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.     

Degradation of Amyloid �� Protein by Purified Myelin Basic Protein

The progressive accumulation of β-amyloid (Aβ) in senile plaques and in the cerebral vasculature is the hallmark of Alzheimer disease and related disorders. Impaired clearance of Aβ from the brain likely contributes to the prevalent sporadic form of Alzheimer disease. Several major pathways for Aβ clearance include receptor-mediated cellular uptake, blood-brain barrier transport, and direct proteolytic degradation. Myelin basic protein (MBP) is the major structural protein component of myelin and plays a functional role in the formation and maintenance of the myelin sheath. MBP possesses endogenous serine proteinase activity and can undergo autocatalytic cleavage liberating distinct fragments. Recently, we showed that MBP binds Aβ and inhibits Aβ fibril formation (Hoos, M. D., Ahmed, M., Smith, S. O., and Van Nostrand, W. E. (2007) J. Biol. Chem. 282, 9952–9961; Hoos, M. D., Ahmed, M., Smith, S. O., and Van Nostrand, W. E. (2009) Biochemistry 48, 4720–4727). Here we show that Aβ40 and Aβ42 peptides are degraded by purified human brain MBP and recombinant human MBP, but not an MBP fragment that lacks autolytic activity. MBP-mediated Aβ degradation is inhibited by serine proteinase inhibitors. Similarly, Cos-1 cells expressing MBP degrade exogenous Aβ40 and Aβ42. In addition, we demonstrate that purified MBP also degrades assembled fibrillar Aβ in vitro. Mass spectrometry analysis identified distinct degradation products generated from Aβ digestion by MBP. Lastly, we demonstrate in situ that purified MBP can degrade parenchymal amyloid plaques as well as cerebral vascular amyloid that form in brain tissue of Aβ precursor protein transgenic mice. Together, these findings indicate that purified MBP possesses Aβ degrading activity in vitro.The progressive accumulation of β-amyloid (Aβ)³ in senile/neuritic plaques and the cerebral vasculature is the hallmark of Alzheimer disease (AD) and is widely used in the pathological diagnosis of the disease. Aβ is generated by proteolytic cleavage of amyloid precursor protein (APP) by β-secretase and γ-secretase (1, 2). The main species of Aβ are 40 and 42 amino acids in length. Aβ42 is much more amyloidogenic than Aβ40 because of its two additional hydrophobic amino acids at the carboxyl-terminal end of the peptide (3). The Aβ42 peptide is the predominant form in senile plaques, forming a β-sheet structure, which is insoluble and resistant to proteolysis.Although increased production of Aβ has been implicated in the onset of familial forms of AD, it has been hypothesized that the more common sporadic forms of AD may be caused by the impaired clearance of Aβ peptides from the CNS. Several major pathways for Aβ clearance have been proposed including receptor-mediated cellular uptake, blood-brain barrier transport into the circulation, and direct proteolytic degradation (4–6). In the latter case, several proteinases or peptidases have been identified that are capable of degrading Aβ, including neprilysin (7, 8), insulin-degrading enzyme (9), the urokinase/tissue plasminogen activator-plasmin system (10), endothelin-converting enzyme (11), angiotensin-converting enzyme (12), gelatinase A (matrix metalloproteinase-2) (13, 14), gelatinase B (matrix metalloproteinase-9) (15), and acylpeptide hydrolase (16). Each of these enzymes has been shown to cleave Aβ peptides at multiple sites (5). However, only neprilysin, insulin-degrading enzyme, endothelin-converting enzyme, and matrix metalloproteinase-9 have been shown to have a significant role in regulating Aβ levels in the brains of experimental animal models (8, 17, 18).The “classic” myelin basic proteins (MBP) are major structural components of myelin sheaths accounting for 30% of total myelin protein. There are four different major isoforms generated from alternative splicing with molecular masses of 17.3, 18.5, 20.2, and 21.5 kDa. The 18.5-kDa variant, composed of 180 amino acids including 19 Arg and 12 Lys basic residues, is most abundant in mature myelin (19). One of the major functions of MBP is to hold together the cytoplasmic leaflets of myelin membranes to maintain proper compaction of the myelin sheath through the electrostatic interaction between the positive Arg and Lys residues of MBP and the negatively charged phosphate groups of the membrane lipid (20). MBP plays an important role in the pathology of multiple sclerosis, which is an autoimmune disease characterized by demyelination within white matter (21). Recently, it was reported that purified MBP exhibits autocleavage activity, generating distinct peptide fragments (22). In this study, serine 151 was reported as the active site serine residue involved in autocatalysis.In the early stages of AD, appreciable and diffuse myelin breakdown in the white matter is observed (23). Also, in white matter regions there are much fewer fibrillar amyloid deposits than are commonly found in gray matter regions. Recently, our laboratory has shown that MBP strongly interacts with Aβ peptides and prevents their assembly into mature amyloid fibrils (24, 25). Through the course of these studies we observed that upon longer incubations the levels of Aβ peptides were reduced upon treatment with MBP. In light of this observation, coupled with the report that MBP possesses proteolytic activity, we hypothesized that MBP may degrade Aβ peptides. In the present study, we show that purified human brain MBP and recombinantly expressed human MBP can degrade soluble Aβ40 and Aβ42 peptides in vitro. Purified MBP also degraded fibrillar Aβ in vitro. Mass spectrometry analysis identified distinct degradation products generated from soluble and fibrillar Aβ digestion by MBP. Furthermore, purified MBP degraded parenchymal and vascular fibrillar amyloid deposits in situ in the brain tissue of APP transgenic mice. Together, these findings indicate that purified MBP possesses Aβ degrading activity in vitro.     

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.     

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.     

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.     

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.     

Regulators of G Protein Signaling Attenuate the G Protein–mediated Inhibition of N-Type Ca Channels

Regulators of G protein signaling (RGS) proteins bind to the α subunits of certain heterotrimeric G proteins and greatly enhance their rate of GTP hydrolysis, thereby determining the time course of interactions among Gα, Gβγ, and their effectors. Voltage-gated N-type Ca channels mediate neurosecretion, and these Ca channels are powerfully inhibited by G proteins. To determine whether RGS proteins could influence Ca channel function, we recorded the activity of N-type Ca channels coexpressed in human embryonic kidney (HEK293) cells with G protein–coupled muscarinic (m2) receptors and various RGS proteins. Coexpression of full-length RGS3T, RGS3, or RGS8 significantly attenuated the magnitude of receptor-mediated Ca channel inhibition. In control cells expressing α1B, α2, and β3 Ca channel subunits and m2 receptors, carbachol (1 μM) inhibited whole-cell currents by ∼80% compared with only ∼55% inhibition in cells also expressing exogenous RGS protein. A similar effect was produced by expression of the conserved core domain of RGS8. The attenuation of Ca current inhibition resulted primarily from a shift in the steady state dose–response relationship to higher agonist concentrations, with the EC₅₀ for carbachol inhibition being ∼18 nM in control cells vs. ∼150 nM in RGS-expressing cells. The kinetics of Ca channel inhibition were also modified by RGS. Thus, in cells expressing RGS3T, the decay of prepulse facilitation was slower, and recovery of Ca channels from inhibition after agonist removal was faster than in control cells. The effects of RGS proteins on Ca channel modulation can be explained by their ability to act as GTPase-accelerating proteins for some Gα subunits. These results suggest that RGS proteins may play important roles in shaping the magnitude and kinetics of physiological events, such as neurosecretion, that involve G protein–modulated Ca channels.     

Role of Molecular Chaperones in G Protein ��5/Regulator of G Protein Signaling Dimer Assembly and G Protein �¦� Dimer Specificity

The G protein βγ subunit dimer (Gβγ) and the Gβ5/regulator of G protein signaling (RGS) dimer play fundamental roles in propagating and regulating G protein pathways, respectively. How these complexes form dimers when the individual subunits are unstable is a question that has remained unaddressed for many years. In the case of Gβγ, recent studies have shown that phosducin-like protein 1 (PhLP1) works as a co-chaperone with the cytosolic chaperonin complex (CCT) to fold Gβ and mediate its interaction with Gγ. However, it is not known what fraction of the many Gβγ combinations is assembled this way or whether chaperones influence the specificity of Gβγ dimer formation. Moreover, the mechanism of Gβ5-RGS assembly has yet to be assessed experimentally. The current study was undertaken to directly address these issues. The data show that PhLP1 plays a vital role in the assembly of Gγ2 with all four Gβ1–4 subunits and in the assembly of Gβ2 with all twelve Gγ subunits, without affecting the specificity of the Gβγ interactions. The results also show that Gβ5-RGS7 assembly is dependent on CCT and PhLP1, but the apparent mechanism is different from that of Gβγ. PhLP1 seems to stabilize the interaction of Gβ5 with CCT until Gβ5 is folded, after which it is released to allow Gβ5 to interact with RGS7. These findings point to a general role for PhLP1 in the assembly of all Gβγ combinations and suggest a CCT-dependent mechanism for Gβ5-RGS7 assembly that utilizes the co-chaperone activity of PhLP1 in a unique way.Eukaryotic cells utilize receptors coupled to heterotrimeric GTP-binding proteins (G proteins)³ to mediate a vast array of responses ranging from nutrient-induced migration of single-celled organisms to neurotransmitter-regulated neuronal activity in the human brain (1). Ligand binding to a G protein-coupled receptor (GPCR) initiates GTP exchange on the G protein heterotrimer (composed of Gα, Gβ, and Gγ subunits), which in turn causes the release of Gα-GTP from the Gβγ dimer (2–4). Both Gα-GTP and Gβγ propagate and amplify the signal by interacting with effector enzymes and ion channels (1, 5). The duration and amplitude of the signal is dictated by receptor phosphorylation coupled with arrestin binding and internalization (6) and by regulators of G protein signaling (RGS) proteins, which serve as GTPase-activating proteins for the GTP-bound Gα subunit (7, 8). The G protein signaling cycle is reset as the inactive Gα-GDP reassembles with the Gβγ dimer and Gαβγ re-associates with the GPCR (5).To fulfill its essential role in signaling, the G protein heterotrimer must be assembled post-translationally from its nascent polypeptides. Significant progress has been made recently regarding the mechanism by which this process occurs. It has been clear for some time that the Gβγ dimer must assemble first, followed by subsequent association of Gα with Gβγ (9). What has not been clear was how Gβγ assembly would occur given the fact that neither Gβ nor Gγ is structurally stable without the other. An important breakthrough was the finding that phosducin-like protein 1 (PhLP1) functions as a co-chaperone with the chaperonin containing tailless complex polypeptide 1 (CCT) in the folding of nascent Gβ and its association with Gγ (10–15). CCT is an important chaperone that assists in the folding of actin and tubulin and many other cytosolic proteins, including many β propeller proteins like Gβ (16). PhLP1 has been known for some time to interact with Gβγ and was initially believed to inhibit Gβγ function (17). However, several recent studies have demonstrated that PhLP1 and CCT work together in a highly orchestrated manner to form the Gβγ dimer (10–15).Studies on the mechanism of PhLP1-mediated Gβγ assembly have focused on the most common dimer Gβ1γ2 (10, 13, 14), leaving open questions about the role of PhLP1 in the assembly of the other Gβγ combinations. These are important considerations given that humans possess 5 Gβ genes and 12 Gγ genes with some important splice variants (18, 19), resulting in more than 60 possible combinations of Gβγ dimers. Gβ1–4 share between 80 and 90% sequence identity and are broadly expressed (18, 19). Gβ5, the more atypical isoform, shares only ∼53% identity with Gβ1, carries a longer N-terminal domain, and is only expressed in the central nervous system and retina (20). The Gγ protein family is more heterogeneous than the Gβ family. The sequence identity of the 12 Gγ isoforms extends from 10 to 70% (21), and the Gγ family can be separated into 5 subfamilies (21–23). All Gγ proteins carry C-terminal isoprenyl modifications, which contribute to their association with the cell membrane, GPCRs, Gαs, and effectors (9). Subfamily I Gγ isoforms are post-translationally farnesylated, whereas all others are geranylgeranylated (22, 24).There is some inherent selectivity in the assembly of different Gβγ combinations, but in general Gβ1–4 can form dimers with most Gγ subunits (25). The physiological purpose of this large number of Gβγ combinations has intrigued researchers in the field for many years, and a large body of research indicates that GPCRs and effectors couple to a preferred subset of Gβγ combinations based somewhat on specific sequence complementarity, but even more so on cellular expression patterns, subcellular localization, and post-translational modifications (18).In contrast to Gβ1–4, Gβ5 does not interact with Gγ subunits in vivo, but it instead forms irreversible dimers with RGS proteins of the R7 family, which includes RGS proteins 6, 7, 9, and 11 (26). All R7 family proteins contain an N-terminal DEP (disheveled, Egl-10, pleckstrin) domain, a central Gγ-like (GGL) domain, and a C-terminal RGS domain (8, 26). The DEP domain interacts with the membrane anchoring/nuclear shuttling R7-binding protein, and the GGL domain binds to Gβ5 in a manner similar to other Gβγ associations (27, 28). Like Gβγs, Gβ5 and R7 RGS proteins form obligate dimers required for their mutual stability (26). Without their partner, Gβ5 and R7 RGS proteins are rapidly degraded in cells (26, 29). Gβ5-R7 RGS complexes act as important GTPase-accelerating proteins for G_i/oα and G_qα subunits in neuronal cells and some immune cells (26).It has been recently shown that all Gβ isoforms are able to interact with the CCT complex, but to varying degrees (15). Gβ4 and Gβ1 bind CCT better than Gβ2 and Gβ3, whereas Gβ5 binds CCT poorly (15). These results suggest that Gβ1 and Gβ4 might be more dependent on PhLP1 than the other Gβs, given the co-chaperone role of PhLP1 with CCT in Gβ1γ2 assembly. However, another report has indicated that Gγ2 assembly with Gβ1 and Gβ2 is more PhLP1-dependent than with Gβ3 and Gβ4 (30). Thus, it is not clear from current information whether PhLP1 and CCT participate in assembly of all Gβγ combinations or whether they contribute to the specificity of Gβγ dimer formation, nor is it clear whether they or other chaperones are involved in Gβ5-R7 RGS dimer formation. This report was designed to address these issues.     

Protein disorder--a breakthrough invention of evolution?

As an operational definition, we refer to regions in proteins that do not adopt regular three-dimensional structures in isolation, as disordered regions. An antipode to disorder would be 'well-structured' rather than 'ordered'. Here, we argue for the following three hypotheses. Firstly, it is more useful to picture disorder as a distinct phenomenon in structural biology than as an extreme example of protein flexibility. Secondly, there are many very different flavors of protein disorder, nevertheless, it seems advantageous to portray the universe of all possible proteins in terms of two main types: well-structured, disordered. There might be a third type 'other' but we have so far no positive evidence for this. Thirdly, nature uses protein disorder as a tool to adapt to different environments. Protein disorder is evolutionarily conserved and this maintenance of disorder is highly nontrivial. Increasingly integrating protein disorder into the toolbox of a living cell was a crucial step in the evolution from simple bacteria to complex eukaryotes. We need new advanced computational methods to study this new milestone in the advance of protein biology.     

Protein reactivity of natural product-derived γ-butyrolactones

The discovery of novel and unique target-drug pairs for the treatment of human diseases such as cancer and bacterial infections is an urgent goal of chemical and pharmaceutical sciences. Natural products represent an inspiring source of compounds for designing chemical biology methods with applications in target identification and characterization. Inspired by the huge structural diversity of γ-butyrolactones, which constitute up to 10% of all known compounds of natural origin, we extended the "activity-based protein profiling" (ABPP) target identification technology to this promising and so far unexplored natural compound class. We designed and synthesized a comprehensive set of natural product-derived γ-lactones and thiolactones that varied in protein reactivity. Several important bacterial enzymes that are involved in diverse cellular functions such as metabolism (dihydrolipoyl dehydrogenase and 6-phosphofructokinase), cell wall biosynthesis (MurA1 and MurA2), and protein folding (trigger factors) were obtained. Especially protein folding in bacteria could represent a novel strategy for antibiotic intervention and requires chemical tools for characterization and inhibition. Future studies that extend structural modifications to protein reactive α-methylene-γ-butyrolactone as well as to reversible binding γ-lactones and thiolactones will reveal if this premise holds true.     

Dynamic Circadian Protein–Protein Interaction Networks Predict Temporal Organization of Cellular Functions

Essentially all biological processes depend on protein–protein interactions (PPIs). Timing of such interactions is crucial for regulatory function. Although circadian (∼24-hour) clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression) suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc.) contributing to temporal organization of cellular physiology in an unprecedented manner.     

Regulation of Oxysterol-binding Protein Golgi Localization through Protein Kinase D–mediated Phosphorylation

Protein kinase D (PKD) plays a critical role at the trans-Golgi network by regulating the fission of transport carriers destined for the plasma membrane. Two known Golgi-localized PKD substrates, PI4-kinase IIIβ and the ceramide transfer protein CERT, mediate PKD signaling to influence vesicle trafficking to the plasma membrane and sphingomyelin synthesis, respectively. PKD is recruited and activated at the Golgi through interaction with diacylglycerol, a pool of which is generated as a by-product of sphingomyelin synthesis from ceramide. Here we identify a novel substrate of PKD at the Golgi, the oxysterol-binding protein OSBP. Using a substrate-directed phospho-specific antibody that recognizes the optimal PKD consensus motif, we show that PKD phosphorylates OSBP at Ser240 in vitro and in cells. We further show that OSBP phosphorylation occurs at the Golgi. Phosphorylation of OSBP by PKD does not modulate dimerization, sterol binding, or affinity for PI(4)P. Instead, phosphorylation attenuates OSBP Golgi localization in response to 25-hydroxycholesterol and cholesterol depletion, impairs CERT Golgi localization, and promotes Golgi fragmentation.     

Identification of G Protein α Subunit-Palmitoylating Enzyme

The heterotrimeric G protein α subunit (Gα) is targeted to the cytoplasmic face of the plasma membrane through reversible lipid palmitoylation and relays signals from G-protein-coupled receptors (GPCRs) to its effectors. By screening 23 DHHC motif (Asp-His-His-Cys) palmitoyl acyl-transferases, we identified DHHC3 and DHHC7 as Gα palmitoylating enzymes. DHHC3 and DHHC7 robustly palmitoylated Gα_q, Gα_s, and Gα_i2 in HEK293T cells. Knockdown of DHHC3 and DHHC7 decreased Gα_q/11 palmitoylation and relocalized it from the plasma membrane into the cytoplasm. Photoconversion analysis revealed that Gα_q rapidly shuttles between the plasma membrane and the Golgi apparatus, where DHHC3 specifically localizes. Fluorescence recovery after photobleaching studies showed that DHHC3 and DHHC7 are necessary for this continuous Gα_q shuttling. Furthermore, DHHC3 and DHHC7 knockdown blocked the α_1A-adrenergic receptor/Gα_q/11-mediated signaling pathway. Together, our findings revealed that DHHC3 and DHHC7 regulate GPCR-mediated signal transduction by controlling Gα localization to the plasma membrane.G-protein-coupled receptors (GPCRs) form the largest family of cell surface receptors, consisting of more than 700 members in humans. GPCRs respond to a variety of extracellular signals, including hormones and neurotransmitters, and are involved in various physiologic processes, such as smooth muscle contraction and synaptic transmission (20, 25). Heterotrimeric G proteins, composed of α, β, and γ subunits, transduce signals from GPCRs to their effectors and play a central role in the GPCR signaling pathway (13, 21, 24, 32). Although the Gα subunit seems to localize stably at the cytosolic face of the plasma membrane (PM), a recent report suggested that Gα_o, a Gα isoform, shuttles rapidly between the PM and intracellular membranes (2). The PM targeting of Gα requires both interaction with the Gβγ complex and subsequent lipid palmitoylation of Gα (22). Thus, palmitoylation of Gα is a critical determinant of membrane targeting of the heterotrimer Gαβγ.Protein palmitoylation is a common posttranslational modification with lipid palmitate and regulates protein trafficking and function (7, 18). Gα is a classic and representative palmitoyl substrate (19, 38), and recent studies revealed that protein palmitoylation modifies virtually almost all the components of G-protein signaling, including GPCRs, Gα subunits, several members of the RGS (regulators of G-protein signaling) family of GTPase-activating proteins, GPCR kinase GRK6, and some small GTPases (7, 33). This common lipid modification plays an important role in compartmentalizing G-protein signaling to the specific microdomain, such as membrane caveolae and lipid raft (26). The palmitoyl thioester bond is relatively labile, and palmitates on substrates turn over rapidly, allowing proteins to shuttle between the cytoplasm/intracellular organelles and the PM (2, 3, 27). For example, binding of isoproterenol to the β-adrenergic receptor markedly accelerates the depalmitoylation of the associated Gα_s, shifting Gα_s to the cytoplasm (37). This receptor activation-induced depalmitoylation was also observed in a major postsynaptic PSD-95 scaffold, which anchors the AMPA (alpha-amino-3-hydroxy-5-methyl-isoxazole-4-propionic acid)-type glutamate receptor at the excitatory postsynapse through stargazin (6). On glutamate receptor activation, accelerated depalmitoylation of PSD-95 dissociates PSD-95 from postsynaptic sites and causes AMPA receptor endocytosis (6). Thus, palmitate turnover on Gα_s and PSD-95 is accelerated by receptor activation, contributing to downregulation of the signaling pathway. However, the enzymes that add palmitate to proteins (palmitoyl-acyl transferases [PATs]) and those that cleave the thioester bond (palmitoyl-protein thioesterases) were long elusive.Recent genetic studies in Saccharomyces cerevisiae identified Erf2/Erf4 (1, 40) and Akr1 (29) as PATs for yeast Ras and yeast casein kinase 2, respectively. Erf2 and Akr1 have four- to six-pass transmembrane domains and share a common domain, referred to as a DHHC domain, a cysteine-rich domain with a conserved Asp-His-His-Cys signature motif. Because the DHHC domain is essential for the PAT activity, we isolated 23 mammalian DHHC domain-containing proteins (DHHC proteins) and developed a systematic screening method to identify the specific enzyme-substrate pairs (11, 12): DHHC2, -3, -7, and -15 for PSD-95 (11); DHHC21 for endothelial NO synthase (10); and DHHC3 and -7 for GABA_A receptor γ2 subunit (9). Several other groups also reported that DHHC9 with GCP16 mediates palmitoylation toward H- and N-Ras (36) and that DHHC17, also known as HIP14, palmitoylates several neuronal proteins: huntingtin (14), SNAP-25, and CSP (14, 23, 35). However, the existence of PATs for Gα has been controversial because spontaneous palmitoylation of Gα could occur in vitro (4).In this study, we screened the 23 DHHC clones to examine which DHHC proteins can palmitoylate Gα. We found that DHHC3 and -7 specifically and robustly palmitoylate Gα at the Golgi apparatus. Inhibition of DHHC3 and -7 reduces Gα_q/11 palmitoylation levels and delocalizes it from the PM to the cytoplasm in HeLa cells and primary hippocampal neurons. Also, DHHC3 and -7 are necessary for the continuous Gα_q shuttling between the Golgi apparatus and the PM. Finally, blocking DHHC3 and -7 inhibits the α_1A-adrenergic receptor [α_1A-AR]/Gα_q-mediated signaling pathway, indicating that DHHC3 and -7 play an essential role in GPCR signaling by regulating Gα localization.     

Protein dynamics of a β-sheet protein

Rhodnius prolixus Nitrophorin 4 (abbreviated NP4) is an almost pure β-sheet heme protein. Its dynamics is investigated by X-ray structure determination at eight different temperatures from 122 to 304 K and by means of Mössbauer spectroscopy. A comparison of this β-sheet protein with the pure α-helical protein myoglobin (abbreviated Mbmet) is performed. The mean square displacement derived from the Mössbauer spectra increases linearly with temperature below a characteristic temperature T _c. It is about 10 K larger than that of myoglobin. Above T _c the mean square displacements increase dramatically. The Mössbauer spectra are analyzed by a two state model. The increased mean square displacements are caused by very slow motions occurring on a time scale faster than 140 ns. With respect to these motions NP4 shows the same protein specific modes as Mbmet. There is, however, a difference in the fast vibration regime. The B values found in the X-ray structures vary linearly over the entire temperature range. The mean square displacements in NP4 increase with slopes which are 60% larger than those observed for Mbmet. This indicates that nitrophorin has a larger structural distribution which makes it more flexible than myoglobin.     

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.