Sequence Based Prediction of DNA-Binding Proteins Based on Hybrid Feature Selection Using Random Forest and Gaussian Na?ve Bayes

Developing an efficient method for determination of the DNA-binding proteins, due to their vital roles in gene regulation, is becoming highly desired since it would be invaluable to advance our understanding of protein functions. In this study, we proposed a new method for the prediction of the DNA-binding proteins, by performing the feature rank using random forest and the wrapper-based feature selection using forward best-first search strategy. The features comprise information from primary sequence, predicted secondary structure, predicted relative solvent accessibility, and position specific scoring matrix. The proposed method, called DBPPred, used Gaussian naïve Bayes as the underlying classifier since it outperformed five other classifiers, including decision tree, logistic regression, k-nearest neighbor, support vector machine with polynomial kernel, and support vector machine with radial basis function. As a result, the proposed DBPPred yields the highest average accuracy of 0.791 and average MCC of 0.583 according to the five-fold cross validation with ten runs on the training benchmark dataset PDB594. Subsequently, blind tests on the independent dataset PDB186 by the proposed model trained on the entire PDB594 dataset and by other five existing methods (including iDNA-Prot, DNA-Prot, DNAbinder, DNABIND and DBD-Threader) were performed, resulting in that the proposed DBPPred yielded the highest accuracy of 0.769, MCC of 0.538, and AUC of 0.790. The independent tests performed by the proposed DBPPred on completely a large non-DNA binding protein dataset and two RNA binding protein datasets also showed improved or comparable quality when compared with the relevant prediction methods. Moreover, we observed that majority of the selected features by the proposed method are statistically significantly different between the mean feature values of the DNA-binding and the non DNA-binding proteins. All of the experimental results indicate that the proposed DBPPred can be an alternative perspective predictor for large-scale determination of DNA-binding proteins.     

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.     

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.     

Selection of Protein–Protein Interactions of Desired Affinities with a Bandpass Circuit

We have developed a genetic circuit in Escherichia coli that can be used to select for protein–protein interactions of different strengths by changing antibiotic concentrations in the media. The genetic circuit links protein–protein interaction strength to β-lactamase activity while simultaneously imposing tuneable positive and negative selection pressure for β-lactamase activity. Cells only survive if they express interacting proteins with affinities that fall within set high- and low-pass thresholds; i.e. the circuit therefore acts as a bandpass filter for protein–protein interactions. We show that the circuit can be used to recover protein–protein interactions of desired affinity from a mixed population with a range of affinities. The circuit can also be used to select for inhibitors of protein–protein interactions of defined strength.     

Prediction of Protein–Protein Interaction Sites in Sequences and 3D Structures by Random Forests

Identifying interaction sites in proteins provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Although there are numerous papers on the prediction of interaction sites using information derived from structure, there are only a few case reports on the prediction of interaction residues based solely on protein sequence. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using (i) a combination of sequence- and structure-derived parameters and (ii) sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and an F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with an F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modeling the Ras–Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information.     

Relative Binding Affinity Prediction of Charge-Changing Sequence Mutations with FEP in Protein–Protein Interfaces

Building on the substantial progress that has been made in using free energy perturbation (FEP) methods to predict the relative binding affinities of small molecule ligands to proteins, we have previously shown that results of similar quality can be obtained in predicting the effect of mutations on the binding affinity of protein–protein complexes. However, these results were restricted to mutations which did not change the net charge of the side chains due to known difficulties with modeling perturbations involving a change in charge in FEP. Various methods have been proposed to address this problem. Here we apply the co-alchemical water approach to study the efficacy of FEP calculations of charge changing mutations at the protein–protein interface for the antibody–gp120 system investigated previously and three additional complexes. We achieve an overall root mean square error of 1.2 kcal/mol on a set of 106 cases involving a change in net charge selected by a simple suitability filter using side-chain predictions and solvent accessible surface area to be relevant to a biologic optimization project. Reasonable, although less precise, results are also obtained for the 44 more challenging mutations that involve buried residues, which may in some cases require substantial reorganization of the local protein structure, which can extend beyond the scope of a typical FEP simulation. We believe that the proposed prediction protocol will be of sufficient efficiency and accuracy to guide protein engineering projects for which optimization and/or maintenance of a high degree of binding affinity is a key objective.     

Computational Assessment of Protein–protein Binding Affinity by Reversely Engineering the Energetics in Protein Complexes

The cellular functions of proteins are maintained by forming diverse complexes. The stability of these complexes is quantified by the measurement of binding affinity, and mutations that alter the binding affinity can cause various diseases such as cancer and diabetes. As a result, accurate estimation of the binding stability and the effects of mutations on changes of binding affinity is a crucial step to understanding the biological functions of proteins and their dysfunctional consequences. It has been hypothesized that the stability of a protein complex is dependent not only on the residues at its binding interface by pairwise interactions but also on all other remaining residues that do not appear at the binding interface. Here, we computationally reconstruct the binding affinity by decomposing it into the contributions of interfacial residues and other non-interfacial residues in a protein complex. We further assume that the contributions of both interfacial and non-interfacial residues to the binding affinity depend on their local structural environments such as solvent-accessible surfaces and secondary structural types. The weights of all corresponding parameters are optimized by Monte-Carlo simulations. After cross-validation against a large-scale dataset, we show that the model not only shows a strong correlation between the absolute values of the experimental and calculated binding affinities, but can also be an effective approach to predict the relative changes of binding affinity from mutations. Moreover, we have found that the optimized weights of many parameters can capture the first-principle chemical and physical features of molecular recognition, therefore reversely engineering the energetics of protein complexes. These results suggest that our method can serve as a useful addition to current computational approaches for predicting binding affinity and understanding the molecular mechanism of protein–protein interactions.     

Characterization of a Single-Stranded DNA-Binding-Like Protein from Nanoarchaeum equitans—A Nucleic Acid Binding Protein with Broad Substrate Specificity

BackgroundSSB (single-stranded DNA-binding) proteins play an essential role in all living cells and viruses, as they are involved in processes connected with ssDNA metabolism. There has recently been an increasing interest in SSBs, since they can be applied in molecular biology techniques and analytical methods. Nanoarchaeum equitans, the only known representative of Archaea phylum Nanoarchaeota, is a hyperthermophilic, nanosized, obligatory parasite/symbiont of Ignicoccus hospitalis.ResultsThis paper reports on the ssb-like gene cloning, gene expression and characterization of a novel nucleic acid binding protein from Nanoarchaeum equitans archaeon (NeqSSB-like protein). This protein consists of 243 amino acid residues and one OB fold per monomer. It is biologically active as a monomer like as SSBs from some viruses. The NeqSSB-like protein displays a low sequence similarity to the Escherichia coli SSB, namely 10% identity and 29% similarity, and is the most similar to the Sulfolobus solfataricus SSB (14% identity and 32% similarity). The NeqSSB-like protein binds to ssDNA, although it can also bind mRNA and, surprisingly, various dsDNA forms, with no structure-dependent preferences as evidenced by gel mobility shift assays. The size of the ssDNA binding site, which was estimated using fluorescence spectroscopy, is 7±1 nt. No salt-dependent binding mode transition was observed. NeqSSB-like protein probably utilizes a different model for ssDNA binding than the SSB proteins studied so far. This protein is highly thermostable; the half-life of the ssDNA binding activity is 5 min at 100°C and melting temperature (T_m) is 100.2°C as shown by differential scanning calorimetry (DSC) analysis.ConclusionNeqSSB-like protein is a novel highly thermostable protein which possesses a unique broad substrate specificity and is able to bind all types of nucleic acids.     

Mapping Plant Interactomes Using Literature Curated and Predicted Protein–Protein Interaction Data Sets

Most cellular processes are enabled by cohorts of interacting proteins that form dynamic networks within the plant proteome. The study of these networks can provide insight into protein function and provide new avenues for research. This article informs the plant science community of the currently available sources of protein interaction data and discusses how they can be useful to researchers. Using our recently curated IntAct Arabidopsis thaliana protein–protein interaction data set as an example, we discuss potentials and limitations of the plant interactomes generated to date. In addition, we present our efforts to add value to the interaction data by using them to seed a proteome-wide map of predicted protein subcellular locations.For well over two decades, plant scientists have studied protein interactions within plants using many different and evolving approaches. Their findings are represented by a large and growing corpus of peer-reviewed literature reflecting the increasing activity in this area of plant proteomic research. More recently, a number of predicted interactomes have been reported in plants and, while these predictions remain largely untested, they could act as a useful guide for future research. These studies have allowed researchers to better understand the function of protein complexes and to refine our understanding of protein function within the cell (Uhrig, 2006; Morsy et al., 2008). The extraction of protein interaction data from the literature and its standardized deposition and representation within publicly available databases remains a challenging task. Aggregating the data in databases allows researchers to leverage visualization, data mining, and integrative approaches to produce new insights that would be unachievable when the data are dispersed within largely inaccessible formats (Rodriguez et al., 2009).Currently, there are three databases that act as repositories of plant protein interaction data. These are IntAct (http://www.ebi.ac.uk/intact/; Aranda et al., 2010), The Arabidopsis Information Resource (TAIR; http://www.Arabidopsis.org/; Poole, 2007), and BioGRID (http://www.thebiogrid.org/; Breitkreutz et al., 2008). These databases curate experimentally established interactions available from the peer-reviewed literature (as opposed to predicted interactions, which will be discussed below). Each repository takes its own approach to the capture, storage, and representation of protein interaction data. TAIR focuses on Arabidopsis thaliana protein–protein interaction data exclusively; BioGRID currently focuses on the plant species Arabidopsis and rice (Oryza sativa), while IntAct attempts to capture protein interaction data from any plant species. Unlike the other repositories, IntAct follows a deep curation strategy that captures detailed experimental and biophysical details, such as binding regions and subcellular locations of interactions using controlled vocabularies (Aranda et al., 2010). While the majority of plant interaction data held by IntAct concern protein–protein interaction data in Arabidopsis, there is a small but growing content of interaction data relating to protein–DNA, protein–RNA, and protein–small molecule interactions, as well as interaction data from other plant species.Using the IntAct Arabidopsis data set as an example, we outline how the accumulating knowledge captured in these repositories can be used to further our understanding of the plant proteome. We compare the characteristics of predicted interactomes with the IntAct protein–protein interaction data set, which consists entirely of experimentally measured protein interactions, to gauge the predictive accuracy of these studies. Finally, we show how the IntAct data set can be used together with a recently developed Divide and Conquer k-Nearest Neighbors Method (DC-kNN; K. Lee et al., 2008) to predict the subcellular locations for most Arabidopsis proteins. This data set predicts high confidence subcellular locations for many unannotated Arabidopsis proteins and should act as a useful resource for future studies of protein function. Although this article focuses on the IntAct Arabidopsis protein–protein interaction data set, readers are also encouraged to explore the resources offered by our colleagues at TAIR and BioGRID.Each database employs its own system to report molecular interactions, as represented in the referenced source publications, and each avoids making judgments on interaction reliability or whether two participants in a complex have a direct interaction. Thus, the user should carefully filter these data sets for their specific purpose based on the full annotation of the data sets. In particular, the user should consider the experimental methods and independent observation of the same interaction in different publications when assessing the reliability and type of interaction of the proteins (e.g., direct or indirect). Confidence scoring schemes for interaction data are discussed widely in the literature (Yu and Finley, 2009).     

Separation,Characterization, and Specificity of α-Mannosidases from Vigna umbellata

Information about the specificity of glycosidase enzymes is important since it affects their use for characterization and synthesis of oligosaccharides. Two α-mannosidases (EC 3.2.1.24), I and II, were isolated from rice beans (Vigna umbellata). The native molecular weight of both isozymes was estimated to be 329,000, but pIs of form I were 5.03-5.34 and pIs of form II were 5.46-6.20. The two isozymes were characterized in terms of optimal pH and temperature, effects of metal ions, inhibition by swainsonine and 1-deoxymannojirimycin, and kinetic parameters for p-nitrophenyl-α-D-mannopyranoside and Manα(1-2)Man. Both enzymes were more specific towards Manα(1-2)Man in both hydrolysis and synthesis, but their hydrolytic specificities towards Manα(1-3)[Manα(1-6)]Man were different.     

Protein–DNA binding: complexities and multi-protein codes

Binding of proteins to particular DNA sites across the genome is a primary determinant of specificity in genome maintenance and gene regulation. DNA-binding specificity is encoded at multiple levels, from the detailed biophysical interactions between proteins and DNA, to the assembly of multi-protein complexes. At each level, variation in the mechanisms used to achieve specificity has led to difficulties in constructing and applying simple models of DNA binding. We review the complexities in protein–DNA binding found at multiple levels and discuss how they confound the idea of simple recognition codes. We discuss the impact of new high-throughput technologies for the characterization of protein–DNA binding, and how these technologies are uncovering new complexities in protein–DNA recognition. Finally, we review the concept of multi-protein recognition codes in which new DNA-binding specificities are achieved by the assembly of multi-protein complexes.     

Selection of Peptide Inhibitors of Soluble Aβ1-42 Oligomer Formation by Phage Display

There have been many reports suggesting that soluble oligomers of amyloid β (Aβ) are neurotoxins causing Alzheimer’s disease (AD). Although inhibition of the soluble oligomerization of Aβ is considered to be effective in the treatment of AD, almost all peptide inhibitors have been designed from the β-sheet structure (H14-D23) of Aβ_1-42. To obtain more potent peptides than the known inhibitors of the soluble-oligomer formation of Aβ_1-42, we performed random screening by phage display. After fifth-round panning of a hepta-peptide library against soluble Aβ_1-42, novel peptides containing arginine residues were enriched. These peptides were found to suppress specifically 37/48 kDa oligomer formation and to keep the monomeric form of Aβ_1-42 even after 24 h of incubation, as disclosed by SDS–PAGE and size-exclusion chromatography. Thus we succeeded in acquiring novel efficient peptides for inhibition of soluble 37/48 kDa oligomer formation of Aβ_1-42.     

Selection and Characterization of Artificial Proteins Targeting the Tubulin α Subunit

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Generation and Characterization of ATP Analog-specific Protein Kinase Cδ

To better study the role of PKCδ in normal function and disease, we developed an ATP analog-specific (AS) PKCδ that is sensitive to specific kinase inhibitors and can be used to identify PKCδ substrates. AS PKCδ showed nearly 200 times higher affinity (K_m) and 150 times higher efficiency (k_cat/K_m) than wild type (WT) PKCδ toward N⁶-(benzyl)-ATP. AS PKCδ was uniquely inhibited by 1-(tert-butyl)-3-(1-naphthyl)-1H-pyrazolo[3,4-d]pyrimidin-4-amine (1NA-PP1) and 1-(tert-butyl)-3-(2-methylbenzyl)-1H-pyrazolo[3,4-d]pyrimidin-4-amine (2MB-PP1) but not by other 4-amino-5-(4-methylphenyl)-7-(t-butyl)pyrazolo[3,4-d]pyrimidine (PP1) analogs tested, whereas WT PKCδ was insensitive to all PP1 analogs. To understand the mechanisms for specificity and affinity of these analogs, we created in silico WT and AS PKCδ homology models based on the crystal structure of PKCι. N⁶-(Benzyl)-ATP and ATP showed similar positioning within the purine binding pocket of AS PKCδ, whereas N⁶-(benzyl)-ATP was displaced from the pocket of WT PKCδ and was unable to interact with the glycine-rich loop that is required for phosphoryl transfer. The adenine rings of 1NA-PP1 and 2MB-PP1 matched the adenine ring of ATP when docked in AS PKCδ, and this interaction prevented the potential interaction of ATP with Lys-378, Glu-428, Leu-430, and Phe-633 residues. 1NA-PP1 failed to effectively dock within WT PKCδ. Other PP1 analogs failed to interact with either AS PKCδ or WT PKCδ. These results provide a structural basis for the ability of AS PKCδ to efficiently and specifically utilize N⁶-(benzyl)-ATP as a phosphate donor and for its selective inhibition by 1NA-PP1 and 2MB-PP1. Such homology modeling could prove useful in designing molecules to target PKCδ and other kinases to understand their function in cell signaling and to identify unique substrates.     

Ras Regulates SCFβ-TrCP Protein Activity and Specificity via Its Effector Protein NORE1A

Ras is the most frequently activated oncogene found in human cancer, but its mechanisms of action remain only partially understood. Ras activates multiple signaling pathways to promote transformation. However, Ras can also exhibit a potent ability to induce growth arrest and death. NORE1A (RASSF5) is a direct Ras effector that acts as a tumor suppressor by promoting apoptosis and cell cycle arrest. Expression of NORE1A is frequently lost in human tumors, and its mechanism of action remains unclear. Here we show that NORE1A forms a direct, Ras-regulated complex with β-TrCP, the substrate recognition component of the SCF^β-TrCP ubiquitin ligase complex. This interaction allows Ras to stimulate the ubiquitin ligase activity of SCF^β-TrCP toward its target β-catenin, resulting in degradation of β-catenin by the 26 S proteasome. However, the action of Ras/NORE1A/β-TrCP is substrate-specific because IκB, another substrate of SCF^β-TrCP, is not sensitive to NORE1A-promoted degradation. We identify a completely new signaling mechanism for Ras that allows for the specific regulation of SCF^β-TrCP targets. We show that the NORE1A levels in a cell may dictate the effects of Ras on the Wnt/β-catenin pathway. Moreover, because NORE1A expression is frequently impaired in tumors, we provide an explanation for the observation that β-TrCP can act as a tumor suppressor or an oncogene in different cell systems.     

Repair of DNA–Protein Cross-links in Mammalian Cells

DNA–protein cross-links are generated by both endogenous and exogenous DNA damaging agents, as intermediates during normal DNA metabolism, and during abortive base excision repair. Cross-links are relatively common lesions that are lethal when they block progression of DNA polymerases. DNA–protein cross-links may be broadly categorized into four groups by the DNA and protein chemistries near the cross-link and by the source of the cross-link: DNA–protein cross-links may be found (1) in nicked DNA at the 3' end of one strand (topo I), (2) in nicked DNA at the 5' end of one strand (pol beta), (3) at the 5' ends of both strands adjacent to nicks in close proximity (topo II; Spo 11), and (4) in one strand of duplex DNA (UV irradiation; bifunctional carcinogens and chemotherapeutic agents). Repair mechanisms are reasonably well-defined for groups 1 and 3, and suggested for groups 2 and 4. Our work is focused on the recognition and removal of DNA–protein cross-links in duplex DNA (group 4).