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Transglutaminases form a large family of intracellular and extracellular enzymes that catalyse the Ca2+-dependent post-translational modification of proteins. Despite significant advances in our understanding of the biological role of most mammalian transglutaminase isoforms, recent findings suggest new scenarios, most notably for the ubiquitous tissue transglutaminase. It is becoming apparent that some transglutaminases, normally expressed at low levels in many tissue types, are activated and/or overexpressed in a variety of diseases, thereby resulting in enhanced concentrations of cross-linked proteins. As applies to all enzymes that exert their metabolic function by modifying the properties of target proteins, the identification and characterization of the modified proteins will cast light on the functions of transglutaminases and their involvement in human diseases. In this paper we review data on the properties of mammalian transglutaminases, particularly as regards their protein substrates and the relevance of transglutaminase-catalysed reactions in physiological and disease conditions. [ABSTRACT FROM AUTHOR]

While it is generally accepted that α‐synuclein oligomers (αSOs) play an important role in neurodegeneration in Parkinson's disease, the basis for their cytotoxicity remains unclear. We have previously shown that docosahexaenoic acid (DHA) stabilizes αSOs against dissociation without compromising their ability to colocalize with glutamatergic synapses of primary hippocampal neurons, suggesting that they bind to synaptic proteins. Here, we develop a proteomic screen for putative αSO binding partners in rat primary neurons using DHA‐stabilized human αSOs as a bait protein. The protocol involved co‐immunoprecipitation in combination with a photoactivatable heterobifunctional sulfo‐LC‐SDA crosslinker which did not compromise neuronal binding and preserved the interaction between the αSOs‐binding partners. We identify in total 29 proteins associated with DHA‐αSO of which eleven are membrane proteins, including synaptobrevin‐2B (VAMP‐2B), the sodium–potassium pump (Na+/K+ ATPase), the V‐type ATPase, the voltage‐dependent anion channel and calcium‐/calmodulin‐dependent protein kinase type II subunit gamma; only these five hits were also found in previous studies which used unmodified αSOs as bait. We also identified Rab‐3A as a target with likely disease relevance. Three out of four selected hits were subsequently validated with dot‐blot binding assays. In addition, likely binding sites on these ligands were identified by computational analysis, highlighting a diversity of possible interactions between αSOs and target proteins. These results constitute an important step in the search for disease‐modifying treatments targeting toxic αSOs. [ABSTRACT FROM AUTHOR]

Hybrid cluster proteins (HCPs) are metalloproteins characterized by the presence of an iron‐sulfur‐oxygen cluster. These proteins occur in all three domains of life. In eukaryotes, HCPs have so far been found only in a few anaerobic parasites and photosynthetic microalgae. With respect to all species harboring an HCP, the green microalga Chlamydomonas reinhardtii stands out by the presence of four HCP genes. The study of the gene and protein structures as well as the phylogenetic analyses strongly support a model in which the HCP family in the alga has emerged from a single gene of alpha proteobacterial origin and then expanded by several rounds of duplications. The spectra and redox properties of HCP1 and HCP3, produced heterologously in Escherichia coli, were analyzed by electron paramagnetic resonance spectroscopy on redox‐titrated samples. Both proteins contain a [4Fe‐4S]‐cluster as well as a [4Fe‐2O‐2S]‐hybrid cluster with paramagnetic properties related to those of HCPs from Desulfovibrio species. Immunoblotting experiments combined with mass spectrometry‐based proteomics showed that both nitrate and darkness contribute to the strong upregulation of the HCP levels in C. reinhardtii growing under oxic conditions. The link to the nitrate metabolism is discussed in the light of recent data on the potential role of HCP in S‐nitrosylation in bacteria. [ABSTRACT FROM AUTHOR]

For the characterization of protein sequences and post-translational modifications by MS, the ‘top-down’ proteomics approach utilizes molecular and fragment ion mass data obtained by ionizing and dissociating a protein in the mass spectrometer. This requires more complex instrumentation and methodology than the far more widely used ‘bottom-up’ approach, which instead uses such data of peptides from the protein's digestion, but the top-down data are far more specific. The ESI MS spectrum of a 14 protein mixture provides full separation of its molecular ions for MS/MS dissociation of the individual components. False-positive rates for the identification of proteins are far lower with the top-down approach, and quantitation of multiply modified isomers is more efficient. Bottom-up proteolysis destroys the information on the size of the protein and the connectivities of the peptide fragments, but it has no size limit for protein digestion. In contrast, the top-down approach has a ∼ 500 residue, ∼ 50 kDa limitation for the extensive molecular ion dissociation required. Basic studies indicate that this molecular ion intractability arises from greatly strengthened electrostatic interactions, such as hydrogen bonding, in the gas-phase molecular ions. This limit is now greatly extended by variable thermal and collisional activation just after electrospray (‘prefolding dissociation’). This process can cleave 287 inter-residue bonds in the termini of a 1314 residue (144 kDa) protein, specify previously unidentified disulfide bonds between eight of 27 cysteines in a 1714 residue (200 kDa) protein, and correct sequence predictions in two proteins, one of 2153 residues (229 kDa). [ABSTRACT FROM AUTHOR]

The two-hybrid method detects the interaction of two proteins by their ability to reconstitute the activity of a split transcription factor, thus allowing the use of a simple growth selection in yeast to identify new interactions. Since its introduction about 15 years ago, the assay largely has been applied to single proteins, successfully uncovering thousands of novel protein partners. In the last few years, however, two-hybrid experiments have been scaled up to focus on the entire complement of proteins found in an organism. Although a single such effort can itself result in thousands of interactions, the validity of these high-throughput approaches has been questioned as a result of the prevalence of numerous false positives in these large data sets. Such artifacts may not be an obstacle to continued scale-up of the method, because the classification of true and false positives has proven to be a computational challenge that can be met by a growing number of creative strategies. Two examples are provided of this combination of high-throughput experimentation and computational analysis, focused on the interaction of Plasmodium falciparum proteins and of Saccharomyces cerevisiae membrane proteins. [ABSTRACT FROM AUTHOR]

New technologies enable acquisition of large data-sets containing genomic, proteomic and metabolic information that describe the state of a cell. These data-sets call for systematic methods enabling relevant information about the inner workings of the cell to be extracted. One important issue at hand is the understanding of the functional interactions between genes, proteins and metabolites. We here present a method for identifying the dynamic interactions between biochemical components within the cell, in the vicinity of a steady-state. Key features of the proposed method are that it can deal with data obtained under perturbations of any system parameter, not only concentrations of specific components, and that the direct effect of the perturbations does not need to be known. This is important as concentration perturbations are often difficult to perform in biochemical systems and the specific effects of general type perturbations are usually highly uncertain, or unknown. The basis of the method is a linear least-squares estimation, using time-series measurements of concentrations and expression profiles, in which system states and parameter perturbations are estimated simultaneously. An important side-effect of also employing estimation of the parameter perturbations is that knowledge of the system's steady-state concentrations, or activities, is not required and that deviations from steady-state prior to the perturbation can be dealt with. Time derivatives are computed using a zero-order hold discretization, shown to yield significant improvements over the widely used Euler approximation. We also show how network interactions with dynamics that are too fast to be captured within the available sampling time can be determined and excluded from the network identification. Known and unknown moiety conservation relationships can be processed in the same manner. The method requires that the number of samples equals at least the number of network components and, hence, is at present restricted to relatively small-scale networks. We demonstrate herein the performance of the method on two small-scalein silicogenetic networks. [ABSTRACT FROM AUTHOR]