iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates

Limelight: An Open, Web-Based Tool for Visualizing, Sharing, and Analyzing Mass Spectrometry Data from DDA Pipelines

Liquid chromatography-tandem mass spectrometry employing data-dependent acquisition (DDA) is a mature, widely used proteomics technique routinely applied to proteome profiling, protein-protein interaction studies, biomarker discovery, and protein modification analysis. Numerous tools exist for searching DDA data and myriad file formats are output as results. While some search and post processing tools include data visualization features to aid biological interpretation, they are often limited or tied to specific software pipelines. This restricts the accessibility, sharing and interpretation of data, and hinders comparison of results between different software pipelines. We developed Limelight, an easy-to-use, open-source, freely available tool that provides data sharing, analysis and visualization and is not tied to any specific software pipeline. Limelight is a data visualization tool specifically designed to provide access to the whole "data stack", from raw and annotated scan data to peptide-spectrum matches, quality control, peptides, proteins, and modifications. Limelight is designed from the ground up for sharing and collaboration and to support data from any DDA workflow. We provide tools to import data from many widely used open-mass and closed-mass search software workflows. Limelight helps maximize the utility of data by providing an easy-to-use interface for finding and interpreting data, all using the native scores from respective workflows.

Candida albicans: A Comprehensive View of the Proteome

We describe a new release of the Candida albicans PeptideAtlas proteomics spectral resource (build 2024-03), providing a sequence coverage of 79.5% at the canonical protein level, matched mass spectrometry spectra, and experimental evidence identifying 3382 and 536 phosphorylated serine and threonine sites with false localization rates of 1% and 5.3%, respectively. We provide a tutorial on how to use the PeptideAtlas and associated tools to access this information. The C. albicans PeptideAtlas summary web page provides "Build overview", "PTM coverage", "Experiment contribution", and "Data set contribution" information. The protein and peptide information can also be accessed via the Candida Genome Database via hyperlinks on each protein page. This allows users to peruse identified peptides, protein coverage, post-translational modifications (PTMs), and experiments that identify each protein. Given the value of understanding the PTM landscape in the sequence of each protein, a more detailed explanation of how to interpret and analyze PTM results is provided in the PeptideAtlas of this important pathogen. Candida albicans PeptideAtlas web page: https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/buildDetails?atlas_build_id=578.

α-Catenin force-sensitive binding and sequestration of LZTS2 leads to cytokinesis failure

Epithelial cells can become polyploid upon tissue injury, but mechanosensitive cues that trigger this state are poorly understood. Using an Madin Darby Canine Kidney (MDCK) cell knock-out/reconstitution system, we show that α-catenin mutants that alter force-sensitive binding to F-actin or middle (M)-domain promote cytokinesis failure and binucleation, particularly near epithelial wound-fronts. We identified Leucine Zipper Tumor Suppressor 2 (LZTS2), a factor previously implicated in abscission, as a conformation sensitive proximity partner of α-catenin. We show that LZTS2 enriches not only at midbody/intercellular bridges but also at apical adhering junctions. α-Catenin mutants with persistent M-domain opening show elevated junctional enrichment of LZTS2 compared with wild-type cells. LZTS2 knock-down leads to elevated rates of binucleation. These data implicate LZTS2 as a mechanosensitive effector of α-catenin that is critical for cytokinetic fidelity. This model rationalizes how persistent mechanoactivation of α-catenin may drive tension-induced polyploidization of epithelia after injury and suggests an underlying mechanism for how pathogenic α-catenin M-domain mutations drive macular dystrophy.

The BLM-TOP3A-RMI1-RMI2 proximity map reveals that RAD54L2 suppresses sister chromatid exchanges

Homologous recombination is a largely error-free DNA repair mechanism conserved across all domains of life and is essential for the maintenance of genome integrity. Not only are the mutations in homologous recombination repair genes probable cancer drivers, some also cause genetic disorders. In particular, mutations in the Bloom (BLM) helicase cause Bloom Syndrome, a rare autosomal recessive disorder characterized by increased sister chromatid exchanges and predisposition to a variety of cancers. The pathology of Bloom Syndrome stems from the impaired activity of the BLM-TOP3A-RMI1-RMI2 (BTRR) complex which suppresses crossover recombination to prevent potentially deleterious genome rearrangements. We provide a comprehensive BTRR proximal proteome, revealing proteins that suppress crossover recombination. We find that RAD54L2, a SNF2-family protein, physically interacts with BLM and suppresses sister chromatid exchanges. RAD54L2 is important for recruitment of BLM to chromatin and requires an intact ATPase domain to promote non-crossover recombination. Thus, the BTRR proximity map identifies a regulator of recombination.

A pan-gene catalogue of Asian cultivated rice

The rice genome underpins fundamental research and breeding, but the Nipponbare (japonica) reference does not fully encompass the genetic diversity of Asian rice. To address this gap, the Rice Population Reference Panel (RPRP) was developed, comprising high-quality assemblies of 16 rice cultivars to represent japonica , indica , aus , and aromatic varietal groups. The RPRP has been consistently annotated, supported by extensive experimental data and here we report the computational assignment, characterization and dissemination of stably identified pan-genes. We identified 25,178 core pan-genes shared across all cultivars, alongside cultivar-specific and family-enriched genes. Core genes exhibit higher gene expression and proteomic evidence, higher confidence protein domains and AlphaFold structures, while cultivar-specific genes were enriched for domains under selective breeding pressure, such as for disease resistance. This resource, integrated into public databases, enables researchers to explore genetic and functional diversity via a population-aware "reference guide" across rice genomes, advancing both basic and applied research.

Candida albicans: a comprehensive view of the proteome

We describe a new release of the Candida albicans PeptideAtlas proteomics spectral resource (build 2024-03), providing a sequence coverage of 79.5% at the canonical protein level, matched mass spectrometry spectra, and experimental evidence identifying 3382 and 536 phosphorylated serine and threonine sites with false localization rates of 1% and 5.3%, respectively. We provide a tutorial on how to use the PeptideAtlas and associated tools to access this information. The C. albicans PeptideAtlas summary web page provides "Build overview", "PTM coverage", "Experiment contribution", and "Dataset contribution" information. The protein and peptide information can also be accessed via the Candida Genome Database via hyperlinks on each protein page. This allows users to peruse identified peptides, protein coverage, post-translational modifications (PTMs), and experiments identifying each protein. Given the value of understanding the PTM landscape in the sequence of each protein, a more detailed explanation of how to interpret and analyse PTM results is provided in the PeptideAtlas of this important pathogen. Candida albicans PeptideAtlas web page: https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/buildDetails?atlas_build_id=578.

PeptideForest: Semisupervised Machine Learning Integrating Multiple Search Engines for Peptide Identification

The first step in bottom-up proteomics is the assignment of measured fragmentation mass spectra to peptide sequences, also known as peptide spectrum matches. In recent years novel algorithms have pushed the assignment to new heights; unfortunately, different algorithms come with different strengths and weaknesses and choosing the appropriate algorithm poses a challenge for the user. Here we introduce PeptideForest, a semisupervised machine learning approach that integrates the assignments of multiple algorithms to train a random forest classifier to alleviate that issue. Additionally, PeptideForest increases the number of peptide-to-spectrum matches that exhibit a q-value lower than 1% by 25.2 ± 1.6% compared to MS-GF+ data on samples containing mixed HEK and Escherichia coli proteomes. However, an increase in quantity does not necessarily reflect an increase in quality and this is why we devised a novel approach to determine the quality of the assigned spectra through TMT quantification of samples with known ground truths. Thereby, we could show that the increase in PSMs below 1% q-value does not come with a decrease in quantification quality and as such PeptideForest offers a possibility to gain deeper insights into bottom-up proteomics. PeptideForest has been integrated into our pipeline framework Ursgal and can therefore be combined with a wide array of algorithms.

Deep Learning Predicts Non-Normal Transmission Distributions in High-Field Asymmetric Waveform Ion Mobility (FAIMS) Directly from Peptide Sequence

Peptide ion mobility adds an extra dimension of separation to mass spectrometry-based proteomics. The ability to accurately predict peptide ion mobility would be useful to expedite assay development and to discriminate true answers in a database search. There are methods to accurately predict peptide ion mobility through drift tube devices, but methods to predict mobility through high-field asymmetric waveform ion mobility (FAIMS) are underexplored. Here, we successfully model peptide ions' FAIMS mobility using a multi-label classification scheme to account for non-normal transmission distributions. We trained two models from over 100,000 human peptide precursors: a random forest and a long-term short-term memory (LSTM) neural network. Both models had different strengths, and the ensemble average of model predictions produced a higher F2 score than either model alone. Finally, we explored cases where the models make mistakes and demonstrate the predictive performance of F2 = 0.66 (AUROC = 0.928) on a new test data set of nearly 40,000 E. coli peptide ions. The deep learning model is easily accessible via https://faims.xods.org.

The small GTPase MRAS is a broken switch

Intense research on founding members of the RAS superfamily has defined our understanding of these critical signalling proteins, leading to the premise that small GTPases function as molecular switches dependent on differential nucleotide loading. The closest homologs of H/K/NRAS are the three-member RRAS family, and interest in the MRAS GTPase as a regulator of MAPK activity has recently intensified. We show here that MRAS does not function as a classical switch and is unable to exchange GDP-to-GTP in solution or when tethered to a lipid bilayer. The exchange defect is unaffected by inclusion of the GEF SOS1 and is conserved in a distal ortholog from nematodes. Synthetic activating mutations widely used to study the function of MRAS in a presumed GTP-loaded state do not increase exchange, but instead drive effector binding due to sampling of an activated conformation in the GDP-loaded state. This includes nucleation of the SHOC2-PP1Cα holophosphatase complex. Acquisition of NMR spectra from isotopically labeled MRAS in live cells validated the GTPase remains fully GDP-loaded, even a supposed activated mutant. These data show that RAS GTPases, including those most similar to KRAS, have disparate biochemical activities and challenge current dogma on MRAS, suggesting previous data may need reinterpretation.

PD-1 interactome in osteosarcoma: identification of a novel PD-1/AXL interaction conserved between humans and dogs

The PD-1/PDL-1 immune checkpoint inhibitors revolutionized cancer treatment, yet osteosarcoma remains a therapeutic challenge. In some types of cancer, PD-1 receptor is not solely expressed by immune cells but also by cancer cells, acting either as a tumor suppressor or promoter. While well-characterized in immune cells, little is known about the role and interactome of the PD-1 pathway in cancer. We investigated PD-1 expression in human osteosarcoma cells and studied PD-1 protein-protein interactions in cancer. Using U2OS cells as a model, we confirmed PD-1 expression by western blotting and characterized its intracellular as well as surface localization through flow cytometry and immunofluorescence. High-throughput analysis of PD-1 interacting proteins was performed using a pull-down assay and quantitative mass spectrometry proteomic analysis. For validation and molecular modeling, we selected tyrosine kinase receptor AXL-a recently reported cancer therapeutic target. We confirmed the PD-1/AXL interaction by immunoblotting and proximity ligation assay (PLA). Molecular dynamics (MD) simulations uncovered binding affinities and domain-specific interactions between extracellular (ECD) and intracellular (ICD) domains of PD-1 and AXL. ECD complexes exhibited strong binding affinity, further increasing for the ICD complexes, emphasizing the role of ICDs in the interaction. PD-1 phosphorylation mutant variants (Y223F and Y248F) did not disrupt the interaction but displayed varying strengths and binding affinities. Using bemcentinib, a selective AXL inhibitor, we observed reduced binding affinity in the PD-1/AXL interaction, although it was not abrogated. To facilitate the future translation of this finding into clinical application, we sought to validate the interaction in canine osteosarcoma. Osteosarcoma spontaneously occurs at significantly higher frequency in dogs and shares close genetic and pathological similarities with humans. We confirmed endogenous expression of PD-1 and AXL in canine osteosarcoma cells, with PD-1/AXL interaction preserved in the dog cells. Also, the interacting residues remain conserved in both species, indicating an important biological function of the interaction. Our study shed light on the molecular basis of the PD-1/AXL interaction with the implication for its conservation across species, providing a foundation for future research aimed at improving immunotherapy strategies and developing novel therapeutic approaches.

Phosphorylation in the Plasmodium falciparum Proteome: A Meta-Analysis of Publicly Available Data Sets

Malaria is a deadly disease caused by Apicomplexan parasites of the Plasmodium genus. Several species of the Plasmodium genus are known to be infectious to humans, of which P. falciparum is the most virulent. Post-translational modifications (PTMs) of proteins coordinate cell signaling and hence regulate many biological processes in P. falciparum homeostasis and host infection, of which the most highly studied is phosphorylation. Phosphosites on proteins can be identified by tandem mass spectrometry (MS) performed on enriched samples (phosphoproteomics), followed by downstream computational analyses. We have performed a large-scale meta-analysis of 11 publicly available phosphoproteomics data sets to build a comprehensive atlas of phosphosites in the P. falciparum proteome, using robust pipelines aimed at strict control of false identifications. We identified a total of 26,609 phosphorylated sites on P. falciparum proteins, split across three categories of data reliability (gold/silver/bronze). We identified significant sequence motifs, likely indicative of different groups of kinases responsible for different groups of phosphosites. Conservation analysis identified clusters of phosphoproteins that are highly conserved and others that are evolving faster within the Plasmodium genus, and implicated in different pathways. We were also able to identify over 180,000 phosphosites within Plasmodium species beyond falciparum, based on orthologue mapping. We also explored the structural context of phosphosites, identifying a strong enrichment for phosphosites on fast-evolving (low conservation) intrinsically disordered regions (IDRs) of proteins. In other species, IDRs have been shown to have an important role in modulating protein-protein interactions, particularly in signaling, and thus warranting further study for their roles in host-pathogen interactions. All data have been made available via UniProtKB, PRIDE, and PeptideAtlas, with visualization interfaces for exploring phosphosites in the context of other data on Plasmodium proteins.

Borrelia PeptideAtlas: A proteome resource of common Borrelia burgdorferi isolates for Lyme research

Lyme disease is caused by an infection with the spirochete Borrelia burgdorferi, and is the most common vector-borne disease in North America. B. burgdorferi isolates harbor extensive genomic and proteomic variability and further comparison of isolates is key to understanding the infectivity of the spirochetes and biological impacts of identified sequence variants. Here, we applied both transcriptome analysis and mass spectrometry-based proteomics to assemble peptide datasets of B. burgdorferi laboratory isolates B31, MM1, and the infective isolate B31-5A4, to provide a publicly available Borrelia PeptideAtlas. Included are total proteome, secretome, and membrane proteome identifications of the individual isolates. Proteomic data collected from 35 different experiment datasets, totaling 386 mass spectrometry runs, have identified 81,967 distinct peptides, which map to 1,113 proteins. The Borrelia PeptideAtlas covers 86% of the total B31 proteome of 1,291 protein sequences. The Borrelia PeptideAtlas is an extensible comprehensive peptide repository with proteomic information from B. burgdorferi isolates useful for Lyme disease research.

The hGIDGID4 E3 ubiquitin ligase complex targets ARHGAP11A to regulate cell migration

The human CTLH/GID (hGID) complex emerged as an important E3 ligase regulating multiple cellular processes, including cell cycle progression and metabolism. However, the range of biological functions controlled by hGID remains unexplored. Here, we used proximity-dependent biotinylation (BioID2) to identify proteins interacting with the hGID complex, among them, substrate candidates that bind GID4 in a pocket-dependent manner. Biochemical and cellular assays revealed that the hGIDGID4 E3 ligase binds and ubiquitinates ARHGAP11A, thereby targeting this RhoGAP for proteasomal degradation. Indeed, GID4 depletion or impeding the GID4 substrate binding pocket with the PFI-7 inhibitor stabilizes ARHGAP11A protein amounts, although it carries no functional N-terminal degron. Interestingly, GID4 inactivation impairs cell motility and directed cell movement by increasing ARHGAP11A levels at the cell periphery, where it inactivates RhoA. Together, we identified a wide range of hGIDGID4 E3 ligase substrates and uncovered a unique function of the hGIDGID4 E3 ligase regulating cell migration by targeting ARHGAP11A.

Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines

Next-generation T-cell-directed vaccines for COVID-19 focus on establishing lasting T-cell immunity against current and emerging SARS-CoV-2 variants. Precise identification of conserved T-cell epitopes is critical for designing effective vaccines. Here we introduce a comprehensive computational framework incorporating a machine learning algorithm-MHCvalidator-to enhance mass spectrometry-based immunopeptidomics sensitivity. MHCvalidator identifies unique T-cell epitopes presented by the B7 supertype, including an epitope from a + 1-frameshift in a truncated Spike antigen, supported by ribosome profiling. Analysis of 100,512 COVID-19 patient proteomes shows Spike antigen truncation in 0.85% of cases, revealing frameshifted viral antigens at the population level. Our EpiTrack pipeline tracks global mutations of MHCvalidator-identified CD8 + T-cell epitopes from the BNT162b4 vaccine. While most vaccine epitopes remain globally conserved, an immunodominant A*01-associated epitope mutates in Delta and Omicron variants. This work highlights SARS-CoV-2 antigenic features and emphasizes the importance of continuous adaptation in T-cell vaccine development.

The promiscuous biotin ligase TurboID reveals the proxisome of the T3SS chaperone IpgC in Shigella flexneri

Promiscuous biotin ligases derived from the bacterial enzyme BirA are used to identify proteins vicinal to a bait protein, thereby defining its proxisome. Despite the popularity of this approach, surprisingly little is known about its use in prokaryotes. Here, we compared the activity of four widely used promiscuous biotin ligases in the cytoplasm of Shigella flexneri, a pathogenic subgroup of Escherichia coli. Our data indicate that the kinetics of TurboID's biotinylating activity is the highest of those tested. In addition, TurboID showed reduced interaction with the natural BirA binding partners, BccP and the biotin operator, when compared to its ancestor BioID. We therefore evaluated the ability of TurboID to probe the proxisome of the type III secretion system (T3SS) chaperone IpgC and the transcriptional activator MxiE. When the T3SS is inactive (off-state), these proteins are inhibited by forming complexes with the T3SS substrates OspD1 and IpaBC, respectively. In contrast, when the T3SS is active (on-state), OspD1 and IpaBC are secreted allowing MxiE and IpgC to interact together and activate their target genes. The results obtained with the IpgC and TurboID fusions capture a good fraction of these known interactions. It also suggests that the availability of IpgC increases in the on-state, resulting in a greater number of proteins detected in its vicinity. Among these is the T3SS ATPase SpaL (also known as Spa47 or SctN), further supporting the notion that chaperones escort their substrate to the T3SS. Interestingly, a specific subset of proteins conserved in E. coli completes the IpgC proxisome in the on-state.IMPORTANCEPromiscuous biotin ligases are widely used to study protein function in eukaryotes. Strikingly, their use in prokaryotes has been rare. Indeed, the small volume and the cytoplasmic location of the biotin ligase's natural binding partners in these organisms pose unique challenges that can interfere with the study of the proxisome of proteins of interest. Here, we evaluated four of the most common promiscuous biotin ligases and found TurboID was best suited for use in the cytoplasm of Shigella flexneri. Using this method, we extended the proxisome of IpgC beyond its known direct binding partners involved in the regulation of the type III secretion system (T3SS) signaling cascade. Of particular interest for further study are transcription factors and housekeeping proteins that are enriched around IpgC when the T3SS is active. We propose a model in which the increased availability of IpgC in the on-state may allow cross-talk of the T3SS with other cellular processes.

Pervasive mislocalization of pathogenic coding variants underlying human disorders

Widespread sequencing has yielded thousands of missense variants predicted or confirmed as disease causing. This creates a new bottleneck: determining the functional impact of each variant-typically a painstaking, customized process undertaken one or a few genes and variants at a time. Here, we established a high-throughput imaging platform to assay the impact of coding variation on protein localization, evaluating 3,448 missense variants of over 1,000 genes and phenotypes. We discovered that mislocalization is a common consequence of coding variation, affecting about one-sixth of all pathogenic missense variants, all cellular compartments, and recessive and dominant disorders alike. Mislocalization is primarily driven by effects on protein stability and membrane insertion rather than disruptions of trafficking signals or specific interactions. Furthermore, mislocalization patterns help explain pleiotropy and disease severity and provide insights on variants of uncertain significance. Our publicly available resource extends our understanding of coding variation in human diseases.

Extracellular proximal interaction profiling by cell surface-targeted TurboID reveals LDLR as a partner of liganded EGFR

Plasma membrane proteins play pivotal roles in receiving and transducing signals from other cells and from the environment and are vital for cellular functionality. Enzyme-based, proximity-dependent approaches, such as biotin identification (BioID), combined with mass spectrometry have begun to illuminate the landscape of proximal protein interactions within intracellular compartments. To extend the potential of these approaches to study the extracellular environment, we developed extracellular TurboID (ecTurboID), a method designed to profile the interactions between proteins on the surfaces of living cells over short timescales using the fast-acting biotin ligase TurboID. After optimizing our experimental and data analysis strategies to capture extracellular proximity interactions, we used ecTurboID to reveal the proximal interactomes of several plasma membrane proteins, including the epidermal growth factor receptor (EGFR). We found that EGF stimulation induced an association between EGFR and the low-density lipoprotein receptor (LDLR) and changed the interactome of LDLR by increasing its proximity with proteins that regulate EGFR signaling. The identification of this interaction between two well-studied and clinically relevant receptors illustrates the utility of our modified proximity labeling methodology for identifying dynamic extracellular associations between plasma membrane proteins.

Detection and Quantification of Drug-Protein Adducts in Human Liver

Covalent protein adducts formed by drugs or their reactive metabolites are risk factors for adverse reactions, and inactivation of cytochrome P450 (CYP) enzymes. Characterization of drug-protein adducts is limited due to lack of methods identifying and quantifying covalent adducts in complex matrices. This study presents a workflow that combines data-dependent and data-independent acquisition (DDA and DIA) based liquid chromatography with tandem mass spectrometry (LC-MS/MS) to detect very low abundance adducts resulting from CYP mediated drug metabolism in human liver microsomes (HLMs). HLMs were incubated with raloxifene as a model compound and adducts were detected in 78 proteins, including CYP3A and CYP2C family enzymes. Experiments with recombinant CYP3A and CYP2C enzymes confirmed adduct formation in all CYPs tested, including CYPs not subject to time-dependent inhibition by raloxifene. These data suggest adducts can be benign. DIA analysis showed variable adduct abundance in many peptides between livers, but no concomitant decrease of unadducted peptides. This study sets a new standard for adduct detection in complex samples, offering insights into the human adductome resulting from reactive metabolite exposure. The methodology presented will aid mechanistic studies to identify, quantify and differentiate between adducts that result in adverse drug reactions and those that are benign.

Bulk and selective autophagy cooperate to remodel a fungal proteome in response to changing nutrient availability

Cells remodel their proteomes in response to changing environments by coordinating changes in protein synthesis and degradation. In yeast, such degradation involves both proteasomal and vacuolar activity, with a mixture of bulk and selective autophagy delivering many of the vacuolar substrates. Although these pathways are known to be generally important for such remodeling, their relative contributions have not been reported on a proteome-wide basis. To assess this, we developed a method to pulse-label the methylotrophic yeast Komagataella phaffii (i.e. Pichia pastoris) with isotopically labeled nutrients, which, when coupled to quantitative proteomics, allowed us to globally monitor protein degradation on a protein-by-protein basis following an environmental perturbation. Using genetic ablations, we found that a targeted combination of bulk and selective autophagy drove the vast majority of the observed proteome remodeling activity, with minimal non-autophagic contributions. Cytosolic proteins and protein complexes, including ribosomes, were degraded via Atg11-independent bulk autophagy, whereas proteins targeted to the peroxisome and mitochondria were primarily degraded in an Atg11-dependent manner. Notably, these degradative pathways were independently regulated by environmental cues. Taken together, our new approach greatly increases the range of known autophagic substrates and highlights the outsized impact of autophagy on proteome remodeling. Moreover, the resulting datasets, which we have packaged in an accessible online database, constitute a rich resource for identifying proteins and pathways involved in fungal proteome remodeling.

PTMoreR-enabled cross-species PTM mapping and comparative phosphoproteomics across mammals

To support PTM proteomic analysis and annotation in different species, we developed PTMoreR, a user-friendly tool that considers the surrounding amino acid sequences of PTM sites during BLAST, enabling a motif-centric analysis across species. By controlling sequence window similarity, PTMoreR can map phosphoproteomic results between any two species, perform site-level functional enrichment analysis, and generate kinase-substrate networks. We demonstrate that the majority of real P-sites in mice can be inferred from experimentally derived human P-sites with PTMoreR mapping. Furthermore, the compositions of 129 mammalian phosphoproteomes can also be predicted using PTMoreR. The method also identifies cross-species phosphorylation events that occur on proteins with an increased tendency to respond to the environmental factors. Moreover, the classic kinase motifs can be extracted across mammalian species, offering an evolutionary angle for refining current motifs. PTMoreR supports PTM proteomics in non-human species and facilitates quantitative phosphoproteomic analysis.

The Zea mays PeptideAtlas: A New Maize Community Resource

This study presents the Maize PeptideAtlas resource (www.peptideatlas.org/builds/maize) to help solve questions about the maize proteome. Publicly available raw tandem mass spectrometry (MS/MS) data for maize collected from ProteomeXchange were reanalyzed through a uniform processing and metadata annotation pipeline. These data are from a wide range of genetic backgrounds and many sample types and experimental conditions. The protein search space included different maize genome annotations for the B73 inbred line from MaizeGDB, UniProtKB, NCBI RefSeq, and for the W22 inbred line. 445 million MS/MS spectra were searched, of which 120 million were matched to 0.37 million distinct peptides. Peptides were matched to 66.2% of proteins in the most recent B73 nuclear genome annotation. Furthermore, most conserved plastid- and mitochondrial-encoded proteins (NCBI RefSeq annotations) were identified. Peptides and proteins identified in the other B73 genome annotations will improve maize genome annotation. We also illustrate the high-confidence detection of unique W22 proteins. N-terminal acetylation, phosphorylation, ubiquitination, and three lysine acylations (K-acetyl, K-malonyl, and K-hydroxyisobutyryl) were identified and can be inspected through a PTM viewer in PeptideAtlas. All matched MS/MS-derived peptide data are linked to spectral, technical, and biological metadata. This new PeptideAtlas is integrated in MaizeGDB with a peptide track in JBrowse.

SeFilter-DIA: Squeeze-and-Excitation Network for Filtering High-Confidence Peptides of Data-Independent Acquisition Proteomics

Mass spectrometry is crucial in proteomics analysis, particularly using Data Independent Acquisition (DIA) for reliable and reproducible mass spectrometry data acquisition, enabling broad mass-to-charge ratio coverage and high throughput. DIA-NN, a prominent deep learning software in DIA proteome analysis, generates peptide results but may include low-confidence peptides. Conventionally, biologists have to manually screen peptide fragment ion chromatogram peaks (XIC) for identifying high-confidence peptides, a time-consuming and subjective process prone to variability. In this study, we introduce SeFilter-DIA, a deep learning algorithm, aiming at automating the identification of high-confidence peptides. Leveraging compressed excitation neural network and residual network models, SeFilter-DIA extracts XIC features and effectively discerns between high and low-confidence peptides. Evaluation of the benchmark datasets demonstrates SeFilter-DIA achieving 99.6% AUC on the test set and 97% for other performance indicators. Furthermore, SeFilter-DIA is applicable for screening peptides with phosphorylation modifications. These results demonstrate the potential of SeFilter-DIA to replace manual screening, providing an efficient and objective approach for high-confidence peptide identification while mitigating associated limitations.

FAM72A degrades UNG2 through the GID/CTLH complex to promote mutagenic repair during antibody maturation

A diverse antibody repertoire is essential for humoral immunity. Antibody diversification requires the introduction of deoxyuridine (dU) mutations within immunoglobulin genes to initiate somatic hypermutation (SHM) and class switch recombination (CSR). dUs are normally recognized and excised by the base excision repair (BER) protein uracil-DNA glycosylase 2 (UNG2). However, FAM72A downregulates UNG2 permitting dUs to persist and trigger SHM and CSR. How FAM72A promotes UNG2 degradation is unknown. Here, we show that FAM72A recruits a C-terminal to LisH (CTLH) E3 ligase complex to target UNG2 for proteasomal degradation. Deficiency in CTLH complex components result in elevated UNG2 and reduced SHM and CSR. Cryo-EM structural analysis reveals FAM72A directly binds to MKLN1 within the CTLH complex to recruit and ubiquitinate UNG2. Our study further suggests that FAM72A hijacks the CTLH complex to promote mutagenesis in cancer. These findings show that FAM72A is an E3 ligase substrate adaptor critical for humoral immunity and cancer development.

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

Quantitative proteomic analysis of plasma membranes from the fish pathogen Saprolegnia parasitica reveals promising targets for disease control

The phylum Oomycota contains economically important pathogens of animals and plants, including Saprolegnia parasitica, the causal agent of the fish disease saprolegniasis. Due to intense fish farming and banning of the most effective control measures, saprolegniasis has re-emerged as a major challenge for the aquaculture industry. Oomycete cells are surrounded by a polysaccharide-rich cell wall matrix that, in addition to being essential for cell growth, also functions as a protective "armor." Consequently, the enzymes responsible for cell wall synthesis provide potential targets for disease control. Oomycete cell wall biosynthetic enzymes are predicted to be plasma membrane proteins. To identify these proteins, we applied a quantitative (iTRAQ) mass spectrometry-based proteomics approach to the plasma membrane of the hyphal cells of S. parasitica, providing the first complete plasma membrane proteome of an oomycete species. Of significance is the identification of 65 proteins enriched in detergent-resistant microdomains (DRMs). In silico analysis showed that DRM-enriched proteins are mainly involved in molecular transport and β-1,3-glucan synthesis, potentially contributing to pathogenesis. Moreover, biochemical characterization of the glycosyltransferase activity in these microdomains further supported their role in β-1,3-glucan synthesis. Altogether, the knowledge gained in this study provides a basis for developing disease control measures targeting specific plasma membrane proteins in S. parasitica.IMPORTANCEThe significance of this research lies in its potential to combat saprolegniasis, a detrimental fish disease, which has resurged due to intensive fish farming and regulatory restrictions. By targeting enzymes responsible for cell wall synthesis in Saprolegnia parasitica, this study uncovers potential avenues for disease control. Particularly noteworthy is the identification of several proteins enriched in membrane microdomains, offering insights into molecular mechanisms potentially involved in pathogenesis. Understanding the role of these proteins provides a foundation for developing targeted disease control measures. Overall, this research holds promise for safeguarding the aquaculture industry against the challenges posed by saprolegniasis.

The TSC22D, WNK, and NRBP gene families exhibit functional buffering and evolved with Metazoa for cell volume regulation

The ability to sense and respond to osmotic fluctuations is critical for the maintenance of cellular integrity. We used gene co-essentiality analysis to identify an unappreciated relationship between TSC22D2, WNK1, and NRBP1 in regulating cell volume homeostasis. All of these genes have paralogs and are functionally buffered for osmo-sensing and cell volume control. Within seconds of hyperosmotic stress, TSC22D, WNK, and NRBP family members physically associate into biomolecular condensates, a process that is dependent on intrinsically disordered regions (IDRs). A close examination of these protein families across metazoans revealed that TSC22D genes evolved alongside a domain in NRBPs that specifically binds to TSC22D proteins, which we have termed NbrT (NRBP binding region with TSC22D), and this co-evolution is accompanied by rapid IDR length expansion in WNK-family kinases. Our study reveals that TSC22D, WNK, and NRBP genes evolved in metazoans to co-regulate rapid cell volume changes in response to osmolarity.

Repression of mRNA translation initiation by GIGYF1 via disrupting the eIF3-eIF4G1 interaction

Viruses can selectively repress the translation of mRNAs involved in the antiviral response. RNA viruses exploit the Grb10-interacting GYF (glycine-tyrosine-phenylalanine) proteins 2 (GIGYF2) and eukaryotic translation initiation factor 4E (eIF4E) homologous protein 4EHP to selectively repress the translation of transcripts such as Ifnb1, which encodes the antiviral cytokine interferon-β (IFN-β). Herein, we reveal that GIGYF1, a paralog of GIGYF2, robustly represses cellular mRNA translation through a distinct 4EHP-independent mechanism. Upon recruitment to a target mRNA, GIGYF1 binds to subunits of eukaryotic translation initiation factor 3 (eIF3) at the eIF3-eIF4G1 interaction interface. This interaction disrupts the eIF3 binding to eIF4G1, resulting in transcript-specific translational repression. Depletion of GIGYF1 induces a robust immune response by derepressing IFN-β production. Our study highlights a unique mechanism of translational regulation by GIGYF1 that involves sequestering eIF3 and abrogating its binding to eIF4G1. This mechanism has profound implications for the host response to viral infections.

Waves of regulated protein expression and phosphorylation rewire the proteome to drive gametogenesis in budding yeast

Sexually reproducing eukaryotes employ a developmentally regulated cell division program-meiosis-to generate haploid gametes from diploid germ cells. To understand how gametes arise, we generated a proteomic census encompassing the entire meiotic program of budding yeast. We found that concerted waves of protein expression and phosphorylation modify nearly all cellular pathways to support meiotic entry, meiotic progression, and gamete morphogenesis. Leveraging this comprehensive resource, we pinpointed dynamic changes in mitochondrial components and showed that phosphorylation of the FoF1-ATP synthase complex is required for efficient gametogenesis. Furthermore, using cryoET as an orthogonal approach to visualize mitochondria, we uncovered highly ordered filament arrays of Ald4ALDH2, a conserved aldehyde dehydrogenase that is highly expressed and phosphorylated during meiosis. Notably, phosphorylation-resistant mutants failed to accumulate filaments, suggesting that phosphorylation regulates context-specific Ald4ALDH2 polymerization. Overall, this proteomic census constitutes a broad resource to guide the exploration of the unique sequence of events underpinning gametogenesis.

Meta-Analysis of Rice Phosphoproteomics Data to Understand Variation in Cell Signaling Across the Rice Pan-Genome

Phosphorylation is the most studied post-translational modification, and has multiple biological functions. In this study, we have reanalyzed publicly available mass spectrometry proteomics data sets enriched for phosphopeptides from Asian rice (Oryza sativa). In total we identified 15,565 phosphosites on serine, threonine, and tyrosine residues on rice proteins. We identified sequence motifs for phosphosites, and link motifs to enrichment of different biological processes, indicating different downstream regulation likely caused by different kinase groups. We cross-referenced phosphosites against the rice 3,000 genomes, to identify single amino acid variations (SAAVs) within or proximal to phosphosites that could cause loss of a site in a given rice variety and clustered the data to identify groups of sites with similar patterns across rice family groups. The data has been loaded into UniProt Knowledge-Base─enabling researchers to visualize sites alongside other data on rice proteins, e.g., structural models from AlphaFold2, PeptideAtlas, and the PRIDE database─enabling visualization of source evidence, including scores and supporting mass spectra.

APIR: Aggregating Universal Proteomics Database Search Algorithms for Peptide Identification with FDR Control

Advances in mass spectrometry (MS) have enabled high-throughput analysis of proteomes in biological systems. The state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide-spectrum matches (PSMs), which convert mass spectra to peptide sequences. Different database search algorithms use distinct search strategies and thus may identify unique PSMs. However, no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and a control on the false discovery rate (FDR). To fill in this gap, we proposed a statistical framework, Aggregation of Peptide Identification Results (APIR), that is universally compatible with all database search algorithms. Notably, under an FDR threshold, APIR is guaranteed to identify at least as many, if not more, peptides as individual database search algorithms do. Evaluation of APIR on a complex proteomics standard dataset showed that APIR outpowers individual database search algorithms and empirically controls the FDR. Real data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search algorithms. The APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis, e.g., differential gene expression analysis on RNA sequencing data. The APIR R package is available at https://github.com/yiling0210/APIR.

Soil Metaproteomics for Microbial Community Profiling: Methodologies and Challenges

Soil represents a complex and dynamic ecosystem, hosting a myriad of microorganisms that coexist and play vital roles in nutrient cycling and organic matter transformation. Among these microorganisms, bacteria and fungi are key members of the microbial community, profoundly influencing the fate of nitrogen, sulfur, and carbon in terrestrial environments. Understanding the intricacies of soil ecosystems and the biological processes orchestrated by microbial communities necessitates a deep dive into their composition and metabolic activities. The advent of next-generation sequencing and 'omics' techniques, such as metagenomics and metaproteomics, has revolutionized our understanding of microbial ecology and the functional dynamics of soil microbial communities. Metagenomics enables the identification of microbial community composition in soil, while metaproteomics sheds light on the current biological functions performed by these communities. However, metaproteomics presents several challenges, both technical and computational. Factors such as the presence of humic acids and variations in extraction methods can influence protein yield, while the absence of high-resolution mass spectrometry and comprehensive protein databases limits the depth of protein identification. Notwithstanding these limitations, metaproteomics remains a potent tool for unraveling the intricate biological processes and functions of soil microbial communities. In this review, we delve into the methodologies and challenges of metaproteomics in soil research, covering aspects such as protein extraction, identification, and bioinformatics analysis. Furthermore, we explore the applications of metaproteomics in soil bioremediation, highlighting its potential in addressing environmental challenges.

Functional surface expression of immunoglobulin cleavage systems in a candidate Mycoplasma vaccine chassis

The Mycoplasma Immunoglobulin Binding/Protease (MIB-MIP) system is a candidate 'virulence factor present in multiple pathogenic species of the Mollicutes, including the fast-growing species Mycoplasma feriruminatoris. The MIB-MIP system cleaves the heavy chain of host immunoglobulins, hence affecting antigen-antibody interactions and potentially facilitating immune evasion. In this work, using -omics technologies and 5'RACE, we show that the four copies of the M. feriruminatoris MIB-MIP system have different expression levels and are transcribed as operons controlled by four different promoters. Individual MIB-MIP gene pairs of M. feriruminatoris and other Mollicutes were introduced in an engineered M. feriruminatoris strain devoid of MIB-MIP genes and were tested for their functionality using newly developed oriC-based plasmids. The two proteins are functionally expressed at the surface of M. feriruminatoris, which confirms the possibility to display large membrane-associated proteins in this bacterium. However, functional expression of heterologous MIB-MIP systems introduced in this engineered strain from phylogenetically distant porcine Mollicutes like Mesomycoplasma hyorhinis or Mesomycoplasma hyopneumoniae could not be achieved. Finally, since M. feriruminatoris is a candidate for biomedical applications such as drug delivery, we confirmed its safety in vivo in domestic goats, which are the closest livestock relatives to its native host the Alpine ibex.

Novel FFPE proteomics method suggests prolactin induced protein as hormone induced cytoskeleton remodeling spatial biomarker

Robotically assisted proteomics provides insights into the regulation of multiple proteins achieving excellent spatial resolution. However, developing an effective method for spatially resolved quantitative proteomics of formalin fixed paraffin embedded tissue (FFPE) in an accessible and economical manner remains challenging. We introduce non-robotic In-insert FFPE proteomics approach, combining glass insert FFPE tissue processing with spatial quantitative data-independent mass spectrometry (DIA). In-insert approach identifies 450 proteins from a 5 µm thick breast FFPE tissue voxel with 50 µm lateral dimensions covering several tens of cells. Furthermore, In-insert approach associated a keratin series and moesin (MOES) with prolactin-induced protein (PIP) indicating their prolactin and/or estrogen regulation. Our data suggest that PIP is a spatial biomarker for hormonally triggered cytoskeletal remodeling, potentially useful for screening hormonally affected hotspots in breast tissue. In-insert proteomics represents an alternative FFPE processing method, requiring minimal laboratory equipment and skills to generate spatial proteotype repositories from FFPE tissue.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability

Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors for a wide range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed "DrugMap," an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NF-κB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NF-κB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription-factor activity.

Mapping the global interactome of the ARF family reveals spatial organization in cellular signaling pathways

The ADP-ribosylation factors (ARFs) and ARF-like (ARL) GTPases serve as essential molecular switches governing a wide array of cellular processes. In this study, we used proximity-dependent biotin identification (BioID) to comprehensively map the interactome of 28 out of 29 ARF and ARL proteins in two cellular models. Through this approach, we identified ∼3000 high-confidence proximal interactors, enabling us to assign subcellular localizations to the family members. Notably, we uncovered previously undefined localizations for ARL4D and ARL10. Clustering analyses further exposed the distinctiveness of the interactors identified with these two GTPases. We also reveal that the expression of the understudied member ARL14 is confined to the stomach and intestines. We identified phospholipase D1 (PLD1) and the ESCPE-1 complex, more precisely, SNX1, as proximity interactors. Functional assays demonstrated that ARL14 can activate PLD1 in cellulo and is involved in cargo trafficking via the ESCPE-1 complex. Overall, the BioID data generated in this study provide a valuable resource for dissecting the complexities of ARF and ARL spatial organization and signaling.

Evolution of chromosome-arm aberrations in breast cancer through genetic network rewiring

The basal breast cancer subtype is enriched for triple-negative breast cancer (TNBC) and displays consistent large chromosomal deletions. Here, we characterize evolution and maintenance of chromosome 4p (chr4p) loss in basal breast cancer. Analysis of The Cancer Genome Atlas data shows recurrent deletion of chr4p in basal breast cancer. Phylogenetic analysis of a panel of 23 primary tumor/patient-derived xenograft basal breast cancers reveals early evolution of chr4p deletion. Mechanistically we show that chr4p loss is associated with enhanced proliferation. Gene function studies identify an unknown gene, C4orf19, within chr4p, which suppresses proliferation when overexpressed-a member of the PDCD10-GCKIII kinase module we name PGCKA1. Genome-wide pooled overexpression screens using a barcoded library of human open reading frames identify chromosomal regions, including chr4p, that suppress proliferation when overexpressed in a context-dependent manner, implicating network interactions. Together, these results shed light on the early emergence of complex aneuploid karyotypes involving chr4p and adaptive landscapes shaping breast cancer genomes.

Regulation of urea cycle by reversible high-stoichiometry lysine succinylation

The post-translational modification lysine succinylation is implicated in the regulation of various metabolic pathways. However, its biological relevance remains uncertain due to methodological difficulties in determining high-impact succinylation sites. Here, using stable isotope labelling and data-independent acquisition mass spectrometry, we quantified lysine succinylation stoichiometries in mouse livers. Despite the low overall stoichiometry of lysine succinylation, several high-stoichiometry sites were identified, especially upon deletion of the desuccinylase SIRT5. In particular, multiple high-stoichiometry lysine sites identified in argininosuccinate synthase (ASS1), a key enzyme in the urea cycle, are regulated by SIRT5. Mutation of the high-stoichiometry lysine in ASS1 to succinyl-mimetic glutamic acid significantly decreased its enzymatic activity. Metabolomics profiling confirms that SIRT5 deficiency decreases urea cycle activity in liver. Importantly, SIRT5 deficiency compromises ammonia tolerance, which can be reversed by the overexpression of wild-type, but not succinyl-mimetic, ASS1. Therefore, lysine succinylation is functionally important in ammonia metabolism.

ESRP1 controls biogenesis and function of a large abundant multiexon circRNA

While the majority of circRNAs are formed from infrequent back-splicing of exons from protein coding genes, some can be produced at quite high level and in a regulated manner. We describe the regulation, biogenesis and function of circDOCK1(2-27), a large, abundant circular RNA that is highly regulated during epithelial-mesenchymal transition (EMT) and whose formation depends on the epithelial splicing regulator ESRP1. CircDOCK1(2-27) synthesis in epithelial cells represses cell motility both by diverting transcripts from DOCK1 mRNA production to circRNA formation and by direct inhibition of migration by the circRNA. HITS-CLIP analysis and CRISPR-mediated deletions indicate ESRP1 controls circDOCK1(2-27) biosynthesis by binding a GGU-containing repeat region in intron 1 and detaining its splicing until Pol II completes its 157 kb journey to exon 27. Proximity-dependent biotinylation (BioID) assay suggests ESRP1 may modify the RNP landscape of intron 1 in a way that disfavours communication of exon 1 with exon 2, rather than physically bridging exon 2 to exon 27. The X-ray crystal structure of RNA-bound ESRP1 qRRM2 domain reveals it binds to GGU motifs, with the guanines embedded in clamp-like aromatic pockets in the protein.

WOMBAT-P: Benchmarking Label-Free Proteomics Data Analysis Workflows

The inherent diversity of approaches in proteomics research has led to a wide range of software solutions for data analysis. These software solutions encompass multiple tools, each employing different algorithms for various tasks such as peptide-spectrum matching, protein inference, quantification, statistical analysis, and visualization. To enable an unbiased comparison of commonly used bottom-up label-free proteomics workflows, we introduce WOMBAT-P, a versatile platform designed for automated benchmarking and comparison. WOMBAT-P simplifies the processing of public data by utilizing the sample and data relationship format for proteomics (SDRF-Proteomics) as input. This feature streamlines the analysis of annotated local or public ProteomeXchange data sets, promoting efficient comparisons among diverse outputs. Through an evaluation using experimental ground truth data and a realistic biological data set, we uncover significant disparities and a limited overlap in the quantified proteins. WOMBAT-P not only enables rapid execution and seamless comparison of workflows but also provides valuable insights into the capabilities of different software solutions. These benchmarking metrics are a valuable resource for researchers in selecting the most suitable workflow for their specific data sets. The modular architecture of WOMBAT-P promotes extensibility and customization. The software is available at https://github.com/wombat-p/WOMBAT-Pipelines.

Detection of the Arabidopsis Proteome and Its Post-translational Modifications and the Nature of the Unobserved (Dark) Proteome in PeptideAtlas

This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource (build 2023-10) providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected post-translational modifications (PTMs), and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18,267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins, and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome: the "dark" proteome. This dark proteome is highly enriched for E3 ligases, transcription factors, and for certain (e.g., CLE, IDA, PSY) but not other (e.g., THIONIN, CAP) signaling peptides families. A machine learning model trained on RNA expression data and protein properties predicts the probability that proteins will be detected. The model aids in discovery of proteins with short half-life (e.g., SIG1,3 and ERF-VII TFs) and for developing strategies to identify the missing proteins. PeptideAtlas is linked to TAIR, tracks in JBrowse, and several other community proteomics resources.

Multidimensional Cross-Linking and Real-Time Informatics for Multiprotein Interaction Studies

Chemical cross-linking combined with mass spectrometry is a technique used to study protein structures and identify protein complexes. Traditionally, chemical cross-linkers contain two reactive groups, allowing them to covalently bond a pair of proximal residues, either within a protein or between two proteins. The output of a cross-linking experiment is a list of interacting site pairs that provide structural constraints for modeling of new structures and complexes. Due to the binary reactive nature of cross-linking reagents, only pairs of interacting sites can be directly observed, and assembly of higher-order structures typically requires prior knowledge of complex composition or iterative docking to produce a putative model. Here, we describe a new tetrameric cross-linker bearing four amine-reactive groups, allowing it to covalently link up to four proteins simultaneously and a real-time instrument method to facilitate the identification of these tetrameric cross-links. We applied this new cross-linker to isolated mitochondria and identified a number of higher-order cross-links in various OXPHOS complexes and ATP synthase, demonstrating its utility in characterizing complex interfaces. We also show that higher-order cross-links can be used to effectively filter models of large protein assemblies generated by using Alphafold. Higher-dimensional cross-linking provides a new avenue for characterizing multiple protein interfaces, even in complex samples such as intact mitochondria.

Trace Sample Proteome Quantification by Data-Dependent Acquisition without Dynamic Exclusion

Despite continuous technological improvements in sample preparation, mass-spectrometry-based proteomics for trace samples faces the challenges of sensitivity, quantification accuracy, and reproducibility. Herein, we explored the applicability of turboDDA (a method that uses data-dependent acquisition without dynamic exclusion) for quantitative proteomics of trace samples. After systematic optimization of acquisition parameters, we compared the performance of turboDDA with that of data-dependent acquisition with dynamic exclusion (DEDDA). By benchmarking the analysis of trace unlabeled human cell digests, turboDDA showed substantially better sensitivity in comparison with DEDDA, whether for unfractionated or high pH fractionated samples. Furthermore, through designing an iTRAQ-labeled three-proteome model (i.e., tryptic digest of protein lysates from yeast, human, and E. coli) to document the interference effect, we evaluated the quantification interference, accuracy, reproducibility of iTRAQ labeled trace samples, and the impact of PIF (precursor intensity fraction) cutoff for different approaches (turboDDA and DEDDA). The results showed that improved quantification accuracy and reproducibility could be achieved by turboDDA, while a more stringent PIF cutoff resulted in more accurate quantification but less peptide identification for both approaches. Finally, the turboDDA strategy was applied to the differential analysis of limited amounts of human lung cancer cell samples, showing great promise in trace proteomics sample analysis.

The Identification of Nuclear FMRP Isoform Iso6 Partners

A deficiency of FMRP, a canonical RNA-binding protein, causes the development of Fragile X Syndrome (FXS), which is characterised by multiple phenotypes, including neurodevelopmental disorders, intellectual disability, and autism. Due to the alternative splicing of the encoding FMR1 gene, multiple FMRP isoforms are produced consisting of full-length predominantly cytoplasmic (i.e., iso1) isoforms involved in translation and truncated nuclear (i.e., iso6) isoforms with orphan functions. However, we recently implicated nuclear FMRP isoforms in DNA damage response, showing that they negatively regulate the accumulation of anaphase DNA genomic instability bridges. This finding provided evidence that the cytoplasmic and nuclear functions of FMRP are uncoupled played by respective cytoplasmic and nuclear isoforms, potentially involving specific interactions. While interaction partners of cytoplasmic FMRP have been reported, the identity of nuclear FMRP isoform partners remains to be established. Using affinity purification coupled with mass spectrometry, we mapped the nuclear interactome of the FMRP isoform iso6 in U2OS. In doing so, we found FMRP nuclear interaction partners to be involved in RNA processing, pre-mRNA splicing, ribosome biogenesis, DNA replication and damage response, chromatin remodeling and chromosome segregation. By comparing interactions between nuclear iso6 and cytoplasmic iso1, we report a set of partners that bind specifically to the nuclear isoforms, mainly proteins involved in DNA-associated processes and proteasomal proteins, which is consistent with our finding that proteasome targets the nuclear FMRP iso6. The specific interactions with the nuclear isoform 6 are regulated by replication stress, while those with the cytoplasmic isoform 1 are largely insensitive to such stress, further supporting a specific role of nuclear isoforms in DNA damage response induced by replicative stress, potentially regulated by the proteasome.

A meta-analysis of rice phosphoproteomics data to understand variation in cell signalling across the rice pan-genome

Phosphorylation is the most studied post-translational modification, and has multiple biological functions. In this study, we have re-analysed publicly available mass spectrometry proteomics datasets enriched for phosphopeptides from Asian rice (Oryza sativa). In total we identified 15,522 phosphosites on serine, threonine and tyrosine residues on rice proteins. We identified sequence motifs for phosphosites, and link motifs to enrichment of different biological processes, indicating different downstream regulation likely caused by different kinase groups. We cross-referenced phosphosites against the rice 3,000 genomes, to identify single amino acid variations (SAAVs) within or proximal to phosphosites that could cause loss of a site in a given rice variety. The data was clustered to identify groups of sites with similar patterns across rice family groups, for example those highly conserved in Japonica, but mostly absent in Aus type rice varieties - known to have different responses to drought. These resources can assist rice researchers to discover alleles with significantly different functional effects across rice varieties. The data has been loaded into UniProt Knowledge-Base - enabling researchers to visualise sites alongside other data on rice proteins e.g. structural models from AlphaFold2, PeptideAtlas and the PRIDE database - enabling visualisation of source evidence, including scores and supporting mass spectra.

Comprehensive Overview of Bottom-Up Proteomics using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods to aid the novice and experienced researcher. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this work to serve as a basic resource for new practitioners in the field of shotgun or bottom-up proteomics.

Efficient Analysis of Proteome-Wide FPOP Data by FragPipe

Monitoring protein structure before and after environmental alterations (e.g., different cell states) can give insights into the role and function of proteins. Fast photochemical oxidation of proteins (FPOP) coupled with mass spectrometry (MS) allows for monitoring of structural rearrangements by exposing proteins to OH radicals that oxidize solvent-accessible residues, indicating protein regions undergoing movement. Some of the benefits of FPOP include high throughput and a lack of scrambling due to label irreversibility. However, the challenges of processing FPOP data have thus far limited its proteome-scale uses. Here, we present a computational workflow for fast and sensitive analysis of FPOP data sets. Our workflow, implemented as part of the FragPipe computational platform, combines the speed of the MSFragger search with a unique hybrid search method to restrict the large search space of FPOP modifications. Together, these features enable more than 10-fold faster FPOP searches that identify 150% more modified peptide spectra than previous methods. We hope this new workflow will increase the accessibility of FPOP to enable more protein structure and function relationships to be explored.

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability

Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors of a wide-range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed DrugMap , an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NFκB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NFκB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription factor activity.

A comprehensive spectral assay library to quantify the Halobacterium salinarum NRC-1 proteome by DIA/SWATH-MS

Data-Independent Acquisition (DIA) is a mass spectrometry-based method to reliably identify and reproducibly quantify large fractions of a target proteome. The peptide-centric data analysis strategy employed in DIA requires a priori generated spectral assay libraries. Such assay libraries allow to extract quantitative data in a targeted approach and have been generated for human, mouse, zebrafish, E. coli and few other organisms. However, a spectral assay library for the extreme halophilic archaeon Halobacterium salinarum NRC-1, a model organism that contributed to several notable discoveries, is not publicly available yet. Here, we report a comprehensive spectral assay library to measure 2,563 of 2,646 annotated H. salinarum NRC-1 proteins. We demonstrate the utility of this library by measuring global protein abundances over time under standard growth conditions. The H. salinarum NRC-1 library includes 21,074 distinct peptides representing 97% of the predicted proteome and provides a new, valuable resource to confidently measure and quantify any protein of this archaeon. Data and spectral assay libraries are available via ProteomeXchange (PXD042770, PXD042774) and SWATHAtlas (SAL00312-SAL00319).

Spectroscape enables real-time query and visualization of a spectral archive in proteomics

In proteomics, spectral archives organize the enormous amounts of publicly available peptide tandem mass spectra by similarity, offering opportunities for error correction and novel discoveries. Here we adapt an indexing algorithm developed by Facebook for organizing online multimedia resources to tandem mass spectra and achieve practically instantaneous retrieval and clustering of approximate nearest neighbors in a large spectral archive. An interactive web-based graphical user interface enables the user to view a query spectrum in its clustered neighborhood, which facilitates contextual validation of peptide identifications and exploration of the dark proteome.