Liquid Chromatography Of Peptides Research Articles

Paradigm Shift: Major Role of Ion-Pairing-Dependent Size Exclusion Effects in Bottom-Up Proteomics Reversed-Phase Peptide Separations.

Can reversed-phase peptide retention be the same for C8 and C18 columns? or increase for otherwise identical columns with a smaller surface area? Can replacing trifluoroacetic acid (TFA) with formic acid (FA) improve the peak shape? According to our common understanding of peptide chromatography, absolutely not. Surprisingly, a thorough comparison of the peptide separation selectivity of 100 and 120 Å fully porous C18 sorbents to maximize the performance of our in-house proteomics LC-MS/MS setup revealed an unexpectedly higher peptide retentivity for a wider pore packing material, despite it having a smaller surface area. Concurrently, the observed increase in peptide retention─which drives variation in separation selectivity between 100 and 120 Å pore size materials─was more pronounced for smaller peptides. These findings contradict the central dogmas that underlie the development of all peptide RP-HPLC applications: (i) a larger surface area leads to higher retention and (ii) increasing the pore size should benefit the retention of larger analytes. Based on our intriguing findings, we compared reversed-phase high-performance liquid chromatography peptide retention for a total of 20 columns with pore sizes between 60 and 300 Å using FA- and TFA-based eluents. Our results unequivocally attest that the larger size of ion pairs in FA- vs TFA-based eluents leads to the observed impact on selectivity and peptide retention. For FA, peptide retention peaks at 200 Å pore size, compared to between 120 and 200 Å for TFA. However, the decrease in retention for narrow-pore particles is more profound in FA. Our findings suggest that common assumptions about analyte size and accessible surface area should be revisited for ion-pair RP separation of small peptides, typical for proteomic applications that are predominantly applying FA eluents. Hybrid silica-based materials with pore sizes of 130-200 Å should be specifically targeted for bottom-up proteomic applications to obtain both superior peak shape and peptide retentivity. This challenging task of attaining the best RPLC column for proteomics calls for closer collaboration between LC column manufacturers and proteomic LC specialists.

Factors Influencing the Prediction Accuracy of Model Peptides in Reversed-Phase Liquid Chromatography

Hydrophobicity is an important physicochemical property of peptides in solution. As well as being strongly associated with peptide stability and aggregation, hydrophobicity governs the solution based chromatographic separation processes, specifically reversed-phase liquid chromatography (RPLC). In addition, hydrophobicity is a major physicochemical property of peptides in comparison to H-bonding, electrostatic, and aromatic properties in intermolecular interactions. However, a wide range of molecular factors can influence peptide hydrophobicity, with accurate predictions depending on specific peptide amino acid compositions, structure, and conformation. It is noticeable that peptide composition, the position of the amino acid, and its neighbouring groups play a crucial role in the elution process. In light of this, the same amino acid behaved differently depending on its position and neighbouring amino acid in the peptide chain. Extra attention should be paid to the denaturation process during the course of elution, as it has been shown to complicate and alter the elution pattern. This paper reports on the key peptide properties that can alter hydrophobicity and, consequently, the RPLC elution behaviour of the peptides, and it will conclude by proposing improved prediction algorithms for peptide elution in RPLC.

Temperature Dependence of Retention Behavior of Phosphorylated Peptides in Ion-Pair Reversed-Phase Liquid Chromatography

Temperature Dependence of Retention Behavior of Phosphorylated Peptides in Ion-Pair Reversed-Phase Liquid Chromatography

Preparation and Evaluation of 2 m Long Open Tubular Capillary Columns of 50 μm Internal Diameter for Separation of Peptides in Liquid Chromatography

A 2 m long open tubular column with a copolymer layer fabricated on the inner surface of silica capillary of 50 μm id has been prepared. A ligand with a terminal halogen was bound to the capillary inner surface via silanol-isocyanate reaction and an initiator moiety was introduced by reacting with the terminal halogen. Then a thick layer of linear copolymer chains was generated on the inner surface of capillary by reversible addition-fragmentation transfer polymerization of styrene and N-phenyl acrylamide. The resultant open tubular column exhibited a remarkably high separation efficiency for the separation of a synthetic mixture of five peptides in liquid capillary chromatography (in the pressure mode). The effect of monomer mixing ratio (styrene versus N-phenyl acrylamide) on the chromatographic separation efficiency has been studied. The column prepared with the optimum mixing ratio produced the number of theoretical plates (N) of 391,200 per column under the optimized elution pressure. The column to column repeatability estimated as relative standard deviation in plate count, retention factor, asymmetry factor, and resolution was found better than 3.0%.

Accurate modelling of the retention behaviour of peptides in gradient-elution hydrophilic interaction liquid chromatography

The applicability of models to describe peptide retention in hydrophilic interaction liquid chromatography (HILIC) was investigated. A tryptic digest of bovine-serum-albumin (BSA) was used as a test sample. Several different models were considered, including adsorption, mixed-mode, exponential, quadratic and Neue–Kuss models. Gradient separations were performed on three different HILIC stationary-phases under three different mobile-phase conditions to obtain model parameters. Methods to track peaks for specific peptides across different chromatograms are shown to be essential. The optimal mobile-phase additive for the separation of BSA digest on each of the three columns was selected by considering the retention window, peak width and peak intensity with mass-spectrometric detection. The performance of the models was investigated using the Akaike information criterion (AIC) to measure the goodness-of-fit and evaluated using prediction errors. The F-test for regression was applied to support model selection. RPLC separations of the same sample were used to test the models. The adsorption model showed the best performance for all the HILIC columns investigated and the lowest prediction errors for two of the three columns. In most cases prediction errors were within 1%.

Styrene-N-phenylacrylamide co-polymer modified silica monolith particles with an optimized mixing ratio of monomers as a new stationary phase for the separation of peptides in high performance liquid chromatography.

A stationary phase was prepared by chemical derivatization of the support particles with a layer of copolymer composed of styrene and N-phenyl acrylamide. Silica monolith particles of ca. 2.6µm (volume-based average) have been prepared as the support particles by sol-gel reaction followed by differential sedimentation. The particles were reacted with 3-chloropropyl trimethoxysilane followed by sodium diethyldithiocarbamate to introduce an initiator moiety. Then, the copolymer layer was immobilized via reversible addition-fragmentation transfer polymerization. The resultant phase was packed in glass-lined stainless-steel micro-columns (1 x 150mm) and evaluated for the separation of a mixture composed of five peptides (Trp-Gly, Thr-Tyr-Ser, angiotensin I, isotocin and bradykinin). The effect of monomer mixing ratio (styrene versus N-phenyl acrylamide) on the chromatographic separation efficiency of the stationary phase was examined. A number of theoretical plates (N) as high as 33600 plates/column (224000 plates/m, 4.46µm plate height) was achieved using the column packed with the optimized stationary phase. The column-to-column reproducibility based on three columns packed with three different batches of stationary phase was found satisfactory in separation efficiency, retention factor, and asymmetry factor.

Proteolysis and volatile profile in the Algerian traditional Bouhezza cheese made using raw goat’s milk

ABSTRACTBouhezza is an Algerian cheese, which is ripened in a goat-skin bag, called Chekoua. The aim of the study was to determine the protein profiles and aromatics, which contribute to sensory properties of Bouhezza cheese. The chemical composition, proteolysis, and volatile profile have been carried out in cheese made from raw goat milk. The results showed that, the dry matter ranged from 23.07 to 51.95%, the fat-in-dry matter ranged from 10.58 to 31.77%, and the protein ranged from 28.27 to 42.09%. Water-soluble and 12% trichloroacetic acid-soluble nitrogen fractions decreased at the beginning of the ripening process and then they increased until the end of ripening. The reverse phase-high-performance liquid chromatography peptide profiles of the cheese showed the modifications occurred during the ripening process. The volatile compounds showed a diversity of odorous components which contribute to give to the cheese its particular organoleptic. There were 109 compounds identified in Bouhezza cheese. Carboxylic acids, esters, and alcohols were the main classes of the volatile components in the cheese.

Atypical carcinoid and large cell neuroendocrine carcinoma of the lung: a proteomic dataset from formalin-fixed archival samples.

Here we present a dataset generated using formalin-fixed paraffin-embedded archival samples from two rare lung neuroendocrine tumor subtypes (namely, two atypical carcinoids, ACs, and two large-cell neuroendocrine carcinomas, LCNECs). Samples were subjected to a shotgun proteomics pipeline, comprising full-length protein extraction, SDS removal through spin columns, in solution trypsin digestion, long gradient liquid chromatography peptide separation and LTQ-Orbitrap mass spectrometry analysis. A total of 1260 and 2436 proteins were identified in the AC and LCNEC samples, respectively, with FDR <1%. MS data are available in the PeptideAtlas repository at http://www.peptideatlas.org/PASS/PASS00375.

Examination of segmental average mass spectra from liquid chromatography–tandem mass spectrometric (LC–MS/MS) data enables screening of multiple types of protein modifications

It has been observed that a modified peptide and its non-modified counterpart, when analyzed with reverse phase liquid chromatography, usually share a very similar elution property [1–3]. Inasmuch as this property is common to many different types of protein modifications, we propose an informatics-based approach, featuring the generation of segmental average mass spectra (saMS), that is capable of locating different types of modified peptides in two-dimensional liquid chromatography–mass spectrometric (LC–MS) data collected for regular protease digests from proteins in gels or solutions. To enable the localization of these peptides in the LC–MS map, we have implemented a set of computer programs, or the saMS package, that perform the needed functions, including generating a complete set of segmental average mass spectra, compiling the peptide inventory from the Sequest/TurboSequest results, searching modified peptide candidates and annotating a tandem mass spectrum for final verification. Using ROCK2 as an example, our programs were applied to identify multiple types of modified peptides, such as phosphorylated and hexosylated ones, which particularly include those peptides that could have been ignored due to their peculiar fragmentation patterns and consequent low search scores. Hence, we demonstrate that, when complemented with peptide search algorithms, our approach and the entailed computer programs can add the sequence information needed for bolstering the confidence of data interpretation by the present analytical platforms and facilitate the mining of protein modification information out of complicated LC–MS/MS data.

Support Vector Regression Based QSPR for the Prediction of Retention Time of Peptides in Reversed-Phase Liquid Chromatography

In the present study, Quantitative Structure–Property Relationship (QSPR) models were developed to investigate the retention times (t R ) of various peptides in seven reversed-phase liquid chromatography systems using Partial Least Squares (PLS), Artificial Neural Network (ANN) and Support Vector Machine (SVM) techniques. Different types of molecular descriptors were calculated to represent the molecular structures of the various compounds studied. Important descriptors were selected by a Genetic Algorithm-Partial Least Square (GA-PLS) method. The four descriptors selected using GA-PLS were used as inputs for PLS, ANN and SVM to build models to predict the retention times. Our study reveals that the relation between the chemical properties and retention time is a nonlinear phenomenon and that the PLS method is not capable to properly model it. The results obtained demonstrate that, for all seven data sets, the t R values estimated by SVM were in good agreement with the experimental data, and the performances of the SVM models were comparable or superior to those of PLS and ANN.

Proteomic analysis of protein deamidation.

Deamidation of asparagines and glutamines occurs spontaneously in proteins and results in protein degradation. Deamidation of asparaginyl residues in proteins produces a mixture of asparaginyl, n-aspartyl, and isoaspartyl residues, which has been linked to the pathology of some neurodegenerative diseases. However, accurate proteomic analysis of deamidation is challenging since it occurs quickly during conventional proteomic sample preparation, and the co-elution of the two resulting isomeric deamidated peptides in reversed-phase liquid chromatography (RPLC) compromises their identification and quantification using RPLC-MS/MS. To overcome these difficulties, a novel sample preparation protocol to minimize artificial deamidation has been developed alongside an offline RP-ERLIC-MS/MS (reversed-phase chromatography fractionation followed by electrostatic repulsion-hydrophilic interaction chromatography coupled with MS/MS) strategy to separate and quantify the three deamidation products from the same peptide on a proteome-wide scale. These protocols are detailed in this unit.

Characteristics of Miniature Cheddar‐Type Cheese Made by Microbial Rennet from Bacillus amyloliquefaciens: A Comparison with Commercial Calf Rennet

Miniature Cheddar-type cheeses were produced using microbial rennet from Bacillus amyloliquefaciens (milk-clotting enzyme [MCE]) or calf rennet (CAR). With the exception of pH, there were no significant differences in gross composition between MCE-cheese (MCE-C) and CAR-cheese (CAR-C). The pH value of CAR-C was significantly higher than that of MCE-C at 40 and 60 d of ripening. The total nitrogen content of the pH 4.6-soluble fraction obtained from MCE-C was higher than that obtained from CAR-C. However, nitrogen content of the 12% TCA-soluble fraction was similar between CAR-C and MCE-C. The extent of α(s1)-casein and β-casein hydrolysis, measured by urea-PAGE, was similar in both cheese samples. The hydrolysis of β-casein was lower than that of α(s1)-casein. Different reverse phase-high-performance liquid chromatography peptide profiles of ethanol-soluble and ethanol-insoluble fractions were obtained from CAR-C and MCE-C. The peptide content in the 2 cheese samples increased throughout ripening; the ratio of hydrophobic to hydrophilic peptides was lower in MCE-C than in CAR-C. Compared with CAR-C, MCE-C was softer as a result of higher protein hydrolysis. Microbial rennet from B. amyloliquefaciens contributed to higher proteolytic rates, which reduced ripening time.

Influence of pressure and temperature on molar volume and retention properties of peptides in ultra-high pressure liquid chromatography

In this study, pressure induced changes in retention were measured for model peptides possessing molecular weights between ∼1 and ∼4kDa. The goal of the present work was to evaluate if such changes were only attributed to the variation of molar volume and if they could be estimated prior to the experiments, using theoretical models. Restrictor tubing was employed to generate pressures up to 1000bar and experiments were conducted for mobile phase temperatures comprised between 30 and 80°C. As expected, the retention increases significantly with pressure, up to 200% for glucagon at around 1000bar compared to ∼100bar. The obtained data were fitted with a theoretical model and the determination coefficients were excellent (r2>0.9992) for the peptides at various temperatures. On the other hand, the pressure induced change in retention was found to be temperature dependent and was more pronounced at 30°C vs. 60 or 80°C. Finally, using the proposed model, it was possible to easily estimate the pressure induced increase in retention for any peptide and mobile phase temperature. This allows to easily estimating the expected change in retention, when increasing the column length under UHPLC conditions.

Five functional adipokinetic peptides expressed in the corpus cardiacum of the moth genus Hippotion (Lepidoptera, Sphingidae)

This is the first study that finds five adipokinetic hormones (AKHs) in the corpus cardiacum of an insect. From two species of the sphingid moth genus Hippotion, eson and celerio, three novel and two known AKHs were isolated and sequenced by deduction from multiple MSn electrospray mass data: two octapeptides are pGlu-Leu-Thr-Phe-Thr-Ser-Ser-Trp amide (denoted Hipes-AKH-I) and its Thr7 analogue (Hipes-AKH-II); two nonapeptides are pGlu-Leu-Thr-Phe-Thr-Ser-Ser-Trp-Gly amide (Manse-AKH) and its Thr7 analogue (Hipes-AKH-III), as well as a decapeptide pGlu-Leu-Thr-Phe-Ser-Ser-Gly-Trp-Gly-Gln amide (Manse-AKH-II). All sequences were confirmed by identical behaviour of natural and synthetic peptides in reversed-phase HPLC and liquid chromatography coupled to electrospray mass spectrometry, resulting in identical retention times and tandem mass spectral data. High resolution mass spectrometry and retention time data also confirmed that the amino acid at position 10 in Manse-AKH-II is Gln and not the isobaric Lys. Conspecific injections of all five peptides in synthetic form and low doses caused hyperlipaemia in H. eson. Our results and pertaining literature suggest that five genes code for the mature peptides, which are very likely released during flight to provide energy for long distance migration in this genus via lipid oxidation; as all five peptides are active at low doses in a conspecific bioassay, it may be speculated, but not proven, that there is only one AKH receptor present in Hippotion that can bind all five peptides with high affinity.

Chromatographic retention time prediction for posttranslationally modified peptides

Retention time prediction of peptides in liquid chromatography has proven to be a valuable tool for mass spectrometry-based proteomics, especially in designing more efficient procedures for state-of-the-art targeted workflows. Additionally, accurate retention time predictions can also be used to increase confidence in identifications in shotgun experiments. Despite these obvious benefits, the use of such methods has so far not been extended to (posttranslationally) modified peptides due to the absence of efficient predictors for such peptides. We here therefore describe a new retention time predictor for modified peptides, built on the foundations of our existing Elude algorithm. We evaluated our software by applying it on five types of commonly encountered modifications. Our results show that Elude now yields equally good prediction performances for modified and unmodified peptides, with correlation coefficients between predicted and observed retention times ranging from 0.93 to 0.98 for all the investigated datasets. Furthermore, we show that our predictor handles peptides carrying multiple modifications as well. This latest version of Elude is fully portable to new chromatographic conditions and can readily be applied to other types of posttranslational modifications. Elude is available under the permissive Apache2 open source License at http://per-colator.com or can be run via a web-interface at http://elude.sbc.su.se.

A proteome reference map and virulence factors analysis of Yersinia pestis 91001

In this report, we carried out the in-depth proteomic analysis of Yersinia pestis strain 91001 under in vitro flea-simulated condition using three technique routes, SDS-PAGE combined with LTQ-FT, two-dimensional liquid chromatography peptide (2D-LC peptide) separation combined with LTQ-FT and intact protein separation followed by 2D-LC peptide separation combined with LTQ-FT. Totally, 1926 proteins (13082 peptides) were identified, covering 46.50% (1926/4142) of the predicted proteome. Transcriptome analysis based on a whole genome DNA microarray of Y. pestis defined 1655 genes with the coincidence of 56.65% to the proteomic results. Through analyzing the identifications of virulence factors involving in the life cycle of Y. pestis, it was found that Hms system and murine toxin, which are virulence factors involved in Y. pestis maintenance in flea, were highly expressed in our analysis. Moreover, some virulence factors also appeared with different extents, such as plasminogen activator, PhoP/PhoQ two-component system, type III secretion system, iron acquisition systems (Ybt, Yfe and Yfu) and ferric uptake regulator. These results indicated that Y. pestis may prepare itself with various strategies in advance for its survival when it evades the hosts. The protein identifications can be accessed through PRIDE database (http://www.ebi.ac.uk/pride) with accession number 18578-18605

Alternative methods for verifying the results of the mass spectrometric identification of peptides in shotgun proteomics

Database search is the most popular approach used for the identification of peptides in contemporary shotgun proteomics; it utilizes only mass spectrometric data. In this work, we introduce three criteria for the verification of peptide identification; these are based on the analysis of data orthogonal to tandem mass spectra. The first one utilizes chromatographic retention times of peptides. The development of such approaches has been hindered by the relatively low accuracy of retention time prediction algorithms. In this work, we suggest the use of two independent models of the liquid chromatography of peptides, which increase the reliability of the results. The second criterion utilizes the mean number of missed tryptic cleavages per peptide. The third one results from the analysis of the difference between theoretical and experimentally measured peptide masses. The proposed criteria were applied to the tandem mass spectra of tryptic peptides from rat kidney tissue, which were processed by the Mascot search engine. All the criteria showed that Mascot significantly overestimated the reliability of an identification. This conclusion was supported by the PeptideProphet algorithm.

Cod (Gadus morhua) Muscle Proteome Cataloging Using 1D-PAGE Protein Separation, Nano-Liquid Chromatography Peptide Fractionation, and Linear Trap Quadrupole (LTQ) Mass Spectrometry

Because Atlantic cod (Gadus morhua) has high economic value and its protein-rich muscle tissue is a food source, an increased understanding of the effects and consequences of environmental, nutritional, biological, and industrial factors on meat quality is necessary. To gain insight into cod muscle tissue protein composition, a large-scale proteomics approach has been used. One-dimensional polyacrylamide gel electrophoresis, nanoflow liquid chromatography peptide separation, and linear trap quadrupole mass spectrometry were used to identify 4804 peptides, which retrieved 9113 cod expressed sequence tags (ESTs), which in turn were mapped to 446 unique proteins. The same data set identified 3924 proteins from the zebrafish protein database, which highlights the complementary value of the two approaches. The generated data sets will act as a foundation for studies related to physiological status assessment of cod under different environmental conditions, screening for diseases, and biomarker identification for assessment of fish quality during industrial processing and preservation.

Nucleophosmin in the pathogenesis of arsenic-related bladder carcinogenesis revealed by quantitative proteomics

To investigate the molecular mechanisms of arsenic (As)-associated carcinogenesis, we performed proteomic analysis on E7 immortalized human uroepithelial cells after treatment with As in vitro. Quantitative proteomics was performed using stable isotope dimethyl labeling coupled with two-dimensional liquid chromatography peptide separation and mass spectrometry (MS)/MS analysis. Among 285 proteins, a total of 26 proteins were upregulated (ratio > 2.0) and 18 proteins were downregulated (ratio < 0.65) by As treatment, which are related to nucleotide binding, lipid metabolism, protein folding, protein biosynthesis, transcription, DNA repair, cell cycle control, and signal transduction. This study reports the potential significance of nucleophosmin (NPM) in the As-related bladder carcinogenesis. NPM was universally expressed in all of uroepithelial cell lines examined, implying that NPM may play a role in human bladder carcinogenesis. Upregulation of NPM tends to be dose- and time-dependent after As treatment. Expression of NPM was associated with cell proliferation, migration and anti-apoptosis . On the contrary, soy isoflavones inhibited the expression of NPM in vitro. The results suggest that NPM may play a role in the As-related bladder carcinogenesis, and soybean-based foods may have potential in the suppression of As/NPM-related tumorigenesis.

Comprehensive comparison of eight statistical modelling methods used in quantitative structure–retention relationship studies for liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome

In this study, we propose a new peptide characterization method that gives attention to both the amino acid composition and the residue local environment. Using this approach, structural characteristics of peptides derived from Escherichia coli proteome were parameterized and, based upon that, the performance profile of eight statistical modelling methods were validated rigorously and compared comprehensively by applying them to modelling relationship between the sequence structure and retention ability for 816 experimentally measured peptides and to predicting normalized retention times for 121,273 unmeasured peptides in liquid chromatography. Results show that the regression models constructed by nonlinear approaches are more robust and predictable but time-consuming than those by linear ones. In these modelling methods, Gaussian process and back-propagation neural network possess the best stability, unbiased ability and predictive power, thus they can be used to accurately model the peptide structure–retention relationships; multiple linear regression and partial least squares regression perform worse compared to nonlinear modelling techniques but they are computationally efficient, so they are promising candidates for solving the qualitative problems involved in massive data. In addition, by investigating the descriptor importance in different models we found that the amino acid composition presents a significantly linear correlation with the retention time of peptides, whereas the residue environment is mainly correlated nonlinearly with peptide retention. The polar Arg and strongly hydrophobic amino acids such as Leu, Ile, Phe, Trp and Val are the critical factors influencing peptide retention behavior.