pSiteExplorer: Visualization of the mass spectrometric data underlying phospho-site quantification

Mass spectrometry based phospho-proteomics is a widely used approach to assess protein phosphorylation. Intensities of phospho-peptide ions are obtained by integrating the MS signal over their chromatographic peaks. How individual peptide measurements mapping to the same phospho-site are combined for the quantification of the given site is, however, in most cases hidden from researchers conducting, reviewing, and reading these studies. I here describe pSiteExplorer, an R script that visualizes the peak intensities associated with phospho-sites in MaxQuant output tables. Barplots of MS intensities originating from phospho-peptides with distinct amino acid sequences due to missed cleavages, different numbers of phosphates and from all off-line chromatographic fractions and charge states are displayed. This tool will help gaining a deeper insight into phospho-site quantifications by contrasting individual and summed phospho-peptide intensities with the site-level values derived by MaxQuant. This will support the validation of quantification results, for example, for the selection of candidates for follow-up studies.

A nanopore ion source delivers single amino acid and peptide ions directly into the gas phase

A technology for sequencing single proteins would expand our understanding of biology and improve the detection and treatment of disease. Approaches based on fluorosequencing, nanopores, and tunneling spectroscopy are under development and show promise. However, only mass spectrometry (MS) has demonstrated an ability to identify amino acids with minimal degeneracy. We envision sequencing a protein by fragmenting it and delivering its constituent amino acids into a mass spectrometer in sequential order, but existing ion sources employ a background gas that scrambles the spatial ordering of ions and degrades their transmission. Here we report an ion source comprising a glass capillary with a sub-100 nm diameter pore that emits amino acid ions from aqueous solution directly into vacuum. Emitted ions travel collision-less trajectories before striking a single-ion detector. We measured unsolvated ions of 16 different amino acids as well as glutathione and two of its post-translationally modified variants.

Chimera Spectrum Diagnostics for Peptides Using Two-Dimensional Partial Covariance Mass Spectrometry

The rate of successful identification of peptide sequences by tandem mass spectrometry (MS/MS) is adversely affected by the common occurrence of co-isolation and co-fragmentation of two or more isobaric or isomeric parent ions. This results in so-called `chimera spectra’, which feature peaks of the fragment ions from more than a single precursor ion. The totality of the fragment ion peaks in chimera spectra cannot be assigned to a single peptide sequence, which contradicts a fundamental assumption of the standard automated MS/MS spectra analysis tools, such as protein database search engines. This calls for a diagnostic method able to identify chimera spectra to single out the cases where this assumption is not valid. Here, we demonstrate that, within the recently developed two-dimensional partial covariance mass spectrometry (2D-PC-MS), it is possible to reliably identify chimera spectra directly from the two-dimensional fragment ion spectrum, irrespective of whether the co-isolated peptide ions are isobaric up to a finite mass accuracy or isomeric. We introduce ‘3-57 chimera tag’ technique for chimera spectrum diagnostics based on 2D-PC-MS and perform numerical simulations to examine its efficiency. We experimentally demonstrate the detection of a mixture of two isomeric parent ions, even under conditions when one isomeric peptide is at one five-hundredth of the molar concentration of the second isomer.

Trapped Ion Mobility Spectrometry Reduces Spectral Complexity in Mass Spectrometry Based Workflow

In bottom-up mass spectrometry based proteomics, deep proteome coverage is limited by high cofragmentation rates. This occurs when more than one analyte is isolated by the quadrupole and the subsequent fragmentation event produces fragment ions of heterogeneous origin. One strategy to reduce cofragmentation rates is through effective peptide separation techniques such as chromatographic separation and, the more recently popularized, ion mobility (IM) spectrometry which separates peptides by their collisional cross section. Here we investigate the capability of the Trapped Ion Mobility Spectrometry (TIMS) device to effectively separate peptide ions and quantify the separation power of the TIMS device in the context of a Parallel Accumulation-Serial Fragmentation (PASEF) workflow. We found that TIMS IM separation increases the number of interference-free MS1 features 9.2-fold, while decreasing the average peptide density in precursor spectra 6.5 fold. In a Data Dependent Acquisition (DDA) PASEF workflow, IM separation increased the number of spectra without cofragmentation by a factor of 4.1 and the number of high quality spectra 17-fold. This observed decrease in spectral complexity results in a substantial increase in peptide identification rates when using our data-driven model. In the context of a Data Independent Acquisition (DIA), the reduction in spectral complexity resulting from IM separation is estimated to be equivalent to a 4-fold decrease in isolation window width (from 25Da to 6.5Da). Our study shows that TIMS IM separation dramatically reduces cofragmentation rates leading to an increase in peptide identification rates.

Deep learning the collisional cross sections of the peptide universe from a million experimental values

The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To investigate the nature and utility of the peptide collisional cross section (CCS) space, we measure more than a million data points from whole-proteome digests of five organisms with trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF). The scale and precision (CV < 1%) of our data is sufficient to train a deep recurrent neural network that accurately predicts CCS values solely based on the peptide sequence. Cross section predictions for the synthetic ProteomeTools peptides validate the model within a 1.4% median relative error (R > 0.99). Hydrophobicity, proportion of prolines and position of histidines are main determinants of the cross sections in addition to sequence-specific interactions. CCS values can now be predicted for any peptide and organism, forming a basis for advanced proteomics workflows that make full use of the additional information.

Peak identification and quantification by proteomic mass spectrogram decomposition

Recent advances in liquid chromatography/mass spectrometry (LC/MS) technology have notably improved the sensitivity, resolution, and speed of proteome analysis, resulting in increasing demand for more sophisticated algorithms to interpret complex mass spectrograms. Here, we propose a novel statistical method that we call proteomic mass spectrogram decomposition (ProtMSD) for joint identification and quantification of peptides and proteins. Given the proteomic mass spectrogram and the reference mass spectra of all possible peptide ions associated with proteins as a dictionary, our method directly estimates the temporal intensity curves of those peptide ions, i.e., the chromatograms, under a group sparsity constraint without using the conventional careful pre-processing (e.g., thresholding and peak picking). We show that the accuracy of protein identification was significantly improved by using the protein-peptide hierarchical relationships, the isotopic distribution profiles and predicted retention times of peptide ions and the pre-learned mass spectra of noise. In the analysis of E. coli cell lysate, our ProtMSD showed excellent agreement (3277 peptide ions (94.79%) and 493 proteins (98.21%)) with the conventional cascading approach to identification and quantification based on Mascot and Skyline. This is the first attempt to use a matrix decomposition technique as a tool for LC/MS-based joint proteome identification and quantification.

Effect of Phosphorylation on the Collision Cross Sections of Peptide Ions in Ion Mobility Spectrometry

The insertion of ion mobility spectrometry (IMS) between LC and MS can improve peptide identification in both proteomics and phosphoproteomics by providing structural information that is complementary to LC and MS, because IMS separates ions on the basis of differences in their shapes and charge states. However, it is necessary to know how phosphate groups affect the peptide collision cross sections (CCS) in order to accurately predict phosphopeptide CCS values and to maximize the usefulness of IMS. In this work, we systematically characterized the CCS values of 4,433 pairs of mono-phosphopeptide and corresponding unphosphorylated peptide ions using trapped ion mobility spectrometry (TIMS). Nearly one-third of the mono-phosphopeptide ions evaluated here showed smaller CCS values than their unphosphorylated counterparts, even though phosphorylation results in a mass increase of 80 Da. Significant changes of CCS upon phosphorylation occurred mainly in structurally extended peptides with large numbers of basic groups, possibly reflecting intramolecular interactions between phosphate and basic groups.

Deep learning the collisional cross sections of the peptide universe from a million training samples

ABSTRACTThe size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To explore the nature and utility of the entire peptide collisional cross section (CCS) space, we measure more than a million data points from whole-proteome digests of five organisms with trapped ion mobility spectrometry (TIMS) and parallel accumulation – serial fragmentation (PASEF). The scale and precision (CV <1%) of our data is sufficient to train a deep recurrent neural network that accurately predicts CCS values solely based on the peptide sequence. Cross section predictions for the synthetic ProteomeTools library validate the model within a 1.3% median relative error (R > 0.99). Hydrophobicity, position of prolines and histidines are main determinants of the cross sections in addition to sequence-specific interactions. CCS values can now be predicted for any peptide and organism, forming a basis for advanced proteomics workflows that make full use of the additional information.

Glycopeptide variable window SWATH for improved Data Independent Acquisition glycoprotein analysis

N-glycosylation plays an essential role in regulating protein folding and function in eukaryotic cells. Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH) has proven useful as a data independent acquisition (DIA) MS method for analysis of glycoproteins and their glycan modifications. By separating the entire m/z range into consecutive isolation windows, DIA-MS allows comprehensive MS data acquisition and high-sensitivity detection of molecules of interest. Variable width DIA windows allow optimal analyte measurement, as peptide ions are not evenly distributed across the full m/z range. However, the m/z distribution of glycopeptides is different to that of unmodified peptides because of their large glycan structures. Here, we improved the performance of DIA glycoproteomics by using variable width windows optimized for glycopeptides. This method allocates narrow windows at m/z ranges rich in glycopeptides, improving analytical specificity and performance. We show that related glycoforms must fall in separate windows to allow accurate glycopeptide measurement. We demonstrate the utility of the method by comparing the cell wall glycoproteomes of wild-type and N-glycan biosynthesis deficient yeast and showing improved measurement of glycopeptides with different glycan structures. Our results highlight the importance of appropriately optimized DIA methods for measurement of post-translationally modified peptides.