounds such as peptides. In addition, CE is orthogonal to high-performance liquid chromatography (HPLC) as both techniques are based on different physicochemical separation principles. For the successful development of peptide separations by CE, operational parameters including puffer pH, buffer concentration and buffer type, applied voltage, capillary dimensions, as well as background electrolyte additives such as detergents, ion-pairing reagents, cyclodextrins, (poly)amines, and soluble polymers have to be considered and optimized.

Modelling the electrophoretic migration behaviour of peptides and glycopeptides from glycoprotein digests in capillary electrophoresis-mass spectrometry

In this study, the classical semiempirical relationships between the electrophoretic mobility and the charge-to-mass ratio (me vs. q/M(α)) were used to model the migration behaviour of peptides and glycopeptides originated from the digestion of recombinant human erythropoietin (rhEPO), a biologically and therapeutically relevant glycoprotein. The Stoke's law (α=1/3), the classical polymer model (α=1/2) and the Offord's surface law (α=2/3) were evaluated to predict migration of peptides and glycopeptides, with and without sialic acids (SiA), in rhEPO digested with trypsin and trypsin-neuraminidase. The Stoke's law resulted in better correlations for the set of peptides used to evaluate the models, while glycopeptides fitted better with the classical polymer model. Once predicted migration times with both models, it was easy to simulate their separation electropherogram. Results were later validated predicting migration and simulating separation of a different set of rhEPO glycopeptides and also human transferrin (Tf) peptides and glycopeptides. The excellent agreement between the experimental and the simulated electropherograms with rhEPO and Tf digests confirmed the potential applicability of this simple strategy to predict, in general, the peptide-glycopeptide electrophoretic map of any digested glycoprotein.

Identification of bioactive peptides in hypoallergenic infant milk formulas by CE-TOF-MS assisted by semiempirical model of electromigration behavior

Biologically active peptides derived from complex bovine milk protein hydrolysates are of particular interest in food science and nutrition because they have been shown to play different physiological roles, providing benefits in human health. In this study, we used CE-TOF-MS for separation and identification of bioactive peptides in three hypoallergenic infant milk formulas. An appropriate sample cleanup using a citrate buffer with DTT and urea followed by SPE with Sep-Pack® C18 and StrataX™ cartridges allowed the detection of a large number of low molecular mass bioactive peptides. This preliminary identification was solely based on the measured experimental monoisotopic molecular mass values (M(exp)). Later, we evaluated the classical semiempirical relationships between electrophoretic mobility and charge-to-mass ratio (m(e) vs. q/M(α), α = 1/2 for the classical polymer model) to describe their migration behavior. The assistance of migration prediction proved to be useful to improve reliability of the identification, avoiding misinterpretations and solving some identity conflicts. After revision, the identity of 24, 30, and 38 bioactive peptides was confirmed in each of the three infant milk formulas. A significant number of these peptides were reported as inhibitors of angiotensin-converting enzyme, however, the presence of sequences with other biological activities such as antihypertensive, antithrombotic, hypocholesterolemic, immunomodulation, cytotoxicity, antioxidant, antimicrobial, antigenic, or opioid was also confirmed.

SEC ICP MS and CZE ICP MS investigation of medium and high molecular weight complexes formed by cadmium ions with phytochelatins

Size-exclusion chromatography (SEC) and capillary zone electrophoresis (CZE) coupled with inductively coupled plasma mass spectrometry were applied to characterize low, medium, and high molecular weight cadmium complexes with glutathione and phytochelatins (PCs). The dominant stoichiometry of the complexes formed in vitro was established as 1:1 using electrospray ionization mass spectrometry. Calculated molecular masses of Cd₁L₁ complexes were used for calibration of the SEC and CZE methods. The results showed a lower (2 kDa) SEC column exclusion limit for cadmium complexes compared with free peptides (10 kDa), and most of the high molecular weight cadmium species were eluted in the void volume of the column. Moreover, the CZE method based on the semiempirical model of Offord to elucidate peptide migration allowed us to show a high propensity of Cd–PC complexes for polymorphism on complexation, which was also observed for extracts of Arabidopsis thaliana treated with cadmium. All the information presented is vital for understanding the mechanism of metal deactivation in plants.

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Quantum dot applications endowing novelty to analytical proteomics

This review surveys all the state-of-art applications of quantum dots (QDs) in conventional and modern analytical methods in proteomic studies. A brief introduction of QDs and their properties is initially presented followed by outlining the application of QDs in fluorescence, MS, imaging, and cancer-based proteomics. The in-depth application of QDs in MALDI-MS and surface assisted laser desorption/ionization-MS has been elaborately discussed, summarizing the speculated mechanism behind the protein-QDs interactions during QD matrix applications leading to enhanced detection sensitivity.

Predicting retention times of naturally occurring phenolic compounds in reversed-phase liquid chromatography: a Quantitative Structure-Retention Relationship (QSRR) approach

Quantitative structure-retention relationships (QSRRs) have successfully been developed for naturally occurring phenolic compounds in a reversed-phase liquid chromatographic (RPLC) system. A total of 1519 descriptors were calculated from the optimized structures of the molecules using MOPAC2009 and DRAGON softwares. The data set of 39 molecules was divided into training and external validation sets. For feature selection and mapping we used step-wise multiple linear regression (SMLR), unsupervised forward selection followed by step-wise multiple linear regression (UFS-SMLR) and artificial neural networks (ANN). Stable and robust models with significant predictive abilities in terms of validation statistics were obtained with negation of any chance correlation. ANN models were found better than remaining two approaches. HNar, IDM, Mp, GATS2v, DISP and 3D-MoRSE (signals 22, 28 and 32) descriptors based on van der Waals volume, electronegativity, mass and polarizability, at atomic level, were found to have significant effects on the retention times. The possible implications of these descriptors in RPLC have been discussed. All the models are proven to be quite able to predict the retention times of phenolic compounds and have shown remarkable validation, robustness, stability and predictive performance.

The design and synthesis of alanine-based indolicidin derivatives with identical physicochemical properties and their separation using capillary electrophoresis

Four novel alanine-based indolicidin peptide derivatives were designed containing one WPW motif and two alanine residues, resulting in peptides of similar sequence. The separation of these peptides with identical physicochemical properties including molar mass, charge, and secondary structure as characterized by circular dichroism spectroscopy is very difficult; and the separation of peptides with differing physicochemical properties has only previously been reported. Capillary electrophoresis parameters such as separation buffer concentration, separation buffer pH, capillary length, and separation voltage were investigated to optimize the analysis. Using optimized conditions of a background electrolyte containing 5 mM formic acid of pH 2.0, total capillary length of 51 cm and a voltage of 10 kV enabled a baseline separation of the four peptides. The relative standard deviation of the peak areas and migration times for method repeatability (n = 3) were found to be lower than 8% and 3%, respectively. In addition, reasoning for the separation of these peptides is proposed based on the acidity of the formic acid buffer and the hydrophobic grouping of the tryptophan residues in the peptide primary sequence.

QSPR studies on soot-water partition coefficients of persistent organic pollutants by using artificial neural network

Two quantitative structure property relationship (QSPR) models for predicting soot-water partition coefficients (K(sc)) of 25 persistent organic pollutants (POPs) were developed. One model was established with linear artificial neural network (L-ANN), the other model was developed by using back propagation artificial neural network (BP-ANN). Leave one out cross validation was adopted to assess the predictive ability of the developed models. For the L-ANN model, the square of correlation coefficient (R(2)) between the predicted and experimental log K(SC) is 0.8358 and the RMS%RE is 6.32 for all the compounds. For the BP-ANN model, R(2) is 0.9628 and the RMS%RE is 4.12 for all the compounds. The result of leave one out cross validation demonstrates that both L-ANN and BP-ANN are practicable for developing the QSPR model for K(SC) of the investigated POPs. However, the model established with BP-ANN is better than the model established with L-ANN in prediction accuracy. It is shown that BP-ANN is a promising method for developing QSPR models for K(SC) of POPs.