Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification.

Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types for proteogenomic-based discovery of neoantigens. By using an optimized computational approach, we discover a large number of tumor-specific and tumor-associated antigens. To create a pipeline for the identification of neoantigens in our cohort, we combine DNA and RNA sequencing with MS-based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity and an in-depth validation process. We detect a broad variety of non-canonical HLA-binding peptides in the majority of patients demonstrating partially immunogenicity. Our validation process allows for the selection of 32 potential neoantigen candidates. The majority of neoantigen candidates originates from variants identified in the RNA data set, illustrating the relevance of RNA as a still understudied source of cancer antigens. This study underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates.

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.

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.

Chemical Phosphoproteomics Sheds New Light on the Targets and Modes of Action of AKT Inhibitors.

Due to its important roles in oncogenic signaling, AKT has been subjected to extensive drug discovery efforts leading to small molecule inhibitors investigated in advanced clinical trials. To better understand how these drugs exert their therapeutic effects at the molecular level, we combined chemoproteomic target affinity profiling using kinobeads and phosphoproteomics to analyze the five clinical AKT inhibitors AZD5363 (Capivasertib), GSK2110183 (Afuresertib), GSK690693, Ipatasertib, and MK-2206 in BT-474 breast cancer cells. Kinobead profiling identified between four and 29 nM targets for these compounds and showed that AKT1 and AKT2 were the only common targets. Similarly, measuring the response of the phosphoproteome to the same inhibitors identified ∼1700 regulated phosphorylation sites, 276 of which were perturbed by all five compounds. This analysis expanded the known AKT signaling network by 119 phosphoproteins that may represent direct or indirect targets of AKT. Within this new network, 41 regulated phosphorylation sites harbor the AKT substrate motif, and recombinant kinase assays validated 16 as novel AKT substrates. These included CEP170 and FAM83H, suggesting a regulatory function of AKT in mitosis and cytoskeleton organization. In addition, a specific phosphorylation pattern on the ULK1-FIP200-ATG13-VAPB complex was found to determine the active state of ULK1, leading to elevated autophagy in response to AKT inhibition.

Mass-spectrometry-based draft of the Arabidopsis proteome.

Plants are essential for life and are extremely diverse organisms with unique molecular capabilities1. Here we present a quantitative atlas of the transcriptomes, proteomes and phosphoproteomes of 30 tissues of the model plant Arabidopsis thaliana. Our analysis provides initial answers to how many genes exist as proteins (more than 18,000), where they are expressed, in which approximate quantities (a dynamic range of more than six orders of magnitude) and to what extent they are phosphorylated (over 43,000 sites). We present examples of how the data may be used, such as to discover proteins that are translated from short open-reading frames, to uncover sequence motifs that are involved in the regulation of protein production, and to identify tissue-specific protein complexes or phosphorylation-mediated signalling events. Interactive access to this resource for the plant community is provided by the ProteomicsDB and ATHENA databases, which include powerful bioinformatics tools to explore and characterize Arabidopsis proteins, their modifications and interactions.

Discovery of suppressors of CRMP2 phosphorylation reveals compounds that mimic the behavioral effects of lithium on amphetamine-induced hyperlocomotion.

The effective treatment of bipolar disorder (BD) represents a significant unmet medical need. Although lithium remains a mainstay of treatment for BD, limited knowledge regarding how it modulates affective behavior has proven an obstacle to discovering more effective mood stabilizers with fewer adverse side effects. One potential mechanism of action of lithium is through inhibition of the serine/threonine protein kinase GSK3ß, however, relevant substrates whose change in phosphorylation may mediate downstream changes in neuroplasticity remain poorly understood. Here, we used human induced pluripotent stem cell (hiPSC)-derived neuronal cells and stable isotope labeling by amino acids in cell culture (SILAC) along with quantitative mass spectrometry to identify global changes in the phosphoproteome upon inhibition of GSK3α/ß with the highly selective, ATP-competitive inhibitor CHIR-99021. Comparison of phosphorylation changes to those induced by therapeutically relevant doses of lithium treatment led to the identification of collapsin response mediator protein 2 (CRMP2) as being highly sensitive to both treatments as well as an extended panel of structurally distinct GSK3α/ß inhibitors. On this basis, a high-content image-based assay in hiPSC-derived neurons was developed to screen diverse compounds, including FDA-approved drugs, for their ability to mimic lithium's suppression of CRMP2 phosphorylation without directly inhibiting GSK3ß kinase activity. Systemic administration of a subset of these CRMP2-phosphorylation suppressors were found to mimic lithium's attenuation of amphetamine-induced hyperlocomotion in mice. Taken together, these studies not only provide insights into the neural substrates regulated by lithium, but also provide novel human neuronal assays for supporting the development of mechanism-based therapeutics for BD and related neuropsychiatric disorders.

Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC-MS/MS.

Nano-flow liquid chromatography tandem mass spectrometry (nano-flow LC-MS/MS) is the mainstay in proteome research because of its excellent sensitivity but often comes at the expense of robustness. Here we show that micro-flow LC-MS/MS using a 1 × 150 mm column shows excellent reproducibility of chromatographic retention time (<0.3% coefficient of variation, CV) and protein quantification (<7.5% CV) using data from >2000 samples of human cell lines, tissues and body fluids. Deep proteome analysis identifies >9000 proteins and >120,000 peptides in 16 h and sample multiplexing using tandem mass tags increases throughput to 11 proteomes in 16 h. The system identifies >30,000 phosphopeptides in 12 h and protein-protein or protein-drug interaction experiments can be analyzed in 20 min per sample. We show that the same column can be used to analyze >7500 samples without apparent loss of performance. This study demonstrates that micro-flow LC-MS/MS is suitable for a broad range of proteomic applications.

Mining the Human Tissue Proteome for Protein Citrullination.

Citrullination is a posttranslational modification of arginine catalyzed by five peptidylarginine deiminases (PADs) in humans. The loss of a positive charge may cause structural or functional alterations, and while the modification has been linked to several diseases, including rheumatoid arthritis (RA) and cancer, its physiological or pathophysiological roles remain largely unclear. In part, this is owing to limitations in available methodology to robustly enrich, detect, and localize the modification. As a result, only a few citrullination sites have been identified on human proteins with high confidence. In this study, we mined data from mass-spectrometry-based deep proteomic profiling of 30 human tissues to identify citrullination sites on endogenous proteins. Database searching of ∼70 million tandem mass spectra yielded ∼13,000 candidate spectra, which were further triaged by spectrum quality metrics and the detection of the specific neutral loss of isocyanic acid from citrullinated peptides to reduce false positives. Because citrullination is easily confused with deamidation, we synthetized ∼2,200 citrullinated and 1,300 deamidated peptides to build a library of reference spectra. This led to the validation of 375 citrullination sites on 209 human proteins. Further analysis showed that >80% of the identified modifications sites were new, and for 56% of the proteins, citrullination was detected for the first time. Sequence motif analysis revealed a strong preference for Asp and Gly, residues around the citrullination site. Interestingly, while the modification was detected in 26 human tissues with the highest levels found in the brain and lung, citrullination levels did not correlate well with protein expression of the PAD enzymes. Even though the current work represents the largest survey of protein citrullination to date, the modification was mostly detected on high abundant proteins, arguing that the development of specific enrichment methods would be required in order to study the full extent of cellular protein citrullination.

High pH Reversed-Phase Micro-Columns for Simple, Sensitive, and Efficient Fractionation of Proteome and (TMT labeled) Phosphoproteome Digests.

Despite recent advances in mass spectrometric sequencing speed and improved sensitivity, the in-depth analysis of proteomes still widely relies on off-line peptide separation and fractionation to deal with the enormous molecular complexity of shotgun digested proteomes. While a multitude of methods has been established for off-line peptide separation using HPLC columns, their use can be limited particularly when sample quantities are scarce. In this protocol, we describe an approach which combines high pH reversed-phase peptide separation into few fractions in StageTip micro-columns. This miniaturized sample preparation method enhances peptide recovery and hence improves sensitivity. This is particularly useful when working with limited sample amounts obtained from e.g., phosphopeptide enrichments or tissue biopsies. Essentially the same approach can also be applied for multiplexed analysis using tandem mass tags (TMT) and can be parallelized in order to deliver the required throughput. Here, we provide a step-by-step protocol for TMT6plex labeling of peptides, the construction of StageTips, sample fractionation and pooling schemes adjusted to different types of analytes, mass spectrometric sample measurement, and downstream data processing using MaxQuant. To illustrate the expected results using this protocol, we provide results from an unlabeled and a TMT6plex labeled phosphopeptide sample leading to the identification of >17,000 phosphopeptides in 8 h (Q Exactive HF) and >23,000 TMT6plex labeled phosphopeptides (Q Exactive Plus) in 12 h of measurement time. Importantly, this protocol is equally applicable to the fractionation of full proteome digests.

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.