Systematic analysis of bacterial effector-postsynaptic density 95/disc large/zonula occludens-1 (PDZ) domain interactions demonstrates Shigella OspE protein promotes protein kinase C activation via PDLIM proteins.

Diseases caused by many Gram-negative bacterial pathogens depend on the activities of bacterial effector proteins that are delivered into eukaryotic cells via specialized secretion systems. Effector protein function largely depends on specific subcellular targeting and specific interactions with cellular ligands. PDZ domains are common domains that serve to provide specificity in protein-protein interactions in eukaryotic systems. We show that putative PDZ-binding motifs are significantly enriched among effector proteins delivered into mammalian cells by certain bacterial pathogens. We use PDZ domain microarrays to identify candidate interaction partners of the Shigella flexneri effector proteins OspE1 and OspE2, which contain putative PDZ-binding motifs. We demonstrate in vitro and in cells that OspE proteins interact with PDLIM7, a member of the PDLIM family of proteins, which contain a PDZ domain and one or more LIM domains, protein interaction domains that participate in a wide variety of functions, including activation of isoforms of protein kinase C (PKC). We demonstrate that activation of PKC during S. flexneri infection is attenuated in the absence of PDLIM7 or OspE proteins and that the OspE PDZ-binding motif is required for wild-type levels of PKC activation. These results are consistent with a model in which binding of OspE to PDLIM7 during infection regulates the activity of PKC isoforms that bind to the PDLIM7 LIM domain.

The nuclear receptor-coactivator interaction surface as a target for peptide antagonists of the peroxisome proliferator-activated receptors.

The peroxisome proliferator-activated receptors (PPARalpha, PPARdelta, and PPARgamma) constitute a family of nuclear receptors that regulates metabolic processes involved in lipid and glucose homeostasis. Although generally considered to function as ligand-regulated receptors, all three PPARs exhibit a high level of constitutive activity that may result from their stimulation by intracellularly produced endogenous ligands. Consequently, complete inhibition of PPAR signaling requires the development of inverse agonists. However, the currently available small molecule antagonists for the PPARs function only as partial agonists, or their efficacy is not sufficient to inhibit the constitutive activity of these receptors. Due to the lack of efficacious antagonists that interact with the ligand-binding domain of the PPARs, we decided to target an interaction that is central to nuclear receptor-mediated gene transcription: the nuclear receptor-coactivator interaction. We utilized phage display technology to identify short LXXLL-containing peptides that bind to the PPARs. Analysis of these peptides revealed a consensus binding motif consisting of HPLLXXLL. Cross-screening of these peptides for binding to other nuclear receptors enabled the identification of a high-affinity PPAR-selective peptide that has the ability to repress PPARgamma1-dependent transcription of transfected reporter genes. Most importantly, when introduced into HepG2 cells, the peptide inhibited the expression of endogenous PPARgamma1 target genes, adipose differentiation-related protein and mitochondrial 3-hydroxy-3-methylglutaryl coenzyme A synthase 2. This work lends support for the rational development of peptidomimetics that block receptor-mediated transcription by targeting the nuclear receptor-coactivator interaction surface.

Predicting ligand-dependent tumors from multi-dimensional signaling features.

Targeted therapies have shown significant patient benefit in about 5-10% of solid tumors that are addicted to a single oncogene. Here, we explore the idea of ligand addiction as a driver of tumor growth. High ligand levels in tumors have been shown to be associated with impaired patient survival, but targeted therapies have not yet shown great benefit in unselected patient populations. Using an approach of applying Bagged Decision Trees (BDT) to high-dimensional signaling features derived from a computational model, we can predict ligand dependent proliferation across a set of 58 cell lines. This mechanistic, multi-pathway model that features receptor heterodimerization, was trained on seven cancer cell lines and can predict signaling across two independent cell lines by adjusting only the receptor expression levels for each cell line. Interestingly, for patient samples the predicted tumor growth response correlates with high growth factor expression in the tumor microenvironment, which argues for a co-evolution of both factors in vivo.

Critical issues for modeling dust transport in tokamaks.

Dust produced in tokamaks is an important issue for fusion. Dust particles can introduce health and safety issues when in the same time can have an impact on reactor performance. Apart from the associated problems there are also potential benefits that make the better understanding of their behavior important. In this work the dust transport code Dust in TOKamakS will be used to explore the effect that variations in the plasma background and the physical model, describing the plasma-dust interaction, have on their predicted trajectories.

Quantifying protein-protein interactions in high throughput using protein domain microarrays.

Protein microarrays provide an efficient way to identify and quantify protein-protein interactions in high throughput. One drawback of this technique is that proteins show a broad range of physicochemical properties and are often difficult to produce recombinantly. To circumvent these problems, we have focused on families of protein interaction domains. Here we provide protocols for constructing microarrays of protein interaction domains in individual wells of 96-well microtiter plates, and for quantifying domain-peptide interactions in high throughput using fluorescently labeled synthetic peptides. As specific examples, we will describe the construction of microarrays of virtually every human Src homology 2 (SH2) and phosphotyrosine binding (PTB) domain, as well as microarrays of mouse PDZ domains, all produced recombinantly in Escherichia coli. For domains that mediate high-affinity interactions, such as SH2 and PTB domains, equilibrium dissociation constants (K(D)s) for their peptide ligands can be measured directly on arrays by obtaining saturation binding curves. For weaker binding domains, such as PDZ domains, arrays are best used to identify candidate interactions, which are then retested and quantified by fluorescence polarization. Overall, protein domain microarrays provide the ability to rapidly identify and quantify protein-ligand interactions with minimal sample consumption. Because entire domain families can be interrogated simultaneously, they provide a powerful way to assess binding selectivity on a proteome-wide scale and provide an unbiased perspective on the connectivity of protein-protein interaction networks.

Predicting PDZ domain-peptide interactions from primary sequences.

PDZ domains constitute one of the largest families of interaction domains and function by binding the C termini of their target proteins. Using Bayesian estimation, we constructed a three-dimensional extension of a position-specific scoring matrix that predicts to which peptides a PDZ domain will bind, given the primary sequences of the PDZ domain and the peptides. The model, which was trained using interaction data from 82 PDZ domains and 93 peptides encoded in the mouse genome, successfully predicts interactions involving other mouse PDZ domains, as well as PDZ domains from Drosophila melanogaster and, to a lesser extent, PDZ domains from Caenorhabditis elegans. The model also predicts the differential effects of point mutations in peptide ligands on their PDZ domain-binding affinities. Overall, we show that our approach captures, in a single model, the binding selectivity of the PDZ domain family.

Biochemical basis for enhanced binding of peptide dimers to X-linked inhibitor of apoptosis protein.

XIAP (X-linked inhibitor of apoptosis protein) is involved in the mediation of programmed cell death and, therefore, is a target for the development of cancer therapeutics. Peptide mimetics based upon Smac, the natural binding partner of XIAP, and specifically, dimeric peptides, have shown great promise in drug development. In the present work, the basis for enhanced dimer efficacy has been explored. Comparisons are made between the peptide binding site on the BIR3 domain of XIAP alone (residues 238-358) and a less truncated construct that includes both BIR2 and BIR3 domains (residues 151-350). This contingency differentially enhances the binding of dimeric tetrapeptides, potentially by providing additional hydrophobic binding surface. The effect of BIR2 on the BIR3 binding site is sustained, even if the BIR2 binding site is disrupted by mutagenesis, as shown by both a fluorescent competition assay and a polarity sensitive dye, badan. FRET measurements reveal an observed separation of >or=45 A between the BIR2 and BIR3 peptide binding pockets, thereby precluding a direct simultaneous interaction of the dimer molecules with both binding domains. Furthermore, variations in the linker length between dimeric tetrapeptides did not show a predictable trend in binding affinities, suggesting that local concentration effects were also an unlikely explanation for the enhanced dimeric affinities. Taken together, the results suggest that enhanced binding of dimeric peptides likely reflects the increased hydrophobic surface area on or near the BIR3 site and have significant ramifications for the design of therapeutics that target this class of proteins.

PDZ domain binding selectivity is optimized across the mouse proteome.

PDZ domains have long been thought to cluster into discrete functional classes defined by their peptide-binding preferences. We used protein microarrays and quantitative fluorescence polarization to characterize the binding selectivity of 157 mouse PDZ domains with respect to 217 genome-encoded peptides. We then trained a multidomain selectivity model to predict PDZ domain-peptide interactions across the mouse proteome with an accuracy that exceeds many large-scale, experimental investigations of protein-protein interactions. Contrary to the current paradigm, PDZ domains do not fall into discrete classes; instead, they are evenly distributed throughout selectivity space, which suggests that they have been optimized across the proteome to minimize cross-reactivity. We predict that focusing on families of interaction domains, which facilitates the integration of experimentation and modeling, will play an increasingly important role in future investigations of protein function.

Tryptophan and tyrosine to terbium fluorescence resonance energy transfer as a method to "map" aromatic residues and monitor docking.

Fluorescent lanthanide ions, with large Stokes shifts and narrow emission bands, are excellent tools for the development of FRET-based assays. In this work, a terbium ion is tethered to a peptide which binds to the BIR3 domain of XIAP, an anti-apoptotic protein. Excitation of tryptophan and tyrosine residues in the BIR3 domain causes the peptide bound terbium ion to fluoresce relative to its distance from these aromatic residues. By developing ligands with terbium ions tethered at different residues, the relative terbium emission can be used to "map" the aromatic residues within the ligand binding pocket.