Analysis of Human Nuclear Protein Complexes by Quantitative Mass Spectrometry Profiling

Analysis of protein complexes provides insights into how the ensemble of expressed proteome is organized into functional units. While there have been advances in techniques for proteome‐wide profiling of cytoplasmic protein complexes, information about human nuclear protein complexes are very limited. To close this gap, we combined native size exclusion chromatography (SEC) with label‐free quantitative MS profiling to characterize hundreds of nuclear protein complexes isolated from human glioblastoma multiforme T98G cells. We identified 1794 proteins that overlapped between two biological replicates of which 1244 proteins were characterized as existing within stably associated putative complexes. co‐IP experiments confirmed the interaction of PARP1 with Ku70/Ku80 proteins and HDAC1 (histone deacetylase complex 1) and CHD4. HDAC1/2 also co‐migrated with various SIN3A and nucleosome remodeling and deacetylase components in SEC fractionation including SIN3A, SAP30, RBBP4, RBBP7, and NCOR1. Co‐elution of HDAC1/2/3 with both the KDM1A and RCOR1 further confirmed that these proteins are integral components of human deacetylase complexes. Our approach also demonstrated the ability to identify potential moonlighting complexes and novel complexes containing uncharacterized proteins. Overall, the results demonstrated the utility of SEC fractionation and LC–MS analysis for system‐wide profiling of proteins to predict the existence of distinct forms of nuclear protein complexes.     

Quantitative GTPase Affinity Purification Identifies Rho Family Protein Interaction Partners

Although Rho GTPases are essential molecular switches involved in many cellular processes, an unbiased experimental comparison of their interaction partners was not yet performed. Here, we develop quantitative GTPase affinity purification (qGAP) to systematically identify interaction partners of six Rho GTPases (Cdc42, Rac1, RhoA, RhoB, RhoC, and RhoD), depending on their nucleotide loading state. The method works with cell line or tissue-derived protein lysates in combination with SILAC-based or label-free quantification, respectively. We demonstrate that qGAP identifies known and novel binding partners that can be validated in an independent assay. Our interaction network for six Rho GTPases contains many novel binding partners, reveals highly promiscuous interaction of several effectors, and mirrors evolutionary relationships among Rho GTPases.     

Identification of Protein Complexes in Escherichia coli using Sequential Peptide Affinity Purification in Combination with Tandem Mass Spectrometry

Since most cellular processes are mediated by macromolecular assemblies, the systematic identification of protein-protein interactions (PPI) and the identification of the subunit composition of multi-protein complexes can provide insight into gene function and enhance understanding of biological systems^{1, 2}. Physical interactions can be mapped with high confidence vialarge-scale isolation and characterization of endogenous protein complexes under near-physiological conditions based on affinity purification of chromosomally-tagged proteins in combination with mass spectrometry (APMS). This approach has been successfully applied in evolutionarily diverse organisms, including yeast, flies, worms, mammalian cells, and bacteria^1-6. In particular, we have generated a carboxy-terminal Sequential Peptide Affinity (SPA) dual tagging system for affinity-purifying native protein complexes from cultured gram-negative Escherichia coli, using genetically-tractable host laboratory strains that are well-suited for genome-wide investigations of the fundamental biology and conserved processes of prokaryotes^{1, 2, 7}. Our SPA-tagging system is analogous to the tandem affinity purification method developed originally for yeast^{8, 9}, and consists of a calmodulin binding peptide (CBP) followed by the cleavage site for the highly specific tobacco etch virus (TEV) protease and three copies of the FLAG epitope (3X FLAG), allowing for two consecutive rounds of affinity enrichment. After cassette amplification, sequence-specific linear PCR products encoding the SPA-tag and a selectable marker are integrated and expressed in frame as carboxy-terminal fusions in a DY330 background that is induced to transiently express a highly efficient heterologous bacteriophage lambda recombination system¹⁰. Subsequent dual-step purification using calmodulin and anti-FLAG affinity beads enables the highly selective and efficient recovery of even low abundance protein complexes from large-scale cultures. Tandem mass spectrometry is then used to identify the stably co-purifying proteins with high sensitivity (low nanogram detection limits).Here, we describe detailed step-by-step procedures we commonly use for systematic protein tagging, purification and mass spectrometry-based analysis of soluble protein complexes from E. coli, which can be scaled up and potentially tailored to other bacterial species, including certain opportunistic pathogens that are amenable to recombineering. The resulting physical interactions can often reveal interesting unexpected components and connections suggesting novel mechanistic links. Integration of the PPI data with alternate molecular association data such as genetic (gene-gene) interactions and genomic-context (GC) predictions can facilitate elucidation of the global molecular organization of multi-protein complexes within biological pathways. The networks generated for E. coli can be used to gain insight into the functional architecture of orthologous gene products in other microbes for which functional annotations are currently lacking.     

Modeling Protein Complexes Using Restraints from Crosslinking Mass Spectrometry

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Accurate Protein Complex Retrieval by Affinity Enrichment Mass Spectrometry (AE-MS) Rather than Affinity Purification Mass Spectrometry (AP-MS)

Protein–protein interactions are fundamental to the understanding of biological processes. Affinity purification coupled to mass spectrometry (AP-MS) is one of the most promising methods for their investigation. Previously, complexes were purified as much as possible, frequently followed by identification of individual gel bands. However, todays mass spectrometers are highly sensitive, and powerful quantitative proteomics strategies are available to distinguish true interactors from background binders. Here we describe a high performance affinity enrichment-mass spectrometry method for investigating protein–protein interactions, in which no attempt at purifying complexes to homogeneity is made. Instead, we developed analysis methods that take advantage of specific enrichment of interactors in the context of a large amount of unspecific background binders. We perform single-step affinity enrichment of endogenously expressed GFP-tagged proteins and their interactors in budding yeast, followed by single-run, intensity-based label-free quantitative LC-MS/MS analysis. Each pull-down contains around 2000 background binders, which are reinterpreted from troubling contaminants to crucial elements in a novel data analysis strategy. First the background serves for accurate normalization. Second, interacting proteins are not identified by comparison to a single untagged control strain, but instead to the other tagged strains. Third, potential interactors are further validated by their intensity profiles across all samples. We demonstrate the power of our AE-MS method using several well-known and challenging yeast complexes of various abundances. AE-MS is not only highly efficient and robust, but also cost effective, broadly applicable, and can be performed in any laboratory with access to high-resolution mass spectrometers.Protein–protein interactions are key to protein-mediated biological processes and influence all aspects of life. Therefore, considerable efforts have been dedicated to the mapping of protein–protein interactions. A classical experimental approach consists of co-immunoprecipitation of protein complexes combined with SDS-PAGE followed by Western blotting to identify complex members. More recently, high-throughput techniques have been introduced; among these affinity purification-mass spectrometry (AP-MS)¹ (1–3) and the yeast two-hybrid (Y2H) approach (4–6) are the most prominent. AP-MS, in particular, has great potential for detecting functional interactions under near-physiological conditions, and has already been employed for interactome mapping in several organisms (7–15). Various AP-MS approaches have evolved over time, that differ in expression, tagging, and affinity purification of the bait protein; fractionation, LC-MS measurement, and quantification of the sample; and in data analysis. Recent progress in the AP-MS field has been driven by two factors: A new generation of mass spectrometers (16) providing higher sequencing speed, sensitivity, and mass accuracy, and the development of quantitative MS strategies.In the early days of AP-MS, tagged bait proteins were mostly overexpressed, enhancing their recovery in the pull-down. However, overexpression comes at the cost of obscuring the true situation in the cell, potentially leading to the detection of false interactions (17). Today, increased MS instrument power helps in the detection of bait proteins and interactors expressed at endogenous levels, augmenting the chances to detect functional interactions. In some simple organisms like yeast, genes of interest can directly be tagged in their genetic loci and expressed under their native promoter. In higher organisms, tagging proteins in their endogenous locus is more challenging, but also for mammalian cells, methods for close to endogenous expression are available. For instance, in controlled inducible expression systems, the concentration of the tagged bait protein can be titrated to close to endogenous levels (18). A very powerful approach is BAC transgenomics (19), as used in our QUBIC protocol (20), where a bacterial artificial chromosome (BAC) containing a tagged version of the gene of interest including all regulatory sequences and the natural promoter is stably transfected into a host cell line.The affinity purification step has also been subject to substantial changes over time. Previously, AP has been combined with nonquantitative MS as the readout, meaning all proteins identified by MS were considered potential interactors. Therefore, to reduce co-purifying “contaminants,” stringent two-step AP protocols using dual affinity tags like the TAP-tag (21) had to be employed. However, such stringent and multistep protocols can result in the loss of weak or transient interactors (3), whereas laborious and partially subjective filtering still has to be applied to clean up the list of identified proteins. The introduction of quantitative mass spectrometry (22–25) to the interactomics field about ten years ago was a paradigm shift, as it offered a proper way of dealing with unspecific binding and true interactors could be directly distinguished from background binders (26, 27). Importantly, quantification enables the detection of true interactors even under low-stringent conditions (28). In turn, this allowed the return to single-step AP protocols, which are milder and faster, and hence more suitable for detecting weak and transient interactors.Despite these advances, nonquantitative methods—often in combination with the TAP-tagging approach—are still popular and widely used, presumably because of reagent expenses and labeling protocols used in label-based approaches. However, there are ways to determine relative protein abundances in a label-free format. A simple, semiquantitative label-free way to estimate protein abundance is spectral counting (29). Another relative label-free quantification strategy is based on peptide intensities (30). In recent years high resolution MS has become much more widely accessible and there has been great progress in intensity-based label-free quantification (LFQ) approaches. Together with development of sophisticated LFQ algorithms, this has boosted obtainable accuracy. Intensity-based LFQ now offers a viable and cost-effective alternative to label-based methods in most applications (31). The potential of intensity-based LFQ approaches as tools for investigating protein–protein interactions has already been demonstrated by us (20, 32, 33) and others (34, 35). We have further refined intensity-based LFQ in the context of the MaxQuant framework (36) using sophisticated normalization algorithms, achieving excellent accuracy and robustness of the measured “MaxLFQ” intensities (37).Another important advance in AP-MS, again enabled by increased MS instrument power, was the development of single-shot LC-MS methods with comprehensive coverage. Instead of extensive fractionation, which was previously needed to reduce sample complexity, nowadays even entire model proteomes can be measured in single LC-MS runs (38). The protein mixture resulting from pull-downs is naturally of lower complexity compared with the entire proteome. Therefore, modern MS obviates the need for gel-based (or other) fractionation and samples can be analyzed in single runs. Apart from avoiding selection of gel bands by visual examination, this has many advantages, including decreased sample preparation and measurement time, increased sensitivity, and higher quantitative accuracy in a label-free format.In this work, we build on many of the recent advances in the field to establish a state of the art LFQ AE-MS method. Based on our previous QUBIC pipeline (20), we developed an approach for investigating protein–protein interactions, which we exemplify in Saccharomyces cerevisiae. We extended the data analysis pipeline to extract the wealth of information contained in the LFQ data, by establishing a novel concept that specifically makes use of the signature of background binders instead of eliminating them from the data set. The large amount of unspecific binders detected in our experiments rendered the use of a classic untagged control strain unnecessary and enabled comparing to a control group consisting of many unrelated pull-downs instead. Our protocol is generic, practical, and fast, uses low input amounts, and identifies interactors with high confidence. We propose that single-step pull-down experiments, especially when coupled to high-sensitivity MS, should now be regarded as affinity enrichment rather than affinity purification methods.     

Profiling of Parkin-Binding Partners Using Tandem Affinity Purification

Parkinson''s disease (PD) is a progressive neurodegenerative disorder affecting approximately 1–2% of the general population over age 60. It is characterized by a rather selective loss of dopaminergic neurons in the substantia nigra and the presence of α-synuclein-enriched Lewy body inclusions. Mutations in the Parkin gene (PARK2) are the major cause of autosomal recessive early-onset parkinsonism. The Parkin protein is an E3 ubiquitin ligase with various cellular functions, including the induction of mitophagy upon mitochondrial depolarizaton, but the full repertoire of Parkin-binding proteins remains poorly defined. Here we employed tandem affinity purification interaction screens with subsequent mass spectrometry to profile binding partners of Parkin. Using this approach for two different cell types (HEK293T and SH-SY5Y neuronal cells), we identified a total of 203 candidate Parkin-binding proteins. For the candidate proteins and the proteins known to cause heritable forms of parkinsonism, protein-protein interaction data were derived from public databases, and the associated biological processes and pathways were analyzed and compared. Functional similarity between the candidates and the proteins involved in monogenic parkinsonism was investigated, and additional confirmatory evidence was obtained using published genetic interaction data from Drosophila melanogaster. Based on the results of the different analyses, a prioritization score was assigned to each candidate Parkin-binding protein. Two of the top ranking candidates were tested by co-immunoprecipitation, and interaction to Parkin was confirmed for one of them. New candidates for involvement in cell death processes, protein folding, the fission/fusion machinery, and the mitophagy pathway were identified, which provide a resource for further elucidating Parkin function.     

Discover Protein Complexes in Protein-Protein Interaction Networks Using Parametric Local Modularity

Background  

Recent advances in proteomic technologies have enabled us to create detailed protein-protein interaction maps in multiple species and in both normal and diseased cells. As the size of the interaction dataset increases, powerful computational methods are required in order to effectively distil network models from large-scale interactome data.     

Quantitative Multiplex Substrate Profiling of Peptidases by Mass Spectrometry

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Highlights

• •Quantitative substrate profiling method for characterizing peptidase specificity.
• •Applicable to both purified peptidases and peptidases in complex biological samples.
• •TMT labeling improves throughput, accuracy and reproducibility of the assay.
• •Design of fluorescent probes to monitor peptidase activity based on substrate data.

     

Analyzing Large Protein Complexes by Structural Mass Spectrometry

Living cells control and regulate their biological processes through the coordinated action of a large number of proteins that assemble themselves into an array of dynamic, multi-protein complexes¹. To gain a mechanistic understanding of the various cellular processes, it is crucial to determine the structure of such protein complexes, and reveal how their structural organization dictates their function. Many aspects of multi-protein complexes are, however, difficult to characterize, due to their heterogeneous nature, asymmetric structure, and dynamics. Therefore, new approaches are required for the study of the tertiary levels of protein organization.One of the emerging structural biology tools for analyzing macromolecular complexes is mass spectrometry (MS)^2-5. This method yields information on the complex protein composition, subunit stoichiometry, and structural topology. The power of MS derives from its high sensitivity and, as a consequence, low sample requirement, which enables examination of protein complexes expressed at endogenous levels. Another advantage is the speed of analysis, which allows monitoring of reactions in real time. Moreover, the technique can simultaneously measure the characteristics of separate populations co-existing in a mixture. Here, we describe a detailed protocol for the application of structural MS to the analysis of large protein assemblies. The procedure begins with the preparation of gold-coated capillaries for nanoflow electrospray ionization (nESI). It then continues with sample preparation, emphasizing the buffer conditions which should be compatible with nESI on the one hand, and enable to maintain complexes intact on the other. We then explain, step-by-step, how to optimize the experimental conditions for high mass measurements and acquire MS and tandem MS spectra. Finally, we chart the data processing and analyses that follow. Rather than attempting to characterize every aspect of protein assemblies, this protocol introduces basic MS procedures, enabling the performance of MS and MS/MS experiments on non-covalent complexes. Overall, our goal is to provide researchers unacquainted with the field of structural MS, with knowledge of the principal experimental tools.     

Analytical Guidelines for co-fractionation Mass Spectrometry Obtained through Global Profiling of Gold Standard Saccharomyces cerevisiae Protein Complexes

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Highlights

• •Guidelines for studying protein complexes via co-fractionation mass spectrometry.
• •A novel procedure for profiling gold standard protein complexes in CF-MS data.
• •Recommendations for efficient CF-MS fractionation collection.
• •Scoring metric recommendations for precise and sensitive CF-MS data analysis.

     

Profiling Thiol Redox Proteome Using Isotope Tagging Mass Spectrometry

Pseudomonas syringae pv. tomato strain DC3000 not only causes bacterial speck disease in Solanum lycopersicum but also on Brassica species, as well as on Arabidopsis thaliana, a genetically tractable host plant^1,2. The accumulation of reactive oxygen species (ROS) in cotyledons inoculated with DC3000 indicates a role of ROS in modulating necrotic cell death during bacterial speck disease of tomato³. Hydrogen peroxide, a component of ROS, is produced after inoculation of tomato plants with Pseudomonas³. Hydrogen peroxide can be detected using a histochemical stain 3''-3'' diaminobenzidine (DAB)⁴. DAB staining reacts with hydrogen peroxide to produce a brown stain on the leaf tissue⁴. ROS has a regulatory role of the cellular redox environment, which can change the redox status of certain proteins⁵. Cysteine is an important amino acid sensitive to redox changes. Under mild oxidation, reversible oxidation of cysteine sulfhydryl groups serves as redox sensors and signal transducers that regulate a variety of physiological processes^6,7. Tandem mass tag (TMT) reagents enable concurrent identification and multiplexed quantitation of proteins in different samples using tandem mass spectrometry^8,9. The cysteine-reactive TMT (cysTMT) reagents enable selective labeling and relative quantitation of cysteine-containing peptides from up to six biological samples. Each isobaric cysTMT tag has the same nominal parent mass and is composed of a sulfhydryl-reactive group, a MS-neutral spacer arm and an MS/MS reporter¹⁰. After labeling, the samples were subject to protease digestion. The cysteine-labeled peptides were enriched using a resin containing anti-TMT antibody. During MS/MS analysis, a series of reporter ions (i.e., 126-131 Da) emerge in the low mass region, providing information on relative quantitation. The workflow is effective for reducing sample complexity, improving dynamic range and studying cysteine modifications. Here we present redox proteomic analysis of the Pst DC3000 treated tomato (Rio Grande) leaves using cysTMT technology. This high-throughput method has the potential to be applied to studying other redox-regulated physiological processes.     

Identification of Protein Interacting Partners Using Tandem Affinity Purification

A critical and often limiting step in understanding the function of host and viral proteins is the identification of interacting cellular or viral protein partners. There are many approaches that allow the identification of interacting partners, including the yeast two hybrid system, as well as pull down assays using recombinant proteins and immunoprecipitation of endogenous proteins followed by mass spectrometry identification¹. Recent studies have highlighted the utility of double-affinity tag mediated purification, coupled with two specific elution steps in the identification of interacting proteins. This approach, termed Tandem Affinity Purification (TAP), was initially used in yeast^2,3 but more recently has been adapted to use in mammalian cells^4-8.As proof-of-concept we have established a tandem affinity purification (TAP) method using the well-characterized eukaryotic translation initiation factor eIF4E^9,10.The cellular translation factor eIF4E is a critical component of the cellular eIF4F complex involved in cap-dependent translation initiation¹⁰. The TAP tag used in the current study is composed of two Protein G units and a streptavidin binding peptide separated by a Tobacco Etch Virus (TEV) protease cleavage sequence. The TAP tag used in the current study is composed of two Protein G units and a streptavidin binding peptide separated by a Tobacco Etch Virus (TEV) protease cleavage sequence⁸. To forgo the need for the generation of clonal cell lines, we developed a rapid system that relies on the expression of the TAP-tagged bait protein from an episomally maintained plasmid based on pMEP4 (Invitrogen). Expression of tagged murine eIF4E from this plasmid was controlled using the cadmium chloride inducible metallothionein promoter.Lysis of the expressing cells and subsequent affinity purification via binding to rabbit IgG agarose, TEV protease cleavage, binding to streptavidin linked agarose and subsequent biotin elution identified numerous proteins apparently specific to the eIF4E pull-down (when compared to control cell lines expressing the TAP tag alone). The identities of the proteins were obtained by excision of the bands from 1D SDS-PAGE and subsequent tandem mass spectrometry. The identified components included the known eIF4E binding proteins eIF4G and 4EBP-1. In addition, other components of the eIF4F complex, of which eIF4E is a component were identified, namely eIF4A and Poly-A binding protein. The ability to identify not only known direct binding partners as well as secondary interacting proteins, further highlights the utility of this approach in the characterization of proteins of unknown function.     

应用A蛋白亲和层析法纯化单克隆抗体

应用Protein A亲和层析法,从采集的小鼠腹水中纯化出了抗凝血因子Ⅶ单克隆抗体,用SDS-PAGE和ELISA法分别检测了纯化后单克隆抗体的纯度及效价,结果显示,电泳为两条带,分别为免疫球蛋白G(IgG)的重链和轻链,纯化后的单克隆抗体纯度达到电泳纯,应用间接ELISA法测定腹水效价为1×10-7左右,与未纯化前无差异。结果表明,应用A蛋白亲和层析法能够得到纯度较高的单克隆抗体,适用于高纯度单克隆抗体的制备。     

Prediction of Heterodimeric Protein Complexes from Weighted Protein-Protein Interaction Networks Using Novel Features and Kernel Functions

Since many proteins express their functional activity by interacting with other proteins and forming protein complexes, it is very useful to identify sets of proteins that form complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this paper, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on reliability. Furthermore, we make use of prior knowledge on protein domains to develop feature space mappings, domain composition kernel and its combination kernel with our proposed features. We perform ten-fold cross-validation computational experiments. These results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method for predicting heterodimeric protein complexes.     

Analyzing Protein Dynamics Using Hydrogen Exchange Mass Spectrometry

All cellular processes depend on the functionality of proteins. Although the functionality of a given protein is the direct consequence of its unique amino acid sequence, it is only realized by the folding of the polypeptide chain into a single defined three-dimensional arrangement or more commonly into an ensemble of interconverting conformations. Investigating the connection between protein conformation and its function is therefore essential for a complete understanding of how proteins are able to fulfill their great variety of tasks. One possibility to study conformational changes a protein undergoes while progressing through its functional cycle is hydrogen-¹H/²H-exchange in combination with high-resolution mass spectrometry (HX-MS). HX-MS is a versatile and robust method that adds a new dimension to structural information obtained by e.g. crystallography. It is used to study protein folding and unfolding, binding of small molecule ligands, protein-protein interactions, conformational changes linked to enzyme catalysis, and allostery. In addition, HX-MS is often used when the amount of protein is very limited or crystallization of the protein is not feasible. Here we provide a general protocol for studying protein dynamics with HX-MS and describe as an example how to reveal the interaction interface of two proteins in a complex.