Molecular Mechanisms of Glutaredoxin Enzymes: Versatile Hubs for Thiol–Disulfide Exchange between Protein Thiols and Glutathione

The tripeptide glutathione (GSH) and its oxidized form glutathione disulfide (GSSG) constitute a key redox couple in cells. In particular, they partner protein thiols in reversible thiol–disulfide exchange reactions that act as switches in cell signaling and redox homeostasis. Disruption of these processes may impair cellular redox signal transduction and induce redox misbalances that are linked directly to aging processes and to a range of pathological conditions including cancer, cardiovascular diseases and neurological disorders. Glutaredoxins are a class of GSH-dependent oxidoreductase enzymes that specifically catalyze reversible thiol–disulfide exchange reactions between protein thiols and the abundant thiol pool GSSG/GSH. They protect protein thiols from irreversible oxidation, regulate their activities under a variety of cellular conditions and are key players in cell signaling and redox homeostasis. On the other hand, they may also function as metal-binding proteins with a possible role in the cellular homeostasis and metabolism of essential metals copper and iron. However, the molecular basis and underlying mechanisms of glutaredoxin action remain elusive in many situations. This review focuses specifically on these aspects in the context of recent developments that illuminate some of these uncertainties.     

Intra- and Extra-cellular Thiol–Disulfide Homeostasis in Blood of Patients with Alcohol Use Disorder

Journal of Evolutionary Biochemistry and Physiology - Alcohol use disorder (AUD) is the loss of a person's quality of life due to increased alcohol consumption and the failure of alcohol...     

Characterization of Protein–Protein Interfaces

We analyze the characteristics of protein–protein interfaces using the largest datasets available from the Protein Data Bank (PDB). We start with a comparison of interfaces with protein cores and non-interface surfaces. The results show that interfaces differ from protein cores and non-interface surfaces in residue composition, sequence entropy, and secondary structure. Since interfaces, protein cores, and non-interface surfaces have different solvent accessibilities, it is important to investigate whether the observed differences are due to the differences in solvent accessibility or differences in functionality. We separate out the effect of solvent accessibility by comparing interfaces with a set of residues having the same solvent accessibility as the interfaces. This strategy reveals residue distribution propensities that are not observable by comparing interfaces with protein cores and non-interface surfaces. Our conclusions are that there are larger numbers of hydrophobic residues, particularly aromatic residues, in interfaces, and the interactions apparently favored in interfaces include the opposite charge pairs and hydrophobic pairs. Surprisingly, Pro-Trp pairs are over represented in interfaces, presumably because of favorable geometries. The analysis is repeated using three datasets having different constraints on sequence similarity and structure quality. Consistent results are obtained across these datasets. We have also investigated separately the characteristics of heteromeric interfaces and homomeric interfaces.     

Computational Prediction of Protein–Protein Interactions

Recently a number of computational approaches have been developed for the prediction of protein–protein interactions. Complete genome sequencing projects have provided the vast amount of information needed for these analyses. These methods utilize the structural, genomic, and biological context of proteins and genes in complete genomes to predict protein interaction networks and functional linkages between proteins. Given that experimental techniques remain expensive, time-consuming, and labor-intensive, these methods represent an important advance in proteomics. Some of these approaches utilize sequence data alone to predict interactions, while others combine multiple computational and experimental datasets to accurately build protein interaction maps for complete genomes. These methods represent a complementary approach to current high-throughput projects whose aim is to delineate protein interaction maps in complete genomes. We will describe a number of computational protocols for protein interaction prediction based on the structural, genomic, and biological context of proteins in complete genomes, and detail methods for protein interaction network visualization and analysis.     

Physicochemical Properties and Biological Activity of the Peptide–Protein Complex from Bovine Scleral Tissue

The peptide–protein complex from bovine sclera was studied. It is shown that it contained a protein with a molecular weight of 66387 Da with the partial N-terminal amino acid sequence DTHKSEIAHRFKDLG-, which is homologous to the mature molecule of bovine serum albumin, and polypeptides with molecular weights of 1300–5080 Da. With a model of the organotypic cultivation of posterior eye tissues of the newt Pleurodeles waltl in vitro, it was shown that the effect of this peptide–protein complex in low doses increased the viability of scleral fibroblasts.     

A Critical Assessment of Information-guided Protein–Protein Docking Predictions

The structures of protein complexes are increasingly predicted via protein–protein docking (PPD) using ambiguous interaction data to help guide the docking. These data often are incomplete and contain errors and therefore could lead to incorrect docking predictions. In this study, we performed a series of PPD simulations to examine the effects of incompletely and incorrectly assigned interface residues on the success rate of PPD predictions. The results for a widely used PPD benchmark dataset obtained using a new interface information-driven PPD (IPPD) method developed in this work showed that the success rate for an acceptable top-ranked model varied, depending on the information content used, from as high as 95% when contact relationships (though not contact distances) were known for all residues to 78% when only the interface/non-interface state of the residues was known. However, the success rates decreased rapidly to ∼40% when the interface/non-interface state of 20% of the residues was assigned incorrectly, and to less than 5% for a 40% incorrect assignment. Comparisons with results obtained by re-ranking a global search and with those reported for other data-guided PPD methods showed that, in general, IPPD performed better than re-ranking when the information used was more complete and more accurate, but worse when it was not, and that when using bioinformatics-predicted information on interface residues, IPPD and other data-guided PPD methods performed poorly, at a level similar to simulations with a 40% incorrect assignment. These results provide guidelines for using information about interface residues to improve PPD predictions and reveal a bottleneck for such improvement imposed by the low accuracy of current bioinformatic interface residue predictions.Proteins work in close association with other proteins to mediate the intricate functions of a cell. The atomic resolution of the structure of a protein complex can therefore help one understand a protein''s function in detail. Protein–protein docking (PPD),¹ a computational approach that complements experimental structure determinations, has attracted increasing research interest (1, 2), in part because it remains challenging to determine most structures of protein complexes via experimental techniques (3).To improve the performance of PPD predictions, experimentally derived data (e.g. distances) and information (e.g. the identity of interface residues) have been used either as a filter allowing less plausible docking solutions to be disregarded (4–9) or as a constraint to guide the docking process (10, 11). Various types of data and information have been used to aid PPD (12); these range from distances between, or the relative orientation of, the two interacting proteins to simple identification of the amino acid residues directly involved in the binding of the two proteins (13). Despite considerable success, the caveat for all these data-guided PPD predictions is that the data or information used must be correct in order to avoid spurious results caused by misguiding (12). It is therefore pertinent and important to evaluate the effects of errors in the incorporated data or information on the quality of PPD solutions.We have recently shown that the use of just a few distance constraints can improve the success rates of PPD such that they rival, or are even better than, those of a global search ranked using a sophisticated energy function, and that errors in the distance data significantly decrease the success rates of prediction (11). However, because distance data for interacting proteins are usually hard to obtain, other types of data or information, even if “ambiguous” (10), are increasingly used in PPD predictions (12, 14). In this study, we investigated the effects of incompletely and incorrectly assigned interface/non-interface residues, a major source of the so-called ambiguous data, on information-guided PPD predictions.As illustrated in Fig. 1, the information content of interface/non-interface residues can be rich enough to reveal the identity of every pair of residues in contact, but not their contact distances, or so poor as to reveal the interface/non-interface state of these residues but not their pairing relationship, for one or both of the two interacting proteins. To determine how these different levels of residue information content can help PPD predictions and the extent to which the use of incorrectly assigned residues degrades prediction success rates, we have developed a new interface information-driven PPD method (IPPD) and carried out a series of PPD simulations on a well-tested benchmark dataset. The results showed that when the information content was rich, excellent predictions (success rates for producing an acceptable top-ranked model > 70%) could be made via IPPD or by re-ranking a global search''s solutions using the same interface information, and that, encouragingly, the success of predictions remained respectable (top-ranked success rates > 15%) when the content was poor. However, when enough of the interface residues were incorrectly assigned, as would be the case when using interface residues predicted by a state-of-the-art bioinformatics method such as CPORT (15), few models ranked first by IPPD or other PPD methods, including HADDOCK (10), a popular ambiguous data-driven PPD method, came close to being acceptable. These results suggest that we can greatly increase the power of PPD predictions for practical applications only if the accuracy of current bioinformatics methods for predicting the interface residues of protein complexes can be significantly improved.Open in a separate windowFig. 1.Contact matrix of two interacting proteins, A and B, and the contact vectors of their residues. In the contact matrix, M_ij = 1 or 0, respectively, denotes contact or a lack of contact between residue i in protein A and residue j in protein B. In the contact vectors, V_Ai = 1 or 0, respectively, when residue Ai has, or does not have, at least one contact with any residue of protein B.     

Nitric Oxide and Protein Disulfide Isomerase Explain the Complexities of Unfolded Protein Response Following Intra-hippocampal Aβ Injection

Several pathways involved in regulation of intracellular protein integrity are known as the protein quality control (PQC) system. Molecular chaperones as the main players are engaged in various aspects of PQC system. According to the importance of these proteins in cell survival, in the present study, we traced endoplasmic reticulum-specific markers and chaperone-mediated autophagy (CMA)-associated factors as two main arms of PQC system in intra-hippocampal amyloid beta (Aβ)-injected rats during 10 days running. Data analysis from Western blot indicated that exposure to Aβ activates immunoglobulin heavy-chain-binding protein (Bip) which is the upstream regulator of unfolded protein responses (UPR). Activation of UPR system eventually led to induction of pro-apoptotic factors like CHOP, calpain, and caspase-12. Moreover, our data revealed that protein disulfide isomerase activity dramatically decreased after Aβ injection, which could be attributed to the increased levels of nitric oxide. Besides, Aβ injection induced levels of 2 members of heat shock proteins (Hsp) 70 and 90. Elevated levels of Hsps family members are accompanied by increased levels of lysosome-associated membrane protein type-2A (Lamp-2A) that are involved in CMA. Despite the reduction in CHOP, calpain, caspase-12, and Lamp-2A protein levels, the levels of molecular chaperones Bip, Hsps70, and 90 increased 10 days after Aβ injection in comparison to the control group. Based on our results, 10 days after Aβ injection, despite the activation of protective chaperones, markers associated with neurotoxicity were still elevated.     

Antiapoptotic Activity of the Cardiovirus Leader Protein,a Viral “Security” Protein

Apoptosis is a common antiviral defensive mechanism that potentially limits viral reproduction and spread. Many viruses possess apoptosis-suppressing tools. Here, we show that the productive infection of HeLa cells with encephalomyocarditis virus (a cardiovirus) was not accompanied by full-fledged apoptosis (although the activation of caspases was detected late in infection) but rather elicited a strong antiapoptotic state, as evidenced by the resistance of infected cells to viral and nonviral apoptosis inducers. The development of the antiapoptotic state appeared to depend on a function(s) of the viral leader (L) protein, since its mutational inactivation resulted in the efflux of cytochrome c from mitochondria, the early activation of caspases, and the appearance of morphological and biochemical signs of apoptosis in a significant proportion of infected cells. Infection with both wild-type and L-deficient viruses induced the fragmentation of mitochondria, which in the former case was not accompanied with cytochrome c efflux. Although the exact nature of the antiapoptotic function(s) of cardioviruses remains obscure, our results suggested that it includes previously undescribed mechanisms operating upstream and possibly downstream of the mitochondrial level, and that L is involved in the control of these mechanisms. We propose that cardiovirus L belongs to a class of viral proteins, dubbed here security proteins, whose roles consist solely, or largely, in counteracting host antidefenses. Unrelated L proteins of other picornaviruses as well as their highly variable 2A proteins also may be security proteins. These proteins appear to be independent acquisitions in the evolution of picornaviruses, implying multiple cases of functional (though not structural) convergence.Cells that are infected with a virus recognize the invader''s presence by their innate immunity machinery and switch on a variety of defensive mechanisms. The infecting virus, on the other hand, may possess tools capable of interfering with host antiviral responses. The outcome of the infection, both in terms of the efficiency of virus growth and the extent of host pathology, depends on the trade-off between these defensive and counterdefensive measures.Cellular innate immunity involves multiple pathways, and one powerful defense is apoptosis, or the programmed self-sacrifice of the infected cell, potentially limiting viral reproduction and spread (10). However, many viruses are able to suppress this defensive mechanism (14, 37). Remarkably, virus-elicited pathology may be specific for a given type of cells and a given virus. Unraveling the interplay between pathways leading to the death or survival of the infected cells is an important task that may provide clues to understanding viral pathogenesis and, possibly, may indicate new directions for searching for antiviral drugs.Picornaviruses are a family of small nonenveloped animal viruses that includes important human and animal pathogens such as polioviruses, rhinoviruses, hepatitis A virus, foot-and-mouth disease viruses, and many others (89). Their genome is represented by a single-stranded 7.2- to 8-kb RNA molecule of positive polarity encoding about a dozen mature proteins (generated by the limited proteolysis of a single polyprotein precursor), nearly all of which are directly involved in the replication of the viral RNA and formation of virions (1).The first picornavirus demonstrated to interact with the host cell apoptotic machinery by both triggering and suppressing the apoptotic response was poliovirus (95). Since then, a wealth of data has been accumulated that shows that the activation of apoptotic pathways is a widespread, though not universal, response to picornavirus infection. Thus, apoptosis-inducing capacity was reported for coxsackieviruses B3, B4, and B5 (22, 54, 82), enteroviruses 70 and 71 (25, 27, 60, 88), human rhinoviruses 1B, 9, 14, and 16 (32, 92, 100), foot-and-mouth disease virus (53, 76), avian encephalomyelitis virus (62, 63), and hepatitis A virus (16, 43) and was the subject of several recent reviews (15, 102). The antiapoptotic activity of picornaviruses was studied predominantly by using poliovirus (3, 8, 13, 72) and coxsackievirus B3 (21, 36, 85).The present study is focused on the interaction of cardioviruses, which are representatives of a genus in the picornavirus family, with the apoptotic machinery of infected cells. Our interest in this topic stemmed from the fact that these viruses, e.g., encephalomyocarditis virus (EMCV) and its strain mengovirus (MV), as well as the less-related Theiler''s murine encephalomyelitis virus (TMEV), while sharing major features of genome organization and reproductive strategy with other family members, encode a unique protein that is not found in other picornaviruses. Indeed, the leader (L) protein, a derivative of the N-terminal portion of the viral polyprotein (55), appears to be a major player in controlling the virus-host interaction. On the one hand, it is devoid of any known enzymatic activity, and L-lacking mutants are viable, at least in certain cultured cells (19, 57, 106). On the other hand, the L protein appears to inhibit host translation (35, 106), suppresses interferon production (46, 83, 98), and impairs nucleocytoplasmic traffic (11, 30, 61, 80, 81). It has been hypothesized that cardiovirus L protein also is involved in the interaction with defensive apoptotic machinery.Previous studies have demonstrated that TMEV infection may induce apoptosis, especially in partially restrictive cells (50, 51). EMCV also exerted a similar effect in certain cell lines (87, 103). The reason(s) underlying variability in the apoptosis-inducing effects of cardioviruses remains unexplained. Here, we demonstrate that the productive cardiovirus infection of susceptible HeLa cells resulted in their cytopathic death, which was not accompanied by clear signs of apoptosis. On the contrary, the infected cells acquired an antiapoptotic state, as evidenced by their failure to develop an apoptotic response to viral and nonviral apoptosis inducers. However, the antiapoptotic state failed to develop in cells infected with a mutant virus with inactivated L, and this mutant instead elicited caspase-dependent apoptosis preceded by cytochrome c efflux. These data suggest that the wild-type (wt) L protein is involved, directly or otherwise, in the control of viral antiapoptotic function(s).     

A Second-generation Protein–Protein Interaction Network of Helicobacter pylori

Helicobacter pylori infections cause gastric ulcers and play a major role in the development of gastric cancer. In 2001, the first protein interactome was published for this species, revealing over 1500 binary protein interactions resulting from 261 yeast two-hybrid screens. Here we roughly double the number of previously published interactions using an ORFeome-based, proteome-wide yeast two-hybrid screening strategy. We identified a total of 1515 protein–protein interactions, of which 1461 are new. The integration of all the interactions reported in H. pylori results in 3004 unique interactions that connect about 70% of its proteome. Excluding interactions of promiscuous proteins we derived from our new data a core network consisting of 908 interactions. We compared our data set to several other bacterial interactomes and experimentally benchmarked the conservation of interactions using 365 protein pairs (interologs) of E. coli of which one third turned out to be conserved in both species.Helicobacter pylori is a Gram-negative, microaerophilic bacterium that colonizes the stomach, an unusual highly acidic niche for microorganisms. In 1983, Warren and Marshall found it to be associated with gastric inflammation and duodenal ulcer disease (1, 2). A chronic infection with H. pylori can lead to development of stomach carcinoma and MALT lymphoma (reviewed in (3)). Hence, the World Health Organization has classified H. pylori as a class I carcinogen (4). It is estimated that half of the world′s population harbors H. pylori but with large variations in the geographical and socioeconomic distribution while causing annually 700,000 deaths worldwide (reviewed in (5)).The pathogenesis of H. pylori has been extensively studied, including the effector CagA, cytotoxin VacA, its adhesins and urease (reviewed in (3, 5–7)). The latter allows the bacterium to neutralize the stomach acid through ammonia production. However, H. pylori is not a classical model organism and thus many gaps in our knowledge still exist.The genome of H. pylori reference strain 26695 was completely sequenced in 1997 (8) and encodes 1587 proteins of which about 950 (61%) have been assigned functions (excluding “putatives”; Uniprot, CMR (9)). These numbers indicate that a large fraction of the proteins of H. pylori has not been functionally characterized.Protein–protein interactions (PPIs)¹ are required for nearly all biological processes. Unbiased interactomes are helpful to understand proteins or pathways and how they are linking poorly or uncharacterized proteins via their interactions. For instance, our study of the Treponema pallidum interactome (10) has led to the characterization of several previously “unknown” proteins such as YbeB, a ribosomal silencing factor (11), or TP0658, a regulator of flagellar translation and assembly (12, 13). However, only a few other comprehensive bacterial interactome studies have been published to date, including Campylobacter jejuni (14), Synechocystis sp. (15), Mycobacterium tuberculosis (16), Mesorhizobium loti (17), and recently Escherichia coli (18). In addition, partial interactomes are available for Bacillus subtilis (19) and H. pylori (20). Most of them used the yeast two-hybrid (Y2H) screening technology (21) which allows the pairwise detection of PPIs. Furthermore, a few other studies (22–25) systematically identified protein complexes and their compositions in bacteria.In 2001, Rain and colleagues have established a partial interactome of H. pylori, the first published protein interaction network of a bacterium (20). In this study, 261 bait constructs were screened against a random prey pool library resulting in the detection of over 1500 PPIs. Although this network likely represents a small fraction of all PPIs that occur in H. pylori, many downstream studies were motivated by these results (see below).Recent studies have disproved the notion that Y2H data sets are of poor quality (26, 27). Similarly, a high false-negative rate can be avoided by multiple Y2H expression vector systems (28–30) or protein fragments as opposed to full-length constructs (31). The aim of this study was to systematically screen the H. pylori proteome for binary protein interactions using a complementary approach to that of Rain et al. to produce an extended protein–protein interaction map of H. pylori. As a result, we have roughly doubled the number of known binary protein–protein interactions for H. pylori in this study.     

Inhibition of Dopamine Transporter Activity by G Protein βγ Subunits

Uptake through the Dopamine Transporter (DAT) is the primary mechanism of terminating dopamine signaling within the brain, thus playing an essential role in neuronal homeostasis. Deregulation of DAT function has been linked to several neurological and psychiatric disorders including ADHD, schizophrenia, Parkinson’s disease, and drug addiction. Over the last 15 years, several studies have revealed a plethora of mechanisms influencing the activity and cellular distribution of DAT; suggesting that fine-tuning of dopamine homeostasis occurs via an elaborate interplay of multiple pathways. Here, we show for the first time that the βγ subunits of G proteins regulate DAT activity. In heterologous cells and brain tissue, a physical association between Gβγ subunits and DAT was demonstrated by co-immunoprecipitation. Furthermore, in vitro pull-down assays using purified proteins established that this association occurs via a direct interaction between the intracellular carboxy-terminus of DAT and Gβγ. Functional assays performed in the presence of the non-hydrolyzable GTP analog GTP-γ-S, Gβγ subunit overexpression, or the Gβγ activator mSIRK all resulted in rapid inhibition of DAT activity in heterologous systems. Gβγ activation by mSIRK also inhibited dopamine uptake in brain synaptosomes and dopamine clearance from mouse striatum as measured by high-speed chronoamperometry in vivo. Gβγ subunits are intracellular signaling molecules that regulate a multitude of physiological processes through interactions with enzymes and ion channels. Our findings add neurotransmitter transporters to the growing list of molecules regulated by G-proteins and suggest a novel role for Gβγ signaling in the control of dopamine homeostasis.     

Specificity of Intramembrane Protein–Lipid Interactions

Our concept of biological membranes has markedly changed, from the fluid mosaic model to the current model that lipids and proteins have the ability to separate into microdomains, differing in their protein and lipid compositions. Since the breakthrough in crystallizing membrane proteins, the most powerful method to define lipid-binding sites on proteins has been X-ray and electron crystallography. More recently, chemical biology approaches have been developed to analyze protein–lipid interactions. Such methods have the advantage of providing highly specific cellular probes. With the advent of novel tools to study functions of individual lipid species in membranes together with structural analysis and simulations at the atomistic resolution, a growing number of specific protein–lipid complexes are defined and their functions explored. In the present article, we discuss the various modes of intramembrane protein–lipid interactions in cellular membranes, including examples for both annular and nonannular bound lipids. Furthermore, we will discuss possible functional roles of such specific protein–lipid interactions as well as roles of lipids as chaperones in protein folding and transport.Our concept of biological membranes has markedly changed in the last two decades, from the fluid mosaic model (Singer and Nicolson 1972), in which the membrane was thought to be formed by a homogenous lipid fluid phase with proteins embedded, to the current model that lipids and proteins are not homogenously distributed, but have the ability to separate into microdomains, differing in their protein and lipid compositions. A well established example of domains are lipid rafts (see Box 1 for definitions). Raft domains are described as dynamic domain structures enriched in cholesterol, sphingolipids, and membrane proteins (Brown and London 1998; Simons and Ikonen 1997) that have an important role in different cellular processes (Lingwood and Simons 2010). Formation of domains within cellular membranes has been extensively investigated over the past years leading to various models that differ in the primary forces involved in the formation and the recruitment of surrounding membrane components into such domains.

BOX 1.

Definitions

Annular Lipids/Lipid Shell

An annular lipid shell is formed when selected lipid classes or molecular species bind preferentially to the hydrophobic and/or hydrophilic surfaces of a membrane protein. Per definition these lipids show markedly reduced residence times at the protein–lipid interface as compared to bulk lipids.

Bulk Lipids

Lipids within the membrane that diffuse rapidly in the bilayer plane and show a low residence time at the protein–lipid interface following random collisions. Typical diffusion coefficients for bulk lipids in a liquid disordered phase are in the range of D_L = 7×10⁻¹² m²/sec (DOPC) (Filippov et al. 2003).

Hydrophobic Mismatch

A term to describe any deviation from the compatibility of the hydrophobic surface of membrane proteins (their TMDs) to the vertically and laterally encountered hydrophobic surfaces of the lipid bilayer in biological membranes. In the case of a hydrophobic mismatch, the resulting energy penalty may cause the recruitment of a suitable local lipid environment, the deformation of the membrane and/or in conformational changes of the protein to achieve a status of hydrophobic match (for advanced reading, see Killian 1998).

Lateral Pressure Field/Profile of Membranes

Biological membranes can be considered as the “solvent” for membrane proteins that are embedded in them. The lateral pressure profile (Ω(z)) describes the force or pressure that is exerted by the membrane on the matter residing inside it. This pressure is modulated by different extents of lipid–lipid interactions and asymmetries across and within the bilayer, which in turn results in varying lateral pressures that may locally correspond to several hundreds of atmospheres.

Lipid Rafts

Sterol and sphingolipid-dependent microdomains that form a network of lipid–lipid, protein–protein, and protein–lipid interactions; involved in the compartmentalization of processes such as signaling within biological membranes.

Liquid-Disordered Phase (I_d)

A predominantly fluid phase of lipids, characterized by a high degree of mobility (cis-gauche flexibility of acyl chains; lateral diffusion) and a high content of short and/or unsaturated fatty acyl chains.

Liquid-Ordered Phase (I_o)

A liquid crystalline phase (that displays physical properties of both liquids and of solid crystals), characterized by a high degree of acyl chain order (“packing”), a reduced lateral mobility of lipid and protein molecules, and a reduction in the elasticity of the membrane as a result of specific interactions between sterols and phospholipids containing long, saturated acyl chains and/or glycosphingolipids.

Microdomains

Membrane compartments of distinct lipid and protein composition that may modulate the enzymatic functions of membrane proteins.

Molecular Lipid Species

Individual members of a lipid class that differ in their fatty acid composition.

Nonannular Lipids

Lipids that specifically interact with membrane proteins are neither bulk lipids, nor do they belong to the shell/annulus of lipids that surround the membrane protein. These nonannular lipids often reside within membrane protein complexes, in which they may fulfill diverse functions ranging from structural building blocks to allosteric effectors of enzymatic activity (see text). Nonannular lipids bind to distinct hydrophobic sites of membrane proteins or membrane protein complexes.According to one model, membrane domains can form by specific protein–protein interactions (Douglass and Vale 2005). This model is based on single-molecule microscopy experiments. In these studies, single fluorophores were chemically attached to specific proteins, and the dynamics of individual proteins was tracked by monitoring the fluorescent probe. In this kind of set up, a dynamic behavior of lipids is not assessed. Here, proteins involved in signaling processes are trapped within interconnected microdomains created by specific protein–protein interactions, probably involving additional scaffolding proteins. The proteins of such domains can exchange with the surrounding membrane area at individual kinetics, some components are immobile over minutes, and others can diffuse rapidly.Another model emphasizes the importance of lipid–lipid interactions, initiating the formation of subdomains of defined lipid compositions. Transmembrane proteins then can be attracted to such subdomains via various specific interactions with lipids. The resulting lipid–protein complexes then eventually coalesce to form larger lipid–protein assemblies (Anderson and Jacobson 2002).The idea of lipid-dependent domain formation is inherent to the biophysical properties and therefore to the complex lipid composition of cellular membranes that include up to a thousand lipids that vary in structure (van Meer et al. 2008). This wide range of lipid species has been proposed to facilitate the “solvation” of membrane proteins. Taken into account the sum of lipid species present in a cellular membrane, it is important to understand the different interactions and affinities within the bilayer between different lipids. Molecular dynamics simulations have been successfully employed to investigate lipid interactions between different lipid species and found specific interactions of various lipid classes and molecular species (Hofsass et al. 2003; Niemela et al. 2004, 2006, 2009; Pandit et al. 2004; Zaraiskaya and Jeffrey 2005; Bhide et al. 2007). These results are supported and expanded by recent data from our group that suggest a specific order of interactions of sphingomyelin species with cholesterol in membranes (A.M. Ernst, F. Wieland, and B. Brügger, unpubl.). At low cholesterol concentrations, some sphingomyelin species preferentially interact with cholesterol, whereas others prefer their kin. At higher cholesterol concentrations, all sphingomyelin species investigated display an increased affinity for the sterol. These findings open the possibility of differentiated pathways of self-assembly of microdomains, dependent on molecular lipid species.In the present article the various modes of intramembrane protein–lipid interactions in cellular membranes (Fig. 1) will be discussed. This includes possible functional roles of such specific protein–lipid interactions.Open in a separate windowFigure 1.Intramembrane protein–lipid interactions within a cell membrane. (A) Bulk lipids; (B) annular lipids; (C) nonannular lipids/lipid ligands. For details see text.     

Multiple Modes of Protein–Protein Interactions Promote RNP Granule Assembly

Eukaryotic cells are known to contain a wide variety of RNA–protein assemblies, collectively referred to as RNP granules. RNP granules form from a combination of RNA–RNA, protein–RNA, and protein–protein interactions. In addition, RNP granules are enriched in proteins with intrinsically disordered regions (IDRs), which are frequently appended to a well-folded domain of the same protein. This structural organization of RNP granule components allows for a diverse set of protein–protein interactions including traditional structured interactions between well-folded domains, interactions of short linear motifs in IDRs with the surface of well-folded domains, interactions of short motifs within IDRs that weakly interact with related motifs, and weak interactions involving at most transient ordering of IDRs and folded domains with other components. In addition, both well-folded domains and IDRs in granule components frequently interact with RNA and thereby can contribute to RNP granule assembly. We discuss the contribution of these interactions to liquid–liquid phase separation and the possible role of phase separation in the assembly of RNP granules. We expect that these principles also apply to other non-membrane bound organelles and large assemblies in the cell.     

Double Suppression of the Gα Protein Activity by RGS Proteins

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Protein–Protein and Protein–Membrane Associations in the Lignin Pathway

Supramolecular organization of enzymes is proposed to orchestrate metabolic complexity and help channel intermediates in different pathways. Phenylpropanoid metabolism has to direct up to 30% of the carbon fixed by plants to the biosynthesis of lignin precursors. Effective coupling of the enzymes in the pathway thus seems to be required. Subcellular localization, mobility, protein–protein, and protein–membrane interactions of four consecutive enzymes around the main branch point leading to lignin precursors was investigated in leaf tissues of Nicotiana benthamiana and cells of Arabidopsis thaliana. CYP73A5 and CYP98A3, the two Arabidopsis cytochrome P450s (P450s) catalyzing para- and meta-hydroxylations of the phenolic ring of monolignols were found to colocalize in the endoplasmic reticulum (ER) and to form homo- and heteromers. They moved along with the fast remodeling plant ER, but their lateral diffusion on the ER surface was restricted, likely due to association with other ER proteins. The connecting soluble enzyme hydroxycinnamoyltransferase (HCT), was found partially associated with the ER. Both HCT and the 4-coumaroyl-CoA ligase relocalized closer to the membrane upon P450 expression. Fluorescence lifetime imaging microscopy supports P450 colocalization and interaction with the soluble proteins, enhanced by the expression of the partner proteins. Protein relocalization was further enhanced in tissues undergoing wound repair. CYP98A3 was the most effective in driving protein association.     

Computational Prediction of Protein–Protein Interaction Networks: Algo-rithms and Resources

Protein interactions play an important role in the discovery of protein functions and pathways in biological processes. This is especially true in case of the diseases caused by the loss of specific protein-protein interactions in the organism. The accuracy of experimental results in finding protein-protein interactions, however, is rather dubious and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. Computational methods have attracted tremendous attention among biologists because of the ability to predict protein-protein interactions and validate the obtained experimental results. In this study, we have reviewed several computational methods for protein-protein interaction prediction as well as describing major databases, which store both predicted and detected protein-protein interactions, and the tools used for analyzing protein interaction networks and improving protein-protein interaction reliability.     

Human β-Defensin 4 with Non-Native Disulfide Bridges Exhibit Antimicrobial Activity

Human defensins play multiple roles in innate immunity including direct antimicrobial killing and immunomodulatory activity. They have three disulfide bridges which contribute to the stability of three anti-parallel β-strands. The exact role of disulfide bridges and canonical β-structure in the antimicrobial action is not yet fully understood. In this study, we have explored the antimicrobial activity of human β-defensin 4 (HBD4) analogs that differ in the number and connectivity of disulfide bridges. The cysteine framework was similar to the disulfide bridges present in μ-conotoxins, an unrelated class of peptide toxins. All the analogs possessed enhanced antimicrobial potency as compared to native HBD4. Among the analogs, the single disulfide bridged peptide showed maximum potency. However, there were no marked differences in the secondary structure of the analogs. Subtle variations were observed in the localization and membrane interaction of the analogs with bacteria and Candida albicans, suggesting a role for disulfide bridges in modulating their antimicrobial action. All analogs accumulated in the cytosol where they can bind to anionic molecules such as nucleic acids which would affect several cellular processes leading to cell death. Our study strongly suggests that native disulfide bridges or the canonical β-strands in defensins have not evolved for maximal activity but they play important roles in determining their antimicrobial potency.     

Protein Disulfide Isomerase A6 Controls the Decay of IRE1α Signaling via Disulfide-Dependent Association

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