Decoding 2D-PAGE complex maps: relevance to proteomics

This review describes two mathematical approaches useful for decoding the complex signal of 2D-PAGE maps of protein mixtures. These methods are helpful for interpreting the large amount of data of each 2D-PAGE map by extracting all the analytical information hidden therein by spot overlapping. Here the basic theory and application to 2D-PAGE maps are reviewed: the means for extracting information from the experimental data and their relevance to proteomics are discussed. One method is based on the quantitative theory of statistical model of peak overlapping (SMO) using the spot experimental data (intensity and spatial coordinates). The second method is based on the study of the 2D-autocovariance function (2D-ACVF) computed on the experimental digitised map. They are two independent methods that are able to extract equal and complementary information from the 2D-PAGE map. Both methods permit to obtain fundamental information on the sample complexity and the separation performance and to single out ordered patterns present in spot positions: the availability of two independent procedures to compute the same separation parameters is a powerful tool to estimate the reliability of the obtained results. The SMO procedure is an unique tool to quantitatively estimate the degree of spot overlapping present in the map, while the 2D-ACVF method is particularly powerful in simply singling out the presence of order in the spot position from the complexity of the whole 2D map, i.e., spot trains. The procedures were validated by extensive numerical computation on computer-generated maps describing experimental 2D-PAGE gels of protein mixtures. Their applicability to real samples was tested on reference maps obtained from literature sources. The review describes the most relevant information for proteomics: sample complexity, separation performance, overlapping extent, identification of spot trains related to post-translational modifications (PTMs).     

The protein interaction network of the epithelial junctional complex: a system-level analysis

To acquire system-level understanding of the intercellular junctional complex, protein–protein interactions occurring at the junctions of simple epithelial cells have been examined by network analysis. Although proper hubs (i.e., very rare proteins with exceedingly high connectivity) were absent from the junctional network, the most connected (albeit nonhub) proteins displayed a significant association with essential genes and contributed to the “small world” properties of the network (as shown by in vivo and in silico deletion, respectively). In addition, compared with a random network, the junctional network had greater tendency to form modules and subnets of densely interconnected proteins. Module analysis highlighted general organizing principles of the junctional complex. In particular, two major modules (corresponding to the tight junctions and to the adherens junctions/desmosomes) were linked preferentially to two other modules that acted as structural and signaling platforms.     

Identification of additional proteins in differential proteomics using protein interaction networks

In this study, we developed a novel computational approach based on protein–protein interaction networks to identify a list of proteins that might have remained undetected in differential proteomic profiling experiments. We tested our computational approach on two sets of human smooth muscle cell protein extracts that were affected differently by DNase I treatment. Differential proteomic analysis by saturation DIGE resulted in the identification of 41 human proteins. The application of our approach to these 41 input proteins consisted of four steps: (i) Compilation of a human protein–protein interaction network from public databases; (ii) calculation of interaction scores based on functional similarity; (iii) determination of a set of candidate proteins that are needed to efficiently and confidently connect the 41 input proteins; and (iv) ranking of the resulting 25 candidate proteins. Two of the three highest‐ranked proteins, beta‐arrestin 1, and beta‐arrestin 2, were experimentally tested, revealing that their abundance levels in human smooth muscle cell samples were indeed affected by DNase I treatment. These proteins had not been detected during the experimental proteomic analysis. Our study suggests that our computational approach may represent a simple, universal, and cost‐effective means to identify additional proteins that remain elusive for current 2D gel‐based proteomic profiling techniques.     

The inhibitor protein of the cAMP-dependent protein kinase-catalytic subunit interaction. Parameters of complex formation

The formation of a complex between the catalytic subunit of the cAMP-dependent protein kinase and the Inhibitor Protein of this enzyme has been examined by means of nondenaturing gel electrophoresis. Two forms of complex were identified, both containing a 1:1 molar ratio of the component proteins. The formation of the major of the two forms is markedly enhanced by the presence of nucleotide triphosphate and divalent cation. Either Mg2+ or Mn2+ serves to promote complex formation. With Mg2+, only ATP is effective for enhancing complex formation, whereas with Mn2+ complex formation occurs to an equal extent with ATP, GTP, ITP, and adenyl-5'-yl imidodiphosphate. The formation of the two complexes is only minimally dependent upon nucleotide triphosphate. It is suggested that the two types of complex are a result of different species of catalytic subunit. Two principal forms of the complex have been detected occurring maximally in approximately a 2.5:1 ratio. In the accompanying paper (Fletcher, W.H., Van Patten, S.M., Cheng, H-C., and Walsh, D.A. (1986) J. Biol. Chem. 261, 5504-5513), we have described the use of a fluoresceinated derivative of catalytic subunit as a cytochemical probe to localize the Inhibitor Protein and the regulatory subunit of the protein kinase. The integrity of this fluorophore has been further characterized using the method of examining catalytic subunit-Inhibitor Protein interaction delineated here.     

Ontology integration to identify protein complex in protein interaction networks

Background

Protein complexes can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of protein complexes detection algorithms.

Methods

We have developed novel semantic similarity method, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. Following the approach of that of the previously proposed clustering algorithm IPCA which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new weighted Protein-Protein interaction networks for identifying protein complexes.

Results

The algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches.

     

An efficient protein complex purification method for functional proteomics in higher eukaryotes

The ensemble of expressed proteins in a given cell is organized in multiprotein complexes. The identification of the individual components of these complexes is essential for their functional characterization. The introduction of the 'tandem affinity purification' (TAP) methodology substantially improved the purification and systematic genome-wide characterization of protein complexes in yeast. The use of this approach in higher eukaryotic cells has lagged behind its use in yeast because the tagged proteins are normally expressed in the presence of the untagged endogenous version, which may compete for incorporation into multiprotein complexes. Here we describe a strategy in which the TAP approach is combined with double-stranded RNA interference (RNAi) to avoid competition from corresponding endogenous proteins while isolating and characterizing protein complexes from higher eukaryotic cells. This strategy allows the determination of the functionality of the tagged protein and increases the specificity and the efficiency of the purification.     

Mitochondrial ubiquinol-cytochromec reductase complex: Crystallization and protein: Ubiquinone interaction

The ubiquinol-cytochromec reductase complex was crystallized in a thin plate form, which diffracts X-rays to 7 Å resolution in the presence of mother liquor. This crystalline complex contains ten protein subunits and 140 nmol phospholipid per milligram protein. Over 90% of the phospholipid and ubiquinone in the reductase can be removed by repeated ammonium sulfate precipitation in the presence of 0.5% sodium cholate. The delipidated complex has no enzymatic activity and shows significant changes in the circular dichroism spectrum in the near UV region and in the EPR characteristics of both cytochromesb. Enzyme activity and spectral characteristics can be restored by replenishing the phospholipid and ubiquinone. The structural requirements of ubiquinone for electron transport were studied by measuring the ability of a variety of synthetic ubiquinone derivatives to restore the enzymatic activity and native spectroscopic signatures to the delipidated complex. Q-binding proteins and binding domains were identified using photoaffinity labeled Q-derivatives and HPLC separation of photolabeled peptides. Interaction between ubiquinol-cytochromec reductase and succinate-Q reductase was established by differential scanning calorimetry and saturation transfer EPR using spinlabeled ubiquinol-cytochromec reductase. Involvement of iron-sulfur protein in proton translocation by ubiquinol-cytochromec reductase was investigated by hematorporphyrinpromoted photoinactivation of the complex. ThecDNAs encoding the Rieske iron-sulfur protein and a small molecular mass Q-binding protein (QPc-9.5 kDa) were isolated and their nucleotide sequences determined. These will be useful in future structural and mechanistic studies of ubiquinol-cytochromec reductase viain vitro reconstitution between an overexpressed, mutated subunit and a specific subunit-depleted reductase.     

The application of modular protein domains in proteomics

The ability of modular protein domains to independently fold and bind short peptide ligands both in vivo and in vitro has allowed a significant number of protein-protein interaction studies to take advantage of them as affinity and detection reagents. Here, we refer to modular domain based proteomics as "domainomics" to draw attention to the potential of using domains and their motifs as tools in proteomics. In this review we describe core concepts of domainomics, established and emerging technologies, and recent studies by functional category. Accumulation of domain-motif binding data should ultimately provide the foundation for domain-specific interactomes, which will likely reveal the underlying substructure of protein networks as well as the selectivity and plasticity of signal transduction.     

Molecular modelling of glycans: three-dimensional structure and protein fraction interaction. The example of rabbit sero-transferrin

On the basis of experimental data and of computer calculations using the Tripos 5.3 force field in order to examine the three-dimensional structures which are sterically feasible and the conformations which are energetically the most favourable, we have designed a program of molecular modelling of biantennary glycans of the N-acetyllactosaminic type (complex type). We demonstrate that, in absence of any interaction with the protein, a high number of glycan conformations exists which can be classified into five basic conformations, four of which have already been described. In fact, in addition to the Y-, T-, "bird" and "broken-wing" conformations, a "back-folded wing" conformation is energetically feasible. In contrast, the glycan linked to the protein is immobilized into only one conformation: the "broken-wing" conformation. Forming a bridge between the two lobes of the peptide chain, it probably contributes to the maintenance of the protein in a biologically active conformation.     

The advancement of chemical cross-linking and mass spectrometry for structural proteomics: from single proteins to protein interaction networks

During the last 15 years, chemical cross-linking combined with mass spectrometry (MS) and computational modeling has advanced from investigating 3D-structures of isolated proteins to deciphering protein interaction networks. In this article, the author discusses the advent, the development and the current status of the chemical cross-linking/MS strategy in the context of recent technological developments. A direct way to probe in vivo protein–protein interactions is by site-specific incorporation of genetically encoded photo-reactive amino acids or by non-directed incorporation of photo-reactive amino acids. As the chemical cross-linking/MS approach allows the capture of transient and weak interactions, it has the potential to become a routine technique for unraveling protein interaction networks in their natural cellular environment.     

Identifying protein complexes based on density and modularity in protein-protein interaction network

Background

Identifying protein complexes is crucial to understanding principles of cellular organization and functional mechanisms. As many evidences have indicated that the subgraphs with high density or with high modularity in PPI network usually correspond to protein complexes, protein complexes detection methods based on PPI network focused on subgraph's density or its modularity in PPI network. However, dense subgraphs may have low modularity and subgraph with high modularity may have low density, which results that protein complexes may be subgraphs with low modularity or with low density in the PPI network. As the density-based methods are difficult to mine protein complexes with low density, and the modularity-based methods are difficult to mine protein complexes with low modularity, both two methods have limitation for identifying protein complexes with various density and modularity.

Results

To identify protein complexes with various density and modularity, including those have low density but high modularity and those have low modularity but high density, we define a novel subgraph's fitness, f _ρ , as f _ρ = (density) ^ρ *(modularity)^1-ρ, and propose a novel algorithm, named LF_PIN, to identify protein complexes by expanding seed edges to subgraphs with the local maximum fitness value. Experimental results of LF-PIN in S.cerevisiae show that compared with the results of fitness equal to density (ρ = 1) or equal to modularity (ρ = 0), the LF-PIN identifies known protein complexes more effectively when the fitness value is decided by both density and modularity (0<ρ<1). Compared with the results of seven competing protein complex detection methods (CMC, Core-Attachment, CPM, DPClus, HC-PIN, MCL, and NFC) in S.cerevisiae and E.coli, LF-PIN outperforms other seven methods in terms of matching with known complexes and functional enrichment. Moreover, LF-PIN has better performance in identifying protein complexes with low density or with low modularity.

Conclusions

By considering both the density and the modularity, LF-PIN outperforms other protein complexes detection methods that only consider density or modularity, especially in identifying known protein complexes with low density or low modularity.

     

The multiple phosphorylation of the microtubule-associated protein MAP2 controls the MAP2:tubulin interaction

Pre-phosphorylation of the microtubule-associated protein MAP2 with the co-purifying cAMP-independent protein kinase (a) decrease the affinity of MAP2 for taxol-stabilised microtubules, (b) increases the dissociation rate constant for microtubule polymerisation, each of which is dependent upon the level of phosphorylation, but (c) has no effect on the association rate constant. Microtubule assembly has no effect on the kinetics of phosphorylation, whereas phosphorylation of pre-assembled microtubules causes their immediate depolymerisation at a rate which is proportional to the initial rate of phosphorylation. The results suggest that the modulated phosphorylation of MAP2 may regulate microtubule length in vivo.     

Chemical proteomics and functional proteomics strategies for protein kinase inhibitor validation and protein kinase substrate identification: Applications to protein kinase CK2

Since protein kinases have been implicated in numerous human diseases, kinase inhibitors have emerged as promising therapeutic agents. Despite this promise, there has been a relative lag in the development of unbiased strategies to validate both inhibitor specificity and the ability to inhibit target activity within living cells. To overcome these limitations, our efforts have been focused on the development of systematic strategies that employ chemical and functional proteomics. We utilized these strategies to evaluate small molecule inhibitors of protein kinase CK2, a constitutively active kinase that has recently emerged as target for anti-cancer therapy in clinical trials. Our chemical proteomics strategies used ATP or CK2 inhibitors immobilized on sepharose beads together with mass spectrometry to capture and identify binding partners from cell extracts. These studies have verified that interactions between CK2 and its inhibitors occur in complex mixtures. However, in the case of CK2 inhibitors related to 4,5,6,7-tetrabromo-1H-benzotriazole (TBB), our work has also revealed off-targets for the inhibitors. To complement these studies, we devised functional proteomics approaches to identify proteins that exhibit decreases in phosphorylation when cells are treated with CK2 inhibitors. To identify and validate those proteins that are direct substrates for CK2, we have also employed mutants of CK2 with decreased inhibitor sensitivity. Overall, our studies have yielded systematic platforms for studying CK2 inhibitors which we believe will foster efforts to define the biological functions of CK2 and to rigorously investigate its potential as a candidate for molecular-targeted therapy. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases (2012).     

Quantitative proteomics reveal up-regulated protein expression of the SET complex associated with hepatocellular carcinoma

We combined culture-derived isotope tags (CDITs) with two-dimensional liquid chromatography-tandem mass spectrometry (2D-LC-MS/MS) to extensively survey abnormal protein expression associated with hepatocellular carcinoma (HCC) in clinical tissues. This approach yielded an in-depth quantitated proteome of 1360 proteins. Importantly, 267 proteins were significantly regulated with a fold-change of at least 1.5. The proteins up-regulated in HCC tissues are involved in regulatory processes, such as the granzyme A-mediated apoptosis pathway (The GzmA pathway). The SET complex, a central component in the GzmA pathway, was significantly up-regulated in HCC tissue. The elevated expressions of all of the SET complex components were validated by Western blotting. Among them, ANP32A and APEX1 were further investigated by immunohistochemistry staining using tissue microarrays (59 cases), confirming their overexpression in tumors. The up-regulation and nuclear accumulations of APEX1 was associated not only with HCC malignancy but also with HCC differentiation in 96 clinical HCC cases. Our work provided a systematic and quantitative analysis and demonstrated key changes in clinical HCC tissues. These proteomic signatures could help to unveil the underlying mechanisms of hepatocarcinogenesis and may be useful for the discovery of candidate biomarkers.     

Activity-based proteomics: enzymatic activity profiling in complex proteomes

Summary. In the postgenomic era new technologies are emerging for global analysis of protein function. The introduction of active site-directed chemical probes for enzymatic activity profiling in complex mixtures, known as activity-based proteomics has greatly accelerated functional annotation of proteins. Here we review probe design for different enzyme classes including serine hydrolases, cysteine proteases, tyrosine phosphatases, glycosidases, and others. These probes are usually detected by their fluorescent, radioactive or affinity tags and their protein targets are analyzed using established proteomics techniques. Recent developments, such as the design of probes for in vivo analysis of proteomes, as well as microarray technologies for higher throughput screenings of protein specificity and the application of activity-based probes for drug screening are highlighted. We focus on biological applications of activity-based probes for target and inhibitor discovery and discuss challenges for future development of this field.     

PPI spider: A tool for the interpretation of proteomics data in the context of protein–protein interaction networks

Recent advances in experimental technologies allow for the detection of a complete cell proteome. Proteins that are expressed at a particular cell state or in a particular compartment as well as proteins with differential expression between various cells states are commonly delivered by many proteomics studies. Once a list of proteins is derived, a major challenge is to interpret the identified set of proteins in the biological context. Protein–protein interaction (PPI) data represents abundant information that can be employed for this purpose. However, these data have not yet been fully exploited due to the absence of a methodological framework that can integrate this type of information. Here, we propose to infer a network model from an experimentally identified protein list based on the available information about the topology of the global PPI network. We propose to use a Monte Carlo simulation procedure to compute the statistical significance of the inferred models. The method has been implemented as a freely available web‐based tool, PPI spider ( http://mips.helmholtz‐muenchen.de/proj/ppispider ). To support the practical significance of PPI spider, we collected several hundreds of recently published experimental proteomics studies that reported lists of proteins in various biological contexts. We reanalyzed them using PPI spider and demonstrated that in most cases PPI spider could provide statistically significant hypotheses that are helpful for understanding of the protein list.