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.

Charge-based interactions through peptide position 4 drive diversity of antigen presentation by human leukocyte antigen class I molecules.

Human leukocyte antigen class I (HLA-I) molecules bind and present peptides at the cell surface to facilitate the induction of appropriate CD8+ T cell-mediated immune responses to pathogen- and self-derived proteins. The HLA-I peptide-binding cleft contains dominant anchor sites in the B and F pockets that interact primarily with amino acids at peptide position 2 and the C-terminus, respectively. Nonpocket peptide-HLA interactions also contribute to peptide binding and stability, but these secondary interactions are thought to be unique to individual HLA allotypes or to specific peptide antigens. Here, we show that two positively charged residues located near the top of peptide-binding cleft facilitate interactions with negatively charged residues at position 4 of presented peptides, which occur at elevated frequencies across most HLA-I allotypes. Loss of these interactions was shown to impair HLA-I/peptide binding and complex stability, as demonstrated by both in vitro and in silico experiments. Furthermore, mutation of these Arginine-65 (R65) and/or Lysine-66 (K66) residues in HLA-A*02:01 and A*24:02 significantly reduced HLA-I cell surface expression while also reducing the diversity of the presented peptide repertoire by up to 5-fold. The impact of the R65 mutation demonstrates that nonpocket HLA-I/peptide interactions can constitute anchor motifs that exert an unexpectedly broad influence on HLA-I-mediated antigen presentation. These findings provide fundamental insights into peptide antigen binding that could broadly inform epitope discovery in the context of viral vaccine development and cancer immunotherapy.

Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides.

The prediction of fragment ion intensities and retention time of peptides has gained significant attention over the past few years. However, the progress shown in the accurate prediction of such properties focused primarily on unlabeled peptides. Tandem mass tags (TMT) are chemical peptide labels that are coupled to free amine groups usually after protein digestion to enable the multiplexed analysis of multiple samples in bottom-up mass spectrometry. It is a standard workflow in proteomics ranging from single-cell to high-throughput proteomics. Particularly for TMT, increasing the number of confidently identified spectra is highly desirable as it provides identification and quantification information with every spectrum. Here, we report on the generation of an extensive resource of synthetic TMT-labeled peptides as part of the ProteomeTools project and present the extension of the deep learning model Prosit to accurately predict the retention time and fragment ion intensities of TMT-labeled peptides with high accuracy. Prosit-TMT supports CID and HCD fragmentation and ion trap and Orbitrap mass analyzers in a single model. Reanalysis of published TMT data sets show that this single model extracts substantial additional information. Applying Prosit-TMT, we discovered that the expression of many proteins in human breast milk follows a distinct daily cycle which may prime the newborn for nutritional or environmental cues.

Retention Time Prediction for TMT-Labeled Peptides in Proteomic LC-MS Experiments.

We present the first detailed study of chromatographic behavior of peptides labeled with tandem mass tags (TMT and TMTpro) in 2D LC for proteomic applications. Carefully designed experimental procedures have permitted generating data sets of over 100,000 nonlabeled and TMT-labeled peptide pairs for the low pH RP in the second separation dimension and data sets of over 10,000 peptide pairs for high-pH RP, HILIC (amide and silica), and SCX separations in the first separation dimension. The average increase in peptide RPLC (0.1% formic acid) retention upon TMT labeling was found to be 3.3% acetonitrile (linear water/acetonitrile gradients), spanning a range of -4 to 10.3%. In addition to the bulk peptide properties such as length, hydrophobicity, and the number of labeled residues, we found several sequence-dependent features mostly associated with differences in N-terminal chemistry. The behavior of TMTpro-labeled peptides was found to be very similar except for a slightly higher hydrophobicity: an average retention shift of 3.7% acetonitrile. The respective versions of the sequence-specific retention calculator (SSRCalc) model have been developed to accommodate both TMT chemistries, showing identical prediction accuracy (R2 ∼ 0.98) for labeled and nonlabeled peptides. Higher retention for TMT-labeled peptides was observed for high-pH RP and HILIC separations, while SCX selectivity remained virtually unchanged.

Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics.

Characterizing the human leukocyte antigen (HLA) bound ligandome by mass spectrometry (MS) holds great promise for developing vaccines and drugs for immune-oncology. Still, the identification of non-tryptic peptides presents substantial computational challenges. To address these, we synthesized and analyzed >300,000 peptides by multi-modal LC-MS/MS within the ProteomeTools project representing HLA class I & II ligands and products of the proteases AspN and LysN. The resulting data enabled training of a single model using the deep learning framework Prosit, allowing the accurate prediction of fragment ion spectra for tryptic and non-tryptic peptides. Applying Prosit demonstrates that the identification of HLA peptides can be improved up to 7-fold, that 87% of the proposed proteasomally spliced HLA peptides may be incorrect and that dozens of additional immunogenic neo-epitopes can be identified from patient tumors in published data. Together, the provided peptides, spectra and computational tools substantially expand the analytical depth of immunopeptidomics workflows.

INFERYS rescoring: Boosting peptide identifications and scoring confidence of database search results.

Database search engines for bottom-up proteomics largely ignore peptide fragment ion intensities during the automated scoring of tandem mass spectra against protein databases. Recent advances in deep learning allow the accurate prediction of peptide fragment ion intensities. Using these predictions to calculate additional intensity-based scores helps to overcome this drawback. Here, we describe a processing workflow termed INFERYS™ rescoring for the intensity-based rescoring of Sequest HT search engine results in Thermo Scientific™ Proteome Discoverer™ 2.5 software. The workflow is based on the deep learning platform INFERYS capable of predicting fragment ion intensities, which runs on personal computers without the need for graphics processing units. This workflow calculates intensity-based scores comparing peptide spectrum matches from Sequest HT and predicted spectra. Resulting scores are combined with classical search engine scores for input to the false discovery rate estimation tool Percolator. We demonstrate the merits of this approach by analyzing a classical HeLa standard sample and exemplify how this workflow leads to a better separation of target and decoy identifications, in turn resulting in increased peptide spectrum match, peptide and protein identification numbers. On an immunopeptidome dataset, this workflow leads to a 50% increase in identified peptides, emphasizing the advantage of intensity-based scores when analyzing low-intensity spectra or analytes with very similar physicochemical properties that require vast search spaces. Overall, the end-to-end integration of INFERYS rescoring enables simple and easy access to a powerful enhancement to classical database search engines, promising a deeper, more confident and more comprehensive analysis of proteomic data from any organism by unlocking the intensity dimension of tandem mass spectra for identification and more confident scoring.

A streamlined workflow for twoplexing of N-linked glycan analysis using light (12C6) and heavy (13C6) isotopologues of 3-aminobenzenesulfonic acid.

Comparative glycosylation analysis of biopharmaceuticals requires the development of methods that deliver the necessary throughput, support structural elucidation and relative quantitation of glycans released from therapeutics. The current study presents the development and applicability assessment of a twoplex approach using light and heavy isotopolouges of 3-aminobenzenesulfonic acid (3-ASA) under wet labeling conditions followed by UHPLC-MS analysis in data dependent acquisition mode. Excellent labelling efficiency, >90%, was achieved for both the light and heavy variants of the reagent. Glycan distributions of two human IgG lots labeled by light and heavy isotopolouges were identical, demonstrating no labeling bias introduced by either of the isotopologues. Peak area distributions of glycan profiles of two human IgG lots were compared to 2-aminobenzamide (2-AB) and RapiFluor-MS protocols. The comparison led to identical results in peak area distribution across the three dyes, but differences in chromatographic selectivity attributed to the different tags. MS1 based relative quantitation was further validated by releasing glycans from the same lot of human IgG, with glycan pools obtained labeled with light and heavy isotopologues separately, followed by mixing and clean-up of the same amount of light and heavy labeled glycan pools. MS analyses of each glycan resulted in a ratio of light and heavy XIC in the range of 0.97 ≤ x ≤ 1.05, demonstrating the method is amenable for the relative quantitation of glycans. Excellent correlation between the relative quantitation data of N-glycans from two human IgG N-glycan pools using the twoplex approach and ratios from peak area distribution calculated from the fluorescent chromatogram was observed (r = 0.986), further corroborating the reliability of the method and its potential applicability in the biopharmaceutical industry. Highly informative HCD-MS2 spectra dominated mostly by Y- and Z-type single and double glycosidic fragment ions facilitate structural interpretation of the oligosaccharides.

International Ring Trial of a High Resolution Targeted Metabolomics and Lipidomics Platform for Serum and Plasma Analysis.

A challenge facing metabolomics in the analysis of large human cohorts is the cross-laboratory comparability of quantitative metabolomics measurements. In this study, 14 laboratories analyzed various blood specimens using a common experimental protocol provided with the Biocrates AbsoluteIDQ p400HR kit, to quantify up to 408 metabolites. The specimens included human plasma and serum from male and female donors, mouse and rat plasma, as well as NIST SRM 1950 reference plasma. The metabolite classes covered range from polar (e.g., amino acids and biogenic amines) to nonpolar (e.g., diacyl- and triacyl-glycerols), and they span 11 common metabolite classes. The manuscript describes a strict system suitability testing (SST) criteria used to evaluate each laboratory's readiness to perform the assay, and provides the SST Skyline documents for public dissemination. The study found approximately 250 metabolites were routinely quantified in the sample types tested, using Orbitrap instruments. Interlaboratory variance for the NIST SRM-1950 has a median of 10% for amino acids, 24% for biogenic amines, 38% for acylcarnitines, 25% for glycerolipids, 23% for glycerophospholipids, 16% for cholesteryl esters, 15% for sphingolipids, and 9% for hexoses. Comparing to consensus values for NIST SRM-1950, nearly 80% of comparable analytes demonstrated bias of <50% from the reference value. The findings of this study result in recommendations of best practices for system suitability, quality control, and calibration. We demonstrate that with appropriate controls, high-resolution metabolomics can provide accurate results with good precision across laboratories, and the p400HR therefore is a reliable approach for generating consistent and comparable metabolomics data.

Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning.

In mass-spectrometry-based proteomics, the identification and quantification of peptides and proteins heavily rely on sequence database searching or spectral library matching. The lack of accurate predictive models for fragment ion intensities impairs the realization of the full potential of these approaches. Here, we extended the ProteomeTools synthetic peptide library to 550,000 tryptic peptides and 21 million high-quality tandem mass spectra. We trained a deep neural network, termed Prosit, resulting in chromatographic retention time and fragment ion intensity predictions that exceed the quality of the experimental data. Integrating Prosit into database search pipelines led to more identifications at >10× lower false discovery rates. We show the general applicability of Prosit by predicting spectra for proteases other than trypsin, generating spectral libraries for data-independent acquisition and improving the analysis of metaproteomes. Prosit is integrated into ProteomicsDB, allowing search result re-scoring and custom spectral library generation for any organism on the basis of peptide sequence alone.

High Performance Anion Exchange and Hydrophilic Interaction Liquid Chromatography Approaches for Comprehensive Mass Spectrometry-Based Characterization of the N-Glycome of a Recombinant Human Erythropoietin.

Comprehensive characterization of the N-glycome of a therapeutic is challenging because glycans may harbor numerous modifications (e.g., phosphorylation, sulfation, sialic acids with possible O-acetylation). The current report presents a comparison of two chromatographic platforms for the comprehensive characterization of a recombinant human erythropoietin (rhEPO) N-glycome. The two platforms include a common workflow based on 2-AB-derivatization and hydrophilic interaction chromatography (HILIC) and a native N-linked glycan workflow employing high performance anion exchange (HPAE) chromatography. Both platforms were coupled to an Orbitrap mass spectrometer, and data dependent HCD fragmentation allowed confident structural elucidation of the glycans. Each platform identified glycans not revealed by the other, and both exhibited strengths and weaknesses. The reductive amination based HILIC workflow provided better throughput and sensitivity, had good isomer resolution, and revealed the presence of O-acetylated sialic acids. However, it exhibited poor performance toward phosphorylated glycans and did not reveal the presence of sulfated glycans. Furthermore, reductive amination introduced dehydration artifacts and modified the glycosylation profile in the rhEPO glycome. Conversely, HPAE provided unbiased charge classification (sialylation levels), improved isomer resolution, and revealed multiple phosphorylated and sulfated structures, but delivered lower throughput, had artifact peaks due to epimer formation, and loss of sialic acid O-acetylation. The MS2 based identification of phosphorylated and sulfated glycans was not possible in HILIC mode due to their poor solubility caused by the high acetonitrile concentrations employed at the beginning of the gradient. After analyzing the glycome by both approaches and determining the glycans present, a glycan library was created for site specific glycopeptide analyses. Glycopeptide analyses confirmed all the compositions annotated by the combined use of 2-AB- and native glycan workflows and provided site specific location of the glycans. These two platforms were complementary and in combination delivered a more thorough and comprehensive characterization of the rhEPO N-glycome, supporting regulatory conformance for the pharmaceutical industry.

Sensitive and Accurate Quantitation of Phosphopeptides Using TMT Isobaric Labeling Technique.

Phosphorylation is an essential post-translational modification for regulating protein function and cellular signal transduction. Mass spectrometry (MS) combined with isobaric tandem mass tags (TMTs) has become a powerful platform for simultaneous, large-scale phospho-proteome site identification and quantitation. To improve the accuracy of isobaric tag-based quantitation in complex proteomic samples, MS3-based acquisition methods such as Synchronous Precursor Selection (SPS) have been used. However, the method suffers from lower peptide identification rates when applied to enriched phosphopeptide samples compared with unmodified samples due to differences in phosphopeptide fragmentation patterns during tandem MS. We developed and optimized two new acquisition methods for analysis of TMT-labeled multiplexed phosphoproteome samples, which resulted in more phosphopeptide identifications with less ratio distortion when compared with previous methods. We also applied these improved methods to a large-scale study of phosphorylation levels in A549 cell lines treated with insulin or insulin growth factor 1 (IGF-1). Overall, 3378 protein groups and 12 465 phosphopeptides were identified, of which 10 436 were quantified across 10 samples without prefractionation. The accurate measurement enabled us to map to numerous signaling pathways including mechanistic target of rapamycin (mTOR), epidermal growth factor receptor (EGFR, ErbB), and insulin signaling pathways.

Trimodal Mixed Mode Chromatography That Enables Efficient Offline Two-Dimensional Peptide Fractionation for Proteome Analysis.

Offline two-dimensional chromatography is a common means to achieve deep proteome coverage. To reduce sample complexity and dynamic range and to utilize mass spectrometer (MS) time efficiently, high chromatographic resolution of and good orthogonality between the two dimensions are needed. Ion exchange and high pH reversed phase chromatography are often used for this purpose. However, the former requires desalting to be MS-compatible, and the latter requires fraction pooling to create orthogonality. Here, we report an alternative first-dimension separation technique using a commercial trimodal phase incorporating polar embedded reversed phase, weak anion exchange, and strong cation exchange material. The column is capable of retaining polar and nonpolar peptides alike without noticeable breakthrough. It allows separating ordinary and TMT-labeled peptides under mild acidic conditions using an acetonitrile gradient. The direct MS compatibility of solvents and good orthogonality to online coupled C18 columns enable a straightforward workflow without fraction pooling and desalting while showing comparable performance to the other techniques. The method scales from low to high microgram sample quantity and is amenable to full automation. To demonstrate practical utility, we analyzed the proteomes of 10 human pancreatic cancer cell lines to a depth of >8,700 quantified proteins.

PPKs mediate direct signal transfer from phytochrome photoreceptors to transcription factor PIF3.

Upon light-induced nuclear translocation, phytochrome (phy) sensory photoreceptors interact with, and induce rapid phosphorylation and consequent ubiquitin-mediated degradation of, transcription factors, called PIFs, thereby regulating target gene expression and plant development. Nevertheless, the biochemical mechanism of phy-induced PIF phosphorylation has remained ill-defined. Here we identify a family of nuclear protein kinases, designated Photoregulatory Protein Kinases (PPK1-4; formerly called MUT9-Like Kinases (MLKs)), that interact with PIF3 and phyB in a light-induced manner in vivo. Genetic analyses demonstrate that the PPKs are collectively necessary for the normal light-induced phosphorylation and degradation of PIF3. PPK1 directly phosphorylates PIF3 in vitro, with a phosphosite pattern that strongly mimics the light-induced pattern in vivo. These data establish that the PPKs are directly involved in catalysing the photoactivated-phy-induced phosphorylation of PIF3 in vivo, and thereby are critical components of a transcriptionally centred signalling hub that pleiotropically regulates plant growth and development in response to multiple signalling pathways.

In-depth analyses of native N-linked glycans facilitated by high-performance anion exchange chromatography-pulsed amperometric detection coupled to mass spectrometry.

Characterization of glycans present on glycoproteins has become of increasing importance due to their biological implications, such as protein folding, immunogenicity, cell-cell adhesion, clearance, receptor interactions, etc. In this study, the resolving power of high-performance anion exchange chromatography with pulsed amperometric detection (HPAE-PAD) was applied to glycan separations and coupled to mass spectrometry to characterize native glycans released from different glycoproteins. A new, rapid workflow generates glycans from 200 µg of glycoprotein supporting reliable and reproducible annotation by mass spectrometry (MS). With the relatively high flow rate of HPAE-PAD, post-column splitting diverted 60% of the flow to a novel desalter, then to the mass spectrometer. The delay between PAD and MS detectors is consistent, and salt removal after the column supports MS. HPAE resolves sialylated (charged) glycans and their linkage and positional isomers very well; separations of neutral glycans are sufficient for highly reproducible glycoprofiling. Data-dependent MS2 in negative mode provides highly informative, mostly C- and Z-type glycosidic and cross-ring fragments, making software-assisted and manual annotation reliable. Fractionation of glycans followed by exoglycosidase digestion confirms MS-based annotations. Combining the isomer resolution of HPAE with MS2 permitted thorough N-glycan annotation and led to characterization of 17 new structures from glycoproteins with challenging glycan profiles.

Proteomic analysis reveals O-GlcNAc modification on proteins with key regulatory functions in Arabidopsis.

Genetic studies have shown essential functions of O-linked N-acetylglucosamine (O-GlcNAc) modification in plants. However, the proteins and sites subject to this posttranslational modification are largely unknown. Here, we report a large-scale proteomic identification of O-GlcNAc-modified proteins and sites in the model plant Arabidopsis thaliana Using lectin weak affinity chromatography to enrich modified peptides, followed by mass spectrometry, we identified 971 O-GlcNAc-modified peptides belonging to 262 proteins. The modified proteins are involved in cellular regulatory processes, including transcription, translation, epigenetic gene regulation, and signal transduction. Many proteins have functions in developmental and physiological processes specific to plants, such as hormone responses and flower development. Mass spectrometric analysis of phosphopeptides from the same samples showed that a large number of peptides could be modified by either O-GlcNAcylation or phosphorylation, but cooccurrence of the two modifications in the same peptide molecule was rare. Our study generates a snapshot of the O-GlcNAc modification landscape in plants, indicating functions in many cellular regulation pathways and providing a powerful resource for further dissecting these functions at the molecular level.

Building ProteomeTools based on a complete synthetic human proteome.

We describe ProteomeTools, a project building molecular and digital tools from the human proteome to facilitate biomedical research. Here we report the generation and multimodal liquid chromatography-tandem mass spectrometry analysis of >330,000 synthetic tryptic peptides representing essentially all canonical human gene products, and we exemplify the utility of these data in several applications. The resource (available at http://www.proteometools.org) will be extended to >1 million peptides, and all data will be shared with the community via ProteomicsDB and ProteomeXchange.

Metabolomic profiling of anionic metabolites in head and neck cancer cells by capillary ion chromatography with Orbitrap mass spectrometry.

A highly sensitive platform coupling capillary ion chromatography (Cap IC) with Q Exactive mass spectrometer has been developed for metabolic profiling of head and neck squamous cell carcinoma (HNSCC) cells. The Cap IC allowed an excellent separation of anionic polar metabolites, and the sensitivities increased by up to 100-fold compared to reversed-phase liquid chromatography and hydrophilic interaction chromatography performed at either high- or capillary-flow rates. The detection limits for a panel of standard metabolites were between 0.04 to 0.5 nmol/L (0.2 to 3.4 fmol) at a signal-to-noise ratio of 3. This platform was applied to an untargeted metabolomic analysis of head and neck cancer cells and stem-like cancer cells. Differential metabolomics analysis identified significant changes in energy metabolism pathways (e.g., glycolysis and tricarboxylic acid cycle). These experiments demonstrate Cap IC/MS as a powerful metabolomics tool by providing enhanced separation and sensitivity of polar metabolites combined with high resolution and accurate mass measurement (HR/AM) capabilities to differentiate isobaric metabolites.

The chromosome-centric human proteome project: a call to action.

The grand vision of the human proteome project (HPP) is moving closer to reality with the recent announcement by HUPO of the creation of the HPP consortium in charge of the development of a two-part HPP, one focused on the description of proteomes of biological samples or related to diseases (B/D-HPP) and the other dedicated to a systematic description of proteins as gene products encoded in the human genome (the C-HPP). This new initiative of HUPO seeks to identify and characterize at least one representative protein from every gene, create a protein distribution atlas and a protein pathway or network map. This vision for proteomics can be the roadmap of biological and clinical research for years to come if it delivers on its promises. The Industrial Advisory Board (IAB) to HUPO shares the visions of C-HPP. The IAB will support and critically accompany the overall project goals and the definitions of the critical milestones. The member companies are in a unique position to develop hardware and software, reagents and standards, procedures, and workflows to ensure a reliable source of tools available to the proteomics community worldwide. In collaboration with academia, the IAB member companies can and must develop the tools to reach the ambitious project goals. We offer to partner with and challenge the academic groups leading the C-HPP to define both ambitious and obtainable goals and milestones to make the C-HPP a real and trusted resource for future biology.