Integrating Cross-Linking Experiments with Ab Initio Protein–Protein Docking

Ab initio protein–protein docking algorithms often rely on experimental data to identify the most likely complex structure. We integrated protein–protein docking with the experimental data of chemical cross-linking followed by mass spectrometry. We tested our approach using 19 cases that resulted from an exhaustive search of the Protein Data Bank for protein complexes with cross-links identified in our experiments. We implemented cross-links as constraints based on Euclidean distance or void-volume distance. For most test cases, the rank of the top-scoring near-native prediction was improved by at least twofold compared with docking without the cross-link information, and the success rate for the top 5 predictions nearly tripled. Our results demonstrate the delicate balance between retaining correct predictions and eliminating false positives. Several test cases had multiple components with distinct interfaces, and we present an approach for assigning cross-links to the interfaces. Employing the symmetry information for these cases further improved the performance of complex structure prediction.     

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.     

Predictive QM/MM Modeling of Modulations in Protein–Protein Binding by Lysine Methylation

Lysine methylation is a key regulator of protein–protein binding. The amine group of lysine can accept up to three methyl groups, and experiments show that protein–protein binding free energies are sensitive to the extent of methylation. These sensitivities have been rationalized in terms of chemical and structural features present in the binding pockets of methyllysine binding domains. However, understanding their specific roles requires an energetic analysis. Here we propose a theoretical framework to combine quantum and molecular mechanics methods, and compute the effect of methylation on protein–protein binding free energies. The advantages of this approach are that it derives contributions from all local non-trivial effects of methylation on induction, polarizability and dispersion directly from self-consistent electron densities, and at the same time determines contributions from well-characterized hydration effects using a computationally efficient classical mean field method. Limitations of the approach are discussed, and we note that predicted free energies of fourteen out of the sixteen cases agree with experiment. Critical assessment of these cases leads to the following overarching principles that drive methylation-state recognition by protein domains. Methylation typically reduces the pairwise interaction between proteins. This biases binding toward lower methylated states. Simultaneously, however, methylation also makes it easier to partially dehydrate proteins and place them in protein–protein complexes. This latter effect biases binding in favor of higher methylated states. The overall effect of methylation on protein–protein binding depends ultimately on the balance between these two effects, which is observed to be tuned via several combinations of local features.     

Predicting Protein–Protein Interfaces that Bind Intrinsically Disordered Protein Regions

A long-standing goal in biology is the complete annotation of function and structure on all protein–protein interactions, a large fraction of which is mediated by intrinsically disordered protein regions (IDRs). However, knowledge derived from experimental structures of such protein complexes is disproportionately small due, in part, to challenges in studying interactions of IDRs. Here, we introduce IDRBind, a computational method that by combining gradient boosted trees and conditional random field models predicts binding sites of IDRs with performance approaching state-of-the-art globular interface predictions, making it suitable for proteome-wide applications. Although designed and trained with a focus on molecular recognition features, which are long interaction-mediating-elements in IDRs, IDRBind also predicts the binding sites of short peptides more accurately than existing specialized predictors. Consistent with IDRBind's specificity, a comparison of protein interface categories uncovered uniform trends in multiple physicochemical properties, positioning molecular recognition feature interfaces between peptide and globular interfaces.     

Calmodulin-dependent Protein Kinase II/cAMP Response Element-binding Protein/Wnt/β-Catenin Signaling Cascade Regulates Angiotensin II-induced Podocyte Injury and Albuminuria

Angiotensin II (Ang II) plays a pivotal role in promoting podocyte dysfunction and albuminuria, however, the underlying mechanisms have not been fully delineated. In this study, we found that Ang II induced Wnt1 expression and β-catenin nuclear translocation in cultured mouse podocytes. Blocking Wnt signaling with Dickkopf-1 (Dkk1) or β-catenin siRNA attenuated Ang II-induced podocyte injury. Ang II could also induce the phosphorylation of calmodulin-dependent protein kinase (CaMK) II and cAMP response element-binding protein (CREB) in cultured podocytes. Blockade of this pathway with CK59 or CREB siRNA could significantly inhibit Ang II-induced Wnt/β-catenin signaling and podocyte injury. In in vivo studies, administration of Ang II promoted Wnt/β-catenin signaling, aggregated podocyte damage, and albuminuria in mice. CK59 could remarkably ameliorate Ang II-induced podocyte injury and albuminuria. Furthermore, ectopic expression of exogenous Dkk1 also attenuated Ang II-induced podocytopathy in mice. Taken together, this study demonstrates that the CaMK II/CREB/Wnt/β-catenin signaling cascade plays an important role in regulating Ang II-induced podocytopathy. Targeting this signaling pathway may offer renal protection against the development of proteinuric kidney diseases.     

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 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.     

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–Protein Interactions in DNA Base Excision Repair

The system of base excision repair (BER) ensures correction of the most abundant DNA damages in mammalian cells and plays an important role in maintaining genome stability. Enzymes and protein factors participate in the multistage BER in a coordinated fashion, which ensures repair efficiency. The suggested coordination mechanisms are based on formation of protein complexes stabilized via either direct or indirect DNA-mediated interactions. The results of investigation of direct interactions of the proteins participating in BER with each other and with other proteins are outlined in this review. The known protein partners and sites responsible for their interaction are presented for the main participants as well as quantitative characteristics of their affinity. Information on the mechanisms of regulation of protein–protein interactions mediated by DNA intermediates and posttranslational modification is presented. It can be suggested based on all available data that the multiprotein complexes are formed on chromatin independent of the DNA damage with the help of key regulators of the BER process – scaffold protein XRCC1 and poly(ADP-ribose) polymerase 1. The composition of multiprotein complexes changes dynamically depending on the DNA damage and the stage of BER process.     

α/β-Hydrolase Domain Containing Protein 15 (ABHD15) – an Adipogenic Protein Protecting from Apoptosis

Our knowledge about adipocyte metabolism and development is steadily growing, yet many players are still undefined. Here, we show that α/β-hydrolase domain containing protein 15 (Abhd15) is a direct and functional target gene of peroxisome proliferator-activated receptor gamma (PPARγ), the master regulator of adipogenesis. In line, Abhd15 is mainly expressed in brown and white adipose tissue and strongly upregulated during adipogenesis in various murine and human cell lines. Stable knockdown of Abhd15 in 3T3-L1 cells evokes a striking differentiation defect, as evidenced by low lipid accumulation and decreased expression of adipocyte marker genes. In preconfluent cells, knockdown of Abhd15 leads to impaired proliferation, which is caused by apoptosis, as we see an increased SubG1 peak, caspase 3/7 activity, and BAX protein expression as well as a reduction in anti-apoptotic BCL-2 protein. Furthermore, apoptosis-inducing amounts of palmitic acid evoke a massive increase of Abhd15 expression, proposing an apoptosis-protecting role for ABHD15. On the other hand, in mature adipocytes physiological (i.e. non-apoptotic) concentrations of palmitic acid down-regulate Abhd15 expression. Accordingly, we found that the expression of Abhd15 in adipose tissue is reduced in physiological situations with high free fatty acid levels, like high-fat diet, fasting, and aging as well as in genetically obese mice. Collectively, our results position ABHD15 as an essential component in the development of adipocytes as well as in apoptosis, thereby connecting two substantial factors in the regulation of adipocyte number and size. Together with its intricate regulation by free fatty acids, ABHD15 might be an intriguing new target in obesity and diabetes research.     

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.     

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.     

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.     

Regulation of CCAAT/Enhancer-binding Protein Homologous Protein (CHOP) Expression by Interleukin-1β in Pancreatic β Cells

Apoptosis contributes to immune-mediated pancreatic β cell destruction in type I diabetes. Exposure of β cells to interleukin-1β (IL-1β) causes endoplasmic reticulum stress and activates proapoptotic networks. Here, we show that nuclear factor κB (NF-κB) and mitogen-activated protein kinase (MAPK) signaling pathways regulate the expression of CCAAT/enhancer-binding protein homologous protein (CHOP), which mediates endoplasmic reticulum stress-induced apoptosis. Both CHOP mRNA and protein increase in β cells treated with IL-1β. In addition, prolonged exposure to high glucose further increases IL-1β-triggered CHOP expression. IL-1β also causes increased expression of C/EBP-β and a reduction of MafA, NFATc2, and Pdx-1 expression in β cells. Inhibition of the NF-κB and MAPK signaling pathways differentially attenuates CHOP expression. Knocking down CHOP by RNA interference protects β cells from IL-1β-induced apoptosis. These studies provide direct mechanistic links between cytokine-induced signaling pathways and CHOP-mediated apoptosis of β cells.     

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.     

Protein–Protein Docking: Past,Present, and Future

The Protein Journal - The biological significance of proteins attracted the scientific community in exploring their characteristics. The studies shed light on the interaction patterns and functions...