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Results for “Protein Interaction Mapping”

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PubMed2006

The TFG protein, involved in oncogenic rearrangements, interacts with TANK and NEMO, two proteins involved in the NF-kappaB pathway.

TRK-fused gene (TFG) was first identified as a partner of NTRK1 in generating the thyroid TRK-T3 oncogene, and is also involved in oncogenic rearrangements with ALK in anaplastic lymphoma and NOR1 in mixoid chondrosarcoma. The TFG physiological role is still unknown, but the presence of a number of motifs involved in protein interactions suggests that it may function by associating with other proteins. We have recently demonstrated that TFG associates and regulates the activity of the tyrosine phosphatase SHP-1. In this study by yeast two-hybrid screening we identified NEMO and TANK, two proteins modulating the NF-kappaB pathway, as novel TFG-interacting proteins. These interactions were further characterized in vitro and in vivo. We provide evidence that TFG and NEMO may be part of the same high molecular weight complex. TFG enhances the effect of TNF-alpha, TANK, TNF receptor-associated factor (TRAF)2, and TRAF6 in inducing NF-kappaB activity. We suggest that TFG is a novel member of the NF-kappaB pathway.

Adaptor Proteins, Signal Transducing↗

PubMed2006

Regulation of protein phosphatase 2A-mediated recruitment of IQGAP1 to beta1 integrin by EGF through activation of Ca2+/calmodulin-dependent protein kinase II.

Maintenance of beta1 integrin-mediated cell adhesion in quiescent human mammary epithelial (HME) cells requires protein phosphatase (PP) 2A for not only dephosphorylation of beta1 integrin but also recruitment of IQGAP1 to Rac-bound beta1 integrin. However, how PP2A-dependent regulatory machinery of cell adhesion responds to EGF remains to be elucidated. We report here that phosphorylated Ca2+/calmodulin-dependent protein kinase II (CaMKII) at threonine 286 was involved in the beta1 integrin complex that consisted of PP2A, Rac, and IQGAP1 in quiescent HME cells. Stimulation of the cells with EGF concomitantly induced an increase in intracellular Ca2+, activation of CaMKII, and dissociation of PP2A-IQGAP1-CaMKII from beta1 integrin-Rac. Because the activation of CaMKII and dissociation of PP2A-IQGAP1-CaMKII were blocked by either Ca2+-chelator or CaMKII inhibitor, we therefore propose that EGF has the ability to abrogate the PP2A function in the maintenance of beta1 integrin-mediated cell adhesion by dissociation of PP2A-IQGAP1-CaMKII from beta1 integrin-Rac through activation of CaMKII.

Calcium↗

PubMedDate not supplied

Protein-protein interactions: mechanisms and modification by drugs.

Protein-protein interactions form the proteinaceous network, which plays a central role in numerous processes in the cell. This review highlights the main structures, properties of contact surfaces, and forces involved in protein-protein interactions. The properties of protein contact surfaces depend on their functions. The characteristics of contact surfaces of short-lived protein complexes share some similarities with the active sites of enzymes. The contact surfaces of permanent complexes resemble domain contacts or the protein core. It is reasonable to consider protein-protein complex formation as a continuation of protein folding. The contact surfaces of the protein complexes have unique structure and properties, so they represent prospective targets for a new generation of drugs. During the last decade, numerous investigations have been undertaken to find or design small molecules that block protein dimerization or protein(peptide)-receptor interaction, or on the other hand, induce protein dimerization.

Animals↗

PubMedDate not supplied

Proteins, recognition networks and developing interfaces for macromolecular biosensing.

Genomics and proteomics discovery is leading to the identification of all proteins and to the opportunity, and challenge, to reveal the protein recognition networks that drive virtually all biological processes. Over the past decade, biosensors have emerged as a key technology for detection and analysis of biomolecular interactions. An important limitation in developing such biosensors is that the focus has been mainly on sensor platforms, the transducing hardware that converts interaction signals into recorded data, without adequately considering the role of molecular interfaces, the elements of sensors that interact with analytes to produce signals. We have investigated this alternative focus by identifying and, where necessary, designing molecular interfaces that will more effectively drive new biosensor development and utilization in biomedical and biotechnological investigations. Here we describe our recent studies of coiled coil and lipid bilayer interfaces and the potential to use these to expand sensing technologies for multiplexed target detection and analysis in increasingly biologically relevant membrane like environments.

Amino Acid Motifs↗

PubMedDate not supplied

Identification of protein associations in organelles, using mass spectrometry-based proteomics.

Recent literature that highlights the power of using mass spectrometry (MS) for protein identification from preparations of highly purified organelles and other large subcellular structures is covered in this review with an emphasis on techniques that preserve the integrity of the functional protein complexes. Recent advances in distinguishing contaminant proteins from "bonafide" organelle-localized proteins and the affinity capture of protein complexes are reviewed, as well as bioinformatic strategies to predict protein organellar localization and to integrate protein-protein interaction maps obtained from MS-affinity capture methods with data obtained from other techniques. Those developments demonstrate that a revolution in cellular biology, fueled by technical advances in MS-based proteomic techniques, is well underway.

Animals↗

PubMedDate not supplied

Mass spectrometry and the search for moonlighting proteins.

Mass spectrometry has become one of the most important techniques in proteomics because of its use to identify the proteins found in different cell types, organelles, and multiprotein complexes. This information about protein location and binding partners can provide valuable clues to infer a protein's function. However, more and more proteins are found that "moonlight," or have more than one function, and the presence of moonlighting proteins can make more difficult the identification of protein function in those studies. This review discusses examples of moonlighting proteins and how their presence can affect the results of mass spectrometry studies that identify the locations, levels, and changes in protein expression. Although the presence of moonlighting proteins can complicate the results of those studies, mass spectrometry-derived protein-expression profiles potentially provides a very powerful method to find additional moonlighting proteins because they do not require a prior hypothesis of the protein's function.

Gene Expression Profiling↗

PubMed2004

Competition STD NMR for the detection of high-affinity ligands and NMR-based screening.

The reported competition STD NMR method combines saturation transfer difference (STD) NMR with competition binding experiments to allow the detection of high-affinity ligands that undergo slow chemical exchange on the NMR time-scale. With this technique, the presence of a competing high-affinity ligand in the compound mixture can be detected by the disappearance or reduction of the STD signals of a low-affinity indicator ligand. This is demonstrated on a BACE1 (beta-site amyloid precursor protein cleaving enzyme 1) protein-inhibitor system. This method can also be used to derive an approximate value, or a lower limit, for the dissociation constant of the potential ligand based on the reduction of the signal intensity of the STD indicator, which is illustrated on an HSA (human serum albumin) model system. This leads to important applications of the competition STD NMR method for lead discovery: it can be used (i) for compound library screening against a broad range of drug targets to identify both high- and low-affinity ligands and (ii) to rank order analogs rapidly and derive structure-activity relationships, which are used to optimize these NMR hits into viable drug leads.

Algorithms↗

PubMed2004

Analysis of protein interactions on protein arrays by a wavelength interrogation-based surface plasmon resonance biosensor.

We have investigated whether surface plasmon resonance (SPR) sensors based on the wavelength interrogation are able to analyze protein interactions on protein arrays. The spectral SPR sensor was self-constructed and its detection limit, expressed as the minimal refractive index variation, was calculated to be 6.6x10(-5) with the signal fluctuation of 1.0x10(-5). The protein array surface was modified by a mixed thiol monolayer to immobilize proteins. Protein arrays were analyzed by the line-scanning mode of the SPR sensor, which scanned every 100 microm along the central line of array spots and the scanned results were presented by color spectra from blue to red. Glutathione S-transferase (GST)-rac1 caused a concentration-dependent increase of SPR wavelength shift on protein arrays. The surface structure of the protein arrays was analyzed by atomic force microscopy. Specific interactions of antigens with antibodies were analyzed on the protein arrays by using three antibodies and eight proteins. These results suggest that the wavelength interrogation-based SPR sensor can be used as the biosensor for the high-throughput analysis of protein interactions on protein arrays.

Antibodies↗

PubMed2006

High-throughput analysis of GST-fusion protein expression and activity-dependent protein interactions on GST-fusion protein arrays with a spectral surface plasmon resonance biosensor.

We modified gold arrays with a glutathione (GSH) surface, and investigated high-throughput protein interactions with a spectral surface plasmon resonance (SPR) biosensor. We fabricated the GSH exterior on gold surfaces by successive modification with aminoethanethiol, 4-maleimidobutyric acid N-hydroxysuccinimide ester and GSH. We immobilized GST-Rac1, GST-RhoA, the GST-Rho-binding domain of rhotekin and the GST-p21-binding domain of PAK1 onto the GSH surface, and observed specific antigen-antibody interactions on the GST-fusion protein arrays. We determined the expression of GST-fusion proteins in Escherichia coli on the GSH surface with the SPR biosensor. We then analyzed the interactions of tissue transglutaminase (tTGase), a Ca2+-dependent enzyme, with RhoA and Rac1 on the GST-fusion protein arrays with the SPR biosensor. We found that tTGase interacted with RhoA and Rac1 in a Ca2+-dependent manner, indicating that the interactions were dependent on tTGase activity. In addition, transamidation of Rac1 by tTGase was dependent on Ca2+ concentration. We obtained similar results with GST pull-down assays. Thus, protein arrays prepared on the GSH surface provide a useful system for the high-throughput analysis of GST-fusion protein expression and activity-dependent protein interactions with the spectral SPR biosensors.

Apoptosis Regulatory Proteins↗

PubMed2006

Mass spectrometry-based proteomic analysis of the epitope-tag affinity purified protein complexes in eukaryotes.

In recent years, MS has been widely used to study protein complex in eukaryotes. The identification of interacting proteins of a particular target protein may help defining protein-protein interaction and proteins of unknown functions. To isolate protein complexes, high-speed ultracentrifugation, sucrose density-gradient centrifugation, and coimmunoprecipitation have been widely used. However, the probability of getting nonspecific binding is comparatively high. Alternatively, by use of one- or two-step (tandem affinity purification) epitope-tag affinity purification, protein complexes can be isolated by affinity or immunoaffinity columns. These epitope-tags include protein A, hexahistidine (His), c-Myc, hemaglutinin (HA), calmodulin-binding protein, FLAG, maltose-binding protein, Strep, etc. The isolated protein complex can then be subjected to protease (i.e., trypsin) digestion followed by an MS analysis for protein identification. An example, the epitope-tag purification of the Arabidopsis cytosolic ribosomes, is addressed in this article to show the success of the application. Several representative protein complexes in eukaryotes been isolated and characterized by use of this approach are listed. In this review, the comparison among different tag systems, validation of interacting relationship, and choices of MS analysis method are addressed. The successful rate, advantages, limitations, and challenges of the epitope-tag purification are also discussed.

Affinity Labels↗

PubMed2006

Plant proteome analysis: a 2004-2006 update.

Since the appearance of the review entitled "Plant Proteome Analysis" in Proteomics in February 2004 (Cánovas, F. M., Dumas-Gaudot, E., Recorbert, G., Jorrín, J. et al., Proteomics 2004, 4, 285-298), about 200 original articles focusing on plant proteomics have been published. Although this represents less than 1% of the global proteomics output during this period, it nevertheless reflects an increase in activity over the period 1999-2004. These papers concern the proteome of at least 35 plant species but have concentrated mainly on thale cress (Arabidopsis thaliana) and rice (Oryza sativa). The scientific objectives have ranged from a proteomic analysis of organs, tissues, cell suspensions, or subcellular fractions to the study of plant development and response to various stresses. A number of contributions have covered PTMs and protein interactions. The dominant analytical platform has been 2-DE coupled to MS, but "second generation" techniques such as DIGE, multidimensional protein identification technology, isotope-coded affinity tags, and stable isotope labeling by amino acids in cell culture have begun to make an impact. This review aims to provide an update of the contribution of proteomics to plant biology during the period 2004-2006, and is divided into six sections: introduction, subcellular proteomes, plant development, responses to biotic and abiotic stresses, PTMs, and protein interactions. The conclusions summarize a view of the major pitfalls and challenges of plant proteomics.

Arabidopsis↗

PubMed2007

Identification of proteins interacting with the catalytic subunit of PP2A by proteomics.

The protein phosphatase 2A (PP2A) is a serine/threonine phosphatase involved in the regulation of multiple signaling pathways including the Wnt/beta-catenin and the ERK pathways. To understand the complex signaling networking associated with PP2A, we searched proteins interacting with the catalytic subunit of protein phosphatase 2A (PP2Ac) by a pull-down analysis followed by 2-D gel electrophoresis and proteomic analyses. The probability of identification of the proteins interacting with PP2Ac was increased by searching proteins differently interacting with PP2Ac according to stimulation of Wnt3a, which regulates both the Wnt/beta-catenin and the ERK pathways. Around 100 proteins, pulled-down by His-tagged PP2Ac, were identified in 2-D gels stained with CBB. By MALDI-TOF-MS analyses of 45 protein spots, we identified several proteins that were previously known to interact with PP2A, such as Axin and CaMK IV. In addition, we also identified many proteins that potentially interact with PP2Ac. The interactions of several candidate proteins, such as tuberous sclerosis complex 2, RhoB, R-Ras, and Nm23H2, with PP2Ac, were confirmed by in vitro binding analyses and/or coimmunoprecipitation experiments.

Animals↗

PubMed2002

Potential of mean force for protein-protein interaction studies.

Calculating protein-protein interaction energies is crucial for understanding protein-protein associations. On the basis of the methodology of mean-field potential, we have developed an empirical approach to estimate binding free energy for protein-protein interactions. This knowledge-based approach has been used to derive distance-dependent free energies of protein complexes from a nonredundant training set in the Protein Data Bank (PDB), with a careful treatment of homology. We calculate atom pair potentials for 16 pair interactions, which can reflect the importance of hydrophobic interactions and specific hydrogen-bonding interactions. The derived potentials for hydrogen-bonding interactions show a valley of favorable interactions at a distance of approximately 3 A, corresponding to that of an established hydrogen bond. For the test set of 28 protein complexes, the calculated energies have a correlation coefficient of 0.75 compared with experimental binding free energies. The performance of the method in ranking the binding energies of different protein-protein complexes shows that the energy estimation can be applied to value binding free energies for protein-protein associations.

Databases, Protein↗

PubMed2003

Evaluation of protein docking predictions using Hex 3.1 in CAPRI rounds 1 and 2.

This article describes and reviews our efforts using Hex 3.1 to predict the docking modes of the seven target protein-protein complexes presented in the CAPRI (Critical Assessment of Predicted Interactions) blind docking trial. For each target, the structure of at least one of the docking partners was given in its unbound form, and several of the targets involved large multimeric structures (e.g., Lactobacillus HPr kinase, hemagglutinin, bovine rotavirus VP6). Here we describe several enhancements to our original spherical polar Fourier docking correlation algorithm. For example, a novel surface sphere smothering algorithm is introduced to generate multiple local coordinate systems around the surface of a large receptor molecule, which may be used to define a small number of initial ligand-docking orientations distributed over the receptor surface. High-resolution spherical polar docking correlations are performed over the resulting receptor surface patches, and candidate docking solutions are refined by using a novel soft molecular mechanics energy minimization procedure. Overall, this approach identified two good solutions at rank 5 or less for two of the seven CAPRI complexes. Subsequent analysis of our results shows that Hex 3.1 is able to place good solutions within a list of <or=20 for four of the seven targets. This finding shows that useful in silico protein-protein docking predictions can now be made with increasing confidence, even for very large macromolecular complexes.

Algorithms↗

PubMed2003

Guided docking: first step to locate potential binding sites.

The long-range electrostatic forces of the targets in round 2 of the Critical Assessment of PRediction of Interactions (CAPRI) experiment were examined and a simple guided docking method, based on these forces, was applied. The method described consists of calculating an initial rigid body trajectory and an optional final, fully flexible refinement stage. Although only limited success was found in predicting the final complexes, some interesting information was discovered. In particular, the long-range forces seem to give some insight into the unusual binding mode of target 4 while raising some questions about target 7, which warrant further investigation.

Algorithms↗

PubMed2003

CAPRI: a Critical Assessment of PRedicted Interactions.

CAPRI is a communitywide experiment to assess the capacity of protein-docking methods to predict protein-protein interactions. Nineteen groups participated in rounds 1 and 2 of CAPRI and submitted blind structure predictions for seven protein-protein complexes based on the known structure of the component proteins. The predictions were compared to the unpublished X-ray structures of the complexes. We describe here the motivations for launching CAPRI, the rules that we applied to select targets and run the experiment, and some conclusions that can already be drawn. The results stress the need for new scoring functions and for methods handling the conformation changes that were observed in some of the target systems. CAPRI has already been a powerful drive for the community of computational biologists who development docking algorithms. We hope that this issue of Proteins will also be of interest to the community of structural biologists, which we call upon to provide new targets for future rounds of CAPRI, and to all molecular biologists who view protein-protein recognition as an essential process.

Algorithms↗

PubMed2003

ICM-DISCO docking by global energy optimization with fully flexible side-chains.

The ICM-DISCO (Docking and Interface Side-Chain Optimization) protein-protein-docking method is a direct stochastic global energy optimization from multiple starting positions of the ligand. The first step is performed by docking of a rigid all-atom ligand molecule to a set of soft receptor potentials precalculated on a 0.5 A grid from realistic solvent-corrected force-field energies. This step finds the correct solution as the lowest energy conformation in almost 100% of the cases in which interfaces do not change on binding. The second step is needed to deal with the induced changes and includes the global optimization of the interface side-chains of up to 400 best solutions. The CAPRI predictions were performed fully automatically with this method. Available experimental information was included as a filtering step to favor expected docking surfaces. In three of the seven proposed targets, the ICM-DISCO method found a good solution (>50% of correct contacts) within the five submitted models. The procedure is global and fully automated. We demonstrate that the algorithm handles the induced changes of surface side-chains but is less successful if the backbone undergoes large-scale rearrangements.

Algorithms↗

PubMed2003

Protein-protein docking predictions for the CAPRI experiment.

We predicted structures for all seven targets in the CAPRI experiment using a new method in development at the time of the challenge. The technique includes a low-resolution rigid body Monte Carlo search followed by high-resolution refinement with side-chain conformational changes and rigid body minimization. Decoys (approximately 10(6) per target) were discriminated using a scoring function including van der Waals and solvation interactions, hydrogen bonding, residue-residue pair statistics, and rotamer probabilities. Decoys were ranked, clustered, manually inspected, and selected. The top ranked model for target 6 predicted the experimental structure to 1.5 A RMSD and included 48 of 65 correct residue-residue contacts. Target 7 was predicted at 5.3 A RMSD with 22 of 37 correct residue-residue contacts using a homology model from a known complex structure. Using a preliminary version of the protocol in round 1, target 1 was predicted within 8.8 A although few contacts were correct. For targets 2 and 3, the interface locations and a small fraction of the contacts were correctly identified.

Algorithms↗