Interrogation of RNA-protein interaction dynamics in bacterial growth

Characterising RNA-protein interaction dynamics is fundamental to understand how bacteria respond to their environment. In this study, we have analysed the dynamics of 91% of the Escherichia coli expressed proteome and the RNA-interaction properties of 271 RNA-binding proteins (RBPs) at different growth phases. We find that 68% of RBPs differentially bind RNA across growth phases and characterise 17 previously unannotated proteins as bacterial RBPs including YfiF, a ncRNA-binding protein. While these new RBPs are mostly present in Proteobacteria, two of them are orthologs of human mitochondrial proteins associated with rare metabolic disorders. Moreover, we reveal novel RBP functions for proteins such as the chaperone HtpG, a new stationary phase tRNA-binding protein. For the first time, the dynamics of the bacterial RBPome have been interrogated, showcasing how this approach can reveal the function of uncharacterised proteins and identify critical RNA-protein interactions for cell growth which could inform new antimicrobial therapies.

System-wide analysis of RNA and protein subcellular localization dynamics

Although the subcellular dynamics of RNA and proteins are key determinants of cell homeostasis, their characterization is still challenging. Here we present an integrative framework to simultaneously interrogate the dynamics of the transcriptome and proteome at subcellular resolution by combining two methods: localization of RNA (LoRNA) and a streamlined density-based localization of proteins by isotope tagging (dLOPIT) to map RNA and protein to organelles (nucleus, endoplasmic reticulum and mitochondria) and membraneless compartments (cytosol, nucleolus and cytosolic granules). Interrogating all RNA subcellular locations at once enables system-wide quantification of the proportional distribution of RNA. We obtain a cell-wide overview of localization dynamics for 31,839 transcripts and 5,314 proteins during the unfolded protein response, revealing that endoplasmic reticulum-localized transcripts are more efficiently recruited to cytosolic granules than cytosolic RNAs, and that the translation initiation factor eIF3d is key to sustaining cytoskeletal function. Overall, we provide the most comprehensive overview so far of RNA and protein subcellular localization dynamics.

N1-methylpseudouridylation of mRNA causes +1 ribosomal frameshifting

In vitro-transcribed (IVT) mRNAs are modalities that can combat human disease, exemplified by their use as vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). IVT mRNAs are transfected into target cells, where they are translated into recombinant protein, and the biological activity or immunogenicity of the encoded protein exerts an intended therapeutic effect1,2. Modified ribonucleotides are commonly incorporated into therapeutic IVT mRNAs to decrease their innate immunogenicity3-5, but their effects on mRNA translation fidelity have not been fully explored. Here we demonstrate that incorporation of N1-methylpseudouridine into mRNA results in +1 ribosomal frameshifting in vitro and that cellular immunity in mice and humans to +1 frameshifted products from BNT162b2 vaccine mRNA translation occurs after vaccination. The +1 ribosome frameshifting observed is probably a consequence of N1-methylpseudouridine-induced ribosome stalling during IVT mRNA translation, with frameshifting occurring at ribosome slippery sequences. However, we demonstrate that synonymous targeting of such slippery sequences provides an effective strategy to reduce the production of frameshifted products. Overall, these data increase our understanding of how modified ribonucleotides affect the fidelity of mRNA translation, and although there are no adverse outcomes reported from mistranslation of mRNA-based SARS-CoV-2 vaccines in humans, these data highlight potential off-target effects for future mRNA-based therapeutics and demonstrate the requirement for sequence optimization.

PLK1 inhibition dampens NLRP3 inflammasome-elicited response in inflammatory disease models

Unabated activation of the NLR family pyrin domain-containing 3 (NLRP3) inflammasome is linked with the pathogenesis of various inflammatory disorders. Polo-like kinase 1 (PLK1) has been widely studied for its role in mitosis. Here, using both pharmacological and genetic approaches, we demonstrate that PLK1 promoted NLRP3 inflammasome activation at cell interphase. Using an unbiased proximity-dependent biotin identification (Bio-ID) screen for the PLK1 interactome in macrophages, we show an enhanced proximal association of NLRP3 with PLK1 upon NLRP3 inflammasome activation. We further confirmed the interaction between PLK1 and NLRP3 and identified the interacting domains. Mechanistically, we show that PLK1 orchestrated the microtubule-organizing center (MTOC) structure and NLRP3 subcellular positioning upon inflammasome activation. Treatment with a selective PLK1 kinase inhibitor suppressed IL-1β production in in vivo inflammatory models, including LPS-induced endotoxemia and monosodium urate-induced peritonitis in mice. Our results uncover a role of PLK1 in regulating NLRP3 inflammasome activation during interphase and identify pharmacological inhibition of PLK1 as a potential therapeutic strategy for inflammatory diseases with excessive NLRP3 inflammasome activation.

A Bioconductor workflow for processing, evaluating, and interpreting expression proteomics data

Background: Expression proteomics involves the global evaluation of protein abundances within a system. In turn, differential expression analysis can be used to investigate changes in protein abundance upon perturbation to such a system. Methods: Here, we provide a workflow for the processing, analysis and interpretation of quantitative mass spectrometry-based expression proteomics data. This workflow utilizes open-source R software packages from the Bioconductor project and guides users end-to-end and step-by-step through every stage of the analyses. As a use-case we generated expression proteomics data from HEK293 cells with and without a treatment. Of note, the experiment included cellular proteins labelled using tandem mass tag (TMT) technology and secreted proteins quantified using label-free quantitation (LFQ). Results: The workflow explains the software infrastructure before focusing on data import, pre-processing and quality control. This is done individually for TMT and LFQ datasets. The application of statistical differential expression analysis is demonstrated, followed by interpretation via gene ontology enrichment analysis. Conclusions: A comprehensive workflow for the processing, analysis and interpretation of expression proteomics is presented. The workflow is a valuable resource for the proteomics community and specifically beginners who are at least familiar with R who wish to understand and make data-driven decisions with regards to their analyses.

Mechanisms and physiological function of daily haemoglobin oxidation rhythms in red blood cells

Cellular circadian rhythms confer temporal organisation upon physiology that is fundamental to human health. Rhythms are present in red blood cells (RBCs), the most abundant cell type in the body, but their physiological function is poorly understood. Here, we present a novel biochemical assay for haemoglobin (Hb) oxidation status which relies on a redox-sensitive covalent haem-Hb linkage that forms during SDS-mediated cell lysis. Formation of this linkage is lowest when ferrous Hb is oxidised, in the form of ferric metHb. Daily haemoglobin oxidation rhythms are observed in mouse and human RBCs cultured in vitro, or taken from humans in vivo, and are unaffected by mutations that affect circadian rhythms in nucleated cells. These rhythms correlate with daily rhythms in core body temperature, with temperature lowest when metHb levels are highest. Raising metHb levels with dietary sodium nitrite can further decrease daytime core body temperature in mice via nitric oxide (NO) signalling. These results extend our molecular understanding of RBC circadian rhythms and suggest they contribute to the regulation of body temperature.

Enrichment of Membrane Proteins for Downstream Analysis Using Styrene Maleic Acid Lipid Particles (SMALPs) Extraction

Integral membrane proteins are an important class of cellular proteins. These take part in key cellular processes such as signaling transducing receptors to transporters, many operating within the plasma membrane. More than half of the FDA-approved protein-targeting drugs operate via interaction with proteins that contain at least one membrane-spanning region, yet the characterization and study of their native interactions with therapeutic agents remains a significant challenge. This challenge is due in part to such proteins often being present in small quantities within a cell. Effective solubilization of membrane proteins is also problematic, with the detergents typically employed in solubilizing membranes leading to a loss of functional activity and key interacting partners. In recent years, alternative methods to extract membrane proteins within their native lipid environment have been investigated, with the aim of producing functional nanodiscs, maintaining protein-protein and protein-lipid interactions. A promising approach involves extracting membrane proteins in the form of styrene maleic acid lipid particles (SMALPs) that allow the retention of their native conformation. This extraction method offers many advantages for further protein analysis and allows the study of the protein interactions with other molecules, such as drugs. Here, we describe a protocol for efficient SMALP extraction of functionally active membrane protein complexes within nanodiscs. We showcase the method on the isolation of a low copy number plasma membrane receptor complex, the nicotinic acetylcholine receptor (nAChR), from adult Drosophila melanogaster heads. We demonstrate that these nanodiscs can be used to study native receptor-ligand interactions. This protocol can be applied across many biological scenarios to extract the native conformations of low copy number integral membrane proteins.

Mapping diversity in African trypanosomes using high resolution spatial proteomics

African trypanosomes are dixenous eukaryotic parasites that impose a significant human and veterinary disease burden on sub-Saharan Africa. Diversity between species and life-cycle stages is concomitant with distinct host and tissue tropisms within this group. Here, the spatial proteomes of two African trypanosome species, Trypanosoma brucei and Trypanosoma congolense, are mapped across two life-stages. The four resulting datasets provide evidence of expression of approximately 5500 proteins per cell-type. Over 2500 proteins per cell-type are classified to specific subcellular compartments, providing four comprehensive spatial proteomes. Comparative analysis reveals key routes of parasitic adaptation to different biological niches and provides insight into the molecular basis for diversity within and between these pathogen species.

Multi-Attribute Monitoring Method for Process Development of Engineered Antibody for Site-Specific Conjugation

Antibody drug conjugates, a class of biotherapeutic proteins, have been extensively developed in recent years, resulting in new approvals and improved standard of care for cancer patients. Among the numerous strategies of conjugating cytotoxic payloads to monoclonal antibodies, insertion of a cysteine residue achieves a tightly controlled, site-specific drug to antibody ratio. Tailored analytical tools are required to direct the development of processes capable of manufacturing novel antibody scaffolds with the desired product quality. Here, we describe the development of a 12 min, mass-spectrometry-based method capable of monitoring four distinct quality attributes simultaneously: variations in the thiol state of the inserted cysteines, N-linked glycosylation, reduction of interchain disulfide bonds, and polypeptide fragmentation. This method provides new insight into the properties of the antibody intermediate and associated manufacturing processes. Oxidized thiol states are formed within the bioreactor, of which a variant containing an additional disulfide bond was produced and remained relatively constant throughout the fed-batch process; reduced thiol variants were introduced upon harvest. Nearly 20 percent of N-linked glycans contained sialic acid, substantially higher than anticipated for wildtype IgG1. Lastly, previously unreported polypeptide fragmentation sites were identified in the C239i constant domain, and the relationship between fragmentation and glycoform were explored. This work illustrates the utility of applying a high-throughput liquid chromatography-mass spectrometry multi-attribute monitoring method to support the development of engineered antibody scaffolds.

The proteomic landscape of genome-wide genetic perturbations

Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.

Cryptosporidium uses multiple distinct secretory organelles to interact with and modify its host cell

Cryptosporidium is a leading cause of diarrheal disease in children and an important contributor to early childhood mortality. The parasite invades and extensively remodels intestinal epithelial cells, building an elaborate interface structure. How this occurs at the molecular level and the contributing parasite factors are largely unknown. Here, we generated a whole-cell spatial proteome of the Cryptosporidium sporozoite and used genetic and cell biological experimentation to discover the Cryptosporidium-secreted effector proteome. These findings reveal multiple organelles, including an original secretory organelle, and generate numerous compartment markers by tagging native gene loci. We show that secreted proteins are delivered to the parasite-host interface, where they assemble into different structures including a ring that anchors the parasite into its unique epicellular niche. Cryptosporidium thus uses a complex set of secretion systems during and following invasion that act in concert to subjugate its host cell.

Translation of in vitro-transcribed RNA therapeutics

In vitro transcribed, modified messenger RNAs (IVTmRNAs) have been used to vaccinate billions of individuals against the SARS-CoV-2 virus, and are currently being developed for many additional therapeutic applications. IVTmRNAs must be translated into proteins with therapeutic activity by the same cellular machinery that also translates native endogenous transcripts. However, different genesis pathways and routes of entry into target cells as well as the presence of modified nucleotides mean that the way in which IVTmRNAs engage with the translational machinery, and the efficiency with which they are being translated, differs from native mRNAs. This review summarises our current knowledge of commonalities and differences in translation between IVTmRNAs and cellular mRNAs, which is key for the development of future design strategies that can generate IVTmRNAs with improved activity in therapeutic applications.

Semi-Supervised Non-Parametric Bayesian Modelling of Spatial Proteomics

Understanding sub-cellular protein localisation is an essential component in the analysis of context specific protein function. Recent advances in quantitative mass-spectrometry (MS) have led to high resolution mapping of thousands of proteins to sub-cellular locations within the cell. Novel modelling considerations to capture the complex nature of these data are thus necessary. We approach analysis of spatial proteomics data in a non-parametric Bayesian framework, using K-component mixtures of Gaussian process regression models. The Gaussian process regression model accounts for correlation structure within a sub-cellular niche, with each mixture component capturing the distinct correlation structure observed within each niche. The availability of marker proteins (i.e. proteins with a priori known labelled locations) motivates a semi-supervised learning approach to inform the Gaussian process hyperparameters. We moreover provide an efficient Hamiltonian-within-Gibbs sampler for our model. Furthermore, we reduce the computational burden associated with inversion of covariance matrices by exploiting the structure in the covariance matrix. A tensor decomposition of our covariance matrices allows extended Trench and Durbin algorithms to be applied to reduce the computational complexity of inversion and hence accelerate computation. We provide detailed case-studies on Drosophila embryos and mouse pluripotent embryonic stem cells to illustrate the benefit of semi-supervised functional Bayesian modelling of the data.

Operation of a TCA cycle subnetwork in the mammalian nucleus

Nucleic acid and histone modifications critically depend on the tricarboxylic acid (TCA) cycle for substrates and cofactors. Although a few TCA cycle enzymes have been reported in the nucleus, the corresponding pathways are considered to operate in mitochondria. Here, we show that a part of the TCA cycle is operational also in the nucleus. Using 13C-tracer analysis, we identified activity of glutamine-to-fumarate, citrate-to-succinate, and glutamine-to-aspartate routes in the nuclei of HeLa cells. Proximity labeling mass spectrometry revealed a spatial vicinity of the involved enzymes with core nuclear proteins. We further show nuclear localization of aconitase 2 and 2-oxoglutarate dehydrogenase in mouse embryonic stem cells. Nuclear localization of the latter enzyme, which produces succinyl-CoA, changed from pluripotency to a differentiated state with accompanying changes in the nuclear protein succinylation. Together, our results demonstrate operation of an extended metabolic pathway in the nucleus, warranting a revision of the canonical view on metabolic compartmentalization.

dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts

The dia-PASEF technology uses ion mobility separation to reduce signal interferences and increase sensitivity in proteomic experiments. Here we present a two-dimensional peak-picking algorithm and generation of optimized spectral libraries, as well as take advantage of neural network-based processing of dia-PASEF data. Our computational platform boosts proteomic depth by up to 83% compared to previous work, and is specifically beneficial for fast proteomic experiments and those with low sample amounts. It quantifies over 5300 proteins in single injections recorded at 200 samples per day throughput using Evosep One chromatography system on a timsTOF Pro mass spectrometer and almost 9000 proteins in single injections recorded with a 93-min nanoflow gradient on timsTOF Pro 2, from 200 ng of HeLa peptides. A user-friendly implementation is provided through the incorporation of the algorithms in the DIA-NN software and by the FragPipe workflow for spectral library generation.

CFTR Rescue by Lumacaftor (VX-809) Induces an Extensive Reorganization of Mitochondria in the Cystic Fibrosis Bronchial Epithelium

BACKGROUND

Cystic Fibrosis (CF) is a genetic disorder affecting around 1 in every 3000 newborns. In the most common mutation, F508del, the defective anion channel, CFTR, is prevented from reaching the plasma membrane (PM) by the quality check control of the cell. Little is known about how CFTR pharmacological rescue impacts the cell proteome.

METHODS

We used high-resolution mass spectrometry, differential ultracentrifugation, machine learning and bioinformatics to investigate both changes in the expression and localization of the human bronchial epithelium CF model (F508del-CFTR CFBE41o-) proteome following treatment with VX-809 (Lumacaftor), a drug able to improve the trafficking of CFTR.

RESULTS

The data suggested no stark changes in protein expression, yet subtle localization changes of proteins of the mitochondria and peroxisomes were detected. We then used high-content confocal microscopy to further investigate the morphological and compositional changes of peroxisomes and mitochondria under these conditions, as well as in patient-derived primary cells. We profiled several thousand proteins and we determined the subcellular localization data for around 5000 of them using the LOPIT-DC spatial proteomics protocol.

CONCLUSIONS

We observed that treatment with VX-809 induces extensive structural and functional remodelling of mitochondria and peroxisomes that resemble the phenotype of healthy cells. Our data suggest additional rescue mechanisms of VX-809 beyond the correction of aberrant folding of F508del-CFTR and subsequent trafficking to the PM.

Prior Signal Acquisition Software Versions for Orbitrap Underestimate Low Isobaric Mass Tag Intensities, Without Detriment to Differential Abundance Experiments

Tandem mass tags (TMTs) enable simple and accurate quantitative proteomics for multiplexed samples by relative quantification of tag reporter ions. Orbitrap quantification of reporter ions has been associated with a characteristic notch region in intensity distribution, within which few reporter intensities are recorded. This has been resolved in version 3 of the instrument acquisition software Tune. However, 47% of Orbitrap Fusion, Lumos, or Eclipse submissions to PRIDE were generated using prior software versions. To quantify the impact of the notch on existing quantitative proteomics data, we generated a mixed species benchmark and acquired quantitative data using Tune versions 2 and 3. Intensities below the notch are predominantly underestimated with Tune version 2, leading to overestimation of the true differences in intensities between samples. However, when summarizing reporter ion intensities to higher-level features, such as peptides and proteins, few features are significantly affected. Targeted removal of spectra with reporter ion intensities below the notch is not beneficial for differential peptide or protein testing. Overall, we find that the systematic quantification bias associated with the notch is not detrimental for a typical proteomics experiment.

In Depth Exploration of the Alternative Proteome of Drosophila melanogaster

Recent studies have shown that hundreds of small proteins were occulted when protein-coding genes were annotated. These proteins, called alternative proteins, have failed to be annotated notably due to the short length of their open reading frame (less than 100 codons) or the enforced rule establishing that messenger RNAs (mRNAs) are monocistronic. Several alternative proteins were shown to be biologically active molecules and seem to be involved in a wide range of biological functions. However, genome-wide exploration of the alternative proteome is still limited to a few species. In the present article, we describe a deep peptidomics workflow which enabled the identification of 401 alternative proteins in Drosophila melanogaster. Subcellular localization, protein domains, and short linear motifs were predicted for 235 of the alternative proteins identified and point toward specific functions of these small proteins. Several alternative proteins had approximated abundances higher than their canonical counterparts, suggesting that these alternative proteins are actually the main products of their corresponding genes. Finally, we observed 14 alternative proteins with developmentally regulated expression patterns and 10 induced upon the heat-shock treatment of embryos, demonstrating stage or stress-specific production of alternative proteins.

Drosophila nicotinic acetylcholine receptor subunits and their native interactions with insecticidal peptide toxins

Drosophila nicotinic acetylcholine receptors (nAChRs) are ligand-gated ion channels that represent a target for insecticides. Peptide neurotoxins are known to block nAChRs by binding to their target subunits, however, a better understanding of this mechanism is needed for effective insecticide design. To facilitate the analysis of nAChRs we used a CRISPR/Cas9 strategy to generate null alleles for all ten nAChR subunit genes in a common genetic background. We studied interactions of nAChR subunits with peptide neurotoxins by larval injections and styrene maleic acid lipid particles (SMALPs) pull-down assays. For the null alleles, we determined the effects of α-Bungarotoxin (α-Btx) and ω-Hexatoxin-Hv1a (Hv1a) administration, identifying potential receptor subunits implicated in the binding of these toxins. We employed pull-down assays to confirm α-Btx interactions with the Drosophila α5 (Dα5), Dα6, Dα7 subunits. Finally, we report the localisation of fluorescent tagged endogenous Dα6 during Drosophila CNS development. Taken together, this study elucidates native Drosophila nAChR subunit interactions with insecticidal peptide toxins and provides a resource for the in vivo analysis of insect nAChRs.

Unresolved stalled ribosome complexes restrict cell-cycle progression after genotoxic stress

During the translation surveillance mechanism known as ribosome-associated quality control, the ASC-1 complex (ASCC) disassembles ribosomes stalled on the mRNA. Here, we show that there are two distinct classes of stalled ribosome. Ribosomes stalled by translation elongation inhibitors or methylated mRNA are short lived in human cells because they are split by the ASCC. In contrast, although ultraviolet light and 4-nitroquinoline 1-oxide induce ribosome stalling by damaging mRNA, and the ASCC is recruited to these stalled ribosomes, we found that they are refractory to the ASCC. Consequently, unresolved UV- and 4NQO-stalled ribosomes persist in human cells. We show that ribosome stalling activates cell-cycle arrest, partly through ZAK-p38MAPK signaling, and that this cell-cycle delay is prolonged when the ASCC cannot resolve stalled ribosomes. Thus, we propose that the sensitivity of stalled ribosomes to the ASCC influences the kinetics of stall resolution, which in turn controls the adaptive stress response.

Localization of Organelle Proteins by Isotope Tagging: Current status and potential applications in drug discovery research

Spatial proteomics has provided important insights into the relationship between protein function and subcellular location. Localization of Organelle Proteins by Isotope Tagging (LOPIT) and its variants are proteome-wide techniques, not matched in scale by microscopy-based or proximity tagging-based techniques, allowing holistic mapping of protein subcellular location and re-localization events downstream of cellular perturbations. LOPIT can be a powerful and versatile tool in drug discovery for unlocking important information on disease pathophysiology, drug mechanism of action, and off-target toxicity screenings. Here, we discuss technical concepts of LOPIT with its potential applications in drug discovery and development research.

SUBCELLULAR TRANSCRIPTOMICS & PROTEOMICS: A COMPARATIVE METHODS REVIEW

The internal environment of cells is molecularly crowded, which requires spatial organization via subcellular compartmentalization. These compartments harbor specific conditions for molecules to perform their biological functions, such as coordination of the cell cycle, cell survival, and growth. This compartmentalization is also not static, with molecules trafficking between these subcellular neighborhoods to carry out their functions. For example, some biomolecules are multifunctional, requiring an environment with differing conditions or interacting partners, and others traffic to export such molecules. Aberrant localization of proteins or RNA species has been linked to many pathological conditions, such as neurological, cancer, and pulmonary diseases. Differential expression studies in transcriptomics and proteomics are relatively common, but the majority have overlooked the importance of subcellular information. In addition, subcellular transcriptomics and proteomics data do not always colocate because of the biochemical processes that occur during and after translation, highlighting the complementary nature of these fields. In this review, we discuss and directly compare the current methods in spatial proteomics and transcriptomics, which include sequencing- and imaging-based strategies, to give the reader an overview of the current tools available. We also discuss current limitations of these strategies as well as future developments in the field of spatial -omics.

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Subcellular information of protein and RNA give insights into molecular function.

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This review discusses strategies available to measure subcellular information.

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Hybridization of methods shows promise for exploring the composition of organelles.

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Advances are aiding understanding of the organisation and dynamics of cells.

Spatiotemporal proteomic profiling of the pro-inflammatory response to lipopolysaccharide in the THP-1 human leukaemia cell line

Protein localisation and translocation between intracellular compartments underlie almost all physiological processes. The hyperLOPIT proteomics platform combines mass spectrometry with state-of-the-art machine learning to map the subcellular location of thousands of proteins simultaneously. We combine global proteome analysis with hyperLOPIT in a fully Bayesian framework to elucidate spatiotemporal proteomic changes during a lipopolysaccharide (LPS)-induced inflammatory response. We report a highly dynamic proteome in terms of both protein abundance and subcellular localisation, with alterations in the interferon response, endo-lysosomal system, plasma membrane reorganisation and cell migration. Proteins not previously associated with an LPS response were found to relocalise upon stimulation, the functional consequences of which are still unclear. By quantifying proteome-wide uncertainty through Bayesian modelling, a necessary role for protein relocalisation and the importance of taking a holistic overview of the LPS-driven immune response has been revealed. The data are showcased as an interactive application freely available for the scientific community.

Global analysis of protein-RNA interactions in SARS-CoV-2-infected cells reveals key regulators of infection

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). SARS-CoV-2 relies on cellular RNA-binding proteins (RBPs) to replicate and spread, although which RBPs control its life cycle remains largely unknown. Here, we employ a multi-omic approach to identify systematically and comprehensively the cellular and viral RBPs that are involved in SARS-CoV-2 infection. We reveal that SARS-CoV-2 infection profoundly remodels the cellular RNA-bound proteome, which includes wide-ranging effects on RNA metabolic pathways, non-canonical RBPs, and antiviral factors. Moreover, we apply a new method to identify the proteins that directly interact with viral RNA, uncovering dozens of cellular RBPs and six viral proteins. Among them are several components of the tRNA ligase complex, which we show regulate SARS-CoV-2 infection. Furthermore, we discover that available drugs targeting host RBPs that interact with SARS-CoV-2 RNA inhibit infection. Collectively, our results uncover a new universe of host-virus interactions with potential for new antiviral therapies against COVID-19.

Global analysis of protein-RNA interactions in SARS-CoV-2-infected cells reveals key regulators of infection

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). SARS-CoV-2 relies on cellular RNA-binding proteins (RBPs) to replicate and spread, although which RBPs control its life cycle remains largely unknown. Here, we employ a multi-omic approach to identify systematically and comprehensively the cellular and viral RBPs that are involved in SARS-CoV-2 infection. We reveal that SARS-CoV-2 infection profoundly remodels the cellular RNA-bound proteome, which includes wide-ranging effects on RNA metabolic pathways, non-canonical RBPs, and antiviral factors. Moreover, we apply a new method to identify the proteins that directly interact with viral RNA, uncovering dozens of cellular RBPs and six viral proteins. Among them are several components of the tRNA ligase complex, which we show regulate SARS-CoV-2 infection. Furthermore, we discover that available drugs targeting host RBPs that interact with SARS-CoV-2 RNA inhibit infection. Collectively, our results uncover a new universe of host-virus interactions with potential for new antiviral therapies against COVID-19.

Ultra-fast proteomics with Scanning SWATH

Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min-1) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5-5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies.

A Bayesian semi-parametric model for thermal proteome profiling

The thermal stability of proteins can be altered when they interact with small molecules, other biomolecules or are subject to post-translation modifications. Thus monitoring the thermal stability of proteins under various cellular perturbations can provide insights into protein function, as well as potentially determine drug targets and off-targets. Thermal proteome profiling is a highly multiplexed mass-spectrommetry method for monitoring the melting behaviour of thousands of proteins in a single experiment. In essence, thermal proteome profiling assumes that proteins denature upon heating and hence become insoluble. Thus, by tracking the relative solubility of proteins at sequentially increasing temperatures, one can report on the thermal stability of a protein. Standard thermodynamics predicts a sigmoidal relationship between temperature and relative solubility and this is the basis of current robust statistical procedures. However, current methods do not model deviations from this behaviour and they do not quantify uncertainty in the melting profiles. To overcome these challenges, we propose the application of Bayesian functional data analysis tools which allow complex temperature-solubility behaviours. Our methods have improved sensitivity over the state-of-the art, identify new drug-protein associations and have less restrictive assumptions than current approaches. Our methods allows for comprehensive analysis of proteins that deviate from the predicted sigmoid behaviour and we uncover potentially biphasic phenomena with a series of published datasets.

Subcellular proteomics

The eukaryotic cell is compartmentalized into subcellular niches, including membrane-bound and membrane-less organelles. Proteins localize to these niches to fulfil their function, enabling discreet biological processes to occur in synchrony. Dynamic movement of proteins between niches is essential for cellular processes such as signalling, growth, proliferation, motility and programmed cell death, and mutations causing aberrant protein localization are associated with a wide range of diseases. Determining the location of proteins in different cell states and cell types and how proteins relocalize following perturbation is important for understanding their functions, related cellular processes and pathologies associated with their mislocalization. In this Primer, we cover the major spatial proteomics methods for determining the location, distribution and abundance of proteins within subcellular structures. These technologies include fluorescent imaging, protein proximity labelling, organelle purification and cell-wide biochemical fractionation. We describe their workflows, data outputs and applications in exploring different cell biological scenarios, and discuss their main limitations. Finally, we describe emerging technologies and identify areas that require technological innovation to allow better characterization of the spatial proteome.

Pan-cancer analysis of transcripts encoding novel open-reading frames (nORFs) and their potential biological functions

Uncharacterized and unannotated open-reading frames, which we refer to as novel open reading frames (nORFs), may sometimes encode peptides that remain unexplored for novel therapeutic opportunities. To our knowledge, no systematic identification and characterization of transcripts encoding nORFs or their translation products in cancer, or in any other physiological process has been performed. We use our curated nORFs database (nORFs.org), together with RNA-Seq data from The Cancer Genome Atlas (TCGA) and Genotype-Expression (GTEx) consortiums, to identify transcripts containing nORFs that are expressed frequently in cancer or matched normal tissue across 22 cancer types. We show nORFs are subject to extensive dysregulation at the transcript level in cancer tissue and that a small subset of nORFs are associated with overall patient survival, suggesting that nORFs may have prognostic value. We also show that nORF products can form protein-like structures with post-translational modifications. Finally, we perform in silico screening for inhibitors against nORF-encoded proteins that are disrupted in stomach and esophageal cancer, showing that they can potentially be targeted by inhibitors. We hope this work will guide and motivate future studies that perform in-depth characterization of nORF functions in cancer and other diseases.

A direct role for SNX9 in the biogenesis of filopodia

Filopodia are finger-like actin-rich protrusions that extend from the cell surface and are important for cell-cell communication and pathogen internalization. The small size and transient nature of filopodia combined with shared usage of actin regulators within cells confounds attempts to identify filopodial proteins. Here, we used phage display phenotypic screening to isolate antibodies that alter the actin morphology of filopodia-like structures (FLS) in vitro. We found that all of the antibodies that cause shorter FLS interact with SNX9, an actin regulator that binds phosphoinositides during endocytosis and at invadopodia. In cells, we discover SNX9 at specialized filopodia in Xenopus development and that SNX9 is an endogenous component of filopodia that are hijacked by Chlamydia entry. We show the use of antibody technology to identify proteins used in filopodia-like structures, and a role for SNX9 in filopodia.

Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers

Intracellular traffic between compartments of the secretory and endocytic pathways is mediated by vesicle-based carriers. The proteomes of carriers destined for many organelles are ill-defined because the vesicular intermediates are transient, low-abundance and difficult to purify. Here, we combine vesicle relocalisation with organelle proteomics and Bayesian analysis to define the content of different endosome-derived vesicles destined for the trans-Golgi network (TGN). The golgin coiled-coil proteins golgin-97 and GCC88, shown previously to capture endosome-derived vesicles at the TGN, were individually relocalised to mitochondria and the content of the subsequently re-routed vesicles was determined by organelle proteomics. Our findings reveal 45 integral and 51 peripheral membrane proteins re-routed by golgin-97, evidence for a distinct class of vesicles shared by golgin-97 and GCC88, and various cargoes specific to individual golgins. These results illustrate a general strategy for analysing intracellular sub-proteomes by combining acute cellular re-wiring with high-resolution spatial proteomics.

A Comprehensive Subcellular Atlas of the Toxoplasma Proteome via hyperLOPIT Provides Spatial Context for Protein Functions

Apicomplexan parasites cause major human disease and food insecurity. They owe their considerable success to highly specialized cell compartments and structures. These adaptations drive their recognition, nondestructive penetration, and elaborate reengineering of the host's cells to promote their growth, dissemination, and the countering of host defenses. The evolution of unique apicomplexan cellular compartments is concomitant with vast proteomic novelty. Consequently, half of apicomplexan proteins are unique and uncharacterized. Here, we determine the steady-state subcellular location of thousands of proteins simultaneously within the globally prevalent apicomplexan parasite Toxoplasma gondii. This provides unprecedented comprehensive molecular definition of these unicellular eukaryotes and their specialized compartments, and these data reveal the spatial organizations of protein expression and function, adaptation to hosts, and the underlying evolutionary trajectories of these pathogens.

A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection

The cell is compartmentalised into complex micro-environments allowing an array of specialised biological processes to be carried out in synchrony. Determining a protein's sub-cellular localisation to one or more of these compartments can therefore be a first step in determining its function. High-throughput and high-accuracy mass spectrometry-based sub-cellular proteomic methods can now shed light on the localisation of thousands of proteins at once. Machine learning algorithms are then typically employed to make protein-organelle assignments. However, these algorithms are limited by insufficient and incomplete annotation. We propose a semi-supervised Bayesian approach to novelty detection, allowing the discovery of additional, previously unannotated sub-cellular niches. Inference in our model is performed in a Bayesian framework, allowing us to quantify uncertainty in the allocation of proteins to new sub-cellular niches, as well as in the number of newly discovered compartments. We apply our approach across 10 mass spectrometry based spatial proteomic datasets, representing a diverse range of experimental protocols. Application of our approach to hyperLOPIT datasets validates its utility by recovering enrichment with chromatin-associated proteins without annotation and uncovers sub-nuclear compartmentalisation which was not identified in the original analysis. Moreover, using sub-cellular proteomics data from Saccharomyces cerevisiae, we uncover a novel group of proteins trafficking from the ER to the early Golgi apparatus. Overall, we demonstrate the potential for novelty detection to yield biologically relevant niches that are missed by current approaches.

Changes in the Oligodendrocyte Progenitor Cell Proteome with Ageing

Following central nervous system (CNS) demyelination, adult oligodendrocyte progenitor cells (OPCs) can differentiate into new myelin-forming oligodendrocytes in a regenerative process called remyelination. Although remyelination is very efficient in young adults, its efficiency declines progressively with ageing. Here we performed proteomic analysis of OPCs freshly isolated from the brains of neonate, young and aged female rats. Approximately 50% of the proteins are expressed at different levels in OPCs from neonates compared with their adult counterparts. The amount of myelin-associated proteins, and proteins associated with oxidative phosphorylation, inflammatory responses and actin cytoskeletal organization increased with age, whereas cholesterol-biosynthesis, transcription factors and cell cycle proteins decreased. Our experiments provide the first ageing OPC proteome, revealing the distinct features of OPCs at different ages. These studies provide new insights into why remyelination efficiency declines with ageing and potential roles for aged OPCs in other neurodegenerative diseases.

Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection

The COVID-19 pandemic is an unprecedented global challenge, and point-of-care diagnostic classifiers are urgently required. Here, we present a platform for ultra-high-throughput serum and plasma proteomics that builds on ISO13485 standardization to facilitate simple implementation in regulated clinical laboratories. Our low-cost workflow handles up to 180 samples per day, enables high precision quantification, and reduces batch effects for large-scale and longitudinal studies. We use our platform on samples collected from a cohort of early hospitalized cases of the SARS-CoV-2 pandemic and identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19. They include complement factors, the coagulation system, inflammation modulators, and pro-inflammatory factors upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, this work supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets.

Moving Profiling Spatial Proteomics Beyond Discrete Classification

The spatial subcellular proteome is a dynamic environment; one that can be perturbed by molecular cues and regulated by post-translational modifications. Compartmentalization of this environment and management of these biomolecular dynamics allows for an array of ancillary protein functions. Profiling spatial proteomics has proved to be a powerful technique in identifying the primary subcellular localization of proteins. The approach has also been refashioned to study multi-localization and localization dynamics. Here, the analytical approaches that have been applied to spatial proteomics thus far are critiqued, and challenges particularly associated with multi-localization and dynamic relocalization is identified. To meet some of the current limitations in analytical processing, it is suggested that Bayesian modeling has clear benefits over the methods applied to date and should be favored whenever possible. Careful consideration of the limitations and challenges, and development of robust statistical frameworks, will ensure that profiling spatial proteomics remains a valuable technique as its utility is expanded.

Proteomics of intracellular freezing survival

Panagrolaimus sp. DAW1, a nematode cultured from the Antarctic, has the extraordinary physiological ability to survive total intracellular freezing throughout all of its compartments. While a few other organisms, all nematodes, have subsequently also been found to survive freezing in this manner, P. sp. DAW1 has so far shown the highest survival rates. In addition, P. sp. DAW1 is also, depending on the rate or extent of freezing, able to undergo cryoprotective dehydration. In this study, the proteome of P. sp DAW1 is explored, highlighting a number of differentially expressed proteins and pathways that occur when the nematodes undergo intracellular freezing. Among the strongest signals after being frozen is an upregulation of proteases and the downregulation of cytoskeletal and antioxidant activity, the latter possibly accumulated before freezing much in the way the sugar trehalose has been shown to be stored during acclimation.

Identification of the cis‑molecular neighbours of the immune checkpoint protein B7‑H4 in the breast cancer cell‑line SK‑BR‑3 by proteomic proximity labelling

The immune checkpoint protein B7-H4 plays an important role in the positive as well as the negative regulation of immune T-cell responses. When expressed on cancer cells, B7-H4 inhibits T-cell activity, and numerous types of cancer cells use upregulation of B7-H4 as a survival strategy. Thus, B7-H4 is a potential target for anticancer drug therapy. Unfortunately, the cell biology of this molecule has yet to be fully elucidated. Even basic properties, such as the nature of B7-H4 interactors, are controversial. In particular, the cis-inter-actors of B7-H4 on cancer cell plasma membranes have not been investigated to date. The present study used a proteomic proximity-labelling assay to investigate the molecular neighbours of B7-H4 on the surface of the human breast cancer cells SK-BR-3. By comparison to a comprehensive proteome analysis of SK-BR-3 cells, the proximity method detected a relatively small number of low abundance plasma membrane proteins highly enriched for proteins known to modulate cell adhesion and immune recognition. It may be inferred that these molecules contribute to the immunosuppressive behaviour that is characteristic of B7-H4 on cancer cells.

Organic phase separation opens up new opportunities to interrogate the RNA-binding proteome

Protein-RNA interactions regulate all aspects of RNA metabolism and are crucial to the function of catalytic ribonucleoproteins. Until recently, the available technologies to capture RNA-bound proteins have been biased toward poly(A) RNA-binding proteins (RBPs) or involve molecular labeling, limiting their application. With the advent of organic-aqueous phase separation-based methods, we now have technologies that efficiently enrich the complete suite of RBPs and enable quantification of RBP dynamics. These flexible approaches to study RBPs and their bound RNA open up new research avenues for systems-level interrogation of protein-RNA interactions.

Biotin proximity tagging favours unfolded proteins and enables the study of intrinsically disordered regions

Intrinsically Disordered Regions (IDRs) are enriched in disease-linked proteins known to have multiple post-translational modifications, but there is limited in vivo information about how locally unfolded protein regions contribute to biological functions. We reasoned that IDRs should be more accessible to targeted in vivo biotinylation than ordered protein regions, if they retain their flexibility in human cells. Indeed, we observed increased biotinylation density in predicted IDRs in several cellular compartments >20,000 biotin sites from four proximity proteomics studies. We show that in a biotin ‘painting’ time course experiment, biotinylation events in Escherichia coli ribosomes progress from unfolded and exposed regions at 10 s, to structured and less accessible regions after five minutes. We conclude that biotin proximity tagging favours sites of local disorder in proteins and suggest the possibility of using biotin painting as a method to gain unique insights into in vivo condition-dependent subcellular plasticity of proteins.

David-Paul Minde, Manasa Ramakrishna et al. look at intrinsically disordered regions of disease-linked proteins in vivo by biotinylation. They show that biotin “painting” could be used as a method to examine the condition-dependent plasticity of proteins in vivo.

Drought Stress Causes Specific Changes to the Spliceosome and Stress Granule Components

The spliceosome processes RNAs from a pre-RNA state to a mature mRNA thereby influencing RNA availability for translation, localization, and turnover. It consists of complex structures containing RNA-binding proteins (RBPs) essential for post-transcriptional gene expression control. Here we investigate the dynamic modifications of spliceosomal RBPs under stress and in particular drought stress. We do so by mRNA interactome capture in Arabidopsis thaliana using label free quantitation. This approach identified 44 proteins associated with the spliceosome and further 32 proteins associated with stress granules. We noted a high enrichment in the motifs RDRR and RSRSRS that are characteristic of RNA interacting proteins. Identification of splicing factors reflect direct and/or indirect stress induced splicing events that have a direct effect on transcriptome and proteome changes under stress. Furthermore, detection of stress granule components is consistent with transcriptional arrest. Identification of drought induced stress granule components is critical in determining common abiotic stress-induced foci that can have biotechnological applications. This study may therefore open ways to modify plant stress responses at a systems level through the modification of key spliceosome components.

DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput

We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves the identification and quantification performance in conventional DIA proteomic applications, and is particularly beneficial for high-throughput applications, as it is fast and enables deep and confident proteome coverage when used in combination with fast chromatographic methods.

Proteome Mapping of a Cyanobacterium Reveals Distinct Compartment Organization and Cell-Dispersed Metabolism

Cyanobacteria are complex prokaryotes, incorporating a Gram-negative cell wall and internal thylakoid membranes (TMs). However, localization of proteins within cyanobacterial cells is poorly understood. Using subcellular fractionation and quantitative proteomics, we produced an extensive subcellular proteome map of an entire cyanobacterial cell, identifying ∼67% of proteins in Synechocystis sp. PCC 6803, ∼1000 more than previous studies. Assigned to six specific subcellular regions were 1,712 proteins. Proteins involved in energy conversion localized to TMs. The majority of transporters, with the exception of a TM-localized copper importer, resided in the plasma membrane (PM). Most metabolic enzymes were soluble, although numerous pathways terminated in the TM (notably those involved in peptidoglycan monomer, NADP+, heme, lipid, and carotenoid biosynthesis) or PM (specifically, those catalyzing lipopolysaccharide, molybdopterin, FAD, and phylloquinol biosynthesis). We also identified the proteins involved in the TM and PM electron transport chains. The majority of ribosomal proteins and enzymes synthesizing the storage compound polyhydroxybuyrate formed distinct clusters within the data, suggesting similar subcellular distributions to one another, as expected for proteins operating within multicomponent structures. Moreover, heterogeneity within membrane regions was observed, indicating further cellular complexity. Cyanobacterial TM protein localization was conserved in Arabidopsis (Arabidopsis thaliana) chloroplasts, suggesting similar proteome organization in more developed photosynthetic organisms. Successful application of this technique in Synechocystis suggests it could be applied to mapping the proteomes of other cyanobacteria and single-celled organisms. The organization of the cyanobacterial cell revealed here substantially aids our understanding of these environmentally and biotechnologically important organisms.

The Arabidopsis thaliana N-recognin E3 ligase PROTEOLYSIS1 influences the immune response

N-degron pathways of ubiquitin-mediated proteolysis (formerly known as the N-end rule pathway) control the stability of substrate proteins dependent on the amino-terminal (Nt) residue. Unlike yeast or mammalian N-recognin E3 ligases, which each recognize several different classes of Nt residues, in Arabidopsis thaliana, N-recognin functions of different N-degron pathways are carried out independently by PROTEOLYSIS (PRT)1, PRT6, and other unknown proteins. PRT1 recognizes type 2 aromatic Nt-destabilizing residues and PRT6 recognizes type 1 basic residues. These two N-recognin functions diverged as separate proteins early in the evolution of plants, before the conquest of the land. We demonstrate that loss of PRT1 function promotes the plant immune system, as mutant prt1-1 plants showed greater apoplastic resistance than WT to infection by the bacterial hemi-biotroph Pseudomonas syringae pv tomato (Pst) DC3000. Quantitative proteomics revealed increased accumulation of proteins associated with specific components of plant defense in the prt1-1 mutant, concomitant with increased accumulation of salicylic acid. The effects of the prt1 mutation were additional to known effects of prt6 in influencing the immune system, in particular, an observed over-accumulation of pipecolic acid (Pip) in the double-mutant prt1-1 prt6-1. These results demonstrate a potential role for PRT1 in controlling aspects of the plant immune system and suggest that PRT1 limits the onset of the defense response via degradation of substrates with type 2 Nt-destabilizing residues.

Ciprofloxacin binding to GyrA causes global changes in the proteome of Pseudomonas aeruginosa

Ciprofloxacin is one of the most widely-used antibiotics, and has proven especially effective at controlling infections associated with the opportunistic human pathogen, Pseudomonas aeruginosa. In this work, we show that sub-inhibitory concentrations of ciprofloxacin induce discrete changes in the intracellular proteome. Central metabolism and cell envelope-associated functions are particularly affected. In spite of the low magnitude of the intracellular proteomic changes, we found that sub-lethal concentrations of ciprofloxacin had substantial effects on motility and exoprotein secretion. Crucially, the proteomic and phenotypic modulations that we observed were absolutely dependent upon the presence of wild-type GyrA; an isogenic strain of P. aeruginosa carrying a ciprofloxacin-insensitive form of GyrA (a T83→I mutant) did not display ciprofloxacin-dependent changes unless complemented with wild-type gyrA in trans. These results show that the diverse effects of sub-inhibitory ciprofloxacin on the cell are routed through its primary target in the cell, DNA gyrase.

Separating Golgi Proteins from Cis to Trans Reveals Underlying Properties of Cisternal Localization

The order of enzymatic activity across Golgi cisternae is essential for complex molecule biosynthesis. However, an inability to separate Golgi cisternae has meant that the cisternal distribution of most resident proteins, and their underlying localization mechanisms, are unknown. Here, we exploit differences in surface charge of intact cisternae to perform separation of early to late Golgi subcompartments. We determine protein and glycan abundance profiles across the Golgi; over 390 resident proteins are identified, including 136 new additions, with over 180 cisternal assignments. These assignments provide a means to better understand the functional roles of Golgi proteins and how they operate sequentially. Protein and glycan distributions are validated in vivo using high-resolution microscopy. Results reveal distinct functional compartmentalization among resident Golgi proteins. Analysis of transmembrane proteins shows several sequence-based characteristics relating to pI, hydrophobicity, Ser abundance, and Phe bilayer asymmetry that change across the Golgi. Overall, our results suggest that a continuum of transmembrane features, rather than discrete rules, guide proteins to earlier or later locations within the Golgi stack.

A Protocol to Map the Spatial Proteome Using HyperLOPIT in Saccharomyces cerevisiae

The correct subcellular localization of proteins is vital for cellular function and the study of this process at the systems level will therefore enrich our understanding of the roles of proteins within the cell. Multiple methods are available for the study of protein subcellular localization, including fluorescence microscopy, organelle cataloging, proximity labeling methods, and whole-cell protein correlation profiling methods. We provide here a protocol for the systems-level study of the subcellular localization of the yeast proteome, using a version of hyperplexed Localization of Organelle Proteins by Isotope Tagging (hyperLOPIT) that has been optimized for use with Saccharomyces cerevisiae. The entire protocol encompasses cell culture, cell lysis by nitrogen cavitation, subcellular fractionation, monitoring of the fractionation using Western blotting, labeling of samples with TMT isobaric tags and mass spectrometric analysis. Also included is a brief explanation of downstream processing of the mass spectrometry data to produce a map of the spatial proteome. If required, the nitrogen cavitation lysis and Western blotting portions of the protocol may be performed independently of the mass spectrometry analysis. The protocol in its entirety, however, enables the unbiased, systems-level and high-resolution analysis of the localizations of thousands of proteins in parallel within a single experiment.

Co-complex protein membership evaluation using Maximum Entropy on GO ontology and InterPro annotation

Motivation

Protein–protein interactions (PPI) play a crucial role in our understanding of protein function and biological processes. The standardization and recording of experimental findings is increasingly stored in ontologies, with the Gene Ontology (GO) being one of the most successful projects. Several PPI evaluation algorithms have been based on the application of probabilistic frameworks or machine learning algorithms to GO properties. Here, we introduce a new training set design and machine learning based approach that combines dependent heterogeneous protein annotations from the entire ontology to evaluate putative co-complex protein interactions determined by empirical studies.

Results

PPI annotations are built combinatorically using corresponding GO terms and InterPro annotation. We use a S.cerevisiae high-confidence complex dataset as a positive training set. A series of classifiers based on Maximum Entropy and support vector machines (SVMs), each with a composite counterpart algorithm, are trained on a series of training sets. These achieve a high performance area under the ROC curve of ≤0.97, outperforming go2ppi—a previously established prediction tool for protein-protein interactions (PPI) based on Gene Ontology (GO) annotations.

Availability and implementation

https://github.com/ima23/maxent-ppi

Supplementary information

Supplementary data are available at Bioinformatics online.