Discovering cellular protein-protein interactions: Technological strategies and opportunities

Exploring affinity chromatography in proteomics: A comprehensive review

Over the past decades, the proteomics field has undergone rapid growth. Progress in mass spectrometry and bioinformatics, together with separation methods, has brought many innovative approaches to the study of the molecular biology of the cell. The potential of affinity chromatography was recognized immediately after its first application in proteomics, and since that time, it has become one of the cornerstones of many proteomic protocols. Indeed, this chromatographic technique exploiting the specific binding between two molecules has been employed for numerous purposes, from selective removal of interfering (over)abundant proteins or enrichment of scarce biomarkers in complex biological samples to mapping the post-translational modifications and protein interactions with other proteins, nucleic acids or biologically active small molecules. This review presents a comprehensive survey of this versatile analytical tool in current proteomics. To navigate the reader, the haphazard space of affinity separations is classified according to the experiment's aims and the separated molecule's nature. Different types of available ligands and experimental strategies are discussed in further detail for each of the mentioned procedures.

Quantitative proteomics reveals CLR interactome in primary human cells

The G protein-coupled receptor (GPCR) calcitonin receptor-like receptor (CLR) mediates essential functions in several cell types and is implicated in cardiovascular pathologies, skin diseases, migraine and cancer. To date, the network of proteins interacting with CLR ("CLR interactome") in primary cells, where this GPCR is expressed at endogenous (physiologically relevant) levels, remains unknown. To address this knowledge gap, we established a novel integrative methodological workflow/approach for conducting a comprehensive/proteome-wide analysis of Homo sapiens CLR interactome. We used primary human dermal lymphatic endothelial cells and combined immunoprecipitation (IP) utilising anti-human CLR antibody with label-free quantitative nano-liquid chromatography-tandem mass spectrometry (nano LC-MS/MS) and quantitative in situ proximity ligation assay (PLA). By using this workflow, we identified 37 proteins interacting with endogenously expressed CLR amongst 4,902 detected members of the cellular proteome (by quantitative nano LC-MS/MS) and revealed direct interactions of two kinases and two transporters with this GPCR (by in situ PLA). All identified interactors have not been previously reported as members of CLR interactome. Our approach and findings uncover the hitherto unrecognized compositional complexity of the interactome of endogenously expressed CLR and contribute to fundamental understanding of the biology of this GPCR. Collectively, our study provides a first-of-its-kind integrative methodological approach and datasets as valuable resources and robust platform/springboard for advancing the discovery and comprehensive characterization of physiologically relevant CLR interactome at a proteome-wide level in a range of cell types and diseases in future studies.

Protein features fusion using attributed network embedding for predicting protein-protein interaction

BACKGROUND

Protein-protein interactions (PPIs) hold significant importance in biology, with precise PPI prediction as a pivotal factor in comprehending cellular processes and facilitating drug design. However, experimental determination of PPIs is laborious, time-consuming, and often constrained by technical limitations.

METHODS

We introduce a new node representation method based on initial information fusion, called FFANE, which amalgamates PPI networks and protein sequence data to enhance the precision of PPIs' prediction. A Gaussian kernel similarity matrix is initially established by leveraging protein structural resemblances. Concurrently, protein sequence similarities are gauged using the Levenshtein distance, enabling the capture of diverse protein attributes. Subsequently, to construct an initial information matrix, these two feature matrices are merged by employing weighted fusion to achieve an organic amalgamation of structural and sequence details. To gain a more profound understanding of the amalgamated features, a Stacked Autoencoder (SAE) is employed for encoding learning, thereby yielding more representative feature representations. Ultimately, classification models are trained to predict PPIs by using the well-learned fusion feature.

RESULTS

When employing 5-fold cross-validation experiments on SVM, our proposed method achieved average accuracies of 94.28%, 97.69%, and 84.05% in terms of Saccharomyces cerevisiae, Homo sapiens, and Helicobacter pylori datasets, respectively.

CONCLUSION

Experimental findings across various authentic datasets validate the efficacy and superiority of this fusion feature representation approach, underscoring its potential value in bioinformatics.

A fluorescence-based binding assay for proteins using the cell surface as a sensing platform

Protein-protein interaction (PPI) analysis is very important for elucidating the functions of proteins because many proteins execute their functions in living cells by interacting with one another. In PPI analysis, methods using the sensor chips are widely employed to obtain quantitative data. However, these methods require that the target proteins be immobilized on the sensor chips, and the immobilization processes can affect the binding of the target proteins to their binding partners. In the present work, we propose a PPI analysis system in which the surface of the living cells is utilized as a sensing platform. In our approach, the target protein is displayed on the cell surface by expressing it as a fusion protein with a membrane protein, and the PPI analysis is then conducted by applying its binding partner labeled with a fluorescent dye to the cell surface. We have constructed a model of this binding analysis system using the interaction between biotin protein ligase (BPL) and biotin carboxyl carrier protein (BCCP), where BCCP was displayed on the cell surface and BPL labeled with fluorescein was applied to the cell surface. Here, a red fluorescent protein, mApple, was attached to the C-terminus of the fusion protein of BCCP with a membrane protein. We evaluated the binding level of the labeled BPL by using the intensity ratios of fluorescence from fluorescein to that from mApple. We found that the binding level of the labeled BPL was stably evaluated at least across 60 min observation period and estimated the binding dissociation constant between BPL and BCCP by equilibrium analysis to be 0.33 ± 0.05 μM.

Human-virus protein-protein interactions maps assist in revealing the pathogenesis of viral infection

Many significant viral infections have been recorded in human history, which have caused enormous negative impacts worldwide. Human-virus protein-protein interactions (PPIs) mediate viral infection and immune processes in the host. The identification, quantification, localization, and construction of human-virus PPIs maps are critical prerequisites for understanding the biophysical basis of the viral invasion process and characterising the framework for all protein functions. With the technological revolution and the introduction of artificial intelligence, the human-virus PPIs maps have been expanded rapidly in the past decade and shed light on solving complicated biomedical problems. However, there is still a lack of prospective insight into the field. In this work, we comprehensively review and compare the effectiveness, potential, and limitations of diverse approaches for constructing large-scale PPIs maps in human-virus, including experimental methods based on biophysics and biochemistry, databases of human-virus PPIs, computational methods based on artificial intelligence, and tools for visualising PPIs maps. The work aims to provide a toolbox for researchers, hoping to better assist in deciphering the relationship between humans and viruses.

Virus-Host Protein Interaction Network of the Hepatitis E Virus ORF2-4 by Mammalian Two-Hybrid Assays

Throughout their life cycle, viruses interact with cellular host factors, thereby influencing propagation, host range, cell tropism and pathogenesis. The hepatitis E virus (HEV) is an underestimated RNA virus in which knowledge of the virus-host interaction network to date is limited. Here, two related high-throughput mammalian two-hybrid approaches (MAPPIT and KISS) were used to screen for HEV-interacting host proteins. Promising hits were examined on protein function, involved pathway(s), and their relation to other viruses. We identified 37 ORF2 hits, 187 for ORF3 and 91 for ORF4. Several hits had functions in the life cycle of distinct viruses. We focused on SHARPIN and RNF5 as candidate hits for ORF3, as they are involved in the RLR-MAVS pathway and interferon (IFN) induction during viral infections. Knocking out (KO) SHARPIN and RNF5 resulted in a different IFN response upon ORF3 transfection, compared to wild-type cells. Moreover, infection was increased in SHARPIN KO cells and decreased in RNF5 KO cells. In conclusion, MAPPIT and KISS are valuable tools to study virus-host interactions, providing insights into the poorly understood HEV life cycle. We further provide evidence for two identified hits as new host factors in the HEV life cycle.

ADP release can explain spatially-dependent kinesin binding times

The self-organization of cells relies on the profound complexity of protein-protein interactions. Challenges in directly observing these events have hindered progress toward understanding their diverse behaviors. One notable example is the interaction between molecular motors and cytoskeletal systems that combine to perform a variety of cellular functions. In this work, we leverage theory and experiments to identify and quantify the rate-limiting mechanism of the initial association between a cargo-bound kinesin motor and a microtubule track. Recent advances in optical tweezers provide binding times for several lengths of kinesin motors trapped at varying distances from a microtubule, empowering the investigation of competing models. We first explore a diffusion-limited model of binding. Through Brownian dynamics simulations and simulation-based inference, we find this simple diffusion model fails to explain the experimental binding times, but an extended model that accounts for the ADP state of the molecular motor agrees closely with the data, even under the scrutiny of penalizing for additional model complexity. We provide quantification of both kinetic rates and biophysical parameters underlying the proposed binding process. Our model suggests that most but not every motor binding event is limited by their ADP state. Lastly, we predict how these association rates can be modulated in distinct ways through variation of environmental concentrations and spatial distances.

A low-temperature digital microfluidic system used for protein-protein interaction detection

The occurrence, development and prediction of various biological processes and diseases are inseparable from the protein-protein interaction (PPI), so it is extremely meaningful to perfect PPI networks. However, shortcomings of traditional detection methods, such as protein degradation, long detection time, complex operation, poor automation and high cost, restrict the rapid development of PPI networks. Here, a low-temperature digital microfluidic (LTDMF) system-based PPI detection box (LTDMF-PPI-Box) was developed to achieve rapid, lossless and efficient PPI detection. It consists of a PMMA shell, LTDMF-PPI and an integrated temperature control system. LTDMF reduces the PPI detection time from tens of hours to 1.5 hours by programmatically controlling the movement of droplets. Moreover, an integrated thermoelectric cooler (TEC) ensures an operating temperature of 4 °C, resulting in a protein protection up to 90%. The interaction between RILP protein and Rab26 protein which has a close connection to insulin secretion was demonstrated as a prototype to illustrate the feasibility of the LTDMF-PPI-Box. LTDMF with automation characteristics is capable of meeting the requirement of high-throughput screening of interacting proteins; therefore, the LTDMF-PPI-Box is expected to accelerate the establishment of the PPI network in the future.

Molecular complex detection in protein interaction networks through reinforcement learning

BACKGROUND

Proteins often assemble into higher-order complexes to perform their biological functions. Such protein-protein interactions (PPI) are often experimentally measured for pairs of proteins and summarized in a weighted PPI network, to which community detection algorithms can be applied to define the various higher-order protein complexes. Current methods include unsupervised and supervised approaches, often assuming that protein complexes manifest only as dense subgraphs. Utilizing supervised approaches, the focus is not on how to find them in a network, but only on learning which subgraphs correspond to complexes, currently solved using heuristics. However, learning to walk trajectories on a network to identify protein complexes leads naturally to a reinforcement learning (RL) approach, a strategy not extensively explored for community detection. Here, we develop and evaluate a reinforcement learning pipeline for community detection on weighted protein-protein interaction networks to detect new protein complexes. The algorithm is trained to calculate the value of different subgraphs encountered while walking on the network to reconstruct known complexes. A distributed prediction algorithm then scales the RL pipeline to search for novel protein complexes on large PPI networks.

RESULTS

The reinforcement learning pipeline is applied to a human PPI network consisting of 8k proteins and 60k PPI, which results in 1,157 protein complexes. The method demonstrated competitive accuracy with improved speed compared to previous algorithms. We highlight protein complexes such as C4orf19, C18orf21, and KIAA1522 which are currently minimally characterized. Additionally, the results suggest TMC04 be a putative additional subunit of the KICSTOR complex and confirm the involvement of C15orf41 in a higher-order complex with HIRA, CDAN1, ASF1A, and by 3D structural modeling.

CONCLUSIONS

Reinforcement learning offers several distinct advantages for community detection, including scalability and knowledge of the walk trajectories defining those communities. Applied to currently available human protein interaction networks, this method had comparable accuracy with other algorithms and notable savings in computational time, and in turn, led to clear predictions of protein function and interactions for several uncharacterized human proteins.

A feature extraction free approach for protein interactome inference from co-elution data

Protein complexes are key functional units in cellular processes. High-throughput techniques, such as co-fractionation coupled with mass spectrometry (CF-MS), have advanced protein complex studies by enabling global interactome inference. However, dealing with complex fractionation characteristics to define true interactions is not a simple task, since CF-MS is prone to false positives due to the co-elution of non-interacting proteins by chance. Several computational methods have been designed to analyze CF-MS data and construct probabilistic protein-protein interaction (PPI) networks. Current methods usually first infer PPIs based on handcrafted CF-MS features, and then use clustering algorithms to form potential protein complexes. While powerful, these methods suffer from the potential bias of handcrafted features and severely imbalanced data distribution. However, the handcrafted features based on domain knowledge might introduce bias, and current methods also tend to overfit due to the severely imbalanced PPI data. To address these issues, we present a balanced end-to-end learning architecture, Software for Prediction of Interactome with Feature-extraction Free Elution Data (SPIFFED), to integrate feature representation from raw CF-MS data and interactome prediction by convolutional neural network. SPIFFED outperforms the state-of-the-art methods in predicting PPIs under the conventional imbalanced training. When trained with balanced data, SPIFFED had greatly improved sensitivity for true PPIs. Moreover, the ensemble SPIFFED model provides different voting schemes to integrate predicted PPIs from multiple CF-MS data. Using the clustering software (i.e. ClusterONE), SPIFFED allows users to infer high-confidence protein complexes depending on the CF-MS experimental designs. The source code of SPIFFED is freely available at: https://github.com/bio-it-station/SPIFFED.

Effects of Homogeneous and Heterogeneous Crowding on Translational Diffusion of Rigid Bovine Serum Albumin and Disordered Alfa-Casein

Intracellular environment includes proteins, sugars, and nucleic acids interacting in restricted media. In the cytoplasm, the excluded volume effect takes up to 40% of the volume available for occupation by macromolecules. In this work, we tested several approaches modeling crowded solutions for protein diffusion. We experimentally showed how the protein diffusion deviates from conventional Brownian motion in artificial conditions modeling the alteration of medium viscosity and rigid spatial obstacles. The studied tracer proteins were globular bovine serum albumin and intrinsically disordered α-casein. Using the pulsed field gradient NMR, we investigated the translational diffusion of protein probes of different structures in homogeneous (glycerol) and heterogeneous (PEG 300/PEG 6000/PEG 40,000) solutions as a function of crowder concentration. Our results showed fundamentally different effects of homogeneous and heterogeneous crowded environments on protein self-diffusion. In addition, the applied "tracer on lattice" model showed that smaller crowding obstacles (PEG 300 and PEG 6000) create a dense net of restrictions noticeably hindering diffusing protein probes, whereas the large-sized PEG 40,000 creates a "less restricted" environment for the diffusive motion of protein molecules.

Endogenous TOM20 Proximity Labeling: A Swiss-Knife for the Study of Mitochondrial Proteins in Human Cells

Biotin-based proximity labeling approaches, such as BioID, have demonstrated their use for the study of mitochondria proteomes in living cells. The use of genetically engineered BioID cell lines enables the detailed characterization of poorly characterized processes such as mitochondrial co-translational import. In this process, translation is coupled to the translocation of the mitochondrial proteins, alleviating the energy cost typically associated with the post-translational import relying on chaperone systems. However, the mechanisms are still unclear with only few actors identified but none that have been described in mammals yet. We thus profiled the TOM20 proxisome using BioID, assuming that some of the identified proteins could be molecular actors of the co-translational import in human cells. The obtained results showed a high enrichment of RNA binding proteins close to the TOM complex. However, for the few selected candidates, we could not demonstrate a role in the mitochondrial co-translational import process. Nonetheless, we were able to demonstrate additional uses of our BioID cell line. Indeed, the experimental approach used in this study is thus proposed for the identification of mitochondrial co-translational import effectors and for the monitoring of protein entry inside mitochondria with a potential application in the prediction of mitochondrial protein half-life.

Interactomics in plant defence: progress and opportunities

Interactomics is a branch of systems biology that deals with the study of protein-protein interactions and how these interactions influence phenotypes. Identifying the interactomes involved during host-pathogen interaction events may bring us a step closer to deciphering the molecular mechanisms underlying plant defence. Here, we conducted a systematic review of plant interactomics studies over the last two decades and found that while a substantial progress has been made in the field, plant-pathogen interactomics remains a less-travelled route. As an effort to facilitate the progress in this field, we provide here a comprehensive research pipeline for an in planta plant-pathogen interactomics study that encompasses the in silico prediction step to the validation step, unconfined to model plants. We also highlight four challenges in plant-pathogen interactomics with plausible solution(s) for each.

Protein-protein interaction and interference of carcinogenesis by supramolecular modifications

Protein-protein interactions (PPIs) are essential in normal biological processes, but they can become disrupted or imbalanced in cancer. Various technological advancements have led to an increase in the number of PPI inhibitors, which target hubs in cancer cell's protein networks. However, it remains difficult to develop PPI inhibitors with desired potency and specificity. Supramolecular chemistry has only lately become recognized as a promising method to modify protein activities. In this review, we highlight recent advances in the use of supramolecular modification approaches in cancer therapy. We make special note of efforts to apply supramolecular modifications, such as molecular tweezers, to targeting the nuclear export signal (NES), which can be used to attenuate signaling processes in carcinogenesis. Finally, we discuss the strengths and weaknesses of using supramolecular approaches to targeting PPIs.

Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning

Protein-protein interactions (PPIs) govern cellular pathways and processes, by significantly influencing the functional expression of proteins. Therefore, accurate identification of protein-protein interaction binding sites has become a key step in the functional analysis of proteins. However, since most computational methods are designed based on biological features, there are no available protein language models to directly encode amino acid sequences into distributed vector representations to model their characteristics for protein-protein binding events. Moreover, the number of experimentally detected protein interaction sites is much smaller than that of protein-protein interactions or protein sites in protein complexes, resulting in unbalanced data sets that leave room for improvement in their performance. To address these problems, we develop an ensemble deep learning model (EDLM)-based protein-protein interaction (PPI) site identification method (EDLMPPI). Evaluation results show that EDLMPPI outperforms state-of-the-art techniques including several PPI site prediction models on three widely-used benchmark datasets including Dset_448, Dset_72, and Dset_164, which demonstrated that EDLMPPI is superior to those PPI site prediction models by nearly 10% in terms of average precision. In addition, the biological and interpretable analyses provide new insights into protein binding site identification and characterization mechanisms from different perspectives. The EDLMPPI webserver is available at http://www.edlmppi.top:5002/ .

Cytotrap: An Innovative Approach for Protein-Protein Interaction Studies for Cytoplasmic Proteins

Protein-protein interaction mapping has gained immense importance in understanding protein functions in diverse biological pathways. There are various in vivo and in vitro techniques associated with the protein-protein interaction studies but generally, the focus is confined to understanding the protein interaction in the nucleus of the cell, and thus it limits the availability to explore protein interactions that are happening in the cytoplasm of the cell. Since posttranslational modification is a crucial step in signaling pathways and cellular protein interactions harnessing the cytoplasmic protein and evaluating the interaction in the cytoplasm, this protocol will provide more information about studying these types of protein interactions. Cytotrap is a type of yeast-two-hybrid system that differs in its ability to anchor along the membrane, thus directing the protein of interest to anchor along the membrane through the myristoylation signaling unit. The vector containing the target protein contains the myristoylation unit, called the prey, and the bait unit contains the protein of interest as a fusion with the hSos protein. In an event of interaction between the target and the protein of interest, the hSos protein unit will be localized to the membrane and the GDP/GTP exchange unit will trigger the activation of the Ras pathway that leads to the survival of the temperature-sensitive yeast strain at a higher temperature.

Virotrap: Trapping Protein Complexes in Virus-Like Particles

The discovery of protein-protein interactions can provide crucial information on protein function by linking proteins into known pathways or complexes within the cell. Mass spectrometry (MS)-based methods, such as affinity purification (AP)-MS and proximity-dependent biotin identification (BioID), allowed for a vast increase in the number of reported protein complexes. As a more recent addition to the arsenal of MS-based methods, Virotrap represents a unique technology that benefits from the specific properties of the human immunodeficiency virus-1 (HIV-1) Gag polyprotein. More specifically, Virotrap captures protein complexes in virus-like particles budded from human embryonic kidney (HEK293T) cells, bypassing the need for cell lysis and thus supporting identification of their content using MS. Being intrinsically different to its two main predecessors, affinity purification MS (AP-MS) and biotin-dependent identification (BioID), Virotrap was shown to complement data obtained with the existing MS-based toolkit. The proven complementarity of these MS-based strategies underlines the importance of using different techniques to enable comprehensive mapping of protein-protein interactions (PPIs). In this chapter, we provide a detailed overview of the Virotrap protocol to screen for PPIs using a bait protein of interest.

Protein-Protein Interaction Studies Using Molecular Dynamics Simulation

Protein-protein interaction (PPI) is a crucial event for many biological functions. Studying the molecular details of PPI requires structure determination using X-ray crystallography, nuclear magnetic resistance (NMR), and single particle Cryo-EM. However, sometimes it is not easy to solve the complex structure for various reasons. For example, complex may be unstable, not enough protein expression for structural studies, etc. Further, PPI are intricate processes, and its molecular details cannot be fully explained by experimental observations. Here, we describe a quick and simple method to study the PPI using the combinatorial approach of molecular dynamics simulation and biophysical methods.

Next-Generation Yeast Two-Hybrid Screening to Discover Protein-Protein Interactions

Yeast two-hybrid is a powerful approach to discover new protein-protein interactions. Traditional methods involve screening a target protein against a cDNA expression library and assaying individual positive colonies to identify interacting partners. Here we describe a simple approach to perform yeast two-hybrid screens of a cDNA expression library in batch liquid culture. Positive yeast cell populations are enriched under selection and then harvested en masse. Prey cDNAs are amplified and used as input for next-generation sequencing libraries for identification, quantification, and ranking.

Enhanced protein-protein interaction network construction promoted by in vivo cross-linking with acid-cleavable click-chemistry enrichment

Chemical cross-linking coupled with mass spectrometry has emerged as a powerful strategy which enables global profiling of protein interactome with direct interaction interfaces in complex biological systems. The alkyne-tagged enrichable cross-linkers are preferred to improve the coverage of low-abundance cross-linked peptides, combined with click chemistry for biotin conjugation to allow the cross-linked peptide enrichment. However, a systematic evaluation on the efficiency of click approaches (protein-based or peptide-based) and diverse cleavable click-chemistry ligands (acid, reduction, and photo) for cross-linked peptide enrichment and release is lacking. Herein, together with in vivo chemical cross-linking by alkyne-tagged cross-linkers, we explored the click-chemistry-based enrichment approaches on protein and peptide levels with three cleavable click-chemistry ligands, respectively. By comparison, the approach of protein-based click-chemistry conjugation with acid-cleavable tags was demonstrated to permit the most cross-linked peptide identification. The advancement of this strategy enhanced the proteome-wide cross-linking analysis, constructing a 5,518-protein-protein-interaction network among 1,871 proteins with widely abundant distribution in cells. Therefore, all these results demonstrated the guideline value of our work for efficient cross-linked peptide enrichment, thus facilitating the in-depth profiling of protein interactome for functional analysis.

Roles of CCR10/CCL27-CCL28 axis in tumour development: mechanisms, diagnostic and therapeutic approaches, and perspectives

Cancer is now one of the major causes of death across the globe. The imbalance of cytokine and chemokine secretion has been reported to be involved in cancer development. Meanwhile, CC chemokines have received considerable interest in cancer research. CCR10, as the latest identified CC chemokine receptor (CCR), has been implicated in the recruitment and infiltration of immune cells, especially lymphocytes, into epithelia such as skin via ligation to two ligands, CCL27 and CCL28. Other than homoeostatic function, several mechanisms have been shown to dysregulate CCR10/CCL27-CCL28 expression in the tumour microenvironment. As such, these receptors and ligands mediate T-cell trafficking in the tumour microenvironment. Depending on the types of lymphocytes recruited, CCR10/CCL27-CCL28 interaction has been shown to play conflicting roles in cancer development. If they were T helper and cytotoxic T cells and natural killer cells, the role of this axis would be tumour-suppressive. In contrast, if CCR10/CCL27-CCL28 recruited regulatory T cells, cancer-associated fibroblasts or myeloid-derived suppressor cells, it would lead to tumour progression. In addition to the trafficking of lymphocytes and immune cells, CCR10 also leads to the migration of tumour cells or endothelial cells (called angiogenesis and lymphangiogenesis) to promote tumour metastasis. Furthermore, CCR10 signalling triggers tumour-promoting signalling such as PI3K/AKT and mitogen-activated protein kinase/extracellular signal-regulated kinase, resulting in tumour cell growth. Since CCR10/CCL27-CCL28 is dysregulated in the tumour tissues, it is suggested that analysis and measurement of them might predict tumour development. Finally, it is hoped using therapeutic approaches based on this axis might increase our knowledge to overcome tumour progression.

An Unexpected Encounter: Respiratory Syncytial Virus Nonstructural Protein 1 Interacts with Mediator Subunit MED25

Innate immune responses, including the production of type I and III interferons, play a crucial role in the first line of defense against RSV infection. However, only a poor induction of type I IFNs is observed during RSV infection, suggesting that RSV has evolved mechanisms to prevent type I IFN expression by the infected host cell.

De novo designed peptides for cellular delivery and subcellular localisation

Increasingly, it is possible to design peptide and protein assemblies de novo from first principles or computationally. This approach provides new routes to functional synthetic polypeptides, including designs to target and bind proteins of interest. Much of this work has been developed in vitro. Therefore, a challenge is to deliver de novo polypeptides efficiently to sites of action within cells. Here we describe the design, characterisation, intracellular delivery, and subcellular localisation of a de novo synthetic peptide system. This system comprises a dual-function basic peptide, programmed both for cell penetration and target binding, and a complementary acidic peptide that can be fused to proteins of interest and introduced into cells using synthetic DNA. The designs are characterised in vitro using biophysical methods and X-ray crystallography. The utility of the system for delivery into mammalian cells and subcellular targeting is demonstrated by marking organelles and actively engaging functional protein complexes.

Identification of all-against-all protein-protein interactions based on deep hash learning

BACKGROUND

Protein-protein interaction (PPI) is vital for life processes, disease treatment, and drug discovery. The computational prediction of PPI is relatively inexpensive and efficient when compared to traditional wet-lab experiments. Given a new protein, one may wish to find whether the protein has any PPI relationship with other existing proteins. Current computational PPI prediction methods usually compare the new protein to existing proteins one by one in a pairwise manner. This is time consuming.

RESULTS

In this work, we propose a more efficient model, called deep hash learning protein-and-protein interaction (DHL-PPI), to predict all-against-all PPI relationships in a database of proteins. First, DHL-PPI encodes a protein sequence into a binary hash code based on deep features extracted from the protein sequences using deep learning techniques. This encoding scheme enables us to turn the PPI discrimination problem into a much simpler searching problem. The binary hash code for a protein sequence can be regarded as a number. Thus, in the pre-screening stage of DHL-PPI, the string matching problem of comparing a protein sequence against a database with M proteins can be transformed into a much more simpler problem: to find a number inside a sorted array of length M. This pre-screening process narrows down the search to a much smaller set of candidate proteins for further confirmation. As a final step, DHL-PPI uses the Hamming distance to verify the final PPI relationship.

CONCLUSIONS

The experimental results confirmed that DHL-PPI is feasible and effective. Using a dataset with strictly negative PPI examples of four species, DHL-PPI is shown to be superior or competitive when compared to the other state-of-the-art methods in terms of precision, recall or F1 score. Furthermore, in the prediction stage, the proposed DHL-PPI reduced the time complexity from [Formula: see text] to [Formula: see text] for performing an all-against-all PPI prediction for a database with M proteins. With the proposed approach, a protein database can be preprocessed and stored for later search using the proposed encoding scheme. This can provide a more efficient way to cope with the rapidly increasing volume of protein datasets.

Applications and advancements of FT-ICR-MS for interactome studies

The set of all intra- and intermolecular interactions, collectively known as the interactome, is currently an unmet challenge for any analytical method, but if measured, could provide unparalleled insight on molecular function in living systems. Developments and applications of chemical cross-linking and high-performance mass spectrometry technologies are beginning to reveal details on how proteins interact in cells and how protein conformations and interactions inside cells change with phenotype or during drug treatment or other perturbations. A major contributor to these advances is Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) technology and its implementation with accurate mass measurements on cross-linked peptide-pair precursor and fragment ions to enable improved identification methods. However, these applications place increased demands on mass spectrometer performance in terms of high-resolution spectral acquisition rates for on-line MSⁿ experiments. Moreover, FT-ICR-MS also offers unique opportunities to develop and implement parallel ICR cells for multiplexed signal acquisition and the potential to greatly advance accurate mass acquisition rates for interactome studies. This review highlights our efforts to exploit accurate mass FT-ICR-MS technologies with chemical cross-linking and developments being pursued to realize parallel MS array capabilities that will further advance visualization of the interactome.

Generation of Peptides for Highly Efficient Proximity Utilizing Site-Specific Biotinylation in Cells

Protein tags are peptide sequences genetically embedded into a recombinant protein for various purposes, such as affinity purification, Western blotting, and immunofluorescence. Another recent application of peptide tags is in vivo labeling and analysis of protein–protein interactions (PPI) by proteomics methods. One of the common workflows involves site-specific in vivo biotinylation of an AviTag-fused protein in the presence of the biotin ligase BirA. However, due to the rapid kinetics of labeling, this tag is not ideal for analysis of PPI. Here we describe the rationale, design, and protocol for the new biotin acceptor peptides BAP1070 and BAP1108 using modular assembling of biotin acceptor fragments, DNA sequencing, transient expression of proteins in cells, and Western blotting methods. These tags were used in the Proximity Utilizing Biotinylation (PUB) method, which is based on coexpression of BAP-X and BirA-Y in mammalian cells, where X or Y are candidate interacting proteins of interest. By changing the sequence of these peptides, a low level of background biotinylation is achieved, which occurs due to random collisions of proteins in cells. Over 100 plasmid constructs, containing genes of transcription factors, histones, gene repressors, and other nuclear proteins were obtained during implementation of projects related to this method.

Application of Skyline for Analysis of Protein-Protein Interactions In Vivo

Quantitative and qualitative analyses of cell protein composition using liquid chromatography/tandem mass spectrometry are now standard techniques in biological and clinical research. However, the quantitative analysis of protein–protein interactions (PPIs) in cells is also important since these interactions are the bases of many processes, such as the cell cycle and signaling pathways. This paper describes the application of Skyline software for the identification and quantification of the biotinylated form of the biotin acceptor peptide (BAP) tag, which is a marker of in vivo PPIs. The tag was used in the Proximity Utilizing Biotinylation (PUB) method, which is based on the co-expression of BAP-X and BirA-Y in mammalian cells, where X or Y are interacting proteins of interest. A high level of biotinylation was detected in the model experiments where X and Y were pluripotency transcription factors Sox2 and Oct4, or heterochromatin protein HP1γ. MRM data processed by Skyline were normalized and recalculated. Ratios of biotinylation levels in experiment versus controls were 86 ± 6 (3 h biotinylation time) and 71 ± 5 (9 h biotinylation time) for BAP-Sox2 + BirA-Oct4 and 32 ± 3 (4 h biotinylation time) for BAP-HP1γ + BirA-HP1γ experiments. Skyline can also be applied for the analysis and identification of PPIs from shotgun proteomics data downloaded from publicly available datasets and repositories.

OGT Protein Interaction Network (OGT-PIN): A Curated Database of Experimentally Identified Interaction Proteins of OGT

Interactions between proteins are essential to any cellular process and constitute the basis for molecular networks that determine the functional state of a cell. With the technical advances in recent years, an astonishingly high number of protein–protein interactions has been revealed. However, the interactome of O-linked N-acetylglucosamine transferase (OGT), the sole enzyme adding the O-linked β-N-acetylglucosamine (O-GlcNAc) onto its target proteins, has been largely undefined. To that end, we collated OGT interaction proteins experimentally identified in the past several decades. Rigorous curation of datasets from public repositories and O-GlcNAc-focused publications led to the identification of up to 929 high-stringency OGT interactors from multiple species studied (including Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Arabidopsis thaliana, and others). Among them, 784 human proteins were found to be interactors of human OGT. Moreover, these proteins spanned a very diverse range of functional classes (e.g., DNA repair, RNA metabolism, translational regulation, and cell cycle), with significant enrichment in regulating transcription and (co)translation. Our dataset demonstrates that OGT is likely a hub protein in cells. A webserver OGT-Protein Interaction Network (OGT-PIN) has also been created, which is freely accessible.

Recent progress in mass spectrometry-based strategies for elucidating protein-protein interactions

Protein-protein interactions are fundamental to various aspects of cell biology with many protein complexes participating in numerous fundamental biological processes such as transcription, translation and cell cycle. MS-based proteomics techniques are routinely applied for characterising the interactome, such as affinity purification coupled to mass spectrometry that has been used to selectively enrich and identify interacting partners of a bait protein. In recent years, many orthogonal MS-based techniques and approaches have surfaced including proximity-dependent labelling of neighbouring proteins, chemical cross-linking of two interacting proteins, as well as inferring PPIs from the co-behaviour of proteins such as the co-fractionating profiles and the thermal solubility profiles of proteins. This review discusses the underlying principles, advantages, limitations and experimental considerations of these emerging techniques. In addition, a brief account on how MS-based techniques are used to investigate the structural and functional properties of protein complexes, including their topology, stoichiometry, copy number and dynamics, are discussed.

Protein-fragment complementation assays for large-scale analysis of protein-protein interactions

Protein-protein interactions (PPIs) orchestrate nearly all biological processes. They are also considered attractive drug targets for treating many human diseases, including cancers and neurodegenerative disorders. Protein-fragment complementation assays (PCAs) provide a direct and straightforward way to study PPIs in living cells or multicellular organisms. Importantly, PCAs can be used to detect the interaction of proteins expressed at endogenous levels in their native cellular environment. In this review, we present the principle of PCAs and discuss some of their advantages and limitations. We describe their application in large-scale experiments to investigate PPI networks and to screen or profile PPI targeting compounds.

Visualizing protein-protein interactions in plants by rapamycin-dependent delocalization

Identifying protein-protein interactions (PPIs) is crucial for understanding biological processes. Many PPI tools are available, yet only some function within the context of a plant cell. Narrowing down even further, only a few tools allow complex multi-protein interactions to be visualized. Here, we present a conditional in vivo PPI tool for plant research that meets these criteria. Knocksideways in plants (KSP) is based on the ability of rapamycin to alter the localization of a bait protein and its interactors via the heterodimerization of FKBP and FRB domains. KSP is inherently free from many limitations of other PPI systems. This in vivo tool does not require spatial proximity of the bait and prey fluorophores and it is compatible with a broad range of fluorophores. KSP is also a conditional tool and therefore the visualization of the proteins in the absence of rapamycin acts as an internal control. We used KSP to confirm previously identified interactions in Nicotiana benthamiana leaf epidermal cells. Furthermore, the scripts that we generated allow the interactions to be quantified at high throughput. Finally, we demonstrate that KSP can easily be used to visualize complex multi-protein interactions. KSP is therefore a versatile tool with unique characteristics and applications that complements other plant PPI methods.

A System-Wide Spatiotemporal Characterization of ErbB Receptor Complexes by Subcellular Fractionation Integrated Quantitative Mass Spectrometry

Precise spatiotemporal regulation of protein complex assembly is essential for cells to achieve a meaningful rely of information flow via intracellular signaling networks in response to extracellular cues, whose disruption would lead to disease. Although various attempts have been made for spatial and/or temporal analysis of protein complexes, it is still a challenge to track cell-wide dynamics of a particular protein complex under physiological conditions. Here we describe a workflow that combines endogenous expression of tagged proteins, organelle marker distribution-directed subcellular fractionation, scaffold protein-mediated receptor complex purification, and targeted proteomics for spatiotemporal quantification of protein complexes in whole cell scale. We applied our method to investigate the assembly kinetics of EGF-dependent ErbB receptor complexes. After fractionation using the density gradient centrifugation and organelle assignment based on organelle markers, endogenous ErbB complex in different subcellular fractionation was efficiently enriched. By using targeted mass spectrometry, ErbB complex components that expressed medium to low level was precisely quantified with in-depth coverage, simultaneously in time and subcellular spaces. Our results revealed a sophisticated scheme of complex behaviors characterized by multiple subcomplexes with distinct molecular composition formed across subcellular fractions enriched with cytosol, plasma membrane, endosome, or mitochondria, implying organelle-specific ErbB functions. Remarkably, our results demonstrated for the first time that activated ErbB receptors might increase their signaling range through promoting a cytosolic, receptor-free subcomplex, consisting of Shc1, Grb2, Arhgef5, Garem1, and Lrrk1. These findings emphasize the potential of our strategy as a powerful tool to study spatiotemporal dynamics of protein complexes.

Mapping the plant proteome: tools for surveying coordinating pathways

Plants rapidly respond to environmental fluctuations through coordinated, multi-scalar regulation, enabling complex reactions despite their inherently sessile nature. In particular, protein post-translational signaling and protein-protein interactions combine to manipulate cellular responses and regulate plant homeostasis with precise temporal and spatial control. Understanding these proteomic networks are essential to addressing ongoing global crises, including those of food security, rising global temperatures, and the need for renewable materials and fuels. Technological advances in mass spectrometry-based proteomics are enabling investigations of unprecedented depth, and are increasingly being optimized for and applied to plant systems. This review highlights recent advances in plant proteomics, with an emphasis on spatially and temporally resolved analysis of post-translational modifications and protein interactions. It also details the necessity for generation of a comprehensive plant cell atlas while highlighting recent accomplishments within the field.

Regulation of membrane NMDA receptors by dynamics and protein interactions

Understanding neurotransmitter system crosstalk in the brain is a major challenge in neurobiology. Several intracellular and genomic cascades have been identified in this crosstalk. However, the discovery that neurotransmitter receptors are highly diffusive in the plasma membrane of neurons, where they form heterocomplexes with other proteins, has profoundly changed our view of neurotransmitter signaling. Here, we review new insights into neurotransmitter crosstalk at the plasma membrane. We focus on the membrane organization and interactome of the ionotropic glutamate N-methyl-D-aspartate receptor (NMDAR) that plays a central role in excitatory synaptic and network physiology and is involved in the etiology of several major neuropsychiatric disorders. The nanoscale organization and dynamics of NMDAR is a key regulatory process for glutamate synapse transmission, plasticity, and crosstalk with other neurotransmitter systems, such as the monoaminergic ones. The plasma membrane appears to be a prime regulatory compartment for spatial and temporal crosstalk between neurotransmitter systems in the healthy and diseased brain. Understanding the molecular mechanisms regulating membrane neurotransmitter receptor crosstalk will likely open research avenues for innovative therapeutical strategies.

Detecting Protein Communities in Native Cell Extracts by Machine Learning: A Structural Biologist's Perspective

Native cell extracts hold great promise for understanding the molecular structure of ordered biological systems at high resolution. This is because higher-order biomolecular interactions, dubbed as protein communities, may be retained in their (near-)native state, in contrast to extensively purifying or artificially overexpressing the proteins of interest. The distinct machine-learning approaches are applied to discover protein-protein interactions within cell extracts, reconstruct dedicated biological networks, and report on protein community members from various organisms. Their validation is also important, e.g., by the cross-linking mass spectrometry or cell biology methods. In addition, the cell extracts are amenable to structural analysis by cryo-electron microscopy (cryo-EM), but due to their inherent complexity, sorting structural signatures of protein communities derived by cryo-EM comprises a formidable task. The application of image-processing workflows inspired by machine-learning techniques would provide improvements in distinguishing structural signatures, correlating proteomic and network data to structural signatures and subsequently reconstructed cryo-EM maps, and, ultimately, characterizing unidentified protein communities at high resolution. In this review article, we summarize recent literature in detecting protein communities from native cell extracts and identify the remaining challenges and opportunities. We argue that the progress in, and the integration of, machine learning, cryo-EM, and complementary structural proteomics approaches would provide the basis for a multi-scale molecular description of protein communities within native cell extracts.

Protein Interaction Network Biology in Neuroscience

Mapping the intricate networks of cellular proteins in the human brain has the potential to address unsolved questions in molecular neuroscience, including the molecular basis of cognition, synaptic plasticity, long-term potentiation, learning, and memory. Perturbations to the protein-protein interaction networks (PPIN) present in neurons, glia, and other cell-types have been linked to multifactorial neurological disorders. Yet while knowledge of brain PPINs is steadily improving, the complexity and dynamic nature of the heterogeneous central nervous system in normal and disease contexts poses a formidable experimental challenge. In this review, the recent applications of functional proteomics and systems biology approaches to study PPINs central to normal neuronal function, during neurodevelopment, and in neurodegenerative disorders are summarized. How systematic PPIN analysis offers a unique mechanistic framework to explore intra- and inter-cellular functional modules governing neuronal activity and brain function is also discussed. Finally, future technological advancements needed to address outstanding questions facing neuroscience are outlined.

Proteomics of host-bacterial interactions: new insights from dual perspectives

Mass-spectrometry (MS)-based proteomics is a powerful and robust platform for studying the interactions between biological systems during health and disease. Bacterial infections represent a significant threat to global health and drive the pursuit of novel therapeutic strategies to combat emerging and resistant pathogens. During infection, the interplay between a host and pathogen determines the ability of the microbe to survive in a hostile environment and promotes an immune response by the host as a protective measure. It is the protein-level changes from either biological system that define the outcome of infection, and MS-based proteomics provides a rapid and effective platform to identify such changes. In particular, proteomics detects alterations in protein abundance, quantifies protein secretion and (or) release, measures an array of post-translational modifications that influence signaling cascades, and profiles protein-protein interactions through protein complex and (or) network formation. Such information provides new insight into the role of known and novel bacterial effectors, as well as the outcome of host cell activation. In this Review, we highlight the diverse applications of MS-based proteomics in profiling the relationship between bacterial pathogens and the host. Our work identifies a plethora of strategies for exploring mechanisms of infection from dual perspectives (i.e., host and pathogen), and we suggest opportunities to extrapolate the current knowledgebase to other biological systems for applications in therapeutic discovery.

Hypothesis: The electrical properties of coronavirus

To help investigate the relationship between inflammatory and other symptoms of coronavirus and the protein-protein interactions (PPI) that occur between viral proteins and protein molecules of the host cell, I propose that the electrostatic discharge (ESD) exists including corona discharge to lead to ozone gas. I cite evidence in support of this hypothesis. I hope that the proposed will inspire new studies in finding effective treatments and vaccines for individuals with coronavirus disease in 2019. I suggest possible future studies that may lend more credibility to the proposed.

Keeping in Touch with Type-III Secretion System Effectors: Mass Spectrometry-Based Proteomics to Study Effector-Host Protein-Protein Interactions

Manipulation of host cellular processes by translocated bacterial effectors is key to the success of bacterial pathogens and some symbionts. Therefore, a comprehensive understanding of effectors is of critical importance to understand infection biology. It has become increasingly clear that the identification of host protein targets contributes invaluable knowledge to the characterization of effector function during pathogenesis. Recent advances in mapping protein-protein interaction networks by means of mass spectrometry-based interactomics have enabled the identification of host targets at large-scale. In this review, we highlight mass spectrometry-driven proteomics strategies and recent advances to elucidate type-III secretion system effector-host protein-protein interactions. Furthermore, we highlight approaches for defining spatial and temporal effector-host interactions, and discuss possible avenues for studying natively delivered effectors in the context of infection. Overall, the knowledge gained when unravelling effector complexation with host factors will provide novel opportunities to control infectious disease outcomes.

Fully Automated Protein Proximity Assay in Formalin-Fixed, Paraffin-Embedded Tissue Using Caged Haptens

The ability to interrogate for the presence and distribution of protein-protein complexes (PPCs) is of high importance for the advancement of diagnostic capabilities such as determining therapeutic effects in the context of pharmaceutical development. Herein, we report a novel assay for detecting and visualizing PPCs on formalin-fixed, paraffin-embedded material using a caged hapten. To this end, we synthetically modified a nitropyrazole hapten with an alkaline phosphatase (AP)-responsive self-immolative caging group. The AP-labile caging group abrogates antibody binding; however, upon exposure to AP, the native hapten is regenerated. These caged haptens were applied in a proximity assay format by the use of a first antibody labeled with caged haptens that can be uncaged by AP conjugated to the second antibody. Only when the two epitopes of interest are in close proximity to one another will the AP interact with the caged hapten and uncage it. The native hapten, which represents the population of PPCs, was then visualized by an anti-hapten antibody conjugated to horseradish peroxidase, followed by diaminobenzidine detection. We provide proof of concept for the detection of protein proximity pairs (β-catenin-E-cadherin and EGFR-GRB2), and confirm assay specificity through technical controls involving reagent omission experiments, and biologically by treatment with small-molecule kinase inhibitors that interrupt kinase-adaptor complexes.

The design and development of covalent protein-protein interaction inhibitors for cancer treatment

Protein-protein interactions (PPIs) are central to a variety of biological processes, and their dysfunction is implicated in the pathogenesis of a range of human diseases, including cancer. Hence, the inhibition of PPIs has attracted significant attention in drug discovery. Covalent inhibitors have been reported to achieve high efficiency through forming covalent bonds with cysteine or other nucleophilic residues in the target protein. Evidence suggests that there is a reduced risk for the development of drug resistance against covalent drugs, which is a major challenge in areas such as oncology and infectious diseases. Recent improvements in structural biology and chemical reactivity have enabled the design and development of potent and selective covalent PPI inhibitors. In this review, we will highlight the design and development of therapeutic agents targeting PPIs for cancer therapy.

Targeted Identification of Protein Interactions in Eukaryotic mRNA Translation

To identify protein-protein interactions and phosphorylated amino acid sites in eukaryotic mRNA translation, replicate TAP-MudPIT and control experiments are performed targeting Saccharomyces cerevisiae genes previously implicated in eukaryotic mRNA translation by their genetic and/or functional roles in translation initiation, elongation, termination, or interactions with ribosomal complexes. Replicate tandem affinity purifications of each targeted yeast TAP-tagged mRNA translation protein coupled with multidimensional liquid chromatography and tandem mass spectrometry analysis are used to identify and quantify copurifying proteins. To improve sensitivity and minimize spurious, nonspecific interactions, a novel cross-validation approach is employed to identify the most statistically significant protein-protein interactions. Using experimental and computational strategies discussed herein, the previously described protein composition of the canonical eukaryotic mRNA translation initiation, elongation, and termination complexes is calculated. In addition, statistically significant unpublished protein interactions and phosphorylation sites for S. cerevisiae's mRNA translation proteins and complexes are identified.

Next-generation Interactomics: Considerations for the Use of Co-elution to Measure Protein Interaction Networks

Understanding how proteins interact is crucial to understanding cellular processes. Among the available interactome mapping methods, co-elution stands out as a method that is simultaneous in nature and capable of identifying interactions between all the proteins detected in a sample. The general workflow in co-elution methods involves the mild extraction of protein complexes and their separation into several fractions, across which proteins bound together in the same complex will show similar co-elution profiles when analyzed appropriately. In this review we discuss the different separation, quantification and bioinformatic strategies used in co-elution studies, and the important considerations in designing these studies. The benefits of co-elution versus other methods makes it a valuable starting point when asking questions that involve the perturbation of the interactome.

Fluorogenic Photoaffinity Labeling of Proteins in Living Cells

Genetically encoded fluorescent proteins or small-molecule probes that recognize specific protein binding partners can be used to label proteins to study their localization and function with fluorescence microscopy. However, these approaches are limited in signal-to-background resolution and the ability to temporally control labeling. Herein, we describe a covalent protein labeling technique using a fluorogenic malachite green probe functionalized with a photoreactive cross-linker. This enables a controlled covalent attachment to a genetically encodable fluorogen activating protein (FAP) with low background signal. We demonstrate covalent labeling of a protein in vitro as well as in live mammalian cells. This method is straightforward, displays high labeling specificity, and results in improved signal-to-background ratios in photoaffinity labeling of target proteins. Additionally, this probe provides temporal control over reactivity, enabling future applications in real-time monitoring of cellular events.

Emerging roles of allosteric modulators in the regulation of protein-protein interactions (PPIs): A new paradigm for PPI drug discovery

Protein-protein interactions (PPIs) are closely implicated in various types of cellular activities and are thus pivotal to health and disease states. Given their fundamental roles in a wide range of biological processes, the modulation of PPIs has enormous potential in drug discovery. However, owing to the general properties of large, flat, and featureless interfaces of PPIs, previous attempts have demonstrated that the generation of therapeutic agents targeting PPI interfaces is challenging, rendering them almost "undruggable" for decades. To date, rapid progress in chemical and structural biology techniques has promoted the exploitation of allostery as a novel approach in drug discovery. By attaching to allosteric sites that are topologically and spatially distinct from PPI interfaces, allosteric modulators can achieve improved physiochemical properties. Thus, allosteric modulators may represent an alternative strategy to target intractable PPIs and have attracted intense pharmaceutical interest. In this review, we first briefly introduce the characteristics of PPIs and then present different approaches for investigating PPIs, as well as the latest methods for modulating PPIs. Importantly, we comprehensively review the recent progress in the development of allosteric modulators to inhibit or stabilize PPIs. Finally, we conclude with future perspectives on the discovery of allosteric PPI modulators, especially the application of computational methods to aid in allosteric PPI drug discovery.

Sample Preparation for Proteomic Analysis of Neisseria meningitidis

Mass spectrometry (MS) proteomics allows systematic identification, characterization, and relative quantification of the full suite of proteins in a biological sample, and is a powerful analytical approach for investigation of many aspects of the biology of Neisseria meningitidis. Here, we describe methods for robust and efficient sample preparation of the proteome of N. meningitidis suitable for diverse MS proteomics workflows.

A protein-protein interaction map of the TNF-induced NF-κB signal transduction pathway

Tumor Necrosis Factor (TNF) has a crucial role in inflammation, cell proliferation and cell death. Dysregulation of TNF receptor 1 (TNFR1)-induced Nuclear Factor-kappa B (NF-κB) signaling leads to chronic inflammation and is associated with several human inflammatory pathologies. Hence, TNF neutralization suppresses inflammation and attenuates inflammatory pathology. However, despite its beneficial effects, anti-TNF therapy suffers from efficacy issues and severe immune side effects. There is thus an urging need to identify novel targets for pharmaceutical intervention in the NF-κB signaling pathway. Here, we present a protein-protein interaction dataset of the TNFR1-induced signaling pathway. For this, we used Virotrap, a novel method for studying protein complexes without disrupting the cellular integrity, on 12 central proteins controlling NF-κB and cell death signaling, both under resting conditions as well as upon TNF stimulation. Our dataset reveals dynamic interactions in TNFR1-induced NF-κB signaling and identifies both known as well as novel interactors that may help to further unravel the molecular mechanisms steering TNF-induced inflammatory signaling and pathology.