Utility of Retention Prediction Model for Investigation of Peptide Separation Selectivity in Reversed-Phase Liquid Chromatography: Impact of Concentration of Trifluoroacetic Acid, Column Temperature, Gradient Slope and Type of Stationary Phase

Peptide retention time prediction for hydrophilic interaction liquid chromatography at acidic pH in formic-acid based eluents

Peptide separation selectivity was evaluated for hydrophilic interaction liquid chromatography (HILIC) ZIC-HILIC, ZIC-cHILIC, and XBridge Amide sorbents using formic acid as eluent additive (pH 2.7). Sequence-specific retention prediction algorithms were trained using retention datasets of ∼30,000 peptides for each column. Our retention models were able to attain ∼0.98 R2-value and yielded retention coefficients that can be probed to understand peptide-stationary phase interaction. Overall, the hydrophilicity for these columns decreased when the mobile phase changed pH from 4.5 to 2.7, when using 0.1 % formic acid in the mobile phase. The acidic residues became protonated, and the resultant hydrophilic interaction is dampened at the lower pH, leaving only the basic residues as the primary hydrophilic interactors. Hence, peptides of increasing charge have higher retention. In this comparison between the three columns, ZIC-HILIC has the highest chromatographic resolution between groups of peptides of different charge. From the position-dependent retention coefficients for ZIC-HILIC at pH 2.7, we found that the amino acids at the terminal positions of the peptide modulate the basicity of the N-terminal amino group or the C-terminal Arg/Lys for tryptic peptides. With respect to the separation orthogonality between HILIC and acidic pH RPLC for two dimensional separations, the orthogonality values were lower at pH 2.7 than operating HILIC at pH 4.5 for the first dimension. We also demonstrate that ZIC-HILIC was able to distinguish citrullinated and deamidated peptides based on predicted retention values.

Toward an Ultimate Solution for Peptide Retention Time Prediction: The Effect of Column Temperature on Separation Selectivity

We studied the effect of the column temperature on the selectivity of reversed-phase peptide separation in bottom-up proteomics. The number of peptide identifications from 2 h liquid chromatography with tandem mass spectrometry (LC-MS/MS) acquisitions reaches a plateau at 45-55 °C, driven simultaneously by improved separation efficiency, a gradual decrease in peptide retention, and possible on-column degradation of peptides at elevated temperatures. Performing 2D LC-MS/MS acquisitions at 25, 35, 45, and 55 °C resulted in the identification of ∼100,000 and ∼120,000 unique peptides for nonmodified and tandem mass tags (TMT)-labeled samples, respectively. These peptide collections were used to investigate the temperature-driven retention features. The latter is governed by the specific temperature response of individual residues, peptide hydrophobicity and length, and amphipathic helicity. On average, peptide retention decreased by 0.56 and 0.5% acetonitrile for each 10 °C increase for label-free and TMT-labeled peptides, respectively. This generally linear response of retention shifts allowed the extrapolation of predictive models beyond the studied temperature range. Thus, (trap) column cooling from room temperature to 0 °C will allow the retention of an additional 3% of detectable tryptic peptides. Meanwhile, the application of 90 °C would result in the loss of ∼20% of tryptic peptides that were amenable to MS/MS-based identification.

Exploring the variable space of shallow machine learning models for reversed-phase retention time prediction

Peptide retention time (RT) prediction algorithms are tools to study and identify the physicochemical properties that drive the peptide-sorbent interaction. Traditional RT algorithms use multiple linear regression with manually curated parameters to determine the degree of direct contribution for each parameter and improvements to RT prediction accuracies relied on superior feature engineering. Deep learning led to a significant increase in RT prediction accuracy and automated feature engineering via chaining multiple learning modules. However, the significance and the identity of these extracted variables are not well understood due to the inherent complexity when interpreting "relationships-of-relationships" found in deep learning variables. To achieve both accuracy and interpretability simultaneously, we isolated individual modules used in deep learning and the isolated modules are the shallow learners employed for RT prediction in this work. Using a shallow convolutional neural network (CNN) and gated recurrent unit (GRU), we find that the spatial features obtained via the CNN correlate with real-world physicochemical properties namely cross-collisional sections (CCS) and variations of assessable surface area (ASA). Furthermore, we determined that the discovered parameters are "micro-coefficients" that contribute to the "macro-coefficient" - hydrophobicity. Manually embedding CCS and the variations of ASA to the GRU model yielded an R2 = 0.981 using only 525 variables and can represent 88% of the ∼110,000 tryptic peptides used in our dataset. This work highlights the feature discovery process of our shallow learners can achieve beyond traditional RT models in performance and have better interpretability when compared with the deep learning RT algorithms found in the literature.

A Comprehensive Study of Gradient Conditions for Deep Proteome Discovery in a Complex Protein Matrix

Bottom–up mass-spectrometry-based proteomics is a well-developed technology based on complex peptide mixtures from proteolytic cleavage of proteins and is widely applied in protein identification, characterization, and quantitation. A tims-ToF mass spectrometer is an excellent platform for bottom–up proteomics studies due to its rapid acquisition with high sensitivity. It remains challenging for bottom–up proteomics approaches to achieve 100% proteome coverage. Liquid chromatography (LC) is commonly used prior to mass spectrometry (MS) analysis to fractionate peptide mixtures, and the LC gradient can affect the peptide fractionation and proteome coverage. We investigated the effects of gradient type and time duration to find optimal gradient conditions. Five gradient types (linear, logarithm-like, exponent-like, stepwise, and step-linear), three different gradient lengths (22 min, 44 min, and 66 min), two sample loading amounts (100 ng and 200 ng), and two loading conditions (the use of trap column and no trap column) were studied. The effect of these chromatography variables on protein groups, peptides, and spectral counts using HeLa cell digests was explored. The results indicate that (1) a step-linear gradient performs best among the five gradient types studied; (2) the optimal gradient duration depends on protein sample loading amount; (3) the use of a trap column helps to enhance protein identification, especially low-abundance proteins; (4) MSFragger and PEAKS Studio have high similarity in protein group identification; (5) MSFragger identified more protein groups among the different gradient conditions compared to PEAKS Studio; and (6) combining results from both database search engines can expand identified protein groups by 9–11%.

Protein analysis and stability: Overcoming trial-and-error by grouping according to physicochemical properties

Today proteins are possibly the most important class of substances. Yet new tasks for proteins are still often solved by trial-and-error approaches. However, in some areas these euphemistically called "screening approaches" are not suitable. E.g. stability tests just take too long and therefore require a more strategic, target-orientated concept. This concept is available by grouping proteins according to their physicochemical properties and then pulling out the right drawer for new tasks. These properties include size, then charge and hydrophobicity as well as their patchinesses, and the degree of order. In addition, solubility, the content of (free) enthalpy, aromatic-amino-acid- and α/β-frequency as well as helix capping, and corresponding patchiness, the number of specific motifs and domains as well as the typical concentration range can be helpful to discriminate between different groups of proteins. Analyzing correlations will reduce the necessary amount of parameters and additional ones, which may be still undiscovered at the present time, can be identified looking at protein subgroups with similar physicochemical properties which still behave heterogeneously. Step-by-step the methodology will be improved. Possibly protein stability will be the driver of this process, but all other areas such as production, purification and analytics including sample pre-treatment and the choice of appropriate separation conditions for e.g. chromatography and electrophoresis will profit from a rational strategy.

Investigation into reversed-phase chromatography peptide separation systems Part IV: Characterisation of mobile phase selectivity differences

The differentiation of mobile phase compositions between sub-classes which exhibit distinct chromatographic selectivity (i.e. termed characterisation) towards a range of peptide probes with diverse functionality and hence the possibility for multi-modal retention mechanisms has been undertaken. Due to the complexity of peptide retention mechanisms in given mobile phase conditions, no attempt has been made to explain these, instead mobile phases have simply been classified into distinct groups with an aim of identifying those yielding differing selectivities for use in strategic method development roadmaps for the analysis of peptide mixtures. The selectivity differences between nine synthetic peptides (fragments of [Ile27]-Bovine GLP-2) were used to assess how fifty-one RPC mobile phase compositions of differing pH (range 1.8 - 7.8), salt types, ionic strengths, ion-pair reagents and chaotropic / kosmotropic additives affected chromatographic selectivity on a new generation C18 stationary phase (Ascentis Express C18). The mobile phase compositions consisted of commonly used and novel UV or MS compatible additives. The chemometric tool of Principal Component Analysis (PCA) was used to visualise the differences in selectivity generated between the various mobile phases evaluated. The results highlight the importance of screening numerous mobile phases of differing pH, ion-pair reagents and ionic strength in order to maximise the probability of achieving separation of all the peptides of interest within a complex mixture. PCA permitted a ranking of the relative importance of the various mobile phase parameters evaluated. The concept of using this approach was proven in the analysis of a sample of Bovine GLP-2 (1-15) containing synthesis related impurities. Mobile phases with high ionic strength were demonstrated to be crucial for the generation of symmetrical peaks. The observations made on the C18 phase were compared on three additional stationary phases (i.e. alkyl amide, fluorophenyl and biphenyl), which had previously been shown to possess large selectivity differences towards these peptides, on a limited sub-set of mobile phases. With the exception of the ion-pair reagent, similar trends were obtained for the C18, fluorophenyl and biphenyl phases intimating the applicability of these findings to the vast majority of RPC columns (i.e. neutral or weakly polar in character) which are suitable for the analysis of peptides. The conclusions were not relevant for columns with a more disparate nature (i.e. containing a high degree of positive charge).

Peptide separation selectivity in proteomics LC-MS experiments: Comparison of formic and mixed formic/heptafluorobutyric acids ion-pairing modifiers

Separation selectivity and detection sensitivity of reversed-phase high-performance liquid chromatography with tandem mass spectrometry analyses were compared for formic (0.1%) and formic/heptafluorobutyric (0.1%/0.005%) acid based eluents using a proteomic data set of ∼12 000 paired peptides. The addition of a small amount of hydrophobic heptafluorobutyric acid ion-pairing modifier increased peptide retention by up to 10% acetonitrile depending on peptide charge, size, and hydrophobicity. Retention increase was greatest for peptides that were short, highly charged, and hydrophilic. There was an ∼3.75-fold reduction in MS signal observed across the whole population of peptides following the addition of heptafluorobutyric acid. This resulted in ∼36% and ∼21% reduction of detected proteins and unique peptides for the whole cell lysate digests, respectively. We also confirmed that the separation selectivity of the formic/heptafluorobutyric acid system was very similar to the commonly used conditions of 0.1% trifluoroacetic acid, and developed a new version of the Sequence-Specific Retention calculator model for the formic/heptafluorobutyric acid system showing the same ∼0.98 R² -value accuracy as the Sequence-Specific Retention calculator formic acid model. In silico simulation of peptide distribution in separation space showed that the addition of 0.005% heptafluorobutyric acid to the 0.1% formic acid system increased potential proteome coverage by ∼11% of detectable species (tryptic peptides ≥ four amino acids).

Peptide retention time prediction in hydrophilic interaction liquid chromatography: Zwitter-ionic sulfoalkylbetaine and phosphorylcholine stationary phases

Peptide retention time prediction models have been developed for zwitter-ionic ZIC-HILIC and ZIC-cHILIC stationary phases (pH 4.5 eluents) using proteomics-derived retention datasets of ~30 thousand tryptic peptides each. Overall, hydrophilicity of these stationary phases was found to be similar to the previously studied Amide HILIC phase, but lower compared to bare silicas. Peptide retention is driven by interactions of all charged (hydrophilic) residues at pH 4.5 (Asp, Glu, Arg, Lys, His), but shows specificity according to orientation of functional groups in zwitter-ionic pair. Thus, ZIC-cHILIC exhibits an increased contribution of negatively charged Asp and Glu due to the distal positioning of positively charged quaternary amines on the stationary phase. These findings confirm that HILIC interactions are driven by both peptide distribution between water layer adsorbed on the stationary phase and by interactions specific to functional groups of the packing material. Sequence-Specific Retention Calculator HILIC models were optimized for these columns showing 0.967-0.976 R²-values between experimental and predicted retention values. ZIC-HILIC separations represent a good choice as a first dimension in 2D LC-MS of peptide mixtures with correlations between retention values of ZIC-HILIC against RPLC found at 0.197 (ZIC-HILIC) and 0.137 (ZIC-cHILIC) R²-values, confirming a good orthogonality.

Peptide barcoding for establishment of new types of genotype-phenotype linkages

Measuring binding properties of binders (e.g., antibodies) is essential for developing useful experimental reagents, diagnostics, and pharmaceuticals. Display technologies can evaluate a large number of binders in a high-throughput manner, but the immobilization effect and the avidity effect prohibit the precise evaluation of binding properties. In this paper, we propose a novel methodology, peptide barcoding, to quantitatively measure the binding properties of multiple binders without immobilization. In the experimental scheme, unique peptide barcodes are fused with each binder, and they represent genotype information. These peptide barcodes are designed to have high detectability for mass spectrometry, leading to low identification bias and a high identification rate. A mixture of different peptide-barcoded nanobodies is reacted with antigen-coated magnetic beads in one pot. Peptide barcodes of functional nanobodies are cleaved on beads by a specific protease, and identified by selected reaction monitoring using triple quadrupole mass spectrometry. To demonstrate proof-of-principle for peptide barcoding, we generated peptide-barcoded anti-CD4 nanobody and anti-GFP nanobody, and determined whether we could simultaneously quantify their binding activities. We showed that peptide barcoding did not affect the properties of the nanobodies, and succeeded in measuring the binding activities of these nanobodies in one shot. The results demonstrate the advantages of peptide barcoding, new types of genotype–phenotype linkages.

Isomer separation of sialylated O- and N-linked glycopeptides using reversed-phase LC-MS/MS at high temperature

Analyses of intact glycopeptides using mass spectrometry is challenging due to the numerous types of isomers of glycan moieties attached to the peptide backbone. Here, we demonstrate that high-temperature reversed-phase liquid chromatography (RPLC) can be used to separate isomeric O- and N-linked glycopeptides. In general, high column temperatures enhanced the resolution for separation of sialylated O- and N-linked glycopeptide isomers with decreased retention times. Using the high-temperature RPLC method, α2-6-linked sialylated N-glycopeptides were eluted first, followed by α2-3-linked isomers. However, highly sialylated N-glycopeptides containing hydrophobic amino acids exhibited increased retention times at high temperature. The separation of sialylated O- and N-glycopeptides with different glycan isoforms using a high-temperature RPLC method was demonstrated. This study indicates that reversed-phase chromatographic separation at high column temperatures is suitable for the separation of glycopeptide structural isomers.

Predictive chromatography of peptides and proteins as a complementary tool for proteomics

In the last couple of decades, considerable effort has been focused on developing methods for quantitative and qualitative proteome characterization. The method of choice in this characterization is mass spectrometry used in combination with sample separation. One of the most widely used separation techniques at the front end of a mass spectrometer is high performance liquid chromatography (HPLC). A unique feature of HPLC is its specificity to the amino acid sequence of separated peptides and proteins. This specificity may provide additional information about the peptides or proteins under study which is complementary to the mass spectrometry data. The value of this information for proteomics has been recognized in the past few decades, which has stimulated significant effort in the development and implementation of computational and theoretical models for the prediction of peptide retention time for a given sequence. Here we review the advances in this area and the utility of predicted retention times for proteomic applications.

Visualization and application of amino acid retention coefficients obtained from modeling of peptide retention

We introduce a method for data inspection in liquid separations of peptides using amino acid retention coefficients and their relative change across experiments. Our method allows for the direct comparison between actual experimental conditions, regardless of sample content and without the use of internal standards. The modeling uses linear regression of peptide retention time as a function of amino acid composition. We demonstrate the pH dependency of the model in a control experiment where the pH of the mobile phase was changed in controlled way. We introduce a score to identify the false discovery rate on peptide spectrum match level that corresponds to the set of most robust models, i.e. to maximize the shared agreement between experiments. We demonstrate the method utility in reversed-phase liquid chromatography using 24 datasets with minimal peptide overlap. We apply our method on datasets obtained from a public repository representing various separation designs, including one-dimensional reversed-phase liquid chromatography followed by tandem mass spectrometry, and two-dimensional online strong cation exchange coupled to reversed-phase liquid chromatography followed by tandem mass spectrometry, and highlight new insights. Our method provides a simple yet powerful way to inspect data quality, in particular for multidimensional separations, improving comparability of data at no additional experimental cost.

Peptide retention time prediction

Most methods for interpreting data from shotgun proteomics experiments are to large degree dependent on being able to predict properties of peptide-ions. Often such predicted properties are limited to molecular mass and fragment spectra, but here we put focus on a perhaps underutilized property, a peptide's chromatographic retention time. We review a couple of different principles of retention time prediction,and their applications within computational proteomics. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:615-623, 2017.

Estimation of Nonlinear Adsorption Isotherms in Gradient Elution RP-LC of Peptides in the Presence of an Adsorbing Additive

ABSTRACT

In electrostatic repulsive interaction chromatography, using a charged surface hybrid sorbent carrying positive charges can improve the peak shape of peptides in reversed-phase liquid chromatography (RP-LC), especially in overloaded conditions, compared with standard C18 sorbents. However, the positive surface charges can interact with anionic additives commonly used in peptide separations, e.g., trifluoroacetic acid (TFA), complicating adsorption isotherm estimation. We investigated how the competition for available adsorption sites between TFA and two peptides influenced the adsorption isotherm in gradient elution. A model accounting for the competition with TFA was compared with a model neglecting TFA adsorption. We found that the two models predicted elution profiles with the same accuracy. We also found that the adsorption isotherms were extremely similar in shape, leading to the conclusion that neglecting the competition with TFA is a valid approximation enabling faster and more robust adsorption isotherm estimation for the studied type of sorbent.

GRAPHICAL ABSTRACT

Proteomic analysis of enterotoxigenic Escherichia coli (ETEC) in neutral and alkaline conditions

Background

Enterotoxigenic Escherichia coli (ETEC) is a major cause of diarrhea in children and travelers to endemic areas. Secretion of the heat labile AB₅ toxin (LT) is induced by alkaline conditions. In this study, we determined the surface proteome of ETEC exposed to alkaline conditions (pH 9) as compared to neutral conditions (pH 7) using a LPI Hexalane FlowCell combined with quantitative proteomics. Relative quantitation with isobaric labeling (TMT) was used to compare peptide abundance and their corresponding proteins in multiple samples at MS/MS level. For protein identification and quantification samples were analyzed using either a 1D-LCMS or a 2D-LCMS approach.

Results

Strong up-regulation of the ATP synthase operon encoding F1Fo ATP synthase and down-regulation of proton pumping proteins NuoF, NuoG, Ndh and WrbA were detected among proteins involved in regulating the proton and electron transport under alkaline conditions. Reduced expression of proteins involved in osmotic stress was found at alkaline conditions while the Sec-dependent transport over the inner membrane and outer membrane protein proteins such as OmpA and the β-Barrel Assembly Machinery (BAM) complex were up-regulated.

Conclusions

ETEC exposed to alkaline environments express a specific proteome profile characterized by up-regulation of membrane proteins and secretion of LT toxin. Alkaline microenvironments have been reported close to the intestinal epithelium and the alkaline proteome may hence represent a better view of ETEC during infection.

Generation of accurate peptide retention data for targeted and data independent quantitative LC-MS analysis: Chromatographic lessons in proteomics

The emergence of data-independent quantitative LC-MS/MS analysis protocols further highlights the importance of high-quality reproducible chromatographic procedures. Knowing, controlling and being able to predict the effect of multiple factors that alter peptide RP-HPLC separation selectivity is critical for successful data collection for the construction of ion libraries. Proteomic researchers have often regarded RP-HPLC as a "black box", while vast amount of research on peptide separation is readily available. In addition to obvious parameters, such as the type of ion-pairing modifier, stationary phase and column temperature, we describe the "mysterious" effects of gradient slope, column size and flow rate on peptide separation selectivity. Retention time variations due to these parameters are governed by the linear solvent strength (LSS) theory on a peptide level by the value of its slope S in the basic LSS equation-a parameter that can be accurately predicted. Thus, the application of shallower gradients, higher flow rates, or smaller columns will each increases the relative retention of peptides with higher S-values (long species with multiple positively charged groups). Simultaneous changes to these parameters that each drive shifts in separation selectivity in the same direction should be avoided. The unification of terminology represents another pressing issue in this field of applied proteomics that should be addressed to facilitate further progress.

Nucleic acid separations using superficially porous silica particles

Ion pair reverse-phase liquid chromatography has been widely employed for nucleic acid separations. A wide range of alternative stationary phases have been utilised in conjunction with ion pair reverse-phase chromatography, including totally porous particles, non-porous particles, macroporous particles and monolithic stationary phases. In this study we have utilised superficially porous silica particles in conjunction with ion pair reverse-phase liquid chromatography for the analysis of nucleic acids. We have investigated a range of different pore-sizes and phases for the analysis of a diverse range of nucleic acids including oligonucleotides, oligoribonucleotides, phosphorothioate oligonucleotides and high molecular weight dsDNA and RNA. The pore size of the superficially porous silica particles was shown to significantly affect the resolution of the nucleic acids. Optimum separations of small oligonucleotides such as those generated in RNase mapping experiments were obtained with 80Å pore sizes and can readily be interfaced with mass spectrometry analysis. Improved resolution of larger oligonucleotides (>19mers) was observed with pore sizes of 150Å. The optimum resolution for larger dsDNA/RNA molecules was achieved using superficially porous silica particles with pore sizes of 400Å. Furthermore, we have utilised 150Å pore size solid-core particles to separate typical impurities of a fully phosphorothioated oligonucleotide, which are often generated in the synthesis of this important class of therapeutic oligonucleotide.

Wide injection zone compression in gradient reversed-phase liquid chromatography

Chromatographic zone broadening is a common issue in microfluidic chromatography, where the sample volume introduced on column often exceeds the column void volume. To better understand the propagation of wide chromatographic zones on a separation device, a series of MS Excel spreadsheets were developed to simulate the process. To computationally simplify these simulations, we investigated the effects of injection related zone broadening and its gradient related zone compression by tracking only the movements of zone boundaries on column. The effects of sample volume, sample solvent, gradient slope, and column length on zone broadening were evaluated and compared to experiments performed on 0.32mm I.D. microfluidic columns. The repetitive injection method (RIM) was implemented to generate experimental chromatograms where large sample volume scenarios can be emulated by injecting two discrete small injection plugs spaced in time. A good match between predicted and experimental RIM chromatograms was observed. We discuss the performance of selected retention models on the accuracy of predictions and use the developed spreadsheets for illustration of gradient zone focusing for both small molecules and peptides.

Combination of Edman degradation of peptides with liquid chromatography/mass spectrometry workflow for peptide identification in bottom-up proteomics

RATIONALE

High-throughput methods of proteomics are essential for identification of proteins in a cell or tissue under certain conditions. Most of these methods require tandem mass spectrometry (MS/MS). A multidimensional approach including predictive chromatography and partial chemical degradation could be a valuable alternative and/or addition to MS/MS.

METHODS

In the proposed strategy peptides are identified in a three-dimensional (3D) search space consisting of retention time (RT), mass, and reduced mass after one-step partial Edman degradation. The strategy was evaluated in silico for two databases: baker's yeast and human proteins. Rates of unambiguous identifications were estimated for mass accuracies from 0.001 to 0.05 Da and RT prediction accuracies from 0.1 to 5 min. Rates of Edman reactions were measured for test peptides.

RESULTS

A 3D description of proteolytic peptides allowing unambiguous identification without employing MS/MS of up to 95% and 80% of tryptic peptides from the yeast and human proteomes, respectively, was considered. Further extension of the search space to a four-dimensional one by incorporating the second N-terminal amino acid residue as the fourth dimension was also considered and was shown to result in up to 90% of human peptides being identified unambiguously.

CONCLUSIONS

The proposed 3D search space can be a useful alternative to MS/MS-based peptide identification approach. Experimental implementations of the proposed method within the on-line liquid chromatography/mass spectrometry (LC/MS) and off-line matrix-assisted laser desorption/ionization (MALDI) workflows are in progress.

Limitation of predictive 2-D liquid chromatography in reducing the database search space in shotgun proteomics: in silico studies

A two-dimensional (2-D) liquid chromatography (LC) separation of complex peptide mixtures that combines a normal phase utilizing hydrophilic interactions and a reversed phase offers reportedly the highest level of 2-D LC orthogonality by providing an even spread of peptides across multiple LC fractions. Matching experimental peptide retention times to those predicted by empirical models describing chromatographic separation in each LC dimension leads to a significant reduction in a database search space. In this work, we calculated the retention times of tryptic peptides separated in the C18 reversed phase at different separation conditions (pH 2 and pH 10) and in TSK gel Amide-80 normal phase. We show that retention times calculated for different 2-D LC separation schemes utilizing these phases start to correlate once the mass range of peptides under analysis becomes progressively narrow. This effect is explained by high degree of correlation between retention coefficients in the considered phases.

Optimal selection of 2D reversed-phase-reversed-phase HPLC separation techniques in bottom-up proteomics

Recent developments in bottom-up proteomics have supplanted the use of gel-based approaches in favor of multidimensional chromatographic separations of peptide mixtures followed by mass spectrometry analysis. This trend is driven by the desire to eliminate labor-intensive in-gel digestion procedures and increase proteome coverage through better recovery of proteolytic fragments. Introduction of reversed-phase-reversed-phase 2D separation techniques is one of the major improvements that have made this possible. In this article, we review recent developments in 2D HPLC and highlight variations in reversed-phase HPLC separation selectivity that allow for superior peak capacity in peptide fractionation.

Using iRT, a normalized retention time for more targeted measurement of peptides

Multiple reaction monitoring (MRM) has recently become the method of choice for targeted quantitative measurement of proteins using mass spectrometry. The method, however, is limited in the number of peptides that can be measured in one run. This number can be markedly increased by scheduling the acquisition if the accurate retention time (RT) of each peptide is known. Here we present iRT, an empirically derived dimensionless peptide-specific value that allows for highly accurate RT prediction. The iRT of a peptide is a fixed number relative to a standard set of reference iRT-peptides that can be transferred across laboratories and chromatographic systems. We show that iRT facilitates the setup of multiplexed experiments with acquisition windows more than four times smaller compared to in silico RT predictions resulting in improved quantification accuracy. iRTs can be determined by any laboratory and shared transparently. The iRT concept has been implemented in Skyline, the most widely used software for MRM experiments.