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

Coupling of surface plasmon resonance and mass spectrometry for molecular interaction studies in drug discovery

Various analytical technologies have been developed for the study of target-ligand interactions. The combination of these technologies gives pivotal information on the binding mechanism, kinetics, affinity, residence time, and changes in molecular structures. Mass spectrometry (MS) offers structural information, enabling the identification and quantification of target-ligand interactions. Surface plasmon resonance (SPR) provides kinetic information on target-ligand interaction in real time. The coupling of MS and SPR complements each other in the studies of target-ligand interactions. Over the last two decades, the capabilities and added values of SPR-MS have been reported. This review summarizes and highlights the benefits, applications, and potential for further research of the SPR-MS approach.

Mapping protein-protein interactions by mass spectrometry

Protein-protein interactions (PPIs) are essential for numerous biological activities, including signal transduction, transcription control, and metabolism. They play a pivotal role in the organization and function of the proteome, and their perturbation is associated with various diseases, such as cancer, neurodegeneration, and infectious diseases. Recent advances in mass spectrometry (MS)-based protein interactomics have significantly expanded our understanding of the PPIs in cells, with techniques that continue to improve in terms of sensitivity, and specificity providing new opportunities for the study of PPIs in diverse biological systems. These techniques differ depending on the type of interaction being studied, with each approach having its set of advantages, disadvantages, and applicability. This review highlights recent advances in enrichment methodologies for interactomes before MS analysis and compares their unique features and specifications. It emphasizes prospects for further improvement and their potential applications in advancing our knowledge of PPIs in various biological contexts.

Systematic HOIP interactome profiling reveals critical roles of linear ubiquitination in tissue homeostasis

Linear ubiquitination catalyzed by HOIL-1-interacting protein (HOIP), the key component of the linear ubiquitination assembly complex, plays fundamental roles in tissue homeostasis by executing domain-specific regulatory functions. However, a proteome-wide analysis of the domain-specific interactome of HOIP across tissues is lacking. Here, we present a comprehensive mass spectrometry-based interactome profiling of four HOIP domains in nine mouse tissues. The interaction dataset provides a high-quality HOIP interactome resource with an average of approximately 90 interactors for each bait per tissue. HOIP tissue interactome presents a systematic understanding of linear ubiquitination functions in each tissue and also shows associations of tissue functions to genetic diseases. HOIP domain interactome characterizes a set of previously undefined linear ubiquitinated substrates and elucidates the cross-talk among HOIP domains in physiological and pathological processes. Moreover, we show that linear ubiquitination of Integrin-linked protein kinase (ILK) decreases focal adhesion formation and promotes the detachment of Shigella flexneri-infected cells. Meanwhile, Hoip deficiency decreases the linear ubiquitination of Smad ubiquitination regulatory factor 1 (SMURF1) and enhances its E3 activity, finally causing a reduced bone mass phenotype in mice. Overall, our work expands the knowledge of HOIP-interacting proteins and provides a platform for further discovery of linear ubiquitination functions in tissue homeostasis.

Uncovering domain motif interactions using high-throughput protein-protein interaction detection methods

Protein-protein interactions (PPIs) are often mediated by short linear motifs (SLiMs) in one protein and domain in another, known as domain-motif interactions (DMIs). During the past decade, SLiMs have been studied to find their role in cellular functions such as post-translational modifications, regulatory processes, protein scaffolding, cell cycle progression, cell adhesion, cell signalling and substrate selection for proteasomal degradation. This review provides a comprehensive overview of the current PPI detection techniques and resources, focusing on their relevance to capturing interactions mediated by SLiMs. We also address the challenges associated with capturing DMIs. Moreover, a case study analysing the BioGrid database as a source of DMI prediction revealed significant known DMI enrichment in different PPI detection methods. Overall, it can be said that current high-throughput PPI detection methods can be a reliable source for predicting DMIs.

Mycobacterial Rv1804c binds to the PEST domain of IκBα and activates macrophage-mediated proinflammatory responses

Recognition of the components of Mycobacterium tuberculosis (Mtb) by macrophages is vital for initiating a cascade of host immune responses. However, the recognition of Mtb-secretory proteins by the receptor-independent pathways of the host remains unclear. Rv1804c is a highly conserved secretory protein in Mtb. However, its exact function and underlying mechanism in Mtb infection remain poorly understood. In the present study, we observed that Rv1804c activates macrophage-mediated proinflammatory responses in an IKKα-independent manner. Furthermore, we noted that Rv1804c inhibits mycobacterial survival. By elucidating the underlying mechanisms, we observed that Rv1804c activates IκBα by directly interacting with its PEST domain. Moreover, Rv1804c was enriched in attenuated but not in virulent mycobacteria and associated with the disease process of tuberculosis. Our findings provide an alternative pathway via which a mycobacterial secretory protein activates macrophage-mediated proinflammatory responses. Our study findings may shed light on the prevention and treatment of tuberculosis.

Graph-theoretical prediction of biological modules in quaternary structures of large protein complexes

MOTIVATION

The functional complexity of biochemical processes is strongly related to the interplay of proteins and their assembly into protein complexes. In recent years, the discovery and characterization of protein complexes have substantially progressed through advances in cryo-electron microscopy, proteomics, and computational structure prediction. This development results in a strong need for computational approaches to analyse the data of large protein complexes for structural and functional characterization. Here, we aim to provide a suitable approach, which processes the growing number of large protein complexes, to obtain biologically meaningful information on the hierarchical organization of the structures of protein complexes.

RESULTS

We modelled the quaternary structure of protein complexes as undirected, labelled graphs called complex graphs. In complex graphs, the vertices represent protein chains and the edges spatial chain-chain contacts. We hypothesized that clusters based on the complex graph correspond to functional biological modules. To compute the clusters, we applied the Leiden clustering algorithm. To evaluate our approach, we chose the human respiratory complex I, which has been extensively investigated and exhibits a known biological module structure experimentally validated. Additionally, we characterized a eukaryotic group II chaperonin TRiC/CCT and the head of the bacteriophage Φ29. The analysis of the protein complexes correlated with experimental findings and indicated known functional, biological modules. Using our approach enables not only to predict functional biological modules in large protein complexes with characteristic features but also to investigate the flexibility of specific regions and coformational changes. The predicted modules can aid in the planning and analysis of experiments.

AVAILABILITY AND IMPLEMENTATION

Jupyter notebooks to reproduce the examples are available on our public GitHub repository: https://github.com/MolBIFFM/PTGLtools/tree/main/PTGLmodulePrediction.

Chicken speckle-type POZ protein (SPOP) negatively regulates MyD88/NF-κB signaling pathway mediated proinflammatory cytokine production to promote the replication of Newcastle disease virus

The speckle-type POZ protein (SPOP) is demonstrated to be a specific adaptor of the cullin-RING-based E3 ubiquitin ligase complex that participates in multiple cellular processes. Up to now, SPOP involved in inflammatory response has attracted more attention, but the association of SPOP with animal virus infection is scarcely reported. In this study, chicken MyD88 (chMyD88), an innate immunity-associated protein, was screened to be an interacting partner of chSPOP using co-immunoprecipitation (Co-IP) combined with liquid chromatography-tandem mass spectrometry methods. This interaction was further confirmed by fluorescence co-localization, Co-IP, and pull-down assays. It was interesting that exogenous recombinant protein HA-chSPOP or endogenous chSPOP alone was mainly located in the nucleus but was translocated to the cytoplasm upon co-expression with chMyD88 or lipopolysaccharide stimulation. In addition, chSPOP reduced chMyD88 expression by ubiquitination in a dose-dependent manner, and the regulation of NF-κB activity by chSPOP was dependent solely on chMyD88. Importantly, chSPOP played a negative regulatory role in the MyD88/NF-κB signaling pathway and the production of proinflammatory cytokines. Moreover, we found that velogenic Newcastle disease virus (NDV) infection changed the subcellular localization of chSPOP and the expression patterns of chSPOP and chMyD88, and overexpression of chSPOP decreased the production of proinflammatory cytokines to enhance velogenic and lentogenic NDV replication, while siRNA-mediated chSPOP knockdown obtained the opposite results, thereby indicating that chSPOP negatively regulated MyD88/NF-κB signaling pathway mediated proinflammatory cytokine production to promote NDV replication. These findings highlight the important role of the SPOP/MyD88/NF-κB signaling pathway in NDV replication and may provide insightful information about NDV pathogenesis.

Uncovering the proximal proteome of CTR1 through TurboID-mediated proximity labeling

Protein-protein interactions play a crucial role in driving cellular processes and enabling appropriate physiological responses in organisms. The plant hormone ethylene signaling pathway is complex and regulated by the spatiotemporal regulation of its signaling molecules. Constitutive Triple Response 1 (CTR1), a key negative regulator of the pathway, regulates the function of Ethylene-Insensitive 2 (EIN2), a positive regulator of ethylene signaling, at the endoplasmic reticulum (ER) through phosphorylation. Our recent study revealed that CTR1 can also translocate from the ER to the nucleus in response to ethylene and positively regulate ethylene responses by stabilizing EIN3. To gain further insights into the role of CTR1 in plants, we used TurboID-based proximity labeling and mass spectrometry to identify the proximal proteomes of CTR1 in Nicotiana benthamiana. The identified proximal proteins include known ethylene signaling components, as well as proteins involved in diverse cellular processes such as mitochondrial respiration, mRNA metabolism, and organelle biogenesis. Our study demonstrates the feasibility of proximity labeling using the N. benthamiana transient expression system and identifies the potential interactors of CTR1 in vivo, uncovering the potential roles of CTR1 in a wide range of cellular processes.

Label-free proteomic comparison reveals ciliary and nonciliary phenotypes of IFT-A mutants

DIFFRAC is a powerful method for systematically comparing proteome content and organization between samples in a high-throughput manner. By subjecting control and experimental protein extracts to native chromatography and quantifying the contents of each fraction using mass spectrometry, it enables the quantitative detection of alterations to protein complexes and abundances. Here, we applied DIFFRAC to investigate the consequences of genetic loss of Ift122, a subunit of the intraflagellar transport-A (IFT-A) protein complex that plays a vital role in the formation and function of cilia and flagella, on the proteome of Tetrahymena thermophila. A single DIFFRAC experiment was sufficient to detect changes in protein behavior that mirrored known effects of IFT-A loss and revealed new biology. We uncovered several novel IFT-A-regulated proteins, which we validated through live imaging in Xenopus multiciliated cells, shedding new light on both the ciliary and non-ciliary functions of IFT-A. Our findings underscore the robustness of DIFFRAC for revealing proteomic changes in response to genetic or biochemical perturbation.

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.

A Tandem-Affinity Purification Method for Identification of Primary Intracellular Drug-Binding Proteins

In the field of drug discovery, understanding how small molecule drugs interact with cellular components is crucial. Our study introduces a novel methodology to uncover primary drug targets using Tandem Affinity Purification for identification of Drug-Binding Proteins (TAP-DBP). Central to our approach is the generation of a FLAG-hemagglutinin (HA)-tagged chimeric protein featuring the FKBP12(F36V) adaptor protein and the TurboID enzyme. Conjugation of drug molecules with the FKBP12(F36V) ligand allows for the coordinated recruitment of drug-binding partners effectively enabling in-cell TurboID-mediated biotinylation. By employing a tandem affinity purification protocol based on FLAG-immunoprecipitation and streptavidin pulldown, alongside mass spectrometry analysis, TAP-DBP allows for the precise identification of drug-primary binding partners. Overall, this study introduces a systematic, unbiased method for identification of drug-protein interactions, contributing a clear understanding of target engagement and drug selectivity to advance the mode of action of a drug in cells.

Cell Fractionation and the Identification of Host Proteins Involved in Plant-Virus Interactions

Plant viruses depend on host cellular factors for their replication and movement. There are cellular proteins that change their localization and/or expression and have a proviral role or antiviral activity and interact with or target viral proteins. Identification of those proteins and their roles during infection is crucial for understanding plant-virus interactions and to design antiviral resistance in crops. Important host proteins have been identified using approaches such as tag-dependent immunoprecipitation or yeast two hybridization that require cloning individual proteins or the entire virus. However, the number of possible interactions between host and viral proteins is immense. Therefore, an alternative method is needed for proteome-wide identification of host proteins involved in host-virus interactions. Here, we present cell fractionation coupled with mass spectrometry as an option to identify protein-protein interactions between viruses and their hosts. This approach involves separating subcellular organelles using differential and/or gradient centrifugation from virus-free and virus-infected cells (1) followed by comparative analysis of the proteomic profiles obtained for each subcellular organelle via mass spectrometry (2). After biological validation, prospect host proteins with proviral or antiviral roles can be subject to fundamental studies in the context of basic biology to shed light on both virus replication and cellular processes. They can also be targeted via gene editing to develop virus-resistant crops.

Proteomics and Metabolomics in Congenital Zika Syndrome: A Review of Molecular Insights and Biomarker Discovery

Zika virus (ZIKV) infection can be transmitted vertically, leading to the development of congenital Zika syndrome (CZS) in infected fetuses. During the early stages of gestation, the fetuses face an elevated risk of developing CZS. However, it is important to note that late-stage infections can also result in adverse outcomes. The differences between CZS and non-CZS phenotypes remain poorly understood. In this review, we provide a summary of the molecular mechanisms underlying ZIKV infection and placental and blood-brain barriers trespassing. Also, we have included molecular alterations that elucidate the progression of CZS by proteomics and metabolomics studies. Lastly, this review comprises investigations into body fluid samples, which have aided to identify potential biomarkers associated with CZS.

Improved in situ characterization of protein complex dynamics at scale with thermal proximity co-aggregation

Cellular activities are carried out vastly by protein complexes but large repertoire of protein complexes remains functionally uncharacterized which necessitate new strategies to delineate their roles in various cellular processes and diseases. Thermal proximity co-aggregation (TPCA) is readily deployable to characterize protein complex dynamics in situ and at scale. We develop a version termed Slim-TPCA that uses fewer temperatures increasing throughputs by over 3X, with new scoring metrics and statistical evaluation that result in minimal compromise in coverage and detect more relevant complexes. Less samples are needed, batch effects are minimized while statistical evaluation cost is reduced by two orders of magnitude. We applied Slim-TPCA to profile K562 cells under different duration of glucose deprivation. More protein complexes are found dissociated, in accordance with the expected downregulation of most cellular activities, that include 55S ribosome and respiratory complexes in mitochondria revealing the utility of TPCA to study protein complexes in organelles. Protein complexes in protein transport and degradation are found increasingly assembled unveiling their involvement in metabolic reprogramming during glucose deprivation. In summary, Slim-TPCA is an efficient strategy for characterization of protein complexes at scale across cellular conditions, and is available as Python package at https://pypi.org/project/Slim-TPCA/ .

Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities

Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.

Elucidating the dynamic remodelling of Escherichia coli interactome in different growth conditions using multiplex co-fractionation MS (mCF-MS)

Most proteins function by forming complexes within a dynamic interconnected network that underlies various biological mechanisms. To systematically investigate such interactomes, high-throughput techniques, including CF-MS, have been developed to capture, identify, and quantify protein-protein interactions (PPIs) on a large scale. Compared to other techniques, CF-MS allows the global identification and quantification of native protein complexes in one setting, without genetic manipulation. Furthermore, quantitative CF-MS can potentially elucidate the distribution of a protein in multiple co-elution features, informing the stoichiometries and dynamics of a target protein complex. In this issue, Youssef et al. (Proteomics 2023, 00, e2200404) combined multiplex CF-MS and a new algorithm to study the dynamics of the PPI network for Escherichia coli grown under ten different conditions. Although the results demonstrated that most proteins remained stable, the authors were able to detect disrupted interactions that were growth condition specific. Further bioinformatics analyses also revealed the biophysical properties and structural patterns that govern such a response.

The Breast Cancer Proteome and Precision Oncology

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

Mind the gap: Methods to study membrane contact sites

Organelles are dynamic entities whose functions are essential for the optimum functioning of cells. It is now known that the juxtaposition of organellar membranes is essential for the exchange of metabolites and their communication. These functional apposition sites are termed membrane contact sites. Dynamic membrane contact sites between various sub-cellular structures such as mitochondria, endoplasmic reticulum, peroxisomes, Golgi apparatus, lysosomes, lipid droplets, plasma membrane, endosomes, etc. have been reported in various model systems. The burgeoning area of research on membrane contact sites has witnessed several manuscripts in recent years that identified the contact sites and components involved. Several methods have been developed to identify, measure and analyze the membrane contact sites. In this manuscript, we aim to discuss important methods developed to date that are used to study membrane contact sites.

Innovation in Cross-Linking Mass Spectrometry Workflows: Toward a Comprehensive, Flexible, and Customizable Data Analysis Platform

Cross-linking mass spectrometry (XL-MS) is widely used in the analysis of protein structure and protein-protein interactions (PPIs). Throughout the entire workflow, the utilization of cross-linkers and the interpretation of cross-linking data are the core steps. In recent years, the development of cross-linkers and analytical software mostly follow up on the classical models of non-cleavable cross-linkers such as BS3/DSS and MS-cleavable cross-linkers such as DSSO. Although such a paradigm promotes the maturity and robustness of the XL-MS field, it confines the innovation and flexibility of new cross-linkers and analytical software. This critical insight will discuss the classification, advantages, and disadvantages of existing data analysis search engines. Take the new platinum-based metal cross-linker as an example, potential pitfalls in characterization of cross-linked peptides using existing software are discussed. Finally, ideas on developing more flexible, comprehensive, and user-friendly cross-linkers and software tools are proposed.

Uncovering Protein Networks in Cardiovascular Proteomics

Biological networks have been widely used in many different diseases to identify potential biomarkers and design drug targets. In the present review, we describe the main computational techniques for reconstructing and analyzing different types of protein networks and summarize the previous applications of such techniques in cardiovascular diseases. Existing tools are critically compared, discussing when each method is preferred such as the use of co-expression networks for functional annotation of protein clusters and the use of directed networks for inferring regulatory associations. Finally, we are presenting examples of reconstructing protein networks of different types (regulatory, co-expression, and protein-protein interaction networks). We demonstrate the necessity to reconstruct networks separately for each cardiovascular tissue type and disease entity and provide illustrative examples of the importance of taking into consideration relevant post-translational modifications. Finally, we demonstrate and discuss how the findings of protein networks could be interpreted using single-cell RNA-sequencing data.

Recent advances in proteomic-based diagnostics of cystic fibrosis

INTRODUCTION

Cystic fibrosis (CF) is a genetic disease characterized by thick and sticky mucus accumulation, which may harm numerous internal organs. Various variables such as gene modifiers, environmental factors, age of diagnosis, and CF transmembrane conductance regulator (CFTR) gene mutations influence phenotypic disease diversity. Biomarkers that are based on genomic information may not accurately represent the underlying mechanism of the disease as well as its lethal complications. Therefore, recent advancements in mass spectrometry (MS)-based proteomics may provide deep insights into CF mechanisms and cellular functions by examining alterations in the protein expression patterns from various samples of individuals with CF.

AREAS COVERED

We present current developments in MS-based proteomics, its application, and findings in CF. In addition, the future roles of proteomics in finding diagnostic and prognostic novel biomarkers.

EXPERT OPINION

Despite significant advances in MS-based proteomics, extensive research in a large cohort for identifying and validating diagnostic, prognostic, predictive, and therapeutic biomarkers for CF disease is highly needed.

A multi-scale map of protein assemblies in the DNA damage response

The DNA damage response (DDR) ensures error-free DNA replication and transcription and is disrupted in numerous diseases. An ongoing challenge is to determine the proteins orchestrating DDR and their organization into complexes, including constitutive interactions and those responding to genomic insult. Here, we use multi-conditional network analysis to systematically map DDR assemblies at multiple scales. Affinity purifications of 21 DDR proteins, with/without genotoxin exposure, are combined with multi-omics data to reveal a hierarchical organization of 605 proteins into 109 assemblies. The map captures canonical repair mechanisms and proposes new DDR-associated proteins extending to stress, transport, and chromatin functions. We find that protein assemblies closely align with genetic dependencies in processing specific genotoxins and that proteins in multiple assemblies typically act in multiple genotoxin responses. Follow-up by DDR functional readouts newly implicates 12 assembly members in double-strand-break repair. The DNA damage response assemblies map is available for interactive visualization and query (ccmi.org/ddram/).

Proteomic applications in identifying protein-protein interactions

There are many things that can be used to characterize a protein. Size, isoelectric point, hydrophobicity, structure (primary to quaternary), and subcellular location are just a few parameters that are used. The most important feature of a protein, however, is its function. While there are many experiments that can indicate a protein's role, identifying the molecules it interacts with is probably the most definitive way of determining its function. Owing to technology limitations, protein interactions have historically been identified on a one molecule per experiment basis. The advent of high throughput multiplexed proteomic technologies in the 1990s, however, made identifying hundreds and thousands of proteins interactions within single experiments feasible. These proteomic technologies have dramatically increased the rate at which protein-protein interactions (PPIs) are discovered. While the improvement in mass spectrometry technology was an early driving force in the rapid pace of identifying PPIs, advances in sample preparation and chromatography have recently been propelling the field. In this chapter, we will discuss the importance of identifying PPIs and describe current state-of-the-art technologies that demonstrate what is currently possible in this important area of biological research.

Unlocking the Mysteries of Alpha-N-Terminal Methylation and its Diverse Regulatory Functions

Protein post-translation modifications (PTMs) are a critical regulatory mechanism of protein function. Protein α-N-terminal (Nα) methylation is a conserved PTM across prokaryotes and eukaryotes. Studies of the Nα methyltransferases responsible for Να methylation and their substrate proteins have shown that the PTM involves diverse biological processes, including protein synthesis and degradation, cell division, DNA damage response, and transcription regulation. This review provides an overview of the progress toward the regulatory function of Να methyltransferases and their substrate landscape. More than 200 proteins in humans and 45 in yeast are potential substrates for protein Nα methylation based on the canonical recognition motif, XP[KR]. Based on recent evidence for a less stringent motif requirement, the number of substrates might be increased, but further validation is needed to solidify this concept. A comparison of the motif in substrate orthologs in selected eukaryotic species indicates intriguing gain and loss of the motif across the evolutionary landscape. We discuss the state of knowledge in the field that has provided insights into the regulation of protein Να methyltransferases and their role in cellular physiology and disease. We also outline the current research tools that are key to understanding Να methylation. Finally, challenges are identified and discussed that would aid in unlocking a system-level view of the roles of Να methylation in diverse cellular pathways.

Network-centric analysis of co-fractionated protein complex profiles using SECAT

The Size-Exclusion Chromatography Analysis Toolkit (SECAT) elucidates protein complex dynamics using co-fractionated bottom-up mass spectrometry (CF-MS) data. Here, we present a protocol for the network-centric analysis and interpretation of CF-MS profiles using SECAT. We describe the technical steps for preprocessing, scoring, semi-supervised machine learning, and quantification, including common pitfalls and their solutions. We further provide guidance for data export, visualization, and the interpretation of SECAT results to discover dysregulated proteins and interactions, supporting new hypotheses and biological insights. For complete details on the use and execution of this protocol, please refer to Rosenberger et al. (2020).1.

Proteomic Profiling of Mouse Brain Pyruvate Kinase Binding Proteins: A Hint for Moonlighting Functions of PKM1?

Affinity-based proteomic profiling is widely used for the identification of proteins involved in the formation of various interactomes. Since protein-protein interactions (PPIs) reflect the role of particular proteins in the cell, identification of interaction partners for a protein of interest can reveal its function. The latter is especially important for the characterization of multifunctional proteins, which can play different roles in the cell. Pyruvate kinase (PK), a classical glycolytic enzyme catalyzing the last step of glycolysis, exists in four isoforms: PKM1, PKM2, PKL, and PKR. The enzyme isoform expressed in actively dividing cells, PKM2, exhibits many moonlighting (noncanonical) functions. In contrast to PKM2, PKM1, predominantly expressed in adult differentiated tissues, lacks well-documented moonlighting functions. However, certain evidence exists that it can also perform some functions unrelated to glycolysis. In order to evaluate protein partners, bound to PKM1, in this study we have combined affinity-based separation of mouse brain proteins with mass spectrometry identification. The highly purified PKM1 and a 32-mer synthetic peptide (PK peptide), sharing high sequence homology with the interface contact region of all PK isoforms, were used as the affinity ligands. This proteomic profiling resulted in the identification of specific and common proteins bound to both affinity ligands. Quantitative affinity binding to the affinity ligands of selected identified proteins was validated using a surface plasmon resonance (SPR) biosensor. Bioinformatic analysis has shown that the identified proteins, bound to both full-length PKM1 and the PK peptide, form a protein network (interactome). Some of these interactions are relevant for the moonlighting functions of PKM1. The proteomic dataset is available via ProteomeXchange with the identifier PXD041321.

Integrated mass spectrometry strategy for functional protein complex discovery and structural characterization

The discovery of functional protein complex and the interrogation of the complex structure-function relationship (SFR) play crucial roles in the understanding and intervention of biological processes. Affinity purification-mass spectrometry (AP-MS) has been proved as a powerful tool in the discovery of protein complexes. However, validation of these novel protein complexes as well as elucidation of their molecular interaction mechanisms are still challenging. Recently, native top-down MS (nTDMS) is rapidly developed for the structural analysis of protein complexes. In this review, we discuss the integration of AP-MS and nTDMS in the discovery and structural characterization of functional protein complexes. Further, we think the emerging artificial intelligence (AI)-based protein structure prediction is highly complementary to nTDMS and can promote each other. We expect the hybridization of integrated structural MS with AI prediction to be a powerful workflow in the discovery and SFR investigation of functional protein complexes.

Label-free proteomic comparison reveals ciliary and non-ciliary phenotypes of IFT-A mutants

DIFFRAC is a powerful method for systematically comparing proteome content and organization between samples in a high-throughput manner. By subjecting control and experimental protein extracts to native chromatography and quantifying the contents of each fraction using mass spectrometry, it enables the quantitative detection of alterations to protein complexes and abundances. Here, we applied DIFFRAC to investigate the consequences of genetic loss of Ift122, a subunit of the intraflagellar transport-A (IFT-A) protein complex that plays a vital role in the formation and function of cilia and flagella, on the proteome of Tetrahymena thermophila . A single DIFFRAC experiment was sufficient to detect changes in protein behavior that mirrored known effects of IFT-A loss and revealed new biology. We uncovered several novel IFT-A-regulated proteins, which we validated through live imaging in Xenopus multiciliated cells, shedding new light on both the ciliary and non-ciliary functions of IFT-A. Our findings underscore the robustness of DIFFRAC for revealing proteomic changes in response to genetic or biochemical perturbation.

Using the Proteomics Toolbox to Resolve Topology and Dynamics of Compartmentalized cAMP Signaling

cAMP is a second messenger that regulates a myriad of cellular functions in response to multiple extracellular stimuli. New developments in the field have provided exciting insights into how cAMP utilizes compartmentalization to ensure specificity when the message conveyed to the cell by an extracellular stimulus is translated into the appropriate functional outcome. cAMP compartmentalization relies on the formation of local signaling domains where the subset of cAMP signaling effectors, regulators and targets involved in a specific cellular response cluster together. These domains are dynamic in nature and underpin the exacting spatiotemporal regulation of cAMP signaling. In this review, we focus on how the proteomics toolbox can be utilized to identify the molecular components of these domains and to define the dynamic cellular cAMP signaling landscape. From a therapeutic perspective, compiling data on compartmentalized cAMP signaling in physiological and pathological conditions will help define the signaling events underlying disease and may reveal domain-specific targets for the development of precision medicine interventions.

Tandem Affinity Purification (TAP) of Interacting Prey Proteins with FLAG- and HA-Tagged Bait Proteins

Proteins often interact with each other to form complexes and play functional roles in almost all cellular processes. The study of protein-protein interactions is therefore critical to understand protein function and biological pathways. Affinity Purification coupled with Mass Spectrometry (AP-MS) is an invaluable technique for identifying the interaction partners in protein complexes. In this approach, the protein of interest is fused to an affinity tag, followed by the expression and purification of the fusion protein. The affinity-purified sample is then analyzed by mass spectrometry to identify the interaction partners of the bait proteins. In this chapter, we detail the protocol for tandem affinity purification (TAP) based on the use of the FLAG (a fusion tag with peptide sequence DYKDDDDK) and hemagglutinin (HA) peptide epitopes. The immunoprecipitation using dual-affinity tags offers the advantage of increasing the specificity of the purification with lower nonspecific-background interactions.

Identification and Quantification of Affinity-Purified Proteins with MaxQuant, Followed by the Discrimination of Nonspecific Interactions with the CRAPome Interface

Affinity purification coupled to mass spectrometry (AP-MS) is a powerful method to analyze protein-protein interactions (PPIs). The AP-MS approach provides an unbiased analysis of the entire protein complex and is useful to identify indirect interactors. However, reliable protein identification from the complex AP-MS experiments requires appropriate control of false identifications and rigorous statistical analysis. Another challenge that can arise from AP-MS analysis is to distinguish bona fide interacting proteins from the non-specifically bound endogenous proteins or the "background contaminants" that co-purified by the bait experiments. In this chapter, we will first describe the protocol for performing in-solution trypsinization for the samples from the AP experiment followed by LC-MS/MS analysis. We will then detail the MaxQuant workflow for protein identification and quantification for the PPI data derived from the AP-MS experiment. Finally, we describe the CRAPome interface to process the data by filtering against contaminant lists, score the interactions and visualize the protein interaction networks.

Where protein structure and cell diversity meet

Protein-protein interaction networks - interactomes - are charted with the hope to understand how phenotypes emerge and how they are altered in disease states. Early efforts to map interactomes have focused on the assembly of context agnostic, reference networks. However, recent studies have mapped interactomes across different cell lines and tissues, finding highly variable interactomes due to the rewiring of protein-protein interactions in different contexts. Increasing evidence points to significant links between protein structure and interactome diversity seen across cell types and tissues. We discuss how recent findings support the key role of alternative splicing and phosphorylation, two well-established regulators of protein structural and functional diversity, in defining cell type- and tissue-specific interactomes. Moreover, we show that intrinsically disordered protein regions are most favorably equipped to support interactome rewiring by acting as hubs of protein structure and function regulation.

Two-Dimensional Blue Native/SDS Polyacrylamide Gel Electrophoresis for Analysis of Brazilian Bothrops Snake Venoms

Viperidae snakes are the most important agents of snakebites in Brazil. The protein composition of snake venoms has been frequently analyzed by means of electrophoretic techniques, but the interaction of proteins in venoms has barely been addressed. An electrophoretic technique that has gained prominence to study this type of interaction is blue native polyacrylamide gel electrophoresis (BN-PAGE), which allows for the high-resolution separation of proteins in their native form. These protein complexes can be further discriminated by a second-dimension gel electrophoresis (SDS-PAGE) from lanes cut from BN-PAGE. Once there is no study on the use of bidimensional BN/SDS-PAGE with snake venoms, this study initially standardized the BN/SDS-PAGE technique in order to evaluate protein interactions in Bothrops atrox, Bothrops erythromelas, and Bothrops jararaca snake venoms. Results of BN/SDS-PAGE showed that native protein complexes were present, and that snake venom metalloproteinases and venom serine proteinases maintained their enzymatic activity after BN/SDS-PAGE. C-type lectin-like proteins were identified by Western blotting. Therefore, bidimensional BN/SDS-PAGE proved to be an easy, practical, and efficient method for separating functional venom proteins according to their assemblage in complexes, as well as to analyze their biological activities in further details.

Short open reading frame genes in innate immunity: from discovery to characterization

Next-generation sequencing (NGS) technologies have greatly expanded the size of the known transcriptome. Many newly discovered transcripts are classified as long noncoding RNAs (lncRNAs) which are assumed to affect phenotype through sequence and structure and not via translated protein products despite the vast majority of them harboring short open reading frames (sORFs). Recent advances have demonstrated that the noncoding designation is incorrect in many cases and that sORF-encoded peptides (SEPs) translated from these transcripts are important contributors to diverse biological processes. Interest in SEPs is at an early stage and there is evidence for the existence of thousands of SEPs that are yet unstudied. We hope to pique interest in investigating this unexplored proteome by providing a discussion of SEP characterization generally and describing specific discoveries in innate immunity.

Twentieth-Century Paleoproteomics: Lessons from Venta Micena Fossils

Simple Summary

Two independent research groups led by Olivares (Spain) and Lowenstein (USA) investigated the immunological reactions of proteins extracted from the controversial Orce skull (VM-0), a 1.3-million-year-old fossil found at the Venta Micena site in Orce, Granada (Spain) and initially believed to come from an unidentified hominin. Work by both groups with polyclonal and monoclonal antibodies showed that proteins from this fossil reacted most strongly to antibodies against modern human proteins. Other hominin and mammal fossils from Venta Micena were also studied.

Abstract

Proteomics methods can identify amino acid sequences in fossil proteins, thus making it possible to determine the ascription or proximity of a fossil to other species. Before mass spectrometry was used to study fossil proteins, earlier studies used antibodies to recognize their sequences. Lowenstein and colleagues, at the University of San Francisco, pioneered the identification of fossil proteins with immunological methods. His group, together with Olivares’s group at the University of Granada, studied the immunological reactions of proteins from the controversial Orce skull fragment (VM-0), a 1.3-million-year-old fossil found at the Venta Micena site in Orce (Granada province, southern Spain) and initially assigned to a hominin. However, discrepancies regarding the morphological features of the internal face of the fossil raised doubts about this ascription. In this article, we review the immunological analysis of the proteins extracted from VM-0 and other Venta Micena fossils assigned to hominins and to other mammals, and explain how these methods helped to determine the species specificity of these fossils and resolve paleontological controversies.

ARFs get the BioID treatment: what have we been missing?

Li et al present the results of a proximity-interaction screen in mammalian cells for the effector proteins of 25 members of the Arf family of small GTPases. This study has generated an important resource for those working in several areas of cell biology and provided an initial characterisation of two new cellular roles for some of the least well studied members of this family, the regulation of PLD1 by ARL11/14 in phagocytosis, and the regulation of PI4KB by ARL5A/5B in the Golgi.

Biological interacting units identified in human protein networks reveal tissue-functional diversification and its impact on disease

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Biological processes are exerted by groups of physically interacting proteins.

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Proteins display variable biological roles depending on tissue-interactomic context.

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Tissue-specific protein-protein interaction networks reveal functional diversification.

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Most disease associated genes/proteins display tissue-specific phenotypes.

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Protein interaction network analysis is a valuable resource to identify disease genes.

Protein-protein interactions (PPI) play an essential role in the biological processes that occur in the cell. Therefore, the dissection of PPI networks becomes decisive to model functional coordination and predict pathological de-regulation. Cellular networks are dynamic and proteins display varying roles depending on the tissue-interactomic context. Thus, the use of centrality measures in individual proteins fall short to dissect the functional properties of the cell. For this reason, there is a need for more comprehensive, relational, and context-specific ways to analyze the multiple actions of proteins in different cells and identify specific functional assemblies within global biomolecular networks. Under this framework, we define Biological Interacting units (BioInt-U) as groups of proteins that interact physically and are enriched in a common Gene Ontology. A search strategy was applied on 33 tissue-specific (TS) PPI networks to generate BioInt libraries associated with each particular human tissue. The cross-tissue comparison showed that housekeeping assemblies incorporate different proteins and exhibit distinct network properties depending on the tissue. Furthermore, disease genes (DGs) of tissue-associated pathologies preferentially accumulate in units in the expected tissues, which in turn were more central in the TS networks. Overall, the study reveals a tissue-specific functional diversification based on the identification of specific protein units and suggests vulnerabilities specific of each tissue network, which can be applied to refine protein-disease association methods.

Scalable multiplex co-fractionation/mass spectrometry platform for accelerated protein interactome discovery

Co-fractionation/mass spectrometry (CF/MS) enables the mapping of endogenous macromolecular networks on a proteome scale, but current methods are experimentally laborious, resource intensive and afford lesser quantitative accuracy. Here, we present a technically efficient, cost-effective and reproducible multiplex CF/MS (mCF/MS) platform for measuring and comparing, simultaneously, multi-protein assemblies across different experimental samples at a rate that is up to an order of magnitude faster than previous approaches. We apply mCF/MS to map the protein interaction landscape of non-transformed mammary epithelia versus breast cancer cells in parallel, revealing large-scale differences in protein-protein interactions and the relative abundance of associated macromolecules connected with cancer-related pathways and altered cellular processes. The integration of multiplexing capability within an optimized workflow renders mCF/MS as a powerful tool for systematically exploring physical interaction networks in a comparative manner.

Co-fractionation/mass spectrometry (CF/MS) allows mapping protein interactomes but efficiency and quantitative accuracy are limited. Here, the authors develop a reproducible multiplexed CF/MS method and apply it to characterize interactome rewiring in breast cancer cells.

MicroID2: A Novel Biotin Ligase Enables Rapid Proximity Dependent Proteomics

Identifying protein-protein and other proximal interactions is central to dissecting signaling and regulatory processes in cells. BioID is a proximity dependent biotinylation method that uses an "abortive" biotin ligase to detect proximal interactions in cells in a highly reproducible manner. Recent advancements in proximity dependent biotinylation tools have improved efficiency and timing of labeling, allowing for measurement of interactions on a cellular timescale. However, issues of size, stability, and background labeling of these constructs persist. Here we modified the structure of BioID2, derived from A. aeolicus BirA, to create a smaller, highly active, biotin ligase that we named MicroID2. Truncation of the c-terminus of BioID2 and addition of mutations to alleviate blockage of biotin/ATP binding at the active site of BioID2 resulted in a smaller, highly active construct with lower background labeling. Several additional point mutations improved the function of our modified MicroID2 construct compared to BioID2 and other biotin ligases, including TurboID and miniTurbo. MicroID2 is the smallest biotin ligase reported so far (180 amino acids, for MicroID2 vs. 257 AA for miniTurbo and 338 AA for TurboID), yet it demonstrates only slightly less labeling activity than TurboID and outperforms miniTurbo. MicroID2 also had lower background labeling than TurboID. For experiments where precise temporal control of labeling is essential, we additionally developed a MicroID2 mutant, termed lbMicroID2, that has lower labeling efficiency, but significantly reduced biotin scavenging compared to BioID2. Finally, we demonstrate utility of MicroID2 in mass spectrometry experiments by localizing MicroID2 constructs to subcellular organelles and measuring proximal interactions.

Membrane Progesterone Receptors (mPRs, PAQRs): Review of Structural and Signaling Characteristics

The role of membrane progesterone receptors (mPRs), which belong to the progestin and adipoQ receptor (PAQR) family, in mediating rapid, nongenomic (non-classical) progestogen actions has been extensively studied since their identification 20 years ago. Although the mPRs have been implicated in progestogen regulation of numerous reproductive and non-reproductive functions in vertebrates, several critical aspects of their structure and signaling functions have been unresolved until recently and remain the subject of considerable debate. This paper briefly reviews recent developments in our understanding of the structure and functional characteristics of mPRs. The proposed membrane topology of mPRα, the structure of its ligand-binding site, and the binding affinities of steroids were predicted from homology modeling based on the structures of other PAQRs, adiponectin receptors, and confirmed by mutational analysis and ligand-binding assays. Extensive data demonstrating that mPR-dependent progestogen regulation of intracellular signaling through mPRs is mediated by activation of G proteins are reviewed. Close association of mPRα with progesterone membrane receptor component 1 (PGRMC1), its role as an adaptor protein to mediate cell-surface expression of mPRα and mPRα-dependent progestogen signaling has been demonstrated in several vertebrate models. In addition, evidence is presented that mPRs can regulate the activity of other hormone receptors.

Short open reading frames (sORFs) and microproteins: an update on their identification and validation measures

A short open reading frame (sORFs) constitutes ≤ 300 bases, encoding a microprotein or sORF-encoded protein (SEP) which comprises ≤ 100 amino acids. Traditionally dismissed by genome annotation pipelines as meaningless noise, sORFs were found to possess coding potential with ribosome profiling (RIBO-Seq), which unveiled sORF-based transcripts at various genome locations. Nonetheless, the existence of corresponding microproteins that are stable and functional was little substantiated by experimental evidence initially. With recent advancements in multi-omics, the identification, validation, and functional characterisation of sORFs and microproteins have become feasible. In this review, we discuss the history and development of an emerging research field of sORFs and microproteins. In particular, we focus on an array of bioinformatics and OMICS approaches used for predicting, sequencing, validating, and characterizing these recently discovered entities. These strategies include RIBO-Seq which detects sORF transcripts via ribosome footprints, and mass spectrometry (MS)-based proteomics for sequencing the resultant microproteins. Subsequently, our discussion extends to the functional characterisation of microproteins by incorporating CRISPR/Cas9 screen and protein–protein interaction (PPI) studies. Our review discusses not only detection methodologies, but we also highlight on the challenges and potential solutions in identifying and validating sORFs and their microproteins. The novelty of this review lies within its validation for the functional role of microproteins, which could contribute towards the future landscape of microproteomics.

Development of Software Workflow for the Rapid Detection of Cross-Linked Dipeptides

Detection and characterization of cross-linked peptides of unknown chemical nature and location is challenging. An analytical workflow based on the use of ¹⁸O-labeling tryptic digestion ( Anal. Chem. 2013, 85, 5900-5908) was previously utilized to identify reduction-resistant scrambled disulfide dipeptides within an IgG that was exposed to light under forced degradation conditions ( Mol. Pharmaceutics 2018, 15, 1598-1606). The analytical workflow denoted as XChem-Finder, while effective, is cumbersome and requires extensive manual effort for detection of ¹⁸O-incorporated peptides and subsequent de novo sequencing of partial peptide sequences to aid in the identification of cross-linked peptides. Here, we provide an automatic workflow using Byos (Protein Metrics Inc.) to facilitate the detection of cross-linked peptides. The LC-MS/MS data files that were subjected to the XChem-Finder workflow that identified the scrambled disulfides were utilized as the test-case data set for the automated ¹⁸O-labeling workflow in Byos. The new workflow resulted in the detection of a photoinduced cross-linked dipeptide with unknown linker chemistry, which was subsequently identified as a cross-linked dipeptide with a novel cysteine-tryptophan (thioether) linkage. This work demonstrates that combining ¹⁸O-labeling tryptic digestion with the Byos workflow enables rapid detection of cross-linked dipeptides.

Uncover New Reactivity of Genetically Encoded Alkyl Bromide Non-Canonical Amino Acids

Genetically encoded non-canonical amino acids (ncAAs) with electrophilic moieties are excellent tools to investigate protein-protein interactions (PPIs) both in vitro and in vivo. These ncAAs, including a series of alkyl bromide-based ncAAs, mainly target cysteine residues to form protein-protein cross-links. Although some reactivities towards lysine and tyrosine residues have been reported, a comprehensive understanding of their reactivity towards a broad range of nucleophilic amino acids is lacking. Here we used a recently developed OpenUaa search engine to perform an in-depth analysis of mass spec data generated for Thioredoxin and its direct binding proteins cross-linked with an alkyl bromide-based ncAA, BprY. The analysis showed that, besides cysteine residues, BprY also targeted a broad range of nucleophilic amino acids. We validated this broad reactivity of BprY with Affibody/Z protein complex. We then successfully applied BprY to map a binding interface between SUMO2 and SUMO-interacting motifs (SIMs). BprY was further applied to probe SUMO2 interaction partners. We identified 264 SUMO2 binders, including several validated SUMO2 binders and many new binders. Our data demonstrated that BprY can be effectively used to probe protein-protein interaction interfaces even without cysteine residues, which will greatly expand the power of BprY in studying PPIs.

Complexome Profiling-Exploring Mitochondrial Protein Complexes in Health and Disease

Complexome profiling (CP) is a state-of-the-art approach that combines separation of native proteins by electrophoresis, size exclusion chromatography or density gradient centrifugation with tandem mass spectrometry identification and quantification. Resulting data are computationally clustered to visualize the inventory, abundance and arrangement of multiprotein complexes in a biological sample. Since its formal introduction a decade ago, this method has been mostly applied to explore not only the composition and abundance of mitochondrial oxidative phosphorylation (OXPHOS) complexes in several species but also to identify novel protein interactors involved in their assembly, maintenance and functions. Besides, complexome profiling has been utilized to study the dynamics of OXPHOS complexes, as well as the impact of an increasing number of mutations leading to mitochondrial disorders or rearrangements of the whole mitochondrial complexome. Here, we summarize the major findings obtained by this approach; emphasize its advantages and current limitations; discuss multiple examples on how this tool could be applied to further investigate pathophysiological mechanisms and comment on the latest advances and opportunity areas to keep developing this methodology.

Label-Free Physical Techniques and Methodologies for Proteins Detection in Microfluidic Biosensor Structures

Proteins in biological fluids (blood, urine, cerebrospinal fluid) are important biomarkers of various pathological conditions. Protein biomarkers detection and quantification have been proven to be an indispensable diagnostic tool in clinical practice. There is a growing tendency towards using portable diagnostic biosensor devices for point-of-care (POC) analysis based on microfluidic technology as an alternative to conventional laboratory protein assays. In contrast to universally accepted analytical methods involving protein labeling, label-free approaches often allow the development of biosensors with minimal requirements for sample preparation by omitting expensive labelling reagents. The aim of the present work is to review the variety of physical label-free techniques of protein detection and characterization which are suitable for application in micro-fluidic structures and analyze the technological and material aspects of label-free biosensors that implement these methods. The most widely used optical and impedance spectroscopy techniques: absorption, fluorescence, surface plasmon resonance, Raman scattering, and interferometry, as well as new trends in photonics are reviewed. The challenges of materials selection, surfaces tailoring in microfluidic structures, and enhancement of the sensitivity and miniaturization of biosensor systems are discussed. The review provides an overview for current advances and future trends in microfluidics integrated technologies for label-free protein biomarkers detection and discusses existing challenges and a way towards novel solutions.