Analysis of protein complexes using mass spectrometry

Dissecting phospho-motif-dependent Shc1 interactome using long synthetic protein fragments

Activated receptor tyrosine kinases (RTKs) rely on the assembly of signaling proteins into high-dimensional protein complexes for signal transduction. Shc1, a prototypical scaffold protein, plays a pivotal role in directing phosphotyrosine (pY)-dependent protein complex formation for numerous RTKs typically through its two pY-binding domains. The three conserved pY sites within its CH1 region (Shc1CH1) hold particular significance due to their substantial contribution to its functions. However, how Shc1 differentially utilizes these sites to precisely coordinate protein complex assembly remains unclear. Here, we employed multiple peptide ligation techniques to synthesize an array of long protein fragments (107 amino acids) covering a significant portion of the Shc1CH1 region with varying phosphorylation states at residues Y239, 240, 313, and S335. By combining these phospho-Shc1CH1 fragments with integrated proteomics sample preparation and quantitative proteomic analysis, we were able to comprehensively resolve the site-specific interactomes of Shc1 with single amino acid resolution. By applying this approach to different cancer cell lines, we demonstrated that these phospho-Shc1CH1 fragments can be effectively used as a diagnostic tool to assess cell type-specific RTK signaling networks. Collectively, these biochemical conclusions help to better understand the sophisticated organization of pY-dependent Shc1 adaptor protein complexes and their functional roles in cancer.

An Optimized Miniaturized Filter-Aided Sample Preparation Method for Sensitive Cross-Linking Mass Spectrometry Analysis of Microscale Samples

Cross-linking mass spectrometry (XL-MS) is a powerful tool for elucidating protein structures and protein-protein interactions (PPIs) at the global scale. However, sensitive XL-MS analysis of mass-limited samples remains challenging, due to serious sample loss during sample preparation of the low-abundance cross-linked peptides. Herein, an optimized miniaturized filter-aided sample preparation (O-MICROFASP) method was presented for sensitive XL-MS analysis of microscale samples. By systematically investigating and optimizing crucial experimental factors, this approach dramatically improves the XL identification of low and submicrogram samples. Compared with the conventional FASP method, more than 7.4 times cross-linked peptides were identified from single-shot analysis of 1 μg DSS cross-linked HeLa cell lysates (440 vs 59). The number of cross-linked peptides identified from 0.5 μg HeLa cell lysates was increased by 58% when further reducing the surface area of the filter to 0.058 mm2 in the microreactor. To deepen the identification coverage of XL-proteome, five different types of cross-linkers were used and each μg of cross-linked HeLa cell lysates was processed by O-MICROFASP integrated with tip-based strong cation exchange (SCX) fractionation. Up to 2741 unique cross-linked peptides were identified from the 5 μg HeLa cell lysates, representing 2579 unique K-K linkages on 1092 proteins. About 96% of intraprotein cross-links were within the maximal distance restraints of 26 Å, and 75% of the identified PPIs reported by the STRING database were with high confidence (scores ≥0.9), confirming the high validity of the identified cross-links for protein structural mapping and PPI analysis. This study demonstrates that O-MICROFASP is a universal and efficient method for proteome-wide XL-MS analysis of microscale samples with high sensitivity and reliability.

Bacterial protein acetylation: mechanisms, functions, and methods for study

Lysine acetylation is an evolutionarily conserved protein modification that changes protein functions and plays an essential role in many cellular processes, such as central metabolism, transcriptional regulation, chemotaxis, and pathogen virulence. It can alter DNA binding, enzymatic activity, protein-protein interactions, protein stability, or protein localization. In prokaryotes, lysine acetylation occurs non-enzymatically and by the action of lysine acetyltransferases (KAT). In enzymatic acetylation, KAT transfers the acetyl group from acetyl-CoA (AcCoA) to the lysine side chain. In contrast, acetyl phosphate (AcP) is the acetyl donor of chemical acetylation. Regardless of the acetylation type, the removal of acetyl groups from acetyl lysines occurs only enzymatically by lysine deacetylases (KDAC). KATs are grouped into three main superfamilies based on their catalytic domain sequences and biochemical characteristics of catalysis. Specifically, members of the GNAT are found in eukaryotes and prokaryotes and have a core structural domain architecture. These enzymes can acetylate small molecules, metabolites, peptides, and proteins. This review presents current knowledge of acetylation mechanisms and functional implications in bacterial metabolism, pathogenicity, stress response, translation, and the emerging topic of protein acetylation in the gut microbiome. Additionally, the methods used to elucidate the biological significance of acetylation in bacteria, such as relative quantification and stoichiometry quantification, and the genetic code expansion tool (CGE), are reviewed.

Translation-independent association of mRNAs encoding protomers of the 5-HT 2A -mGlu2 receptor complex in living cells

The serotonin 2A receptor (5-HT 2A R) and the metabotropic glutamate 2 receptor (mGluR2) form heteromeric G protein-coupled receptor (GPCR) complexes through a direct physical interaction. Co-translational association of mRNAs encoding subunits of heteromeric ion channels has been reported, but whether complex assembly of GPCRs occurs during translation remains unknown. Our in vitro data reveal evidence of co-translational modulation in 5-HT 2A R and mGluR2 mRNAs following siRNA-mediated knockdown. Interestingly, immunoprecipitation of either 5-HT 2A R or mGluR2, using an antibody targeting epitope tags at their N-terminus, results in detection of both transcripts associated with ribonucleoprotein complexes containing RPS24. Additionally, we demonstrate that the mRNA transcripts of 5-HT 2A R and mGluR2 associate autonomously of their respective encoded proteins. Validation of this translation-independent association is extended ex vivo using mouse frontal cortex samples. Together, these findings provide mechanistic insights into the co-translational assembly of GPCR heteromeric complexes, unraveling regulatory processes governing protein-protein interactions and complex formation.

The Traf2 and NcK interacting kinase inhibitor NCB-0846 suppresses seizure activity involving the decrease of GRIA1

Epilepsy, one of the most common neurological disorders, is characterized by spontaneous recurrent seizures. Temporal lobe epilepsy (TLE) is one of the most common medically intractable seizure disorders. Traf2-and NcK-interacting kinase (TNIK) has recently attracted attention as a critical modulation target of many neurological and psychiatric disorders, but its role in epilepsy remains unclear. In this study, we hypothesized the involvement of TNIK in epilepsy and investigated TNIK expression in patients with intractable TLE and in a pilocarpine-induced rat model of epilepsy by western blotting, immunofluorescence, and immunohistochemistry. A pentylenetetrazole (PTZ)-induced epilepsy rat model was used to determine the effect of the TNIK inhibitor NCB-0846 on behavioral manifestations of epilepsy. Coimmunoprecipitation (Co-IP)/mass spectrometry (MS) was used to identify the potential mechanism. Through Co-IP, we detected and confirmed the main potential TNIK interactors. Subcellular fractionation was used to establish the effect of NCB-0846 on the expression of the main interactors in postsynaptic density (PSD) fractions. We found that TNIK was primarily located in neurons and decreased significantly in epilepsy model rats and TLE patients compared with controls. NCB-0846 delayed kindling progression and decreased seizure severity. Co-IP/MS identified 63 candidate TNIK interactors in rat hippocampi, notably CaMKII. Co-IP showed that TNIK might correlate with endogenous GRIA1, SYN2, PSD-95, CaMKIV, GABRG1, and GABRG2. In addition, the significant decrease in GRIA1 in hippocampal total lysate and PSDs after NCB-0846 treatment might help modify the progression of PTZ kindling. Our results suggest that TNIK contributes to epileptic pathology and is a potential antiepileptic drug target.

Pathogenic mutations of human phosphorylation sites affect protein-protein interactions

Despite their lack of a defined 3D structure, intrinsically disordered regions (IDRs) of proteins play important biological roles. Many IDRs contain short linear motifs (SLiMs) that mediate protein-protein interactions (PPIs), which can be regulated by post-translational modifications like phosphorylation. 20% of pathogenic missense mutations are found in IDRs, and understanding how such mutations affect PPIs is essential for unraveling disease mechanisms. Here, we employ peptide-based interaction proteomics to investigate 36 disease-associated mutations affecting phosphorylation sites. Our results unveil significant differences in interactomes between phosphorylated and non-phosphorylated peptides, often due to disrupted phosphorylation-dependent SLiMs. We focused on a mutation of a serine phosphorylation site in the transcription factor GATAD1, which causes dilated cardiomyopathy. We find that this phosphorylation site mediates interaction with 14-3-3 family proteins. Follow-up experiments reveal the structural basis of this interaction and suggest that 14-3-3 binding affects GATAD1 nucleocytoplasmic transport by masking a nuclear localisation signal. Our results demonstrate that pathogenic mutations of human phosphorylation sites can significantly impact protein-protein interactions, offering insights into potential molecular mechanisms underlying pathogenesis.

DIP-MS: ultra-deep interaction proteomics for the deconvolution of protein complexes

Most proteins are organized in macromolecular assemblies, which represent key functional units regulating and catalyzing most cellular processes. Affinity purification of the protein of interest combined with liquid chromatography coupled to tandem mass spectrometry (AP-MS) represents the method of choice to identify interacting proteins. The composition of complex isoforms concurrently present in the AP sample can, however, not be resolved from a single AP-MS experiment but requires computational inference from multiple time- and resource-intensive reciprocal AP-MS experiments. Here we introduce deep interactome profiling by mass spectrometry (DIP-MS), which combines AP with blue-native-PAGE separation, data-independent acquisition with mass spectrometry and deep-learning-based signal processing to resolve complex isoforms sharing the same bait protein in a single experiment. We applied DIP-MS to probe the organization of the human prefoldin family of complexes, resolving distinct prefoldin holo- and subcomplex variants, complex-complex interactions and complex isoforms with new subunits that were experimentally validated. Our results demonstrate that DIP-MS can reveal proteome modularity at unprecedented depth and resolution.

Activity-Guided Proteomic Profiling of Proteasomes Uncovers a Variety of Active (and Inactive) Proteasome Species

Proteasomes are multisubunit, multicatalytic protein complexes present in eukaryotic cells that degrade misfolded, damaged, or unstructured proteins. In this study, we used an activity-guided proteomic methodology based on a fluorogenic peptide substrate to characterize the composition of proteasome complexes in WT yeast and the changes these complexes undergo upon the deletion of Pre9 (Δα3) or of Sem1 (ΔSem1). A comparison of whole-cell proteomic analysis to activity-guided proteasome profiling indicates that the amounts of proteasomal proteins and proteasome interacting proteins in the assembled active proteasomes differ significantly from their total amounts in the cell as a whole. Using this activity-guided profiling approach, we characterized the changes in the abundance of subunits of various active proteasome species in different strains, quantified the relative abundance of active proteasomes across these strains, and charted the overall distribution of different proteasome species within each strain. The distributions obtained by our mass spectrometry-based quantification were markedly higher for some proteasome species than those obtained by activity-based quantification alone, suggesting that the activity of some of these species is impaired. The impaired activity appeared mostly among 20SBlm10 proteasome species which account for 20% of the active proteasomes in WT. To identify the factors behind this impaired activity, we mapped and quantified known proteasome-interacting proteins. Our results suggested that some of the reduced activity might be due to the association of the proteasome inhibitor Fub1. Additionally, we provide novel evidence for the presence of nonmature and therefore inactive proteasomal protease subunits β2 and β5 in the fully assembled proteasomes.

Probing Protein Complexes Composition, Stoichiometry, and Interactions by Peptide-Based Mass Spectrometry

The characterization of a protein complex by mass spectrometry can be conducted at different levels. Initial steps regard the qualitative composition of the complex and subunit identification. After that, quantitative information such as stoichiometric ratios and copy numbers for each subunit in a complex or super-complex is acquired. Peptide-based LC-MS/MS offers a wide number of methods and protocols for the characterization of protein complexes. This chapter concentrates on the applications of peptide-based LC-MS/MS for the qualitative, quantitative, and structural characterization of protein complexes focusing on subunit identification, determination of stoichiometric ratio and number of subunits per complex as well as on cross-linking mass spectrometry and hydrogen/deuterium exchange as methods for the structural investigation of the biological assemblies.

Peptide-Based Mass Spectrometry for the Investigation of Protein Complexes

In the last two decades, biological mass spectrometry has become the gold standard for the identification of proteins in biological samples. The technological advancement of mass spectrometers and the development of methods for ionization, gas phase transfer, peptide fragmentation as well as for acquisition of high-resolution mass spectrometric data marked the success of the technique. This chapter introduces peptide-based mass spectrometry as a tool for the investigation of protein complexes. It provides an overview of the main steps for sample preparation starting from protein fractionation, reduction, alkylation and focus on the final step of protein digestion. The basic concepts of biological mass spectrometry as well as details about instrumental analysis and data acquisition are described. Finally, the most common methods for data analysis and sequence determination are summarized with an emphasis on its application to protein-protein complexes.

Co-fractionation-mass spectrometry to characterize native mitochondrial protein assemblies in mammalian neurons and brain

Human mitochondrial (mt) protein assemblies are vital for neuronal and brain function, and their alteration contributes to many human disorders, e.g., neurodegenerative diseases resulting from abnormal protein-protein interactions (PPIs). Knowledge of the composition of mt protein complexes is, however, still limited. Affinity purification mass spectrometry (MS) and proximity-dependent biotinylation MS have defined protein partners of some mt proteins, but are too technically challenging and laborious to be practical for analyzing large numbers of samples at the proteome level, e.g., for the study of neuronal or brain-specific mt assemblies, as well as altered mtPPIs on a proteome-wide scale for a disease of interest in brain regions, disease tissues or neurons derived from patients. To address this challenge, we adapted a co-fractionation-MS platform to survey native mt assemblies in adult mouse brain and in human NTERA-2 embryonal carcinoma stem cells or differentiated neuronal-like cells. The workflow consists of orthogonal separations of mt extracts isolated from chemically cross-linked samples to stabilize PPIs, data-dependent acquisition MS to identify co-eluted mt protein profiles from collected fractions and a computational scoring pipeline to predict mtPPIs, followed by network partitioning to define complexes linked to mt functions as well as those essential for neuronal and brain physiological homeostasis. We developed an R/CRAN software package, Macromolecular Assemblies from Co-elution Profiles for automated scoring of co-fractionation-MS data to define complexes from mtPPI networks. Presently, the co-fractionation-MS procedure takes 1.5-3.5 d of proteomic sample preparation, 31 d of MS data acquisition and 8.5 d of data analyses to produce meaningful biological insights.

The interaction network of the proteasome assembly chaperone PSMD9 regulates proteostasis

Functional networks in cells are created by physical, genetic, and regulatory interactions. Mapping them and annotating their functions by available methods remains a challenge. We use affinity purification mass spectrometry (AP-MS) coupled with SLiMFinder to discern such a network involving 26S proteasome non-ATPase regulatory subunit 9 (PSMD9), a chaperone of proteasome assembly. Approximately 20% of proteins within the PSMD9 interactome carry a short linear motif (SLiM) of the type 'EXKK'. The binding of purified PSMD9 with the peptide sequence ERKK, proteins heterogeneous nuclear ribonucleoproteins A2/B1 (hnRNPA2B1; containing ERKK), and peroxiredoxin-6 (PRDX6; containing EAKK) provided proof of principle for this motif-driven network. The EXKK motif in the peptide primarily interacts with the coiled-coil N domain of PSMD9, a unique interaction not reported for any coiled-coil domain. PSMD9 knockout (KO) HEK293 cells experience endoplasmic reticulum (ER) stress and respond by increasing the unfolded protein response (UPR) and reducing the formation of aggresomes and lipid droplets. Trans-expression of PSMD9 in the KO cells rescues lipid droplet formation. Overexpression of PSMD9 in HEK293 cells results in reduced UPR, and increased lipid droplet and aggresome formation. The outcome argues for the prominent role of PSMD9 in maintaining proteostasis. Probable mechanisms involve the binding of PSMD9 to binding immunoglobulin protein (BIP/GRP78; containing EDKK), an endoplasmic reticulum chaperone and key regulator of the UPR, and fatty acid synthase (FASN; containing ELKK), involved in fatty acid synthesis/lipid biogenesis. We propose that PSMD9 acts as a buffer in the cellular milieu by moderating the UPR and enhancing aggresome formation to reduce stress-induced proteotoxicity. Akin to waves created in ponds that perpetuate to a distance, perturbing the levels of PSMD9 would cause ripples down the networks, affecting final reactions in the pathway, one of which is altered proteostasis.

Single-molecule quantification of the oligomeric state of ZIP transporters in mammalian cells with fluorescence correlation spectroscopy

The SLC39 family of transporters, otherwise known as ZIPs for Zrt and Irt-like Proteins, function to increase cytosolic levels of transition metals. ZIP transporters have been identified at all phylogenetic levels and are members of the SoLute Carrier (SLC) superfamily. There are fourteen ZIP transporters encoded in the human genome. ZIP transmembrane proteins are expressed in the plasma membrane or membranes of intracellular organelles and have unique expression profiles across cell types. While direct structural efforts including x-ray crystallography, NMR and ab initio approaches have been effective tools in elucidating the structure of ZIPs, direct elucidation of the oligomeric state of these proteins is essential in understanding how wild type ZIP proteins function and whether mutations alter the oligomeric state of ZIPs. Unfortunately, several tools to quantify oligomeric states of proteins require overexpression of proteins which can lead to artifacts in experimental results. In contrast, fluorescence correlation spectroscopy (FCS) is a single-molecule technique which can be used to quantify the oligomeric state of transmembrane proteins. FCS takes advantage of the observation that the molecular brightness of a cluster of fluorescent molecules is directly proportional to the number of fluorescent molecules within the protein complex. This chapter describes how to implement FCS, focused on ZIP transporters, to quantify the oligomeric state of transmembrane in vivo. Included within this chapter are procedures to design constructs for experiments, transfection of mammalian cells as well as data acquisition and analysis. Taken together, FCS is a powerful mechanism to investigate the oligomeric state of proteins embedded within membranes of cells.

Interactome-Based Machine Learning Predicts Potential Therapeutics for COVID-19

COVID-19, the disease caused by SARS-CoV-2, has been disrupting our lives for more than two years now. SARS-CoV-2 interacts with human proteins to pave its way into the human body, thereby wreaking havoc. Moreover, the mutating variants of the virus that take place in the SARS-CoV-2 genome are also a cause of concern among the masses. Thus, it is very important to understand human-spike protein-protein interactions (PPIs) in order to predict new PPIs and consequently propose drugs for the human proteins in order to fight the virus and its different mutated variants, with the mutations occurring in the spike protein. This fact motivated us to develop a complete pipeline where PPIs and drug-protein interactions can be predicted for human-SARS-CoV-2 interactions. In this regard, initially interacting data sets are collected from the literature, and noninteracting data sets are subsequently created for human-SARS-CoV-2 by considering only spike glycoprotein. On the other hand, for drug-protein interactions both interacting and noninteracting data sets are considered from DrugBank and ChEMBL databases. Thereafter, a model based on a sequence-based feature is used to code the protein sequences of human and spike proteins using the well-known Moran autocorrelation technique, while the drugs are coded using another well-known technique, viz., PaDEL descriptors, to predict new human-spike PPIs and eventually new drug-protein interactions for the top 20 predicted human proteins interacting with the original spike protein and its different mutated variants like Alpha, Beta, Delta, Gamma, and Omicron. Such predictions are carried out by random forest as it is found to perform better than other predictors, providing an accuracy of 90.53% for human-spike PPI and 96.15% for drug-protein interactions. Finally, 40 unique drugs like eicosapentaenoic acid, doxercalciferol, ciclesonide, dexamethasone, methylprednisolone, etc. are identified that target 32 human proteins like ACACA, DST, DYNC1H1, etc.

Serial Capture Affinity Purification and Integrated Structural Modeling of the H3K4me3 Binding and DNA Damage Related WDR76:SPIN1 Complex

WDR76 is a multifunctional protein involved in many cellular functions. With a diverse and complicated protein interaction network, dissecting the structure and function of specific WDR76 complexes is needed. We previously demonstrated the ability of the Serial Capture Affinity Purification (SCAP) method to isolate specific complexes by introducing two proteins of interest as baits at the same time. Here, we applied SCAP to dissect a subpopulation of WDR76 in complex with SPIN1, a histone marker reader that specifically recognizes trimethylated histone H3 lysine4 (H3K4me3). In contrast to the SCAP analysis of the SPIN1:SPINDOC complex, H3K4me3 was copurified with the WDR76:SPIN1 complex. In combination with crosslinking mass spectrometry, we built an integrated structural model of the complex which revealed that SPIN1 recognized the H3K4me3 epigenetic mark while interacting with WDR76. Lastly, interaction network analysis of copurifying proteins revealed the potential role of the WDR76:SPIN1 complex in the DNA damage response.

Teaser

In contrast to the SPINDOC/SPIN1 complex, analyses reveal that the WDR76/SPIN1 complex interacts with core histones and is involved in DNA damage.

Translation-A tug of war during viral infection

Viral reproduction is contingent on viral protein synthesis that relies on the host ribosomes. As such, viruses have evolved remarkable strategies to hijack the host translational apparatus in order to favor viral protein production and to interfere with cellular innate defenses. Here, we describe the approaches viruses use to exploit the translation machinery, focusing on commonalities across diverse viral families, and discuss the functional relevance of this process. We illustrate the complementary strategies host cells utilize to block viral protein production and consider how cells ensure an efficient antiviral response that relies on translation during this tug of war over the ribosome. Finally, we highlight potential roles mRNA modifications and ribosome quality control play in translational regulation and innate immunity. We address these topics in the context of the COVID-19 pandemic and focus on the gaps in our current knowledge of these mechanisms, specifically in viruses with pandemic potential.

Rapid Approximate Subset-Based Spectra Prediction for Electron Ionization-Mass Spectrometry

Mass spectrometry is a vital tool in the analytical chemist's toolkit, commonly used to identify the presence of known compounds and elucidate unknown chemical structures. All of these applications rely on having previously measured spectra for known substances. Computational methods for predicting mass spectra from chemical structures can be used to augment existing spectral databases with predicted spectra from previously unmeasured molecules. In this paper, we present a method for prediction of electron ionization-mass spectra (EI-MS) of small molecules that combines physically plausible substructure enumeration and deep learning, which we term rapid approximate subset-based spectra prediction (RASSP). The first of our two models, FormulaNet, produces a probability distribution over chemical subformulae to achieve a state-of-the-art forward prediction accuracy of 92.9% weighted (Stein) dot product and database lookup recall (within top 10 ranked spectra) of 98.0% when evaluated against the NIST 2017 Mass Spectral Library. The second model, SubsetNet, produces a probability distribution over vertex subsets of the original molecule graph to achieve similar forward prediction accuracy and superior generalization in the high-resolution, low-data regime. Spectra predicted by our best model improve upon the previous state-of-the-art spectral database lookup error rate by a factor of 2.9×, reducing the lookup error (top 10) from 5.7 to 2.0%. Both models can train on and predict spectral data at arbitrary resolution. Source code and predicted EI-MS spectra for 73.2M small molecules from PubChem will be made freely accessible online.

Multiplexed Protein Detection and Parallel Binding Kinetics Analysis with Label-Free Digital Single-Molecule Counting

Multiplexed protein detection is critical for improving the drug and biomarker screening efficiency. Here, we show that multiplexed protein detection and parallel protein interaction analysis can be realized by evanescent scattering microscopy (ESM). ESM enables binding kinetics measurement with label-free digital single-molecule counting. We implemented an automatic single-molecule counting strategy with high temporal resolution to precisely determine the binding time, which improves the counting efficiency and accuracy. We show that digital single-molecule counting can recognize proteins with different molecular weights, thus making it possible to monitor the protein binding processes in the solution by real-time tracking of the numbers of free and bound proteins landing on the sensor surface. Furthermore, we show that this strategy can simultaneously analyze the kinetics of two different protein interaction processes on the surface and in the solution. This work may pave a way to investigate complicated protein interactions, such as the competition of biomarker-antibody binding in biofluids with biomarker-protein binding on the cellular membrane.

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.

Analyzing Protein Interactions by MAC-Tag Approaches

Proteomics methods such as affinity purification (AP) and proximity-dependent labeling (PL) coupled with mass spectrometry (MS) are currently commonly utilized to define interaction landscapes. BioID is one of the PL approaches, and it employs the expression of bait proteins fused to a nonspecific biotin ligase (BirA*), to induce in vivo biotinylation of proximal proteins. We developed the multiple approaches combined (MAC)-tag workflow, which allows for both AP and BioID analysis with a single construct and with almost identical protein purification and MS identification procedures. MAC-tag is a well-established method and has been widely used. Recent developed PL tags such as BioID2 and UltraID are smaller versions of BirA* with faster labeling efficiency. We therefore incorporate these tags into our system to develop MAC2-tag (containing BioID2) and MAC3-tag (containing UltraID) to overcome potential limitations of the original MAC-tag system and broaden the spectrum of applications for MAC-tags. Here, we describe a detailed procedure for the MAC-tag system workflow including cell line generation for the MAC/MAC2/MAC3-tagged protein of interest (POI), sample preparation for AP and PL protein purification, and MS analysis.

Proteomics Evaluation of Semen of Clinically Healthy Beagle-Breed Dogs

The objectives of the present work were to evaluate the semen of dogs by means of proteomics methods and to compare with proteomics results of the blood of the animals, in order to increase available knowledge on the topic and present relevant reference values for semen samples. Semen samples were collected from five Beagle-breed dogs. Reproductive assessment of the animals by means of clinical, ultrasonographic and seminological examinations confirmed their reproductive health. The sperm-rich fraction and the prostatic fraction of semen were processed for proteomics evaluation. LC-MS/MS analysis was performed by means of a LTQ Orbitrap Elite system. The technology combines high separation capacity and strong qualitative ability of proteins in biological samples that require deep proteome coverage. Protein classification was performed based on their functional annotations using Gene Ontology (GO). In blood plasma, semen sperm-rich fraction, and semen prostatic fraction, 59, 42 and 43 proteins, respectively, were detected. Two proteins were identified simultaneously in plasma and the semen sperm-rich fraction, 11 proteins in plasma and the semen prostatic fraction, and three proteins in the semen sperm-rich and prostatic fractions. In semen samples, most proteins were related to cell organization and biogenesis, metabolic processes or transport of ions and molecules. Most proteins were located in the cell membrane, the cytosol or the nucleus. Finally, most proteins performed functions related to binding or enzyme regulation. There were no differences between the semen sperm-rich fraction and prostatic fractions in terms of the clustering of proteins. In conclusion, a baseline reference for proteins in the semen of Beagle-breed dogs is provided. These proteins are involved mostly in supporting spermatozoan maturation, survival and motility, enhancing the reproductive performance of male animals. There appears potential for the proteomics examination of semen to become a tool in semen evaluation. This analysis may potentially identify biomarkers for reproductive disorders. This can be particularly useful in stud animals, also given its advantage as a non-invasive method.

Protein-protein interactions in plant antioxidant defense

The regulation of reactive oxygen species (ROS) levels in plants is ensured by mechanisms preventing their over accumulation, and by diverse antioxidants, including enzymes and nonenzymatic compounds. These are affected by redox conditions, posttranslational modifications, transcriptional and posttranscriptional modifications, Ca²⁺, nitric oxide (NO) and mitogen-activated protein kinase signaling pathways. Recent knowledge about protein-protein interactions (PPIs) of antioxidant enzymes advanced during last decade. The best-known examples are interactions mediated by redox buffering proteins such as thioredoxins and glutaredoxins. This review summarizes interactions of major antioxidant enzymes with regulatory and signaling proteins and their diverse functions. Such interactions are important for stability, degradation and activation of interacting partners. Moreover, PPIs of antioxidant enzymes may connect diverse metabolic processes with ROS scavenging. Proteins like receptor for activated C kinase 1 may ensure coordination of antioxidant enzymes to ensure efficient ROS regulation. Nevertheless, PPIs in antioxidant defense are understudied, and intensive research is required to define their role in complex regulation of ROS scavenging.

Long-distance dependency combined multi-hop graph neural networks for protein-protein interactions prediction

BACKGROUND

Protein-protein interactions are widespread in biological systems and play an important role in cell biology. Since traditional laboratory-based methods have some drawbacks, such as time-consuming, money-consuming, etc., a large number of methods based on deep learning have emerged. However, these methods do not take into account the long-distance dependency information between each two amino acids in sequence. In addition, most existing models based on graph neural networks only aggregate the first-order neighbors in protein-protein interaction (PPI) network. Although multi-order neighbor information can be aggregated by increasing the number of layers of neural network, it is easy to cause over-fitting. So, it is necessary to design a network that can capture long distance dependency information between amino acids in the sequence and can directly capture multi-order neighbor information in protein-protein interaction network.

RESULTS

In this study, we propose a multi-hop neural network (LDMGNN) model combining long distance dependency information to predict the multi-label protein-protein interactions. In the LDMGNN model, we design the protein amino acid sequence encoding (PAASE) module with the multi-head self-attention Transformer block to extract the features of amino acid sequences by calculating the interdependence between every two amino acids. And expand the receptive field in space by constructing a two-hop protein-protein interaction (THPPI) network. We combine PPI network and THPPI network with amino acid sequence features respectively, then input them into two identical GIN blocks at the same time to obtain two embeddings. Next, the two embeddings are fused and input to the classifier for predict multi-label protein-protein interactions. Compared with other state-of-the-art methods, LDMGNN shows the best performance on both the SHS27K and SHS148k datasets. Ablation experiments show that the PAASE module and the construction of THPPI network are feasible and effective.

CONCLUSIONS

In general terms, our proposed LDMGNN model has achieved satisfactory results in the prediction of multi-label protein-protein interactions.

Current and emerging target identification methods for novel antimalarials

New antimalarial compounds with novel mechanisms of action are urgently needed to combat the recent rise in antimalarial drug resistance. Phenotypic high-throughput screens have proven to be a successful method for identifying new compounds, however, do not provide mechanistic information about the molecular target(s) responsible for antimalarial action. Current and emerging target identification methods such as in vitro resistance generation, metabolomics screening, chemoproteomic approaches and biophysical assays measuring protein stability across the whole proteome have successfully identified novel drug targets. This review provides an overview of these techniques, comparing their strengths and weaknesses and how they can be utilised for antimalarial target identification.

Protocol for establishing a protein-protein interaction network using tandem affinity purification followed by mass spectrometry in mammalian cells

Identification of protein interactors is fundamental to understanding their functions. Here, we describe a modified protocol for tandem affinity purification coupled with mass spectrometry (TAP/MS), which includes two-step purification. We detail the S-, 2×FLAG-, and Streptavidin-Binding Peptide (SBP)- tandem tags (SFB-tag) system for protein purification. This protocol can be used to identify protein interactors and establish a high-confidence protein-protein interaction network based on computational models. This is particularly useful for identifying bona fide interacting proteins for subsequent functional studies. For complete details on the use and execution of this protocol, please refer to Bian et al. (2021).

Illuminating the dark protein-protein interactome

In living systems, a complex network of protein-protein interactions (PPIs) underlies most biochemical events. The human protein-protein interactome has been surveyed using yeast two-hybrid (Y2H)- and mass spectrometry (MS)-based approaches such as affinity purification coupled to MS (AP-MS). Despite decades of systematic investigations and collaborative multi-disciplinary efforts, there is no "gold standard" for documenting PPIs. A surprisingly large fraction of the human interactome remains uncharted, which we refer to as the "dark interactome." In this review, we highlight the complexity of the human interactome and discuss the current status of the human reference interactome maps. We discuss why a large proportion of the human interactome has remained refractory to traditional approaches. We propose an experimental model that can enable the identification of the dark interactome in a cell-type-specific manner. We also propose a framework to implement when embarking on studies designed to rigorously identify and characterize protein interactions.

Proteomic Discovery and Validation of Novel Fluid Biomarkers for Improved Patient Selection and Prediction of Clinical Outcomes in Alzheimer’s Disease Patient Cohorts

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease characterized by progressive cognitive decline. The two cardinal neuropathological hallmarks of AD include the buildup of cerebral β amyloid (Aβ) plaques and neurofibrillary tangles of hyperphosphorylated tau. The current disease-modifying treatments are still not effective enough to lower the rate of cognitive decline. There is an urgent need to identify early detection and disease progression biomarkers that can facilitate AD drug development. The current established readouts based on the expression levels of amyloid beta, tau, and phospho-tau have shown many discrepancies in patient samples when linked to disease progression. There is an urgent need to identify diagnostic and disease progression biomarkers from blood, cerebrospinal fluid (CSF), or other biofluids that can facilitate the early detection of the disease and provide pharmacodynamic readouts for new drugs being tested in clinical trials. Advances in proteomic approaches using state-of-the-art mass spectrometry are now being increasingly applied to study AD disease mechanisms and identify drug targets and novel disease biomarkers. In this report, we describe the application of quantitative proteomic approaches for understanding AD pathophysiology, summarize the current knowledge gained from proteomic investigations of AD, and discuss the development and validation of new predictive and diagnostic disease biomarkers.

Recent advances in proteomics and metabolomics in plants

Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Intramolecular covalent bonds in Gram-positive bacterial surface proteins

Gram-positive bacteria experience considerable mechanical perturbation when adhering to host surfaces during colonization and infection. They have evolved various adhesion proteins that are mechanically robust to ensure strong surface adhesion. Recently, it was discovered that these adhesion proteins contain rare, extra intramolecular covalent bonds that stabilize protein structures and participate in surface bonding. These intramolecular covalent bonds include isopeptides, thioesters, and ester bonds, which often form spontaneously without the need for additional enzymes. With the development of single-molecule force spectroscopy techniques, the detailed mechanical roles of these intramolecular covalent bonds have been revealed. In this review, we summarize the recent advances in this area of research, focusing on the link between the mechanical stability and function of these covalent bonds in Gram-positive bacterial surface proteins. We also highlight the potential impact of these discoveries on the development of novel antibiotics and chemical biology tools.

EPH receptor tyrosine kinases phosphorylate the PAR-3 scaffold protein to modulate downstream signaling networks

EPH receptors (EPHRs) constitute the largest family among receptor tyrosine kinases in humans. They are mainly involved in short-range cell-cell communication events that regulate cell adhesion, migration, and boundary formation. However, the molecular mechanisms by which EPHRs control these processes are less understood. To address this, we unravel EPHR-associated complexes under native conditions using mass-spectrometry-based BioID proximity labeling. We obtain a composite proximity network from EPHA4, -B2, -B3, and -B4 that comprises 395 proteins, most of which were not previously linked to EPHRs. We examine the contribution of several BioID-identified candidates via loss-of-function in an EPHR-dependent cell-segregation assay. We find that the signaling scaffold PAR-3 is required for cell sorting and that EPHRs directly phosphorylate PAR-3. We also delineate a signaling complex involving the C-terminal SRC kinase (CSK), whose recruitment to PAR-3 is dependent on EPHR signals. Our work describes signaling networks by which EPHRs regulate cellular phenotypes.

PCfun: a hybrid computational framework for systematic characterization of protein complex function

In molecular biology, it is a general assumption that the ensemble of expressed molecules, their activities and interactions determine biological function, cellular states and phenotypes. Stable protein complexes—or macromolecular machines—are, in turn, the key functional entities mediating and modulating most biological processes. Although identifying protein complexes and their subunit composition can now be done inexpensively and at scale, determining their function remains challenging and labor intensive. This study describes Protein Complex Function predictor (PCfun), the first computational framework for the systematic annotation of protein complex functions using Gene Ontology (GO) terms. PCfun is built upon a word embedding using natural language processing techniques based on 1 million open access PubMed Central articles. Specifically, PCfun leverages two approaches for accurately identifying protein complex function, including: (i) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for a protein complex query vector and (ii) a supervised approach using Random Forest (RF) models trained specifically for recovering the GO terms of protein complex queries described in the CORUM protein complex database. PCfun consolidates both approaches by performing a hypergeometric statistical test to enrich the top NN GO terms within the child terms of the GO terms predicted by the RF models. The documentation and implementation of the PCfun package are available at https://github.com/sharmavaruns/PCfun. We anticipate that PCfun will serve as a useful tool and novel paradigm for the large-scale characterization of protein complex function.

Protein interaction networks in neurodegenerative diseases: from physiological function to aggregation

The accumulation of protein inclusions is linked to many neurodegenerative diseases that typically develop in older individuals, due to a combination of genetic and environmental factors. In rare familial neurodegenerative disorders, genes encoding for aggregation-prone proteins are often mutated. While the underlying mechanism leading to these diseases still remains to be fully elucidated, efforts in the past 20 years revealed a vast network of protein–protein interactions that play a major role in regulating the aggregation of key proteins associated with neurodegeneration. Misfolded proteins that can oligomerize and form insoluble aggregates associate with molecular chaperones and other elements of the proteolytic machineries that are the frontline workers attempting to protect the cells by promoting clearance and preventing aggregation. Proteins that are normally bound to aggregation-prone proteins can become sequestered and mislocalized in protein inclusions, leading to their loss of function. In contrast, mutations, posttranslational modifications, or misfolding of aggregation-prone proteins can lead to gain of function by inducing novel or altered protein interactions, which in turn can impact numerous essential cellular processes and organelles, such as vesicle trafficking and the mitochondria. This review examines our current knowledge of protein–protein interactions involving several key aggregation-prone proteins that are associated with Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, or amyotrophic lateral sclerosis. We aim to provide an overview of the protein interaction networks that play a central role in driving or mitigating inclusion formation, while highlighting some of the key proteomic studies that helped to uncover the extent of these networks.

Interaction network among factors involved in heterocyst-patterning in cyanobacteria

The genetically regulated pattern of heterocyst formation in multicellular cyanobacteria represents the simplest model to address how patterns emerge and are established, the signals that control them, and the regulatory pathways that act downstream. Although numerous factors involved in this process have been identified, the mechanisms of action of many of them remain largely unknown. The aim of this study was to identify specific relationships between 14 factors required for cell differentiation and pattern formation by exploring their putative physical interactions in the cyanobacterium model Nostoc sp. PCC 7120 and by probing their evolutionary conservation and distribution across the cyanobacterial phylum. A bacterial two-hybrid assay indicated that 10 of the 14 factors studied here are engaged in more than one protein-protein interaction. The transcriptional regulator PatB was central in this network as it showed the highest number of binary interactions. A phylum-wide genomic survey of the distribution of these factors in cyanobacteria showed that they are all highly conserved in the genomes of heterocyst-forming strains, with the PatN protein being almost restricted to this clade. Interestingly, eight of the factors that were shown to be capable of protein interactions were identified as key elements in the evolutionary genomics analysis. These data suggest that a network of 12 proteins may play a crucial role in heterocyst development and patterning. Unraveling the physical and functional interactions between these factors during heterocyst development will certainly shed light on the mechanisms underlying pattern establishment in cyanobacteria.

Zwitter-ionic monolith-based spintip column coupled with Evosep One liquid chromatography for high-throughput proteomic analysis

A high-throughput proteomic workflow with good sensitivity and reproducibility is highly demanding for proteomic studies of large clinical cohorts. We present a workflow that seamlessly integrates the zwitter-ionic monolith-based spintip (ZIM-Tip) with the Evosep One liquid chromatography system to address this challenge. Disposable ZIM-Tips were prepared with satisfying permeability based on photo-initiated free radical polymerization. Sample preparation steps, including ion-exchange-based protein concentration, reduction, alkylation, and enzymatic digestion, were processed on the ZIM-Tips in 2 h with about 10% sample loss. The peptides recovered from ZIM-Tips were directly loaded on Evotips for desalting and proteomic data acquisition. In one-hour high performance liquid chromatography-MS/MS run, more than 4000 proteins were consistently identified from 1 µg of cell lysate using timsTOF Pro-mass spectrometer in data-dependent acquisition mode (DDA). At least 20 samples with protein amount of 1 µg could be processed each day. Good intra- and inter-day precision in quantification were demonstrated with median coefficient of variation (CV) values of less than 20% and 30%, respectively. The average Pearson correlation coefficients of each two sets of samples are 0.934 and 0.901, respectively. Collectively, the ZIM-Tip technology offers an useful solution for clinical cohort studies with demand for large sample amounts and low sample input while maintaining in-depth proteome coverage.

Tag Thy Neighbour: Nanometre-Scale Insights Into Kinetoplastid Parasites With Proximity Dependent Biotinylation

Proximity labelling is a powerful and rapidly developing technology for exploring the interaction space and molecular environment of a protein of interest at the nanometre scale. In proximity labelling, a promiscuous biotinylating enzyme is genetically fused to the protein of interest, initiation of labelling then results in the biotinylating enzyme generating reactive biotin which covalently 'tags' nearby molecules. Importantly, this labelling takes place in vivo whilst the protein of interest continues to perform its normal functions in the cell. Due to its unique advantageous characteristics, proximity labelling is driving discoveries in an ever increasing range of organisms. Here, we highlight the applications of proximity labelling to the study of kinetoplastids, a group of eukaryotic protozoa that includes trypanosomes and Leishmania which can cause serious disease in humans and livestock. We first provide a general overview of the proximity labelling experimental workflow including key labelling enzymes used, proper experimental design with appropriate controls and robust statistical analysis to maximise the amount of reliable spatial information that is generated. We discuss studies employing proximity labelling in kinetoplastid parasites to illustrate how these key principles of experimental design are applied. Finally, we highlight emerging trends in the development of proximity labelling methodology.

Dot6/Tod6 degradation fine-tunes the repression of ribosome biogenesis under nutrient-limited conditions

Ribosome biogenesis (Ribi) is a complex and energy-consuming process, and should therefore be repressed under nutrient-limited conditions to minimize unnecessary cellular energy consumption. In yeast, the transcriptional repressors Dot6 and Tod6 are phosphorylated and inactivated by the TORC1 pathway under nutrient-rich conditions, but are activated and repress ∼200 Ribi genes under nutrient-limited conditions. However, we show that in the presence of rapamycin or under nitrogen starvation conditions, Dot6 and Tod6 were readily degraded by the proteasome in a SCF^Grr1 and Tom1 ubiquitin ligase-dependent manner, respectively. Moreover, promiscuous accumulation of Dot6 and Tod6 excessively repressed Ribi gene expression as well as translation activity and caused a growth defect in the presence of rapamycin. Thus, we propose that degradation of Dot6 and Tod6 is a novel mechanism to ensure an appropriate level of Ribi gene expression and thereby fine-tune the repression of Ribi and translation activity for cell survival under nutrient-limited conditions.

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Dot6 and Tod6 repress Ribi gene expression under nutrient-limited conditions

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Dot6 and Tod6 are degraded by the proteasome

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Excess repression of Ribi causes a growth defect in the presence of rapamycin

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Dot6 and Tod6 degradation fine-tunes the repression of Ribi and translation activity

Selective Removal of Unhydrolyzed Monolinked Peptides from Enriched Crosslinked Peptides To Improve the Coverage of Protein Complex Analysis

Chemical crosslinking combined with mass spectrometry (CXMS) has allowed the global characterization of protein complexes with high throughput and accuracy. Although enrichable crosslinkers have been introduced to exclude the interference of regular peptides, the crosslinked peptide identification is still severely inhibited by a large amount of monolinked peptides. In this work, we proposed a strategy called MoTE (unhydrolyzed Monolinked peptide Targeting Elimination) to remove the unhydrolyzed monolinked peptides, while enriching crosslinked peptides for regular peptide removal. In this strategy, followed by the crosslinking reaction, an amine biotin reagent was used to block the unreacted reactive groups on the crosslinker, and subsequently, the crosslinked proteins were tagged by a cleavable biotin-azide ligand based on click chemistry for enrichment. The following crosslinked protein digestion, purification by streptavidin beads, and release by chemical cleavage of the biotin-azide ligand were sequentially performed. In this case, the amine biotin-blocked unhydrolyzed monolinked peptides with the unbreakable arm remained on the streptavidin beads, which realized selective removal without any additional steps. By combining in vivo crosslinking with our proposed MoTE strategy for protein complex analysis of the HeLa cell, the number of high reliability (score <E-04) interlinked peptides increased 43% in a single LC-MS run, and the structural and interaction mapping capacity for low-abundance and flexible proteins were greatly enhanced. These results demonstrated that the MoTE strategy has great potential to improve the coverage of CXMS-based protein complex analysis. Notably, it was also the first report focused on removing the highly abundant monolinked peptides.

FAM83A is a potential biomarker for breast cancer initiation

Background

Family with sequence similarity 83 member A (FAM83A) presents oncogenic properties in several cancers including breast cancer. Recently, we reported FAM83A overexpression in normal breast tissues from women at high risk of breast cancer. We now hypothesize that FAM83A is a key factor in breast cancer initiation.

Methods

Immunohistochemical staining was used to evaluate FAM83A protein levels in both a normal breast tissue microarray (TMA, N = 411) and a breast tumor TMA (N = 349). EGFR staining and its correlation with FAM83A expression were also assessed. Lentivirus-mediated manipulation of FAM83A expression in primary and hTERT-immortalized breast epithelial cells was employed. Biological and molecular alterations upon FAM83A overexpression/downregulation and FAM83A’s interaction partners were investigated.

Results

TMA analysis revealed a 1.5-fold increase in FAM83A expression level in breast cancer cases as compared with normal breast tissues (p < 0.0001). FAM83A protein expression was directly correlated with EGFR level in both normal and breast cancer tissues. In in vitro assays, exogenous expression of FAM83A in either primary or immortalized breast epithelial cells promoted cell viability and proliferation. Additionally, Ingenuity Pathway Analysis (IPA) revealed that FAM83A overexpression in primary cells affected the expression of genes involved in cellular morphology and metabolism. Mass spectrometry analysis identified DDX3X and LAMB3 as potential FAM83A interaction partners in primary cells, while we detected FAM83A interaction with cytoskeleton reorganization factors, including LIMA1, MYH10, PLEC, MYL6 in the immortalized cells.

Conclusions

This study shows that FAM83A promotes metabolic activation in primary breast epithelial cells and cell proliferation in both primary and immortalized cells. These findings support its role in early breast oncogenesis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40364-022-00353-9.

Motif-dependent immune co-receptor interactome profiling by photoaffinity chemical proteomics

Identification of the tyrosine phosphorylation (pY)-dependent interactome of immune co-receptors is crucial for understanding signal pathways involved in immunotherapy. However, identifying the motif-specific interactome for each pY commonly found on these multi-phosphorylated membrane proteins remains challenging. Here, we describe a photoaffinity-based chemical proteomic approach to dissect the motif-specific cytoplasmic interactomes of the critical immune co-receptor CD28. Various full-length CD28 cytoplasmic tails (CD28^cyto) with defined pY and selectively replaced photo-methionine were synthesized and applied to explore three pY-motif-dependent CD28^cyto interactomes. We identified a stand-alone interaction of phospholipase PLCG1 with the Y191 motif with enhanced affinity for the sequence neighboring the transmembrane domain. Importantly, taking advantage of native top-down mass spectrometry with a 193-nm laser, we discovered the direct association of a previously undefined pY218 motif with the kinase PKCθ through its C2 domain. This synthetic CD28^cyto-based photoaffinity proteomic approach is generically applicable to the study of other immune co-receptors with multiple pY sites on their linear cytoplasmic tail.

Label-free visual proteomics: Coupling MS- and EM-based approaches in structural biology

Combining diverse experimental structural and interactomic methods allows for the construction of comprehensible molecular encyclopedias of biological systems. Typically, this involves merging several independent approaches that provide complementary structural and functional information from multiple perspectives and at different resolution ranges. A particularly potent combination lies in coupling structural information from cryoelectron microscopy or tomography (cryo-EM or cryo-ET) with interactomic and structural information from mass spectrometry (MS)-based structural proteomics. Cryo-EM/ET allows for sub-nanometer visualization of biological specimens in purified and near-native states, while MS provides bioanalytical information for proteins and protein complexes without introducing additional labels. Here we highlight recent achievements in protein structure and interactome determination using cryo-EM/ET that benefit from additional MS analysis. We also give our perspective on how combining cryo-EM/ET and MS will continue bridging gaps between molecular and cellular studies by capturing and describing 3D snapshots of proteomes and interactomes.

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.

Purification of MAP-kinase protein complexes and identification of candidate components by XL-TAP-MS

The purification of low-abundance protein complexes and detection of in vivo protein-protein interactions in complex biological samples remains a challenging task. Here, we devised crosslinking and tandem affinity purification coupled to mass spectrometry (XL-TAP-MS), a quantitative proteomics approach for analyzing tandem affinity-purified, crosslinked protein complexes from plant tissues. We exemplarily applied XL-TAP-MS to study the MKK2-Mitogen-activated protein kinase (MPK4) signaling module in Arabidopsis thaliana. A tandem affinity tag consisting of an in vivo-biotinylated protein domain flanked by two hexahistidine sequences was adopted to allow for the affinity-based isolation of formaldehyde-crosslinked protein complexes under fully denaturing conditions. Combined with 15N stable isotopic labeling and tandem MS we captured and identified a total of 107 MKK2-MPK4 module-interacting proteins. Consistent with the role of the MPK signaling module in plant immunity, many of the module-interacting proteins are involved in the biotic and abiotic stress response of Arabidopsis. Validation of binary protein-protein interactions by in planta split-luciferase assays and in vitro kinase assays disclosed several direct phosphorylation targets of MPK4. Together, the XL-TAP-MS approach purifies low abundance protein complexes from biological samples and discovers previously unknown protein-protein interactions.

Proteomic analysis of differential expression of lung proteins in response to highly pathogenic avian influenza virus infection in chickens

Elucidation of the molecular pathogenesis underlying virus-host interactions is important for the development of new diagnostic and therapeutic strategies against highly pathogenic avian influenza (HPAI) virus infection in chickens. However, the pathogenesis of HPAI virus in chickens is not completely understood. To identify the intracellular signaling pathways and critical host proteins associated with influenza pathogenesis, we analyzed the lung proteome of a chicken infected with HPAI H5N1 virus (A/duck/India/02CA10/2011/Agartala). Mass spectrometry data sets were searched against the chicken UniProt reference database. At the local false discovery rate level of 5%, a total of 3313 proteins with the presence of at least one unique peptide were identified in the chicken lung proteome datasets. Differential expression analysis of these proteins showed that 247 and 1754 proteins were downregulated at 12 h and 48 h postinfection, respectively. We observed expression of proteins of the predominant signaling pathways, including Toll-like receptors (TLRs), retinoic acid-inducible gene I-like receptors (RLRs), NOD-like receptors (NLRs), and JAK-STAT signaling. Activation of these pathways is associated with the cytokine storm effect and thus may be the cause of the severity of HPAI H5N1 infection in chickens. We also observed the expression of myeloid differentiation primary response protein (MyD88), inhibitor of nuclear factor kappa B kinase subunit beta (IKBKB), interleukin 1 receptor associated kinase 4 (IRAK4), RELA proto-oncogene NF-κB subunit (RELA), and mitochondrial antiviral signaling protein (MAVS), which are involved in critical signaling pathways, as well as other, less-commonly identified proteins such as hepatocyte nuclear factor 4 alpha (HNF4A), ELAV-like RNA binding protein 1 (ELAVL1), fibronectin 1 (FN1), COP9 signalosome subunit 5 (COPS5), cullin 1 (CUL1), breast cancer type 1 susceptibility protein (BRCA1), and the FYN proto-oncogene Src family tyrosine kinase (FYN) as main hub proteins that might play important roles in influenza pathogenesis in chickens. In summary, we identified the signaling pathways and the proteomic determinants associated with disease pathogenesis in chickens infected with HPAI H5N1 virus.

Biotinylation-based proximity labeling proteomics: Basics, applications, and technical considerations

Recent advances in biotinylation-based proximity labeling (PL) have opened up new avenues for mapping the protein composition of cellular compartments and protein complexes in living cells at high spatiotemporal resolution. In particular, PL combined with mass spectrometry-based proteomics has been successfully applied to defining protein-protein interactions, protein-nucleic acid interactions, (membraneless) organelle proteomes, and secretomes in various systems ranging from cultured cells to whole animals. In this review, we first summarize the basics and recent biological applications of PL proteomics, and then highlight recent developments in enrichment techniques for biotinylated proteins and peptides, focusing on the advantages of PL and technical considerations.

CANVS: an easy-to-use application for the analysis and visualization of mass spectrometry-based protein-protein interaction/association data

The elucidation of a protein’s interaction/association network is important for defining its biological function. Mass spectrometry–based proteomic approaches have emerged as powerful tools for identifying protein–protein interactions (PPIs) and protein–protein associations (PPAs). However, interactome/association experiments are difficult to interpret, considering the complexity and abundance of data that are generated. Although tools have been developed to identify protein interactions/associations quantitatively, there is still a pressing need for easy-to-use tools that allow users to contextualize their results. To address this, we developed CANVS, a computational pipeline that cleans, analyzes, and visualizes mass spectrometry–based interactome/association data. CANVS is wrapped as an interactive Shiny dashboard with simple requirements, allowing users to interface easily with the pipeline, analyze complex experimental data, and create PPI/A networks. The application integrates systems biology databases such as BioGRID and CORUM to contextualize the results. Furthermore, CANVS features a Gene Ontology tool that allows users to identify relevant GO terms in their results and create visual networks with proteins associated with relevant GO terms. Overall, CANVS is an easy-to-use application that benefits all researchers, especially those who lack an established bioinformatic pipeline and are interested in studying interactome/association data.

Interactome of human and SARS-CoV-2 proteins to identify human hub proteins associated with comorbidities

SARS-CoV-2 has a higher chance of progression in adults of any age with certain underlying health conditions or comorbidities like cancer, neurological diseases and in certain cases may even lead to death. Like other viruses, SARS-CoV-2 also interacts with host proteins to pave its entry into host cells. Therefore, to understand the behaviour of SARS-CoV-2 and design of effective antiviral drugs, host-virus protein-protein interactions (PPIs) can be very useful. In this regard, we have initially created a human-SARS-CoV-2 PPI database from existing works in the literature which has resulted in 7085 unique PPIs. Subsequently, we have identified at most 10 proteins with highest degrees viz. hub proteins from interacting human proteins for individual virus protein. The identification of these hub proteins is important as they are connected to most of the other human proteins. Consequently, when they get affected, the potential diseases are triggered in the corresponding pathways, thereby leading to comorbidities. Furthermore, the biological significance of the identified hub proteins is shown using KEGG pathway and GO enrichment analysis. KEGG pathway analysis is also essential for identifying the pathways leading to comorbidities. Among others, SARS-CoV-2 proteins viz. NSP2, NSP5, Envelope and ORF10 interacting with human hub proteins like COX4I1, COX5A, COX5B, NDUFS1, CANX, HSP90AA1 and TP53 lead to comorbidities. Such comorbidities are Alzheimer, Parkinson, Huntington, HTLV-1 infection, prostate cancer and viral carcinogenesis. Subsequently, using Enrichr tool possible repurposable drugs which target the human hub proteins are reported in this paper as well. Therefore, this work provides a consolidated study for human-SARS-CoV-2 protein interactions to understand the relationship between comorbidity and hub proteins so that it may pave the way for the development of anti-viral drugs.

Capturing Salmonella SspH2 Host Targets in Virus-Like Particles

In the context of host-pathogen interactions, gram-negative bacterial virulence factors, such as effectors, may be transferred from bacterial to eukaryotic host cytoplasm by multicomponent Type III protein secretion systems (T3SSs). Central to Salmonella enterica serovar Typhimurium (S. Typhimurium) pathogenesis is the secretion of over 40 effectors by two T3SSs encoded within pathogenicity islands SPI-1 and SPI-2. These effectors manipulate miscellaneous host cellular processes, such as cytoskeleton organization and immune signaling pathways, thereby permitting host colonization and bacterial dissemination. Recent research on effector biology provided mechanistic insights for some effectors. However, for many effectors, clearly defined roles and host target repertoires-further clarifying effector interconnectivity and virulence networks-are yet to be uncovered. Here we demonstrate the utility of the recently described viral-like particle trapping technology Virotrap as an effective approach to catalog S. Typhimurium effector-host protein complexes (EH-PCs). Mass spectrometry-based Virotrap analysis of the novel E3 ubiquitin ligase SspH2 previously shown to be implicated in modulating actin dynamics and immune signaling, exposed known host interactors PFN1 and-2 besides several putative novel, interconnected host targets. Network analysis revealed an actin (-binding) cluster among the significantly enriched hits for SspH2, consistent with the known localization of the S-palmitoylated effector with actin cytoskeleton components in the host. We show that Virotrap complements the current state-of-the-art toolkit to study protein complexes and represents a valuable means to screen for effector host targets in a high-throughput manner, thereby bridging the knowledge gap between effector-host interplay and pathogenesis.

Identification of covalent modifications regulating immune signaling complex composition and phenotype

Cells signal through rearrangements of protein communities governed by covalent modifications and reversible interactions of distinct sets of proteins. A method that identifies those post‐transcriptional modifications regulating signaling complex composition and functional phenotypes in one experimental setup would facilitate an efficient identification of novel molecular signaling checkpoints. Here, we devised modifications, interactions and phenotypes by affinity purification mass spectrometry (MIP‐APMS), comprising the streamlined cloning and transduction of tagged proteins into functionalized reporter cells as well as affinity chromatography, followed by MS‐based quantification. We report the time‐resolved interplay of more than 50 previously undescribed modification and hundreds of protein–protein interactions of 19 immune protein complexes in monocytes. Validation of interdependencies between covalent, reversible, and functional protein complex regulations by knockout or site‐specific mutation revealed ISGylation and phosphorylation of TRAF2 as well as ARHGEF18 interaction in Toll‐like receptor 2 signaling. Moreover, we identify distinct mechanisms of action for small molecule inhibitors of p38 (MAPK14). Our method provides a fast and cost‐effective pipeline for the molecular interrogation of protein communities in diverse biological systems and primary cells.

An updated structural model of the A domain of the Pseudomonas putida XylR regulator poses an atypical interplay with aromatic effectors

A revised model of the aromatic binding A domain of the σ⁵⁴ -dependent regulator XylR of Pseudomonas putida mt-2 was produced based on the known 3D structures of homologous regulators PoxR, MopR and DmpR. The resulting frame was instrumental for mapping a number of mutations known to alter effector specificity, which were then reinterpreted under a dependable spatial reference. Some of these changes involved the predicted aromatic binding pocket but others occurred in distant locations, including dimerization interfaces and putative zinc binding site. The effector pocket was buried within the protein structure and accessible from the outside only through a narrow tunnel. Yet, several loop regions of the A domain could provide the flexibility required for widening such a tunnel for passage of aromatic ligands. The model was experimentally validated by treating the cells in vivo and the purified protein in vitro with benzyl bromide, which reacts with accessible nucleophilic residues on the protein surface. Structural and proteomic analyses confirmed the predicted in/out distribution of residues but also supported two additional possible scenarios of interaction of the A domain with aromatic effectors: a dynamic interaction of the fully structured yet flexible protein with the aromatic partner and/or inducer-assisted folding of the A domain.