Functional antagonism between the retinoic acid receptor and the viral transactivator BZLF1 is mediated by protein-protein interactions.

The Epstein-Barr virus-encoded protein BZLF1 is a member of the basic leucine zipper (bZip) family of transcription factors. Like several other members of the bZip family, transcriptional activity of BZLF1 is modulated by retinoic acid receptors (RARs). We present evidence that the RAR alpha and BZLF1 can reciprocally repress each other's transcriptional activation by a newly discovered mechanism. Analysis of RAR alpha mutants in transfection studies reveals that the DNA binding domain is sufficient for inhibition of BZLF1 activity. Analysis of BZLF1 mutants indicates that both the coiled-coil dimerization domain and a region containing the transcriptional activation domain of BZLF1 are required for transrepression. Coimmunoprecipitation experiments demonstrate physical interactions between RAR alpha and BZLF1 in vivo. Furthermore, glutathione S-transferase-pulldown assays reveal that these protein-protein interactions are mediated by the coiled-coil dimerization domain of BZLF1 and the DNA binding domain of RAR alpha. While RAR alpha is unable to recognize BZLF1 binding sites, the RAR alpha can be tethered to the DNA by forming a heteromeric complex with BZLF1 bound to DNA. Tethering RARs via protein-protein interactions onto promoter DNA suggest a mechanism through which RARs might gain additional levels of transcriptional regulation.

Competing Lipid-Protein and Protein-Protein Interactions Determine Clustering and Gating Patterns in the Potassium Channel from Streptomyces lividans (KcsA)

There is increasing evidence to support the notion that membrane proteins, instead of being isolated components floating in a fluid lipid environment, can be assembled into supramolecular complexes that take part in a variety of cooperative cellular functions. The interplay between lipid-protein and protein-protein interactions is expected to be a determinant factor in the assembly and dynamics of such membrane complexes. Here we report on a role of anionic phospholipids in determining the extent of clustering of KcsA, a model potassium channel. Assembly/disassembly of channel clusters occurs, at least partly, as a consequence of competing lipid-protein and protein-protein interactions at nonannular lipid binding sites on the channel surface and brings about profound changes in the gating properties of the channel. Our results suggest that these latter effects of anionic lipids are mediated via the Trp67–Glu71–Asp80 inactivation triad within the channel structure and its bearing on the selectivity filter.

Inhibition of protein interactions with the beta(2) sliding clamp of Escherichia coli DNA polymerase III by peptides from beta(2)-binding proteins

The sliding clamp of the Escherichia coli replisome is now understood to interact with many proteins involved in DNA synthesis and repair. A universal interaction motif is proposed to be one mechanism by which those proteins bind the E. coli sliding clamp, a homodimer of the beta subunit, at a single site on the dimer. The numerous beta(2)-binding proteins have various versions of the consensus interaction motif, including a related hexameric sequence. To determine if the variants of the motif could contribute to the competition of the beta-binding proteins for the beta(2) site, synthetic peptides derived from the putative beta(2)-binding motifs were assessed for their abilities to inhibit protein-beta(2) interactions, to bind directly to beta(2), and to inhibit DNA synthesis in vitro. A hierarchy emerged, which was consistent with sequence similarity to the pentameric consensus motif, QL(S/D)LF, and peptides containing proposed hexameric motifs were shown to have activities comparable to those containing the consensus sequence. The hierarchy of peptide binding may be indicative of a competitive hierarchy for the binding of proteins to beta(2) in various stages or circumstances of DNA replication and repair.

Identifying optimal lipid raft characteristics required to promote nanoscale protein-protein interactions on the plasma membrane

The dynamic lateral segregation of signaling proteins into microdomains is proposed to facilitate signal transduction, but the constraints on microdomain size, mobility, and diffusion that might realize this function are undefined. Here we interrogate a stochastic spatial model of the plasma membrane to determine how microdomains affect protein dynamics. Taking lipid rafts as representative microdomains, we show that reduced protein mobility in rafts segregates dynamically partitioning proteins, but the equilibrium concentration is largely independent of raft size and mobility. Rafts weakly impede small-scale protein diffusion but more strongly impede long-range protein mobility. The long-range mobility of raft-partitioning and raft-excluded proteins, however, is reduced to a similar extent. Dynamic partitioning into rafts increases specific interprotein collision rates, but to maximize this critical, biologically relevant function, rafts must be small (diameter, 6 to 14 nm) and mobile. Intermolecular collisions can also be favored by the selective capture and exclusion of proteins by rafts, although this mechanism is generally less efficient than simple dynamic partitioning. Generalizing these results, we conclude that microdomains can readily operate as protein concentrators or isolators but there appear to be significant constraints on size and mobility if microdomains are also required to function as reaction chambers that facilitate nanoscale protein-protein interactions. These results may have significant implications for the many signaling cascades that are scaffolded or assembled in plasma membrane microdomains.

Quantitative analysis of DNA-protein interactions using double-labeled native gel electrophoresis and fluorescence-based imaging

We have developed a sensitive, non-radioactive method to assess the interaction of transcription factors/DNA-binding proteins with DNA. We have modified the traditional radiolabeled DNA gel mobility shift assay to incorporate a DNA probe end-labeled with a Texas-red fluorophore and a DNA-binding protein tagged with the green fluorescent protein to monitor precisely DNA-protein complexation by native gel electrophoresis. We have applied this method to the DNA-binding proteins telomere release factor-1 and the sex-determining region-Y, demonstrating that the method is sensitive (able to detect 100 fmol of fluorescently labeled DNA), permits direct visualization of both the DNA probe and the DNA-binding protein, and enables quantitative analysis of DNA and protein complexation, and thereby an estimation of the stoichiometry of protein-DNA binding.

Microscale mesoarrays created by dip-pen nanolithography for screening of protein-protein interactions

Using microarrays to probe protein-protein interactions is becoming increasingly attractive due to their compatibility with highly sensitive detection techniques, selectivity of interaction, robustness and capacity for examining multiple proteins simultaneously. The major drawback to using this approach is the relatively large volumes and high concentrations necessary. Reducing the protein array spot size should allow for smaller volumes and lower concentrations to be used as well as opening the way for combination with more sensitive detection technologies. Dip-Pen Nanolithography (DPN) is a recently developed technique for structure creation on the nano to microscale with the capacity to create biological architectures. Here we describe the creation of miniaturised microarrays, 'mesoarrays', using DPN with protein spots 400× smaller by area compared to conventional microarrays. The mesoarrays were then used to probe the ERK2-KSR binding event of the Ras/Raf/MEK/ERK signalling pathway at a physical scale below that previously reported. Whilst the overall assay efficiency was determined to be low, the mesoarrays could detect KSR binding to ERK2 repeatedly and with low non-specific binding. This study serves as a first step towards an approach that can be used for analysis of proteins at a concentration level comparable to that found in the cellular environment.

Protein-protein interactions in ovalbumin solutions studied by small angle scattering: effect of ionic strength, and the chemical nature of cations

The influence of ionic strength and of the chemical nature of cations on the protein-protein interactions in ovalbumin solution was studied using small-angle X-ray and neutron scattering (SAXS/SANS). The globular protein ovalbumin is found in dimeric form in solutions as suggested by SANS/SAXS experiments. Due to the negative charge of the proteins at neutral pH, the protein-protein interactions without any salt addition are dominated by electrostatic repulsion. A structure factor related to screened Coulombic interactions together with an ellipsoid form factor was used to fit the scattering intensity. A monovalent salt (NaCl) and a trivalent salt (YCl3) were used to study the effect of the chemical nature of cations on the interaction in protein solutions. Upon addition of NaCl, with ionic strength below that of physiological conditions (150 mM), the effective interactions are still dominated by the surface charge of the proteins and the scattering data can be understood using the same model. When yttrium chloride was used, a reentrant condensation behavior, i.e., aggregation and subsequent redissolution of proteins with increasing salt concentration, was observed. SAXS measurements reveal a transition from effective repulsion to attraction with increasing salt concentration. The solutions in the reentrant regime become unstable after long times (several days). The results are discussed and compared with those from bovine serum albumin (BSA) in solutions.

Extracting protein-protein interactions from MEDLINE using the hidden vector state model

A major challenge in text mining for biomedicine is automatically extracting protein-protein interactions from the vast amount of biomedical literature. We have constructed an information extraction system based on the Hidden Vector State (HVS) model for protein-protein interactions. The HVS model can be trained using only lightly annotated data whilst simultaneously retaining sufficient ability to capture the hierarchical structure. When applied in extracting protein-protein interactions, we found that it performed better than other established statistical methods and achieved 61.5% in F-score with balanced recall and precision values. Moreover, the statistical nature of the pure data-driven HVS model makes it intrinsically robust and it can be easily adapted to other domains.