Unifying Expression Scale for Peptide Hydrophobicity in Proteomic Reversed Phase High-Pressure Liquid Chromatography Experiments

Compendium of Chromatographic Behavior of Post-translationally and Chemically Modified Peptides in Bottom-Up Proteomic Experiments

We have systematically evaluated the chromatographic behavior of post-translationally/chemically modified peptides using data spanning over 70 of the most relevant modifications. These retention properties were measured for standard bottom-up proteomic settings (fully porous C18 separation media, 0.1% formic acid as ion-pairing modifier) using collections of modified/nonmodified peptide pairs. These pairs were generated by spontaneous degradation, chemical or enzymatic treatment, analysis of synthetic peptides, or the cotranslational incorporation of noncanonical proline analogues. In addition, these measurements were validated using external data acquired for synthetic peptides and enzymatically induced citrullination. Working in units of hydrophobicity index (HI, % ACN) and evaluating the average retention shifts (ΔHI) represent the simplest approach to describe the effect of modifications from a didactic point of view. Plotting HI values for modified (y-axis) vs nonmodified (x-axis) counterparts generates unique slope and intercept values for each modification defined by the chemistry of the modifying moiety: its hydrophobicity, size, pKa of ionizable groups, and position of the altered residue. These composition-dependent correlations can be used for coarse incorporation of PTMs into models for prediction of peptide retention. More accurate predictions would require the development of specific sequence-dependent algorithms to predict ΔHI values.

Deep learning from harmonized peptide libraries enables retention time prediction of diverse post translational modifications

In proteomics experiments, peptide retention time (RT) is an orthogonal property to fragmentation when assessing detection confidence. Advances in deep learning enable accurate RT prediction for any peptide from sequence alone, including those yet to be experimentally observed. Here we present Chronologer, an open-source software tool for rapid and accurate peptide RT prediction. Using new approaches to harmonize and false-discovery correct across independently collected datasets, Chronologer is built on a massive database with >2.2 million peptides including 10 common post-translational modification (PTM) types. By linking knowledge learned across diverse peptide chemistries, Chronologer predicts RTs with less than two-thirds the error of other deep learning tools. We show how RT for rare PTMs, such as OGlcNAc, can be learned with high accuracy using as few as 10-100 example peptides in newly harmonized datasets. This iteratively updatable workflow enables Chronologer to comprehensively predict RTs for PTM-marked peptides across entire proteomes.

Polyglutamylation: biology and analysis

Polyglutamylation is a posttranslational modification (PTM) that adds several glutamates on glutamate residues in the form of conjugated peptide chains by a family of enzymes known as polyglutamylases. Polyglutamylation is well documented in microtubules. Polyglutamylated microtubules consist of different α- and β-tubulin subunits with varied number of added glutamate residues. Kinetic control and catalytic rates of tubulin modification by polyglutamylases influence the polyglutamylation pattern of functional microtubules. The recent studies uncovered catalytic mechanisms of the glutamylation enzymes family, particularly tubulin tyrosine ligase-like (TTLL). Variable length polyglutamylation of primary sequence glutamyl residues have been mapped with a multitude of protein chemistry and proteomics approaches. Although polyglutamylation was initially considered a tubulin-specific modification, the recent studies have uncovered a calmodulin-dependent glutamylase, SidJ. Nano-electrospray ionization (ESI) proteomic approaches have identified quantifiable polyglutamylated sites in specific substrates. Indeed, conjugated glutamylated peptides were used in nano-liquid chromatography gradient delivery due to their relative hydrophobicity for their tandem mass spectrometry (MS/MS) characterization. The recent polyglutamylation characterization has revealed three major sites: E445 in α-tubulin, E435 in β-tubulin, and E860 in SdeA. In this review, we have summarized the progress made using proteomic approaches for large-scale detection of polyglutamylated peptides, including biology and analysis.

Mapping of polyglutamylation in tubulins using nanoLC-ESI-MS/MS

Tubulin polyglutamylation is a polymeric modification that extends from the carboxyl-terminus of tubulins. Molecular description of amino acids and their branching polyglutamyls is a hallmark of tubulin in microtubules. There are different chemical approaches for detecting these polymeric structures, mostly reported prior to development of nESI peptide analysis. Here we demonstrate a novel and simple approach to detect shared regions of amino acid ions from tubulin polyglutamylated peptides in nanoLC-MS/MS. This involves two parallel in gel digestions with trypsin and subtilisin followed by mapping of di- and triglutamyl modifications of α- and β-tubulins using a routine proteomics assay. We present three levels of information: i) identification of proteomics MS/MS data, ii) description of internal fragment ion series common across digests, and iii) extracted ion chromatograms mapped relative to retention time standards for confirmation of relative hydrophobicity values. Our nanoLC assay positive ion ESI detects up to 3 conjugated glutamates in tubulins. We implemented an analytical column only bottom up approach that characterizes molecular features of polyglutamylated tubulins.

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.

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.

Towards further defining the proteome of mouse saliva

Background

Knowledge of the mouse salivary proteome is not well documented and as a result, very limited. Currently, several salivary proteins remain unidentified and for some others, their function yet to be determined. The goal of the present study is to utilize mass spectrometry analysis to widen our knowledge of mouse salivary proteins, and through extensive database searches, provide further insight into the array of proteins that can be found in saliva. A comprehensive mouse salivary proteome will also facilitate the development of mouse models to study specific biomarkers of many human diseases.

Results

Individual saliva samples were collected from male and female mice, and later pooled according to sex. Two pools of saliva from female mice (2 samples/pool) and 2 pools of saliva from male mice were used for analysis utilizing high performance liquid chromatograph mass spectrometry (nano-RPLC-MS/MS). The resulting datasets identified 345 proteins: 174 proteins were represented in saliva obtained from both sexes, as well as 82 others that were more female specific and 89 that were more male specific. Of these sex linked proteins, twelve were identified as exclusively sex-limited; 10 unique to males and 2 unique to females. Functional analysis of the 345 proteins identified 128 proteins with catalytic activity characteristics; indicative of proteins involved in digestion, and 35 proteins associated with stress response, host defense, and wound healing functions. Submission of the list of 345 proteins to the BioMart data mining tool in the Ensembl database further allowed us to identify a total of 283 orthologous human genes, of which, 131 proteins were recently reported to be present in the human salivary proteome.

Conclusions

The present study is the most comprehensive list to date of the proteins that constitute the mouse salivary proteome. The data presented can serve as a useful resource for identifying potentially useful biomarkers of human health and disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s12953-015-0068-3) contains supplementary material, which is available to authorized users.