PDV: an integrative proteomics data viewer

Chemo-phosphoproteomic profiling with ATR inhibitors berzosertib and gartisertib uncovers new biomarkers and DNA damage response regulators

The ATR kinase protects cells against DNA damage and replication stress and represents a promising anti-cancer drug target. The ATR inhibitors (ATRi) berzosertib and gartisertib are both in clinical trials for the treatment of advanced solid tumours as monotherapy or in combination with genotoxic agents. We carried out quantitative phospho-proteomic screening for ATR biomarkers that are highly sensitive to berzosertib and gartisertib, using an optimized mass spectrometry pipeline. Screening identified a range of novel ATR-dependent phosphorylation events, which were grouped into three broad classes: i) targets whose phosphorylation is highly sensitive to ATRi and which could be the next generation of ATR biomarkers; ii) proteins with known genome maintenance roles not previously known to be regulated by ATR; iii) novel targets whose cellular roles are unclear. Class iii targets represent candidate DNA damage response proteins and, with this in mind, proteins in this class were subjected to secondary screening for recruitment to DNA damage sites. We show that one of the proteins recruited, SCAF1, interacts with RNAPII in a phospho-dependent manner and recruitment requires PARP activity and interaction with RNAPII. We also show that SCAF1 deficiency partly rescues RAD51 loading in cells lacking the BRCA1 tumour suppressor. Taken together these data reveal potential new ATR biomarkers and new genome maintenance factors.

diaTracer enables spectrum-centric analysis of diaPASEF proteomics data

Data-independent acquisition (DIA) has become a widely used strategy for peptide and protein quantification in mass spectrometry-based proteomics studies. The integration of ion mobility separation into DIA analysis, such as the diaPASEF technology available on Bruker's timsTOF platform, further improves the quantification accuracy and protein depth achievable using DIA. We introduce diaTracer, a new spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (m/z, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-MS/MS" spectra, facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from cerebrospinal fluid (CSF) and plasma samples, data from phosphoproteomics and HLA immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass offset searches.

O-linked glycosylations in human milk casein and major whey proteins during lactation

As glycosylations are difficult to analyze, their roles and effects are poorly understood. Glycosylations in human milk (HM) differ across lactation. Glycosylations can be involved in antimicrobial activities and may serve as food for beneficial microorganisms. This study aimed to identify and analyze O-linked glycans in HM by high-throughput mass spectrometry. 184 longitudinal HM samples from 66 donors from day 3 and months 1, 2, and 3 postpartum were subjected to a post-translational modification specific enrichment-based strategy using TiO2 and ZrO2 beads for O-linked glycopeptide enrichment. β-CN was found to be a major O-linked glycoprotein, additionally, αS1-CN, κ-CN, lactotransferrin, and albumin also contained O-linked glycans. As glycosyltransferases and glycosidases are involved in assembling the glycans including O-linked glycosylations, these were further investigated. Some glycosyltransferases and glycosidases were found to be significantly decreasing through lactation, including two O-linked glycan initiator enzymes (GLNT1 and GLNT2). Despite their decrease, the overall level of O-linked glycans remained stable in HM over lactation. Three different motifs for O-linked glycosylation were enriched in HM proteins: Gly-Xxx-Xxx-Gly-Ser/Thr, Arg-Ser/Thr and Lys-Ser/Thr. Further O-linked glycan motifs on β-CN were observed to differ between intact proteins and endogenous peptides in HM.

Analysis of Tandem Mass Spectrometry Data with CONGA: Combining Open and Narrow Searches with Group-Wise Analysis

Searching for tandem mass spectrometry proteomics data against a database is a well-established method for assigning peptide sequences to observed spectra but typically cannot identify peptides harboring unexpected post-translational modifications (PTMs). Open modification searching aims to address this problem by allowing a spectrum to match a peptide even if the spectrum's precursor mass differs from the peptide mass. However, expanding the search space in this way can lead to a loss of statistical power to detect peptides. We therefore developed a method, called CONGA (combining open and narrow searches with group-wise analysis), that takes into account results from both types of searches─a traditional "narrow window" search and an open modification search─while carrying out rigorous false discovery rate control. The result is an algorithm that provides the best of both worlds: the ability to detect unexpected PTMs without a concomitant loss of power to detect unmodified peptides.

Functionalizing tandem mass tags for streamlining click-based quantitative chemoproteomics

Mapping the ligandability or potential druggability of all proteins in the human proteome is a central goal of mass spectrometry-based covalent chemoproteomics. Achieving this ambitious objective requires high throughput and high coverage sample preparation and liquid chromatography-tandem mass spectrometry analysis for hundreds to thousands of reactive compounds and chemical probes. Conducting chemoproteomic screens at this scale benefits from technical innovations that achieve increased sample throughput. Here we realize this vision by establishing the silane-based cleavable linkers for isotopically-labeled proteomics-tandem mass tag (sCIP-TMT) proteomic platform, which is distinguished by early sample pooling that increases sample preparation throughput. sCIP-TMT pairs a custom click-compatible sCIP capture reagent that is readily functionalized in high yield with commercially available TMT reagents. Synthesis and benchmarking of a 10-plex set of sCIP-TMT reveal a substantial decrease in sample preparation time together with high coverage and high accuracy quantification. By screening a focused set of four cysteine-reactive electrophiles, we demonstrate the utility of sCIP-TMT for chemoproteomic target hunting, identifying 789 total liganded cysteines. Distinguished by its compatibility with established enrichment and quantification protocols, we expect sCIP-TMT will readily translate to a wide range of covalent chemoproteomic applications.

SpectiCal: m/z Calibration of MS2 Peptide Spectra Using Known Low Mass Ions

Most tandem mass spectrometry fragmentation spectra have small calibration errors that can lead to suboptimal interpretation and annotation. We developed SpectiCal, a software tool that can read mzML files from data-dependent acquisition proteomics experiments in parallel, compute m/z calibrations for each file prior to identification analysis based on known low-mass ions, and produce information about frequently observed peaks and their explanations. Using calibration coefficients, the data can be corrected to generate new calibrated mzML files. SpectiCal was tested using five public data sets, creating a table of commonly observed low-mass ions and their identifications. Information about the calibration and individual peaks is written in PDF and TSV files. This includes information for each peak, such as the number of runs in which it appears, the percentage of spectra in which it appears, and a plot of the aggregated region surrounding each peak. SpectiCal can be used to compute MS run calibrations, examine MS runs for artifacts that might hinder downstream analysis, and generate tables of detected low-mass ions for further analysis. SpectiCal is freely available at https://github.com/PlantProteomes/SpectiCal.

Immunopeptidomics-based identification of naturally presented non-canonical circRNA-derived peptides

Circular RNAs (circRNAs) are covalently closed non-coding RNAs lacking the 5' cap and the poly-A tail. Nevertheless, it has been demonstrated that certain circRNAs can undergo active translation. Therefore, aberrantly expressed circRNAs in human cancers could be an unexplored source of tumor-specific antigens, potentially mediating anti-tumor T cell responses. This study presents an immunopeptidomics workflow with a specific focus on generating a circRNA-specific protein fasta reference. The main goal of this workflow is to streamline the process of identifying and validating human leukocyte antigen (HLA) bound peptides potentially originating from circRNAs. We increase the analytical stringency of our workflow by retaining peptides identified independently by two mass spectrometry search engines and/or by applying a group-specific FDR for canonical-derived and circRNA-derived peptides. A subset of circRNA-derived peptides specifically encoded by the region spanning the back-splice junction (BSJ) are validated with targeted MS, and with direct Sanger sequencing of the respective source transcripts. Our workflow identifies 54 unique BSJ-spanning circRNA-derived peptides in the immunopeptidome of melanoma and lung cancer samples. Our approach enlarges the catalog of source proteins that can be explored for immunotherapy.

Deep Learning Prediction Boosts Phosphoproteomics-Based Discoveries Through Improved Phosphopeptide Identification

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples. One of the primary challenges associated with this technology is the relatively low rate of phosphopeptide identification during data analysis. This limitation hampers the full realization of the potential offered by shotgun phosphoproteomics. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19% to 46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.

Identification of proteoforms of short open reading frame-encoded peptides in Blautia producta under different cultivation conditions

IMPORTANCE

The identification of short open reading frame-encoded peptides (SEP) and different proteoforms in single cultures of gut microbes offers new insights into a largely neglected part of the microbial proteome landscape. This is of particular importance as SEP provide various predicted functions, such as acting as antimicrobial peptides, maintaining cell homeostasis under stress conditions, or even contributing to the virulence pattern. They are, thus, taking a poorly understood role in structure and function of microbial networks in the human body. A better understanding of SEP in the context of human health requires a precise understanding of the abundance of SEP both in commensal microbes as well as pathogens. For the gut beneficial B. producta, we demonstrate the importance of specific environmental conditions for biosynthesis of SEP expanding previous findings about their role in microbial interactions.

Solid-Phase Compatible Silane-Based Cleavable Linker Enables Custom Isobaric Quantitative Chemoproteomics

Mass spectrometry-based chemoproteomics has emerged as an enabling technology for functional biology and drug discovery. To address limitations of established chemoproteomics workflows, including cumbersome reagent synthesis and low throughput sample preparation, here, we established the silane-based cleavable isotopically labeled proteomics (sCIP) method. The sCIP method is enabled by a high yielding and scalable route to dialkoxydiphenylsilane fluorenylmethyloxycarbonyl (DADPS-Fmoc)-protected amino acid building blocks, which enable the facile synthesis of customizable, isotopically labeled, and chemically cleavable biotin capture reagents. sCIP is compatible with both MS1- and MS2-based quantitation, and the sCIP-MS2 method is distinguished by its click-assembled isobaric tags in which the reporter group is encoded in the sCIP capture reagent and balancer in the pan cysteine-reactive probe. The sCIP-MS2 workflow streamlines sample preparation with early stage isobaric labeling and sample pooling, allowing for high coverage and increased sample throughput via customized low cost six-plex sample multiplexing. When paired with a custom FragPipe data analysis workflow and applied to cysteine-reactive fragment screens, sCIP proteomics revealed established and unprecedented cysteine-ligand pairs, including the discovery that mitochondrial uncoupling agent FCCP acts as a covalent-reversible cysteine-reactive electrophile.

The Breast Cancer Proteome and Precision Oncology

The goal of precision oncology is to translate the molecular features of cancer into predictive and prognostic tests that can be used to individualize treatment leading to improved outcomes and decreased toxicity. Success for this strategy in breast cancer is exemplified by efficacy of trastuzumab in tumors overexpressing ERBB2 and endocrine therapy for tumors that are estrogen receptor positive. However, other effective treatments, including chemotherapy, immune checkpoint inhibitors, and CDK4/6 inhibitors are not associated with strong predictive biomarkers. Proteomics promises another tier of information that, when added to genomic and transcriptomic features (proteogenomics), may create new opportunities to improve both treatment precision and therapeutic hypotheses. Here, we review both mass spectrometry-based and antibody-dependent proteomics as complementary approaches. We highlight how these methods have contributed toward a more complete understanding of breast cancer and describe the potential to guide diagnosis and treatment more accurately.

Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery

Identification of tumor antigens presented by the human leucocyte antigen (HLA) molecules is essential for the design of effective and safe cancer immunotherapies that rely on T cell recognition and killing of tumor cells. Mass spectrometry (MS)-based immunopeptidomics enables high-throughput, direct identification of HLA-bound peptides from a variety of cell lines, tumor tissues, and healthy tissues. It involves immunoaffinity purification of HLA complexes followed by MS profiling of the extracted peptides using data-dependent acquisition, data-independent acquisition, or targeted approaches. By incorporating DNA, RNA, and ribosome sequencing data into immunopeptidomics data analysis, the proteogenomic approach provides a powerful means for identifying tumor antigens encoded within the canonical open reading frames of annotated coding genes and non-canonical tumor antigens derived from presumably non-coding regions of our genome. We discuss emerging computational challenges in immunopeptidomics data analysis and tumor antigen identification, highlighting key considerations in the proteogenomics-based approach, including accurate DNA, RNA and ribosomal sequencing data analysis, careful incorporation of predicted novel protein sequences into reference protein database, special quality control in MS data analysis due to the expanded and heterogeneous search space, cancer-specificity determination, and immunogenicity prediction. The advancements in technology and computation is continually enabling us to identify tumor antigens with higher sensitivity and accuracy, paving the way toward the development of more effective cancer immunotherapies.

Proteogenomic data and resources for pan-cancer analysis

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

MSBooster: improving peptide identification rates using deep learning-based features

Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.

Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

No Substrate Left behind-Mining of Shotgun Proteomics Datasets Rescues Evidence of Proteolysis by SARS-CoV-2 3CLpro Main Protease

Proteolytic processing is the most ubiquitous post-translational modification and regulator of protein function. To identify protease substrates, and hence the function of proteases, terminomics workflows have been developed to enrich and detect proteolytically generated protein termini from mass spectrometry data. The mining of shotgun proteomics datasets for such 'neo'-termini, to increase the understanding of proteolytic processing, is an underutilized opportunity. However, to date, this approach has been hindered by the lack of software with sufficient speed to make searching for the relatively low numbers of protease-generated semi-tryptic peptides present in non-enriched samples viable. We reanalyzed published shotgun proteomics datasets for evidence of proteolytic processing in COVID-19 using the recently upgraded MSFragger/FragPipe software, which searches data with a speed that is an order of magnitude greater than many equivalent tools. The number of protein termini identified was higher than expected and constituted around half the number of termini detected by two different N-terminomics methods. We identified neo-N- and C-termini generated during SARS-CoV-2 infection that were indicative of proteolysis and were mediated by both viral and host proteases-a number of which had been recently validated by in vitro assays. Thus, re-analyzing existing shotgun proteomics data is a valuable adjunct for terminomics research that can be readily tapped (for example, in the next pandemic where data would be scarce) to increase the understanding of protease function and virus-host interactions, or other diverse biological processes.

MSFragger-Labile: A Flexible Method to Improve Labile PTM Analysis in Proteomics

Posttranslational modifications of proteins play essential roles in defining and regulating the functions of the proteins they decorate, making identification of these modifications critical to understanding biology and disease. Methods for enriching and analyzing a wide variety of biological and chemical modifications of proteins have been developed using mass spectrometry-based proteomics, largely relying on traditional database search methods to identify the resulting mass spectra of modified peptides. These database search methods treat modifications as static attachments of a mass to particular position in the peptide sequence, but many modifications undergo fragmentation in tandem mass spectrometry experiments alongside, or instead of, the peptide backbone. While this fragmentation can confound traditional search methods, it also offers unique opportunities for improved searches that incorporate modification-specific fragment ions. Here, we present a new labile mode in the MSFragger search engine that provides the flexibility to tailor modification-centric searches to the fragmentation observed. We show that labile mode can dramatically improve spectrum identification rates of phosphopeptides, RNA-crosslinked peptides, and ADP-ribosylated peptides. Each of these modifications presents distinct fragmentation characteristics, showcasing the flexibility of MSFragger labile mode to improve search for a wide variety of biological and chemical modifications.

PepQuery2 democratizes public MS proteomics data for rapid peptide searching

We present PepQuery2, which leverages a new tandem mass spectrometry (MS/MS) data indexing approach to enable ultrafast, targeted identification of novel and known peptides in any local or publicly available MS proteomics datasets. The stand-alone version of PepQuery2 allows directly searching more than one billion indexed MS/MS spectra in the PepQueryDB or any public datasets from PRIDE, MassIVE, iProX, or jPOSTrepo, whereas the web version enables users to search datasets in PepQueryDB with a user-friendly interface. We demonstrate the utilities of PepQuery2 in a wide range of applications including detecting proteomic evidence for genomically predicted novel peptides, validating novel and known peptides identified using spectrum-centric database searching, prioritizing tumor-specific antigens, identifying missing proteins, and selecting proteotypic peptides for targeted proteomics experiments. By putting public MS proteomics data directly into the hands of scientists, PepQuery2 opens many new ways to transform these data into useful information for the broad research community.

A large-scale proteogenomic atlas of pear

Pear is an important fruit tree that is widely distributed around the world. The first pear genome map was reported from our laboratory approximately 10 years ago. To further study global protein expression patterns in pear, we generated pear proteome data based on 24 major tissues. The tissue-resolved profiles provided evidence of the expression of 17 953 proteins. We identified 4294 new coding events and improved the pear genome annotation via the proteogenomic strategy based on 18 090 peptide spectra with peptide spectrum matches >1. Among the eight randomly selected new short coding open reading frames that were expressed in the style, four promoted and one inhibited the growth of pear pollen tubes. Based on gene coexpression module analysis, we explored the key genes associated with important agronomic traits, such as stone cell formation in fruits. The network regulating the synthesis of lignin, a major component of stone cells, was reconstructed, and receptor-like kinases were implicated as core factors in this regulatory network. Moreover, we constructed the online database PearEXP (http://www.peardb.org.cn) to enable access to the pear proteogenomic resources. This study provides a paradigm for in-depth proteogenomic studies of woody plants.

Post-translational modifications reshape the antigenic landscape of the MHC I immunopeptidome in tumors

Post-translational modification (PTM) of antigens provides an additional source of specificities targeted by immune responses to tumors or pathogens, but identifying antigen PTMs and assessing their role in shaping the immunopeptidome is challenging. Here we describe the Protein Modification Integrated Search Engine (PROMISE), an antigen discovery pipeline that enables the analysis of 29 different PTM combinations from multiple clinical cohorts and cell lines. We expanded the antigen landscape, uncovering human leukocyte antigen class I binding motifs defined by specific PTMs with haplotype-specific binding preferences and revealing disease-specific modified targets, including thousands of new cancer-specific antigens that can be shared between patients and across cancer types. Furthermore, we uncovered a subset of modified peptides that are specific to cancer tissue and driven by post-translational changes that occurred in the tumor proteome. Our findings highlight principles of PTM-driven antigenicity, which may have broad implications for T cell-mediated therapies in cancer and beyond.

Deep learning prediction boosts phosphoproteomics-based discoveries through improved phosphopeptide identification

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples, but low phosphopeptide identification rate in data analysis limits the potential of this technology. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19%-46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.

Finding haplotypic signatures in proteins

BACKGROUND

The nonrandom distribution of alleles of common genomic variants produces haplotypes, which are fundamental in medical and population genetic studies. Consequently, protein-coding genes with different co-occurring sets of alleles can encode different amino acid sequences: protein haplotypes. These protein haplotypes are present in biological samples and detectable by mass spectrometry, but they are not accounted for in proteomic searches. Consequently, the impact of haplotypic variation on the results of proteomic searches and the discoverability of peptides specific to haplotypes remain unknown.

FINDINGS

Here, we study how common genetic haplotypes influence the proteomic search space and investigate the possibility to match peptides containing multiple amino acid substitutions to a publicly available data set of mass spectra. We found that for 12.42% of the discoverable amino acid substitutions encoded by common haplotypes, 2 or more substitutions may co-occur in the same peptide after tryptic digestion of the protein haplotypes. We identified 352 spectra that matched to such multivariant peptides, and out of the 4,582 amino acid substitutions identified, 6.37% were covered by multivariant peptides. However, the evaluation of the reliability of these matches remains challenging, suggesting that refined error rate estimation procedures are needed for such complex proteomic searches.

CONCLUSIONS

As these procedures become available and the ability to analyze protein haplotypes increases, we anticipate that proteomics will provide new information on the consequences of common variation, across tissues and time.

Proteomic Profiling of Intra-Islet Features Reveals Substructure-Specific Protein Signatures

Despite their diminutive size, islets of Langerhans play a large role in maintaining systemic energy balance in the body. New technologies have enabled us to go from studying the whole pancreas to isolated whole islets, to partial islet sections, and now to islet substructures isolated from within the islet. Using a microfluidic nanodroplet-based proteomics platform coupled with laser capture microdissection and field asymmetric waveform ion mobility spectrometry, we present an in-depth investigation of protein profiles specific to features within the islet. These features include the islet-acinar interface vascular tissue, inner islet vasculature, isolated endocrine cells, whole islet with vasculature, and acinar tissue from around the islet. Compared to interface vasculature, unique protein signatures observed in the inner vasculature indicate increased innervation and intra-islet neuron-like crosstalk. We also demonstrate the utility of these data for identifying localized structure-specific drug-target interactions using existing protein/drug binding databases.

Enhancing PD-L1 Degradation by ITCH during MAPK Inhibitor Therapy Suppresses Acquired Resistance

MAPK inhibitor (MAPKi) therapy in melanoma leads to the accumulation of tumor-surface PD-L1/L2, which may evade antitumor immunity and accelerate acquired resistance. Here, we discover that the E3 ligase ITCH binds, ubiquitinates, and downregulates tumor-surface PD-L1/L2 in MAPKi-treated human melanoma cells, thereby promoting T-cell activation. During MAPKi therapy in vivo, melanoma cell-intrinsic ITCH knockdown induced tumor-surface PD-L1, reduced intratumoral cytolytic CD8+ T cells, and accelerated acquired resistance only in immune-competent mice. Conversely, tumor cell-intrinsic ITCH overexpression reduced MAPKi-elicited PD-L1 accumulation, augmented intratumoral cytolytic CD8+ T cells, and suppressed acquired resistance in BrafV600MUT, NrasMUT, or Nf1MUT melanoma and KrasMUT-driven cancers. CD8+ T-cell depletion and tumor cell-intrinsic PD-L1 overexpression nullified the phenotype of ITCH overexpression, thereby supporting an in vivo ITCH-PD-L1-T-cell regulatory axis. Moreover, we identify a small-molecular ITCH activator that suppresses acquired MAPKi resistance in vivo. Thus, MAPKi-induced PD-L1 accelerates resistance, and a PD-L1-degrading ITCH activator prolongs antitumor response.

SIGNIFICANCE

MAPKi induces tumor cell-surface PD-L1 accumulation, which promotes immune evasion and therapy resistance. ITCH degrades PD-L1, optimizing antitumor T-cell immunity. We propose degrading tumor cell-surface PD-L1 and/or activating tumor-intrinsic ITCH as strategies to overcome MAPKi resistance. This article is highlighted in the In This Issue feature, p. 1825.

NPvis: An Interactive Visualizer of Peptidic Natural Product–MS/MS Matches

Peptidic natural products (PNPs) represent a medically important class of secondary metabolites that includes antibiotics, anti-inflammatory and antitumor agents. Advances in tandem mass spectra (MS/MS) acquisition and in silico database search methods have enabled high-throughput PNP discovery. However, the resulting spectra annotations are often error-prone and their validation remains a bottleneck. Here, we present NPvis, a visualizer suitable for the evaluation of PNP–MS/MS matches. The tool interactively maps annotated spectrum peaks to the corresponding PNP fragments and allows researchers to assess the match correctness. NPvis accounts for the wide chemical diversity of PNPs that prevents the use of the existing proteomics visualizers. Moreover, NPvis works even if the exact chemical structure of the matching PNP is unknown. The tool is available online and as a standalone application. We hope that it will benefit the community by streamlining PNP data analysis and validation.

A Novel Proteogenomic Integration Strategy Expands the Breadth of Neo-Epitope Sources

Tumor-specific antigens can activate T cell-based antitumor immune responses and are ideal targets for cancer immunotherapy. However, their identification is still challenging. Although mass spectrometry can directly identify human leukocyte antigen (HLA) binding peptides in tumor cells, it focuses on tumor-specific antigens derived from annotated protein-coding regions constituting only 1.5% of the genome. We developed a novel proteogenomic integration strategy to expand the breadth of tumor-specific epitopes derived from all genomic regions. Using the colorectal cancer cell line HCT116 as a model, we accurately identified 10,737 HLA-presented peptides, 1293 of which were non-canonical peptides that traditional database searches could not identify. Moreover, we found eight tumor neo-epitopes derived from somatic mutations, four of which were not previously reported. Our findings suggest that this new proteogenomic approach holds great promise for increasing the number of tumor-specific antigen candidates, potentially enlarging the tumor target pool and improving cancer immunotherapy.

IntroSpect: Motif-Guided Immunopeptidome Database Building Tool to Improve the Sensitivity of HLA I Binding Peptide Identification by Mass Spectrometry

Although database search tools originally developed for shotgun proteome have been widely used in immunopeptidomic mass spectrometry identifications, they have been reported to achieve undesirably low sensitivities or high false positive rates as a result of the hugely inflated search space caused by the lack of specific enzymic digestions in immunopeptidome. To overcome such a problem, we developed a motif-guided immunopeptidome database building tool named IntroSpect, which is designed to first learn the peptide motifs from high confidence hits in the initial search, and then build a targeted database for refined search. Evaluated on 18 representative HLA class I datasets, IntroSpect can improve the sensitivity by an average of 76%, compared to conventional searches with unspecific digestions, while maintaining a very high level of accuracy (~96%), as confirmed by synthetic validation experiments. A distinct advantage of IntroSpect is that it does not depend on any external HLA data, so that it performs equally well on both well-studied and poorly-studied HLA types, unlike the previously developed method SpectMHC. We have also designed IntroSpect to keep a global FDR that can be conveniently controlled, similar to a conventional database search. Finally, we demonstrate the practical value of IntroSpect by discovering neoepitopes from MS data directly, an important application in cancer immunotherapies. IntroSpect is freely available to download and use.

Quantitative Proteogenomic Characterization of Inflamed Murine Colon Tissue Using an Integrated Discovery, Verification, and Validation Proteogenomic Workflow

Chronic inflammation of the colon causes genomic and/or transcriptomic events, which can lead to expression of non-canonical protein sequences contributing to oncogenesis. To better understand these mechanisms, Rag2^−/−Il10^−/− mice were infected with Helicobacter hepaticus to induce chronic inflammation of the cecum and the colon. Transcriptomic data from harvested proximal colon samples were used to generate a customized FASTA database containing non-canonical protein sequences. Using a proteogenomic approach, mass spectrometry data for proximal colon proteins were searched against this custom FASTA database using the Galaxy for Proteomics (Galaxy-P) platform. In addition to the increased abundance in inflammatory response proteins, we also discovered several non-canonical peptide sequences derived from unique proteoforms. We confirmed the veracity of these novel sequences using an automated bioinformatics verification workflow with targeted MS-based assays for peptide validation. Our bioinformatics discovery workflow identified 235 putative non-canonical peptide sequences, of which 58 were verified with high confidence and 39 were validated in targeted proteomics assays. This study provides insights into challenges faced when identifying non-canonical peptides using a proteogenomics approach and demonstrates an integrated workflow addressing these challenges. Our bioinformatic discovery and verification workflow is publicly available and accessible via the Galaxy platform and should be valuable in non-canonical peptide identification using proteogenomics.

Proteogenomic Analysis of Breast Cancer Transcriptomic and Proteomic Data, Using De Novo Transcript Assembly: Genome-Wide Identification of Novel Peptides and Clinical Implications

We have carried out proteogenomic analysis of the breast cancer transcriptomic and proteomic data, available at The Clinical Proteomic Tumor Analysis Consortium resource, to identify novel peptides arising from alternatively spliced events as well as other noncanonical expressions. We used a pipeline that consisted of de novo transcript assembly, six frame-translated custom database, and a combination of search engines to identify novel peptides. A portfolio of 4,387 novel peptide sequences initially identified was further screened through PepQuery validation tool (Clinical Proteomic Tumor Analysis Consortium), which yielded 1,558 novel peptides. We considered the dataset of 1,558 validated through PepQuery to understand their functional and clinical significance, leaving the rest to be further verified using other validation tools and approaches. The novel peptides mapped to the known gene sequences as well as to genomic regions yet undefined for translation, 580 novel peptides mapped to known protein-coding genes, 147 to non–protein-coding genes, and 831 belonged to novel translational sequences. The novel peptides belonging to protein-coding genes represented alternatively spliced events or 5′ or 3′ extensions, whereas others represented translation from pseudogenes, long noncoding RNAs, or novel peptides originating from uncharacterized protein-coding sequences—mostly from the intronic regions of known genes. Seventy-six of the 580 protein-coding genes were associated with cancer hallmark genes, which included key oncogenes, transcription factors, kinases, and cell surface receptors. Survival association analysis of the 76 novel peptide sequences revealed 10 of them to be significant, and we present a panel of six novel peptides, whose high expression was found to be strongly associated with poor survival of patients with human epidermal growth factor receptor 2–enriched subtype. Our analysis represents a landscape of novel peptides of different types that may be expressed in breast cancer tissues, whereas their presence in full-length functional proteins needs further investigations.

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Novel protein variants and peptides from noncoding sequences are rapidly emerging.

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Mining of mass spectrometry data using proteogenomic analysis reveals such entities.

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Novel peptides from coding and noncoding sequences identified in breast cancer.

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Novel peptides mapped to cancer hallmark genes in breast cancer.

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Panel of novel peptides with prognostic potential found for HER2-enriched subtype.

Enhancing Cysteine Chemoproteomic Coverage through Systematic Assessment of Click Chemistry Product Fragmentation

Mass spectrometry-based chemoproteomics has enabled functional analysis and small molecule screening at thousands of cysteine residues in parallel. Widely adopted chemoproteomic sample preparation workflows rely on the use of pan cysteine-reactive probes such as iodoacetamide alkyne combined with biotinylation via copper-catalyzed azide-alkyne cycloaddition (CuAAC) or "click chemistry" for cysteine capture. Despite considerable advances in both sample preparation and analytical platforms, current techniques only sample a small fraction of all cysteines encoded in the human proteome. Extending the recently introduced labile mode of the MSFragger search engine, here we report an in-depth analysis of cysteine biotinylation via click chemistry (CBCC) reagent gas-phase fragmentation during MS/MS analysis. We find that CBCC conjugates produce both known and novel diagnostic fragments and peptide remainder ions. Among these species, we identified a candidate signature ion for CBCC peptides, the cyclic oxonium-biotin fragment ion that is generated upon fragmentation of the N(triazole)-C(alkyl) bond. Guided by our empirical comparison of fragmentation patterns of six CBCC reagent combinations, we achieved enhanced coverage of cysteine-labeled peptides. Implementation of labile searches afforded unique PSMs and provides a roadmap for the utility of such searches in enhancing chemoproteomic peptide coverage.

Multi-attribute Glycan Identification and FDR Control for Glycoproteomics

Rapidly improving methods for glycoproteomics have enabled increasingly large-scale analyses of complex glycopeptide samples, but annotating the resulting mass spectrometry data with high confidence remains a major bottleneck. We recently introduced a fast and sensitive glycoproteomics search method in our MSFragger search engine, which reports glycopeptides as a combination of a peptide sequence and the mass of the attached glycan. In samples with complex glycosylation patterns, converting this mass to a specific glycan composition is not straightforward; however, as many glycans have similar or identical masses. Here, we have developed a new method for determining the glycan composition of N-linked glycopeptides fragmented by collisional or hybrid activation that uses multiple sources of information from the spectrum, including observed glycan B-type (oxonium) and Y-type ions and mass and precursor monoisotopic selection errors to discriminate between possible glycan candidates. Combined with false discovery rate estimation for the glycan assignment, we show that this method is capable of specifically and sensitively identifying glycans in complex glycopeptide analyses and effectively controls the rate of false glycan assignments. The new method has been incorporated into the PTM-Shepherd modification analysis tool to work directly with the MSFragger glyco search in the FragPipe graphical user interface, providing a complete computational pipeline for annotation of N-glycopeptide spectra with false discovery rate control of both peptide and glycan components that is both sensitive and robust against false identifications.

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Identifying the glycan on intact glycopeptides remains difficult in glycoproteomics.

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We developed a method to assign glycan compositions in N-glycoproteomics searches.

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We demonstrate well-controlled glycan FDR in multiple sample types.

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The method annotates more glycopeptide spectra than competing tools.

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The method is included PTM-Shepherd for a full glycoproteomics workflow in FragPipe.

caAtlas: An immunopeptidome atlas of human cancer

Comprehensive characterization of tumor antigens is essential for the design of cancer immunotherapies, and mass spectrometry (MS)-based immunopeptidomics enables high-throughput identification of major histocompatibility complex (MHC)-bound peptide antigens in vivo. Here we construct an immunopeptidome atlas of human cancer through an extensive collection of 43 published immunopeptidomic datasets and standardized analysis of 81.6 million MS/MS spectra using an open search engine. Our analysis greatly expands the current knowledge of MHC-bound antigens, including an unprecedented characterization of post-translationally modified antigens and their cancer-association. We also perform systematic analysis of cancer-testis antigens, cancer-associated antigens, and neoantigens. We make all these data together with annotated MS/MS spectra supporting identification of each antigen in an easily browsable web portal named cancer antigen atlas (caAtlas). caAtlas provides a central resource for the selection and prioritization of MHC-bound peptides for in vitro HLA binding assay and immunogenicity testing, which will pave the way to eventual development of cancer immunotherapies.

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Extensive collection of 43 immunopeptidomic datasets with 1018 samples

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Standardized and rigorous identification of HLA-bound peptides, including PTM peptides

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Comprehensive annotation of CT antigens and cancer-associated antigens

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User-friendly data dissemination through the caAtlas web portal

Discovery of a small protein-encoding cis-regulatory overlapping gene of the tumor suppressor gene Scribble in humans

Intensive gene annotation has revealed many functional and regulatory elements in the human genome. Although eukaryotic protein-coding genes are generally transcribed into monocistronic mRNAs, recent studies have discovered additional short open reading frames (sORFs) in mRNAs. Here, we performed proteogenomic data mining for hidden proteins categorized into sORF-encoded polypeptides (SEPs) in human cancers. We identified a new SEP-encoding overlapping sORF (oORF) on the cell polarity determinant Scribble (SCRIB) that is considered a proto-oncogene with tumor suppressor function in Hippo-YAP/TAZ, MAPK/ERK, and PI3K/Akt/mTOR signaling. Reanalysis of clinical human proteomic data revealed translational dysregulation of both SCRIB and its oORF, oSCRIB, during carcinogenesis. Biochemical analyses suggested that the translatable oSCRIB constitutively limits the capacity of eukaryotic ribosomes to translate the downstream SCRIB. These findings provide a new example of cis-regulatory oORFs that function as a ribosomal roadblock and potentially serve as a fail-safe mechanism to normal cells for non-excessive downstream gene expression, which is hijacked in cancer.

Yuhta Nomura and Naoshi Dohmae report the discovery of a small protein-coding gene that overlaps the tumor suppressor gene Scribble. Their data suggest that the overlapping gene, oSCRIB, limits the translation of downstream Scribble and may have important implications in cancer.

TRIM25 and DEAD-Box RNA Helicase DDX3X Cooperate to Regulate RIG-I-Mediated Antiviral Immunity

The cytoplasmic retinoic acid-inducible gene-I (RIG-I)-like receptors (RLRs) initiate interferon (IFN) production and antiviral gene expression in response to RNA virus infection. Consequently, RLR signalling is tightly regulated by both host and viral factors. Tripartite motif protein 25 (TRIM25) is an E3 ligase that ubiquitinates multiple substrates within the RLR signalling cascade, playing both ubiquitination-dependent and -independent roles in RIG-I-mediated IFN induction. However, additional regulatory roles are emerging. Here, we show a novel interaction between TRIM25 and another protein in the RLR pathway that is essential for type I IFN induction, DEAD-box helicase 3X (DDX3X). In vitro assays and knockdown studies reveal that TRIM25 ubiquitinates DDX3X at lysine 55 (K55) and that TRIM25 and DDX3X cooperatively enhance IFNB1 induction following RIG-I activation, but the latter is independent of TRIM25's catalytic activity. Furthermore, we found that the influenza A virus non-structural protein 1 (NS1) disrupts the TRIM25:DDX3X interaction, abrogating both TRIM25-mediated ubiquitination of DDX3X and cooperative activation of the IFNB1 promoter. Thus, our results reveal a new interplay between two RLR-host proteins that cooperatively enhance IFN-β production. We also uncover a new and further mechanism by which influenza A virus NS1 suppresses host antiviral defence.

A proteogenomic portrait of lung squamous cell carcinoma

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an alternative driver in FGFR1-amplified tumors and low-p63 tumors overexpressing the therapeutic target survivin. SOX2 is considered undruggable, but our analyses provide rationale for exploring chromatin modifiers such as LSD1 and EZH2 to target SOX2-overexpressing tumors. Our data support complex regulation of metabolic pathways by crosstalk between post-translational modifications including ubiquitylation. Numerous immune-related proteogenomic observations suggest directions for further investigation. Proteogenomic dissection of CDKN2A mutations argue for more nuanced assessment of RB1 protein expression and phosphorylation before declaring CDK4/6 inhibition unsuccessful. Finally, triangulation between LSCC, LUAD, and HNSCC identified both unique and common therapeutic vulnerabilities. These observations and proteogenomics data resources may guide research into the biology and treatment of LSCC.

Development of neoantigens: from identification in cancer cells to application in cancer vaccines

Introduction: The discovery of neoantigens as mutated proteins specifically expressed in tumor cells but not in normal cells has led to improved cancer vaccines. Targeting neoantigens can induce anti-tumor T-cell responses to destroy tumors without damaging healthy cells. Extensive advances in genome sequencing technology and bioinformatics analysis have made it possible to discover and design effective neoantigens for use in therapeutic cancer vaccines. Neoantigens-based therapeutic personalized vaccines have shown promising results in cancer immunotherapy.Areas covered: We discuss the types of cancer neoantigens that can be recognized by the immune system in this review. We also summarize the detection, identification, and design of neoantigens and their appliction in developing cancer vaccines. Finally, clinical trials of neoantigen-based vaccines, their advantages, and their limitations are reviewed. From 2015 to 2020, the authors conducted a literature search of controlled randomized trials and laboratory investigations that that focused on neoantigens, their use in the design of various types of cancer vaccines.Expert opinion: Neoantigens are cancer cell-specific antigens, which their expression leads to the immune stimulation against tumor cells. The identification and delivery of specific neoantigens to antigen-presenting cells (APCs) with the help of anti-cancer vaccines promise novel and more effective cancer treatments.

MS_Piano: A Software Tool for Annotating Peaks in CID Tandem Mass Spectra of Peptides and N-Glycopeptides

Annotating product ion peaks in tandem mass spectra is essential for evaluating spectral quality and validating peptide identification. This task is more complex for glycopeptides and is crucial for the confident determination of glycosylation sites in glycoproteins. MS_Piano (Mass Spectrum Peptide Annotation) software was developed for reliable annotation of peaks in collision induced dissociation (CID) tandem mass spectra of peptides or N-glycopeptides for given peptide sequences, charge states, and optional modifications. The program annotates each peak in high or low resolution spectra with possible product ion(s) and the mass difference between the measured and theoretical m/z values. Spectral quality is measured by two major parameters: the ratio between the sum of unannotated vs all peak intensities in the top 20 peaks, and the intensity of the highest unannotated peak. The product ions of peptides, glycans, and glycopeptides in spectra are labeled in different class-type colors to facilitate interpretation. MS_Piano assists validating peptide and N-glycopeptide identification from database and library searches and provides quality control and optimizes search reliability in custom developed peptide mass spectral libraries. The software is freely available in .exe and .dll formats for the Windows operating system.

TopMSV: A Web-Based Tool for Top-Down Mass Spectrometry Data Visualization

Top-down mass spectrometry (MS) investigates intact proteoforms for proteoform identification, characterization, and quantification. Data visualization plays an essential role in top-down MS data analysis because proteoform identification and characterization often involve manual data inspection to determine the molecular masses of highly charged ions and validate unexpected alterations in identified proteoforms. While many software tools have been developed for MS data visualization, there is still a lack of web-based visualization software designed for top-down MS. Here, we present TopMSV, a web-based tool for top-down MS data processing and visualization. TopMSV provides interactive views of top-down MS data using a web browser. It integrates software tools for spectral deconvolution and proteoform identification and uses analysis results of the tools to annotate top-down MS data.

A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of Coronavirus Disease 2019 (COVID-19)

BACKGROUND

The Coronavirus Disease 2019 (COVID-19) global pandemic has had a profound, lasting impact on the world's population. A key aspect to providing care for those with COVID-19 and checking its further spread is early and accurate diagnosis of infection, which has been generally done via methods for amplifying and detecting viral RNA molecules. Detection and quantitation of peptides using targeted mass spectrometry-based strategies has been proposed as an alternative diagnostic tool due to direct detection of molecular indicators from non-invasively collected samples as well as the potential for high-throughput analysis in a clinical setting; many studies have revealed the presence of viral peptides within easily accessed patient samples. However, evidence suggests that some viral peptides could serve as better indicators of COVID-19 infection status than others, due to potential misidentification of peptides derived from human host proteins, poor spectral quality, high limits of detection etc. METHODS: In this study we have compiled a list of 636 peptides identified from Sudden Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) samples, including from in vitro and clinical sources. These datasets were rigorously analyzed using automated, Galaxy-based workflows containing tools such as PepQuery, BLAST-P, and the Multi-omic Visualization Platform as well as the open-source tools MetaTryp and Proteomics Data Viewer (PDV).

RESULTS

Using PepQuery for confirming peptide spectrum matches, we were able to narrow down the 639-peptide possibilities to 87 peptides that were most robustly detected and specific to the SARS-CoV-2 virus. The specificity of these sequences to coronavirus taxa was confirmed using Unipept and BLAST-P. Through stringent p-value cutoff combined with manual verification of peptide spectrum match quality, 4 peptides derived from the nucleocapsid phosphoprotein and membrane protein were found to be most robustly detected across all cell culture and clinical samples, including those collected non-invasively.

CONCLUSION

We propose that these peptides would be of the most value for clinical proteomics applications seeking to detect COVID-19 from patient samples. We also contend that samples harvested from the upper respiratory tract and oral cavity have the highest potential for diagnosis of SARS-CoV-2 infection from easily collected patient samples using mass spectrometry-based proteomics assays.

A tri-functional amino acid enables mapping of binding sites for posttranslational-modification-mediated protein-protein interactions

Posttranslational modification (PTM), through the recruitment of effector proteins (i.e., "readers") that signal downstream events, plays key roles in regulating a variety of cellular processes. To understand how a PTM is recognized, it is necessary to find its readers and, importantly, the location of the binding pockets responsible for PTM recognition. Although various methods have been developed to identify PTM readers, it remains a challenge to directly map the PTM-binding regions, especially for intrinsically disordered domains. Here, we demonstrate a photo-crosslinkable, clickable, and cleavable tri-functional amino acid, ADdis-Cys, that when coupled with mass spectrometry (ADdis-Cys-MS) can not only identify PTM readers from complex proteomes but also simultaneously map their PTM-recognition modules. Using ADdis-Cys-MS, we successfully identify the binding sites of several reader-PTM interactions, among which we discover human C1QBP as a histone chaperone. This robust method should find wide applications in examining other histone or non-histone PTM-mediated protein-protein interactions.

Proteome Discoverer-A Community Enhanced Data Processing Suite for Protein Informatics

Proteomics researchers today face an interesting challenge: how to choose among the dozens of data processing and analysis pipelines available for converting tandem mass spectrometry files to protein identifications. Due to the dominance of Orbitrap technology in proteomics in recent history, many researchers have defaulted to the vendor software Proteome Discoverer. Over the fourteen years since the initial release of the software, it has evolved in parallel with the increasingly complex demands faced by proteomics researchers. Today, Proteome Discoverer exists in two distinct forms with both powerful commercial versions and fully functional free versions in use in many labs today. Throughout the 11 main versions released to date, a central theme of the software has always been the ability to easily view and verify the spectra from which identifications are made. This ability is, even today, a key differentiator from other data analysis solutions. In this review I will attempt to summarize the history and evolution of Proteome Discoverer from its first launch to the versions in use today.

Proteogenomic insights into the biology and treatment of HPV-negative head and neck squamous cell carcinoma

We present a proteogenomic study of 108 human papilloma virus (HPV)-negative head and neck squamous cell carcinomas (HNSCCs). Proteomic analysis systematically catalogs HNSCC-associated proteins and phosphosites, prioritizes copy number drivers, and highlights an oncogenic role for RNA processing genes. Proteomic investigation of mutual exclusivity between FAT1 truncating mutations and 11q13.3 amplifications reveals dysregulated actin dynamics as a common functional consequence. Phosphoproteomics characterizes two modes of EGFR activation, suggesting a new strategy to stratify HNSCCs based on EGFR ligand abundance for effective treatment with inhibitory EGFR monoclonal antibodies. Widespread deletion of immune modulatory genes accounts for low immune infiltration in immune-cold tumors, whereas concordant upregulation of multiple immune checkpoint proteins may underlie resistance to anti-programmed cell death protein 1 monotherapy in immune-hot tumors. Multi-omic analysis identifies three molecular subtypes with high potential for treatment with CDK inhibitors, anti-EGFR antibody therapy, and immunotherapy, respectively. Altogether, proteogenomics provides a systematic framework to inform HNSCC biology and treatment.

A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of Coronavirus Disease 2019 (COVID-19)

The Coronavirus Disease 2019 (COVID-19) global pandemic has had a profound, lasting impact on the world's population. A key aspect to providing care for those with COVID-19 and checking its further spread is early and accurate diagnosis of infection, which has been generally done via methods for amplifying and detecting viral RNA molecules. Detection and quantitation of peptides using targeted mass spectrometry-based strategies has been proposed as an alternative diagnostic tool due to direct detection of molecular indicators from non-invasively collected samples as well as the potential for high-throughput analysis in a clinical setting; many studies have revealed the presence of viral peptides within easily accessed patient samples. However, evidence suggests that some viral peptides could serve as better indicators of COVID-19 infection status than others, due to potential misidentification of peptides derived from human host proteins, poor spectral quality, high limits of detection etc. In this study we have compiled a list of 639 peptides identified from Sudden Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) samples, including from in vitro and clinical sources. These datasets were rigorously analyzed using automated, Galaxy-based workflows containing tools such as PepQuery, BLAST-P, and the Multi-omic Visualization Platform as well as the open-source tools MetaTryp and Proteomics Data Viewer (PDV). Using PepQuery for confirming peptide spectrum matches, we were able to narrow down the 639 peptide possibilities to 87 peptides which were most robustly detected and specific to the SARS-CoV-2 virus. The specificity of these sequences to coronavirus taxa was confirmed using Unipept and BLAST-P. Applying stringent statistical scoring thresholds, combined with manual verification of peptide spectrum match quality, 4 peptides derived from the nucleocapsid phosphoprotein and membrane protein were found to be most robustly detected across all cell culture and clinical samples, including those collected non-invasively. We propose that these peptides would be of the most value for clinical proteomics applications seeking to detect COVID-19 from a variety of sample types. We also contend that samples taken from the upper respiratory tract and oral cavity have the highest potential for diagnosis of SARS-CoV-2 infection from easily collected patient samples using mass spectrometry-based proteomics assays.

The first Conus genome assembly reveals a primary genetic central dogma of conopeptides in C. betulinus

Although there are various Conus species with publicly available transcriptome and proteome data, no genome assembly has been reported yet. Here, using Chinese tubular cone snail (C. betulinus) as a representative, we sequenced and assembled the first Conus genome with original identification of 133 genome-widely distributed conopeptide genes. After integration of our genomics, transcriptomics, and peptidomics data in the same species, we established a primary genetic central dogma of diverse conopeptides, assuming a rough number ratio of ~1:1:1:10s for the total genes: transcripts: proteins: post-translationally modified peptides. This ratio may be special for this worm-hunting Conus species, due to the high diversity of various Conus genomes and the big number ranges of conopeptide genes, transcripts, and peptides in previous reports of diverse Conus species. Only a fraction (45.9%) of the identified conotopeptide genes from our achieved genome assembly are transcribed with transcriptomic evidence, and few genes individually correspond to multiple transcripts possibly due to intraspecies or mutation-based variances. Variable peptide processing at the proteomic level, generating a big diversity of venom conopeptides with alternative cleavage sites, post-translational modifications, and N-/C-terminal truncations, may explain how the 133 genes and ~123 transcripts can generate thousands of conopeptides in the venom of individual C. betulinus. We also predicted many conopeptides with high stereostructural similarities to the putative analgesic ω-MVIIA, addiction therapy AuIB and insecticide ImI, suggesting that our current genome assembly for C. betulinus is a valuable genetic resource for high-throughput prediction and development of potential pharmaceuticals.

Impact of DJ-1 and Helix 8 on the Proteome and Degradome of Neuron-Like Cells

DJ-1 is an abundant and ubiquitous component of cellular proteomes. DJ-1 supposedly exerts a wide variety of molecular functions, ranging from enzymatic activities as a deglycase, protease, and esterase to chaperone functions. However, a consensus perspective on its molecular function in the cellular context has not yet been reached. Structurally, the C-terminal helix 8 of DJ-1 has been proposed to constitute a propeptide whose proteolytic removal transforms a DJ-1 zymogen to an active hydrolase with potential proteolytic activity. To better understand the cell-contextual functionality of DJ-1 and the role of helix 8, we employed post-mitotically differentiated, neuron-like SH-SY5Y neuroblastoma cells with stable over-expression of full length DJ-1 or DJ-1 lacking helix 8 (ΔH8), either with a native catalytically active site (C106) or an inactive site (C106A active site mutation). Global proteome comparison of cells over-expressing DJ-1 ΔH8 with native or mutated active site cysteine indicated a strong impact on mitochondrial biology. N-terminomic profiling however did not highlight direct protease substrate candidates for DJ-1 ΔH8, but linked DJ-1 to elevated levels of activated lysosomal proteases, albeit presumably in an indirect manner. Finally, we show that DJ-1 ΔH8 loses the deglycation activity of full length DJ-1. Our study further establishes DJ-1 as deglycation enzyme. Helix 8 is essential for the deglycation activity but dispensable for the impact on lysosomal and mitochondrial biology; further illustrating the pleiotropic nature of DJ-1.

Parthenolide Destabilizes Microtubules by Covalently Modifying Tubulin

Detyrosination of the α-tubulin C-terminal tail is a post-translational modification (PTM) of microtubules that is key for many biological processes.¹ Although detyrosination is the oldest known microtubule PTM,^2-7 the carboxypeptidase responsible for this modification, VASH1/2-SVBP, was identified only 3 years ago,^8,9 precluding genetic approaches to prevent detyrosination. Studies examining the cellular functions of detyrosination have therefore relied on a natural product, parthenolide, which is widely believed to block detyrosination of α-tubulin in cells, presumably by inhibiting the activity of the relevant carboxypeptidase(s).¹⁰ Parthenolide is a sesquiterpene lactone that forms covalent linkages predominantly with exposed thiol groups; e.g., on cysteine residues.^11-13 Using mass spectrometry, we show that parthenolide forms adducts on both cysteine and histidine residues on tubulin itself, in vitro and in cells. Parthenolide causes tubulin protein aggregation and prevents the formation of microtubules. In contrast to epoY, an epoxide inhibitor of VASH1/2-SVBP,⁹ parthenolide does not block VASH1-SVBP activity in vitro. Lastly, we show that epoY is an efficacious inhibitor of microtubule detyrosination in cells, providing an alternative chemical means to block detyrosination. Collectively, our work supports the notion that parthenolide is a promiscuous inhibitor of many cellular processes and suggests that its ability to block detyrosination may be an indirect consequence of reducing the polymerization-competent pool of tubulin in cells.

Fast and comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco

Recent advances in methods for enrichment and mass spectrometric analysis of intact glycopeptides have produced large-scale glycoproteomics datasets, but interpreting these data remains challenging. We present MSFragger-Glyco, a glycoproteomics mode of the MSFragger search engine, for fast and sensitive identification of N- and O-linked glycopeptides and open glycan searches. Reanalysis of recent N-glycoproteomics data resulted in annotation of 80% more glycopeptide spectrum matches (glycoPSMs) than previously reported. In published O-glycoproteomics data, our method more than doubled the number of glycoPSMs annotated when searching the same glycans as the original search, and yielded 4- to 6-fold increases when expanding searches to include additional glycan compositions and other modifications. Expanded searches also revealed many sulfated and complex glycans that remained hidden to the original search. With greatly improved spectral annotation, coupled with the speed of index-based scoring, MSFragger-Glyco makes it possible to comprehensively interrogate glycoproteomics data and illuminate the many roles of glycosylation.

DeepRescore: Leveraging Deep Learning to Improve Peptide Identification in Immunopeptidomics

The identification of major histocompatibility complex (MHC)-binding peptides in mass spectrometry (MS)-based immunopeptideomics relies largely on database search engines developed for proteomics data analysis. However, because immunopeptidomics experiments do not involve enzymatic digestion at specific residues, an inflated search space leads to a high false positive rate and low sensitivity in peptide identification. In order to improve the sensitivity and reliability of peptide identification, a post-processing tool named DeepRescore is developed. DeepRescore combines peptide features derived from deep learning predictions, namely accurate retention timeand MS/MS spectra predictions, with previously used features to rescore peptide-spectrum matches. Using two public immunopeptidomics datasets, it is shown that rescoring by DeepRescore increases both the sensitivity and reliability of MHC-binding peptide and neoantigen identifications compared to existing methods. It is also shown that the performance improvement is, to a large extent, driven by the deep learning-derived features. DeepRescore is developed using NextFlow and Docker and is available at https://github.com/bzhanglab/DeepRescore.

Fast and comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco

Recent advances in methods for enrichment and mass spectrometric analysis of intact glycopeptides have produced large-scale glycoproteomics datasets, but interpreting this data remains challenging. We present MSFragger-Glyco, a glycoproteomics mode of the MSFragger search engine, for fast and sensitive identification of N- and O-linked glycopeptides and open glycan searches. Reanalysis of recent N-glycoproteomics data resulted in annotation of 80% more glycopeptide-spectrum matches (glycoPSMs) than previously reported. In published O-glycoproteomics data, our method more than doubled the number of glycoPSMs annotated when searching the same glycans as the original search and yielded 4–6-fold increases when expanding searches to include additional glycan compositions and other modifications. Expanded searches also revealed many sulfated and complex glycans that remained hidden to the original search. With greatly improved spectral annotation, coupled with the speed of index-based scoring, MSFragger-Glyco makes it possible to comprehensively interrogate glycoproteomics data and illuminate the many roles of glycosylation.