Molecular & Cellular Proteomics

Proteomic analyses of the phagosome has significantly improved our understanding of the proteins which contribute to critical phagosome functions such as apoptotic cell clearance and microbial killing. However, previous methods of isolating phagosomes for proteomic analysis have relied on cell fractionation with some intrinsic limitations. Here, we present an alternative and modular proximity-labeling based strategy for mass spectrometry proteomic analysis of the phagosome lumen, termed PhagoID. We optimize proximity labeling in the phagosome and apply PhagoID to immortalized murine macrophages as well as primary human macrophages. Analysis of proteins detected by PhagoID in murine macrophages demonstrate that PhagoID corroborates previous proteomic studies, but also nominates novel proteins with unexpected residence at the phagosome for further study. A direct comparison between the proteins detected by PhagoID between mouse and human macrophages further reveals that human macrophage phagosomes have an increased abundance of proteins involved in the oxidative burst and antigen presentation. Our study develops and benchmarks a new approach to measure the protein composition of the phagosome and validates a subset of these findings, ultimately using PhagoID to grant further insight into the core constituent proteins and species differences at the phagosome lumen.

Reversible protein phosphorylation is a central signaling mechanism in eukaryotic cells. While the identification of canonical phosphorylation sites using mass-spectrometry (MS) based proteomics has become routine, annotation of non-canonical phosphorylation has remained a challenge. Here, we report a tailored pyrophosphoproteomics workflow to detect and reliably assign protein pyrophosphorylation in two human cell lines, providing the first direct evidence of endogenous protein pyrophosphorylation. Detection of protein pyrophosphorylation was reproducible, specific and consistent with previous biochemical evidence relating the installation of the modification to inositol pyrophosphates (PP-InsPs). We manually validated 148 pyrophosphosites across 71 human proteins, the most heavily pyrophosphorylated of which were the nucleolar proteins NOLC1 and TCOF1. A predictive workflow based on the MS data set was established to recognize putative pyrophosphorylation sequences, and UBF1, a nucleolar protein incompatible with the proteomics method, was biochemically shown to undergo pyrophosphorylation. When the biosynthesis of PP-InsPs was perturbed in a model cell line, proteins expressed in this background exhibited lower levels of pyrophosphorylation. Disruption of PP-InsP biosynthesis also significantly reduced rDNA transcription, potentially by lowering pyrophosphorylation on regulatory proteins NOLC1, TCOF1, and UBF1. Overall, protein pyrophosphorylation emerges as an archetype of non-canonical phosphorylation, and should be considered in future phosphoproteomic analyses.

Protein activity, abundance, and stability can be regulated by posttranslational modification including ubiquitination. Ubiquitination is conserved among eukaryotes and plays a central role in modulating cellular function and yet we lack comprehensive catalogs of proteins that are modified by ubiquitin in plants. In this study, we describe an antibody-based approach to enrich peptides containing the di-glycine (diGly) remnant of ubiquitin and coupled that with isobaric labeling to enable quantification, from up to 16-multiplexed samples, for plant tissues. Collectively, we identified 7,130 diGly-modified lysine residues sites arising from 3,178 proteins in Arabidopsis primary roots. These data include ubiquitin proteasome dependent ubiquitination events as well as ubiquitination events associated with auxin treatment. Gene Ontology analysis indicated that ubiquitinated proteins are associated with numerous biological processes including hormone signaling, plant defense, protein homeostasis, and root morphogenesis. We determined the ubiquitinated lysine residues that directly regulate the stability of the transcription factors CRYPTOCHROME-INTERACTING BASIC-HELIX-LOOP-HELIX 1 (CIB1), CIB1 LIKE PROTEIN 2 (CIL2), and SENSITIVE TO PROTON RHIZOTOXICITY (STOP1) using site directed mutagenesis and in vivo degradation assays. These comprehensive site-level ubiquitinome profiles provide a wealth of data for future studies related to modulation of biological processes mediated by this posttranslational modification in plants.

Proximity labeling has emerged as a powerful approach for identifying protein-protein interactions within living systems, particularly those involving weak or transient associations. Here, we present a comprehensive proximity labeling study of five conserved Caenorhabditis elegans proteins--NEKL-2, NEKL-3, MLT-2, MLT-3, and MLT-4--that form two NEKL-MLT kinase-scaffold subcomplexes involved in membrane trafficking and actin regulation. Using endogenously expressed TurboID fusions and a data-independent acquisition (DIA) mass spectrometry (MS) pipeline, we profiled NEKL-MLT interactomes across 23 experiments, including several methodological variations, applying stringent controls and filtering strategies. By analyzing and comparing experimental outcomes, we develop a set of intuitive quantitative metrics to assess experimental outcomes and quality. We demonstrate that DIA-based workflows produce sensitive physiologically relevant findings, even in the presence of experimental noise and variability across biological replicates. Our approach is validated through the identification of known NEKL-MLT binding partners and conserved genetic suppressors of nekl-mlt mutant phenotypes. Gene ontology enrichment further supports the involvement of newly identified NEKL-MLT interactors in processes including membrane trafficking, cytoskeletal regulation, and cell adhesion. Additionally, several novel proximate interactors were functionally validated using genetic assays. Our findings underscore the utility of DIA-MS in proximity labeling applications and highlight the value of incorporating internal controls, quantitative metrics, and biological validation to enhance confidence in candidate interactors. Overall, this study provides a scalable, organismal-level strategy for probing endogenous protein networks and offers practical guidelines for proximity labeling in multicellular systems.

Ovarian cancer is the deadliest of gynecological cancers and surgery is often necessary for a final diagnosis. Benign cases could be managed more conservatively, avoiding the risks and complications associated with surgery, if accurate diagnostic biomarkers existed. Underlying differences between circulating protein biomarkers and tumor gene expression also restricts interpretation and prioritization of potential biomarkers for diagnosis and potential drug targets. Here, high-throughput affinity plasma proteomics data encompassing over 5400 proteins in plasma from 404 women from two independent Swedish cohorts were analyzed alone and combined with total RNA sequencing in corresponding benign and malignant tumor tissue. A subset of 191 proteins previously identified as differentially expressed between benign and malignant conditions were used to perform correlation analyses, revealing similar patterns between groups but much stronger signals in malignant cases. Comparison with known protein interactions from the STRING database revealed a highly interconnected network consisting of 154 proteins in plasma. Differential correlation analysis (DCA) was performed on the full set of 5414 proteins and for their corresponding tumor RNA expression. DCA identified 31 plasma proteins with significant differential correlations (adjusted p < 0.05, {Delta}R > 0.5) and 759 tumor transcript pairs with significantly differentially correlating RNA expression. Distinct protein-protein correlation patterns in plasma were discovered and validated with notable differences between benign and malignant tumors. In general, these patterns were distinct from those detected on gene expression level in tumor tissue. In conclusion, our findings reveal clear differences in plasma protein co-regulation, with distinct correlation patterns between malignant and benign cases. The differences between results obtained in tumor transcriptomics and plasma proteomics results from the same patients warrants further studies into the tumor microenvironment to understand the function of promising protein biomarker candidates and the potential of these as future drug targets.

N-glycosylation plays essential roles in the folding, trafficking, and maturation of proteins in the secretory pathways, but how individual protein and residue glycosylation rewires under endoplasmic reticulum (ER) stress is unknown. Particularly, intact glycopeptide data that retain the connectivity between glycosylation sites and the attached glycans are needed to reveal the micro- and macro- heterogeneity of N-glycosylation sites and their permutations in stressed cells. Here, we developed an optimized magnetic polyethyleneimine boronic acid-containing scaffold (mPBA) enrichment workflow to achieve sensitive and broad enrichment of intact glycoproteins for mass spectrometry analysis, quantifying 13759 unique protein-, site-, and glycoform combinations, termed glycopeptidoforms, in normal and stressed cells while requiring only 0.1 to 0.5 mg total peptide input. The data reveals a systems-level shift in the fate of hundreds of glycoproteins. N-glycosylation changes are highly dynamic, with magnitude far exceeding protein expression changes, and showing complex protein-, site-, and glycan-specific granularity. Individual glycoform reconfigurations can be observed that suggest lesions within specific steps in protein maturation and trafficking pathways. Mannose trimming is disrupted across multiple proteins and cell states, suggesting a central feature of ER stress mediated glycoproteome remodeling. Together, these results reveal molecular details into the remodeling of protein secretory pathways upon ER stress and highlight the utility of mPBA for sensitive N-glycoproteomics studies.

Defining the molecular mechanisms that govern heart development is essential for identifying the etiology of congenital heart disease. Here, quantitative proteomics was used to measure temporal changes in the cardiac proteome at eight critical stages of murine embryonic heart development. Global temporal profiles of the over 7,300 identified proteins uncovered signature cardiac protein interaction networks that linked protein dynamics with molecular pathways. Using this integrated dataset, we identified and established a functional role for the mevalonate pathway in the regulation of embryonic cardiomyocyte proliferation and cell signaling. Overall, our proteomic datasets are an invaluable resource for studying molecular events that regulate embryonic heart development and contribute to congenital heart disease.

The evolution of mass spectrometry (MS)-based proteomics has been driven by continuous technological advances in sample preparation, instrumentation, and data acquisition. While chromatographic separation has historically been considered a critical bottleneck in achieving comprehensive proteome coverage, recent developments in ultra-fast data acquisition fundamentally challenge this paradigm. We investigated whether the traditional paradigm that chromatographic performance directly correlates with proteome depth still holds true. Spanning a matrix of experiments with five distinct stationary phases, including C18 chemistries, C8, and Phenyl-Hexyl, across eight column lengths (40-140 mm), we evaluate protein identification performance using data-independent acquisition (DIA) on the Orbitrap Astral mass spectrometer. Despite substantial chromatographic differences, we observed remarkably convergent proteome coverage metrics. All C18 and C8 phases consistently achieved over 150,000 precursor- and approximately 9,000 protein group identifications, regardless of column length variations. While distinct selectivity fingerprints persisted across chemistries, these chromatographic differences did not translate into meaningful variations in proteome coverage under high-speed acquisition conditions at 200 Hz. We conclude that the analytical bottleneck has fundamentally shifted from chromatographic resolution to mass spectrometric sampling efficiency, where comprehensive peptide identification is now gained through advanced spectral deconvolution rather than physical separation alone. This paradigmatic shift is reflected in modern proteomics by method development priorities being directed beyond traditional separation optimization, with greater emphasis placed on operational robustness, analytical throughput, and reproducibility.

Here we present a high-throughput virtual top-down proteomics approach that restores the molecular weight (MW) information in shotgun proteomics, and demonstrate its utility in studying proteolytic events in programmed cell death. With Gel-Assisted Proteome Position Integral Shift (GAPPIS), we quantified over 7000 proteins in staurosporine-induced apoptotic HeLa cells and identified 84 proteins exhibiting in a statistically significant manner at least two of the following features: 1) a negative MW shift; 2) an elevated ratio in a pair of a semi-tryptic and tryptic peptide, 3) a negative shift in the standard deviation of MW estimated for different peptides, and 4) a negative shift in skewness of the same data. Of these proteins, 58 molecules were novel caspase 3 substrates. Further analysis identified the preferred cleavage sites consistent with the known caspase cleavages after the DXXD motif. As a powerful tool for high-throughput MW analysis simultaneously with the conventional expression analysis, GAPPIS assay can prove useful in studying a broad range of biological processes involving proteolytic events.

Most membrane and secretory proteins undergo N-glycosylation, catalyzed by oligosaccharyltransferase (OST), a membrane-bound complex in the endoplasmic reticulum (ER). Proteins failing quality control are degraded via ER-associated degradation (ERAD), involving retrotranslocation to cytosolic proteasomes. Using SILAC proteomics, we identified OST subunits as key interactors with a misfolded ER protein bait, suggesting unexpected involvement in ERAD. Previous reports implied additional roles for OST subunits beyond N-glycosylation, such as quality control by ribophorin I. We tested OST engagement in glycoprotein and non-glycosylated protein ERAD; overexpression or partial knockdown of OST subunits interfered with ERAD in conditions that did not affect glycosylation. Effects were studied on misfolded membrane proteins, BACE476 and asialoglycoprotein receptor H2a, and the luminal 1-antitrypsin NHK variant. OST appears to participate in late ERAD stages, interacting with the E3 ligase HRD1 and facilitating retrotranslocation. Molecular dynamics simulations suggest membrane thinning by OST transmembrane domains, possibly assisting retrotranslocation via membrane distortion.

The molecular chaperone heat shock protein 90 (HSP90) works in concert with co-chaperones to stabilize its client proteins, which include multiple drivers of oncogenesis and malignant progression. Pharmacologic inhibitors of HSP90 have been observed to exert a wide range of effects on the proteome, including depletion of client proteins, induction of heat shock proteins, dissociation of co-chaperones from HSP90, disruption of client protein signaling networks, and recruitment of the protein ubiquitylation and degradation machinery--suggesting widespread remodeling of cellular protein complexes. However, proteomics studies to date have focused on inhibitor-induced changes in total protein levels, often overlooking protein complex alterations. Here, we use size-exclusion chromatography in combination with mass spectrometry (SEC-MS) to characterize the changes in native protein complexes following treatment with the HSP90 inhibitor tanespimycin (17-AAG) in the HT29 colon adenocarcinoma cell line. After confirming the signature cellular response to HSP90 inhibition (e.g., induction of heat shock proteins, decreased total levels of client proteins), we were surprised to find only modest perturbations to the global distribution of protein elution profiles in inhibitor-treated cells. Similarly, co-chaperones that co-eluted with HSP90 displayed no clear difference between control and treated conditions. However, two distinct analysis strategies identified multiple inhibitor-induced changes, including several known components of the HSP90 proteome, as well as numerous proteins and protein complexes with no previous links to HSP90. We present this dataset as a resource for the HSP90, proteostasis, and cancer communities (https://www.bioinformatics.babraham.ac.uk/shiny/HSP90/SEC-MS/), laying the groundwork for future mechanistic and therapeutic studies related to HSP90 pharmacology. Data are available via ProteomeXchange with identifier PXD033459.

Late maturity alpha-amylase (LMA) is a wheat genetic defect causing the synthesis of high isoelectric point (pI) alpha-amylase in the aleurone as a result of a temperature shock during mid-grain development or prolonged cold throughout grain development leading to an unacceptable low falling numbers (FN) at harvest or during storage. High pI alpha-amylase is normally not synthesized until after maturity in seeds when they may sprout in response to rain or germinate following sowing the next seasons crop. Whilst the physiology is well understood, the biochemical mechanisms involved in grain LMA response remain unclear. We have employed high-throughput proteomics to analyse thousands of wheat flours displaying a range of LMA values. We have applied an array of statistical analyses to select LMA-responsive biomarkers and we have mined them using a suite of tools applicable to wheat proteins. To our knowledge, this is not only the first proteomics study tackling the wheat LMA issue, but also the largest plant-based proteomics study published to date. Logistics, technicalities, requirements, and bottlenecks of such an ambitious large-scale high-throughput proteomics experiment along with the challenges associated with big data analyses are discussed. We observed that stored LMA-affected grains activated their primary metabolisms such as glycolysis and gluconeogenesis, TCA cycle, along with DNA- and RNA binding mechanisms, as well as protein translation. This logically transitioned to protein folding activities driven by chaperones and protein disulfide isomerase, as wellas protein assembly via dimerisation and complexing. The secondary metabolism was also mobilised with the up-regulation of phytohormones, chemical and defense responses. LMA further invoked cellular structures among which ribosomes, microtubules, and chromatin. Finally, and unsurprisingly, LMA expression greatly impacted grain starch and other carbohydrates with the up-regulation of alpha-gliadins and starch metabolism, whereas LMW glutenin, stachyose, sucrose, UDP-galactose and UDP-glucose were down-regulated. This work demonstrates that proteomics deserves to be part of the wheat LMA molecular toolkit and should be adopted by LMA scientists and breeders in the future.

Advancements in glycoproteomics software have improved glycopeptide identification; however, algorithm differences cause discrepancies in identified glycopeptide, even when identical datasets. We compared five state-of-the-art glycoproteomics software programs (Byonic, MSFragger-Glyco, pGlyco3, Glyco-Decipher, and GRable), investigating their unique capabilities, and examined their ability to identify rare sialic acid-containing glycopeptides (NeuGc and KDN) derived from BJAB-K20 cells, which lack UDP-N-acetylglucosamine 2-epimerase, the rate-limiting enzyme for sialic acid synthesis. Approximately half of the identified glycopeptides were unique to individual tools. Byonic identified the highest number of glycopeptides, whereas Glyco-Decipher and GRable identified complex highly branched glycan structures. NeuGc- and KDN-containing glycopeptides were identified by specific programs, highlighting their capability to handle rare glycan structures. To assess the reliability of these identifications, we reanalyzed the MS/MS spectra for the presence of diagnostic ions corresponding to each identified glycopeptide. Some software programs identified glycopeptides without detecting the corresponding diagnostic ions, raising concerns regarding result reliability. However, leveraging the distinct capabilities of each software enabled us to achieve a comprehensive and reliable analysis of glycopeptides, including those with rare glycan structures. Combining multiple glycoproteomics software programs with complementary strengths and incorporating post- verification steps, such as diagnostic ion analysis, enhances the accuracy and depth of glycopeptide identification.

Increasing interest in deubiquitinases (DUBs) and ubiquitin E3 ligases as drug targets to modulate critical molecular pathways in disease is driven by the discovery of specific cellular roles of these enzymes. Key to this is the identification of DUB or E3 ligase substrates. While global cellular ubiquitination changes upon perturbation of DUB/E3 ligase activity can be studied using mass spectrometry-based proteomic methods, these datasets include indirect and downstream ubiquitination events. To enrich for direct substrates of DUB/E3 ligase enzymes, we have combined proximity-labelling methodology (APEX2) and subsequent ubiquitination enrichment (based on the K-{varepsilon}-GG motif) to form a proximal-ubiquitome workflow. We have applied this technology to identify altered ubiquitination events in the proximity of the DUB ubiquitin specific protease 30 (USP30) upon its inhibition. We show ubiquitination events previously linked to USP30 on TOMM20 and FKBP8 and the previously undescribed candidate substrate LETM1, which is deubiquitinated in a USP30-dependent manner.

Peptide-spectrum match (PSM) rescoring has become standard in proteomics workflows, improving peptide identification accuracy across diverse search engines. Despite the availability of multiple rescoring strategies, systematic comparisons spanning several search engines, datasets, and database configurations remain limited. Here, we benchmarked seven publicly available search engines, evaluating standard target-decoy-based false discovery rate (FDR) estimation alongside Percolator, MS2Rescore, and Oktoberfest across four datasets acquired on different mass spectrometry platforms and searched against protein databases of varying size and composition. Rescoring substantially increased identification consensus and reduced variability between search engines, with prediction-based approaches yielding the largest gains. While database size had limited impact for human datasets, it significantly affected identification rates on a metaproteomic dataset. Entrapment-based evaluation indicated generally adequate FDR control across methods, although prediction-based rescoring exhibited a slightly higher tendency toward FDR underestimation in specific configurations. Overall, advanced rescoring strategies harmonize peptide identification outcomes across search engines, thereby enhancing robustness and comparability in proteomics analyses. However, careful feature selection and appropriate database choice remain essential to ensure reliable FDR control and optimal performance across diverse experimental settings.

This study investigates the utility of Thermolysin as a proteolytic enzyme to enhance the depth and coverage of proteomic analysis in ovarian cancer (OC) extracellular vesicles (EVs). EVs were isolated from OC cell lines and ascites fluid samples from women diagnosed with high-grade serous carcinoma. Proteins were digested using Thermolysin and Trypsin/LysC, followed by label-free data-dependent acquisition (DDA) and data-independent acquisition (DIA) mass spectrometry. The proteolytic efficiency, sequence coverage, peptide complexity, and proteomic depth were compared between Thermolysin and Trypsin/LysC digests. In silico analyses predicted theoretical benchmarks of these parameters using a core set of 22 proteins, and Gene set enrichment analyses (GSEA) highlighted the biological relevance of proteins identified throughout the study. Thermolysin digestion significantly increased the complexity or of peptide pools compared to Trypsin/LysC leading to limited peptide and protein identification, albeit total sequence coverage was increased through complementation to tryptic peptides. In both cell line and ascites EVs, Thermolysin identified unique proteins not detected by Trypsin/LysC that are known drivers of metastatic solid cancers, such as Ly6E. Offline strong cation exchange (SCX) fractionation improved proteomic depth and sequence coverage obtained with Thermolysin to generate a spectral library for DIA. Our DIA analysis of Thermolysin digests revealed the presence of NODAL in OC ascites EVs, a protein associated with poor clinical prognosis, which was not detected in Trypsin/LysC digests. The importance of NODAL was cross-validated in TGCA-OV and AOCS datasets by clinical cohorts by assessing RNA levels in solid tumors or ascites fluid, respectively. Collectively, we demonstrate that Thermolysin complements traditional enzymes like Trypsin/LysC to provide a more comprehensive proteomic landscape for biomarker discovery.

Single-cell proteomics has emerged as a powerful approach for characterizing cellular heterogeneity. Here, we present an optimized scProteomic workflow that enhances proteome coverage and quantification by refining liquid chromatography (LC) conditions across platforms (both Evosep and nanoElute2). Importantly, our results show that using our optimized LC conditions, even with Bruker timsTOF HT, a machine not designed for scProteomics applications, we achieved solid performance with single cell samples that allows meaningful biological discoveries. First, we compared power to detect differentially-expressed genes/proteins between scRNA-seq and scProteomics at single cell level, and demonstrated that scProteomics showed an excellent performance at small sample sizes. To further validate this finding, we applied our optimized workflow to study the proteomic response to oxidative stress using a limited number of single cells. Our scProteomics results successfully detected 3 distinct cell populations, reflecting correctly the 3 cell lines used, and captured the dysregulation of antioxidant enzymes, molecular chaperones and ubiquitin-proteasome system, reflecting a multi-faceted response to oxidative stress that is uniform across distinct cell lines. Our results highlighted the potential of scProteomics to resolve subtle perturbations and provide a readout of cellular states for the broader community, including users operating non-scProteomics-dedicated machines.

Single-cell proteomics (SCP) offers direct insight into functional protein states that drive cellular heterogeneity, complementing genomic and transcriptomic analyses. Although recent reports have demonstrated improved proteome coverage, their reliance on specialized instrumentation limits broader adoption. Additionally, current evaluation practices remain largely centered on protein and peptide identification counts, which alone do not fully reflect data quality or biological interpretability. Here, we describe an accessible, label-free SCP workflow which implements easily accessible laboratory equipment: a single-cell dispenser, conventional multiwell plates, and an incubator with water-bath-based humidity control. Using trapped ion mobility spectrometry-time-of-flight mass spectrometry (timsTOF), we systematically optimize key sample preparation variables, including trypsin concentration, incubation time, reduction/alkylation, digestion conditions, and plate types, which together maximize data quality and reproducibility. We further introduce a data-quality framework that moves beyond identification counts, emphasizing quantitative consistency and biological interpretability via individual protein coverage completeness across cells, coefficients of variation across technical replicates, peptide-to-protein ratios, and single-cell-to-bulk correlations. Collectively, our approach lowers technical barriers to accessing SCP while enabling more rigorous, interpretable, and scalable SCP analysis across diverse research contexts.

Bcl-2 family proteins govern the intrinsic apoptotic pathway by regulating mitochondrial outer membrane permeabilisation (MOMP), releasing apoptogenic factors into the cytosol. How close a cell is to MOMP, termed mitochondrial priming, is determined by the interactions between different Bcl-2 proteins at the outer mitochondrial membrane (OMM). However, Bcl-2 proteins can also drive diverse processes that do not result in cell death, such as incomplete MOMP, sublethal caspase activation, pro-inflammatory signalling and regulation of cellular metabolism. To understand the wider Bcl-2 family interactome and how it might be involved in these processes, we undertook an unbiased proteomic BioID screen, using both pro- and anti-apoptotic Bcl-2 family members as bait proteins. We found that most high-confidence potential interactions in non-apoptotic cells were outside the canonical Bcl-2 family interactome. Analysis of how this interactome changed in response to either BH3-mimetics or full-length BH3-only proteins revealed dynamic changes at multiple organelles and membrane contact sites in response to altered mitochondrial priming. These findings underscore the complex and varied interactions of the Bcl-2 family proteins, expanding their scope and function beyond the frequently studies intra-family interactions.

Modern vaccine designs and studies of human leukocyte antigen (HLA)-mediated immune responses rely heavily on the knowledge of HLA allele-specific binding motifs and computational prediction of HLA-peptide binding affinity. Breakthroughs in HLA peptidomics have considerably expanded the databases of natural HLA ligands and enabled detailed characterizations of HLA-peptide binding specificity. However, cautions must be made when analyzing HLA peptidomics data because identified peptides may be contaminants in mass spectrometry or may weakly bind to the HLA molecules. Here, a hybrid de novo peptide sequencing approach was applied to large-scale mono-allelic HLA peptidomics datasets to uncover new ligands and refine current knowledge of HLA binding motifs. Up to 12-40% of the peptidomics data were low-binding affinity peptides with an arginine or a lysine at the C-terminus and likely to be tryptic peptide contaminants. Thousands of these peptides have been reported in a community database as legitimate ligands and might be erroneously used for training prediction models. Furthermore, unsupervised clustering of identified ligands revealed additional binding motifs for several HLA class I alleles and effectively isolated outliers that were experimentally confirmed to be false positives. Overall, our findings expanded the knowledge of HLA binding specificity and advocated for more rigorous interpretation of HLA peptidomics data that will ensure the high validity of community HLA ligandome databases.