Accelerating Proteomics Using Broad Specificity Proteases.

Trypsin is the gold-standard protease in bottom-up proteomics, but many sequence stretches of the proteome are inaccessible to trypsin and standard LC-MS approaches. Thus, multienzyme strategies are used to maximize sequence coverage in post-translational modification profiling. We present fast and robust SP3- and STRAP-based protocols for the broad-specificity proteases subtilisin, proteinase K, and thermolysin. All three enzymes are remarkably fast, producing near-complete digests in 1-5 min, and cost 200-1000× less than proteomics-grade trypsin. Using FragPipe resolved a major challenge by drastically reducing the duration of the required "unspecific" searches. In-depth analyses of proteinase K, subtilisin, and thermolysin Jurkat digests identified 7374, 8178, and 8753 unique proteins with average sequence coverages of 21, 29, and 37%, including 10,000s of amino acids not reported in PeptideAtlas' >2400 experiments. While we could not identify distinct cleavage patterns, machine learning could distinguish true protease products from random cleavages, potentially enabling the prediction of cleavage products. Finally, proteinase K, subtilisin, and thermolysin enabled label-free quantitation of 3111, 3659, and 4196 unique Jurkat proteins, which in our hands is comparable to trypsin. Our data demonstrate that broad-specificity proteases enable quantitative proteomics of uncharted areas of the proteome. Their fast kinetics may allow "on-the-fly" digestion of samples in the future.

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 Šfully 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 Špore 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 Šusing 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 Špore size, compared to between 120 and 200 Šfor 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 Šshould 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.

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.

Sequence-Specific Model for Predicting Peptide Collision Cross Section Values in Proteomic Ion Mobility Spectrometry.

The contribution of peptide amino acid sequence to collision cross section values (CCS) has been investigated using a dataset of ∼134 000 peptides of four different charge states (1+ to 4+). The migration data were acquired using a two-dimensional liquid chromatography (LC)/trapped ion mobility spectrometry/quadrupole/time-of-flight mass spectrometry (MS) analysis of HeLa cell digests created using seven different proteases and was converted to CCS values. Following the previously reported modeling approaches using intrinsic size parameters (ISP), we extended this methodology to encode the position of individual residues within a peptide sequence. A generalized prediction model was built by dividing the dataset into eight groups (four charges for both tryptic/nontryptic peptides). Position-dependent ISPs were independently optimized for the eight subsets of peptides, resulting in prediction accuracy of ∼0.981 for the entire population of peptides. We find that ion mobility is strongly affected by the peptide's ability to solvate the positively charged sites. Internal positioning of polar residues and proline leads to decreased CCS values as they improve charge solvation; conversely, this ability decreases with increasing peptide charge due to electrostatic repulsion. Furthermore, higher helical propensity and peptide hydrophobicity result in a preferential formation of extended structures with higher than predicted CCS values. Finally, acidic/basic residues exhibit position-dependent ISP behavior consistent with electrostatic interaction with the peptide macrodipole, which affects the peptide helicity. The MS raw data files have been deposited with the ProteomeXchange Consortium via the jPOST partner repository (http://jpostdb.org) with the dataset identifiers PXD021440/JPST000959, PXD022800/JPST001017, and PXD026087/ JPST001176.

Separation Orthogonality in Liquid Chromatography-Mass Spectrometry for Proteomic Applications: Comparison of 16 Different Two-Dimensional Combinations.

Peptide separation orthogonality for 16 different 2D LC-ESI MS systems has been evaluated. To compare and contrast the behavior of the first dimension columns, a large proteomic retention data set of ∼30 000 tryptic peptides was collected for each 2D pairing. The selection of the first dimension system was made to cover the most popular peptide separation modes applied in proteomics: reversed-phase (RP) separations with different pH, hydrophilic interaction liquid chromatography (HILIC), strong cation and anion exchange (SCX, SAX), and mixed-mode separations. The separation orthogonality generally increases in the order RP < SCX < HILIC < SAX, with the exception of high pH RP-low pH RP system, which showed the second best orthogonality value (68%), just behind PolySAX LP column (74%). The identification output of the 2D LC-MS/MS system is driven by both separation orthogonality and efficiency, making high pH RP the best choice for the first dimension separation. Its performance in combination with a standard C18 at acidic pH can be increased further through the application of pairwise fraction concatenation. The effect of the latter has been evaluated using in silico fraction concatenation, which has been proven to show improvement only for RP separations in the first dimension. Concatenation of two, three, and four-five fractions into one is shown to be the most effective for high pH RP and HFBA- and TFA-based C18 separations, respectively. We also suggest simple guidelines for the unbiased determination of dissimilarity for two separation dimensions and evaluate separation orthogonality in 3D LC-LC-MS separation space for all systems under investigation.

PythoMS: A Python Framework To Simplify and Assist in the Processing and Interpretation of Mass Spectrometric Data.

Mass spectrometric data are copious and generate a processing burden that is best dealt with programmatically. PythoMS is a collection of tools based on the Python programming language that assist researchers in creating figures and video output that is informative, clear, and visually compelling. The PythoMS framework introduces a library of classes and a variety of scripts that quickly perform time-consuming tasks: making proprietary output readable; binning intensity vs time data to simulate longer scan times (and hence reduce noise); calculating theoretical isotope patterns and overlaying them in histogram form on experimental data (an approach that works even for overlapping signals); rendering videos that enable zooming into the baseline of intensity vs time plots (useful to make sense of data collected over a large dynamic range) or that depict the evolution of different species in a time-lapse format; calculating aggregates; and providing a quick first-pass at identifying fragments in MS/MS spectra. PythoMS is a living project that will continue to evolve as additional scripts are developed and deployed.

Oxidation of Titanocene(III): The Deceptive Simplicity of a Color Change.

Reduction of red Cp2TiCl2 (Cp = cyclopentadienyl) with zinc dust in acetonitrile produces a blue solution of [Cp2Ti(NCMe)2]+, which when exposed to air rapidly discolors to bright yellow. This behavior makes the blue solution a handy visual indicator for the presence of oxygen, but the chemistry is considerably more complicated than the primary colors suggest at first glance. Real-time mass spectrometric and colorimetric analysis reveals that oxidation from Ti(III) to Ti(IV) produces a host of oxygen-containing complexes, whose appearance parallels the observed color changes.

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.

Peptide retention time prediction for electrostatic repulsion-hydrophilic interaction chromatography.

Electrostatic Repulsion-Hydrophilic Interaction Chromatography (ERLIC) is one of the legacy separation tools developed by Dr. Andrew Alpert and has been used for developing unique separation methods of hydrophilic compounds, including peptides. In the past it has been studied using designed peptide libraries to elucidate major features of its separation mechanism, while comprehensive peptide retention modeling for ERLIC is still lacking. In this work we employed a proteomics-derived ∼170,000 peptide retention datasets to evaluate major ERLIC retention features using the framework of our Sequence-Specific Retention Calculator model. The separation conditions were adjusted to obtain a wider proteome coverage, particularly for non-modified peptides, resulting in a superior separation orthogonality for a 2D LC combination with reversed-phase C18 LC-MS in the second dimension. The SSRCalc ERLIC model presents a consistent theme with the existing ERLIC retention mechanism, reflecting a dependence on peptide orientation and the position of charged and hydrophilic residues across the peptide backbone. R2 values of 0.935 and 0.955 accuracy were demonstrated for the standard interpretable SSRCalc model and machine learning algorithm, respectively. The effects of various PTMs on peptide retention were evaluated in this study, covering spontaneous (oxidation, deamidation) and enzymatic (N-terminal acetylation, phosphorylation, glycosylation) modifications.

Retention time prediction for post-translationally modified peptides: Ser, Thr, Tyr-phosphorylation.

The development of a peptide retention prediction model for reversed-phase chromatography applications in proteomics is reported for peptides carrying phosphorylated Ser, Thr and Tyr-residues. The major retention features have been assessed using a collection of over 10,000 phosphorylated/non-phosphorylated peptide pairs identified in a series 1D and 2D LC-MS/MS acquisitions using formic acid as ion pairing modifier. Single modification event on average results in increased peptide retention for phosphorylation of Ser (+ 1.46), Thr (+1.33), Tyr (+0.93% acetonitrile, ACN) on gradient elution scale for Luna C18(2) stationary phase. We established several composition and sequence specific features, which drive deviations from these average values. Thus, single phosphorylation of serine results in retention shifts ranging from -2.4 to 5.5% ACN depending on position of the residue, nature of nearest neighbour residues, peptide length, hydrophobicity and pI value, and its propensity to form amphipathic helical structures. We established that the altered ion-pairing environment upon phosphorylation is detrimental for this variability. Hydrophobicity of ion-pairing modifier directly informs the magnitude of expected shifts: (most hydrophilic) 0.5 % acetic acid (larger positive shift upon phosphorylation) > 0.1 % formic acid (positive) > 0.1 % trifluoroacetic (negative) > 0.1 % heptafluorobutyric acid (larger negative shift). The effect of phosphorylation has been also evaluated for several separation conditions used in the first dimension of 2D LC applications: high pH reversed-phase (RP), hydrophilic interaction liquid chromatography (HILIC), strong cation- and strong anion exchange separations.

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.

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.

Chromatographic behaviour of peptides modified with amine-reacting tags for relative protein quantitation in proteomic applications.

Reversed-phase (RP) HPLC separation of peptides labeled with amine-reacting tags for relative protein quantitation (iTRAQ4, iTRAQ8 - isobaric tag for relative and absolute quantitation, TMT - tandem mass tag) has been investigated using large-scale proteomics derived retention datasets. These tags have similar chemistry but use linkers of different length and hydrophobicity, moving the positively charged functional groups further from peptide backbone. Peptide hydrophobicity (RP HPLC retention), on average, increases in the following order: non-labeled < iTRAQ4 < iTRAQ8 < TMT under both low pH (0.1% formic acid) and pH 10 eluent conditions. At the same time, the interplay between hydrophobicity and length of the labeling group drives the deviations from this order. Thus, longer and less hydrophobic iTRAQ8 moiety results in greater retention increase for peptides carrying amphipathic helical structures at the N-terminus. Development of a peptide retention prediction models for these modifications was achieved by predicting correspondent retention shifts ΔHI (hydrophobicity index,% acetonitrile) between unmodified and labelled peptide pairs.

Peptide retention time prediction for peptides with post-translational modifications: N-terminal (α-amine) and lysine (ε-amine) acetylation.

Development of a peptide retention prediction model in reversed-phase chromatography is reported for acetylated peptides - both N-terminal (α-) and side chain of Lys (ε-amine) residues. Large-scale proteomic 2D LC-MS analyses of acetylated/non-acetylated tryptic digest of whole human cell lysate have been used to assemble representative retention data sets of 25,000+ modified/non-modified pairs. This allowed elucidating chromatographic behaviour of modified peptides in three different separation modes: high pH reversed-phase, HILIC separation on amide phase (first dimension of 2D) and reversed-phase separation with formic acid as ion-pairing modifier in the second dimension. On average, N-terminal acetylation increases peptide RP retention at acidic pH by 5 Hydrophobicity Index units (% acetonitrile). Acetylation of first lysine adds another 4.1%. The magnitude of the retention shift varies greatly depending on the number of modified amines, peptide length, and N-terminal peptide sequence. Large retention shifts have been observed for peptides with hydrophobic N-termini and specifically peptides carrying sequences characteristic for amphipathic helical structures - all in complete agreement with major sequence-specific features of RP retention mechanism. The utility of the modified Sequence Specific Retention Calculator model has been verified for the in-vivo N-terminally acetylated peptides detected by 2D LC-MS/MS analysis of a yeast tryptic digest. The effect of N-terminal acetylation was also evaluated for six different HILIC columns, strong cation- and strong anion exchange separations using previously acquired 2D LC-MS/MS data.

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.