Over-producing soluble protein complex and validating protein–protein interaction through a new bacterial co-expression system

Many proteins exert their functions through a protein complex and protein–protein interactions. However, the study of these types of interactions is complicated when dealing with toxic or hydrophobic proteins. It is difficult to use the popular Escherichia coli host for their expression, as these proteins in all likelihood require a critical partner protein to ensure their proper folding and stability. In the present study, we have developed a novel co-expression vector, pHEX, which is compatible with, and thus can be partnered with, many commercially available E. coli vectors, such as pET, pGEX and pMAL. The pHEX contains the p15A origin of replication and a T7 promoter, which can over-produce a His-tagged recombinant protein. The new co-expression system was demonstrated to efficiently co-produce and co-purify heterodimeric protein complexes, for example PE25/PPE41 (Rv2430c/Rv2431c) and ESAT6/CFP10 (Rv3874/Rv3875), from the human pathogen Mycobacterium tuberculosis H37Rv. Furthermore, the system was also effectively used to characterize protein–protein interactions through convenient affinity tags. Using an in vivo pull-down assay, for the first time we have confirmed the presence of three pairs of PE/PPE-related novel protein interactions in this pathogen. In summary, a convenient and efficient co-expression vector system has been successfully developed. The new system should be applicable to any protein complex or any protein–protein interaction of interest in a wide range of biological organisms.     

Predicting protein–protein interactions from protein sequences using meta predictor

A novel method is proposed for predicting protein–protein interactions (PPIs) based on the meta approach, which predicts PPIs using support vector machine that combines results by six independent state-of-the-art predictors. Significant improvement in prediction performance is observed, when performed on Saccharomyces cerevisiae and Helicobacter pylori datasets. In addition, we used the final prediction model trained on the PPIs dataset of S. cerevisiae to predict interactions in other species. The results reveal that our meta model is also capable of performing cross-species predictions. The source code and the datasets are available at     

Identification of antisense RNA stem–loops that inhibit RNA–protein interactions using a bacterial reporter system

Many well-characterized examples of antisense RNAs from prokaryotic systems involve hybridization of the looped regions of stem–loop RNAs, presumably due to the high thermodynamic stability of the resulting loop–loop and loop–linear interactions. In this study, the identification of RNA stem–loops that inhibit U1A protein binding to the hpII RNA through RNA–RNA interactions was attempted using a bacterial reporter system based on phage λ N-mediated antitermination. As a result, loop sequences possessing 7–8 base complementarity to the 5′ region of the boxA element important for functional antitermination complex formation, but not the U1 hpII loop, were identified. In vitro and in vivo mutational analysis strongly suggested that the selected loop sequences were binding to the boxA region, and that the structure of the antisense stem–loop was important for optimal inhibitory activity. Next, in an attempt to demonstrate the ability to inhibit the interaction between the U1A protein and the hpII RNA, the rational design of an RNA stem–loop that inhibits U1A-binding to a modified hpII was carried out. Moderate inhibitory activity was observed, showing that it is possible to design and select antisense RNA stem–loops that disrupt various types of RNA–protein interactions.     

Predicting protein–protein interactions from sequence using correlation coefficient and high-quality interaction dataset

Identifying protein–protein interactions (PPIs) is critical for understanding the cellular function of the proteins and the machinery of a proteome. Data of PPIs derived from high-throughput technologies are often incomplete and noisy. Therefore, it is important to develop computational methods and high-quality interaction dataset for predicting PPIs. A sequence-based method is proposed by combining correlation coefficient (CC) transformation and support vector machine (SVM). CC transformation not only adequately considers the neighboring effect of protein sequence but describes the level of CC between two protein sequences. A gold standard positives (interacting) dataset MIPS Core and a gold standard negatives (non-interacting) dataset GO-NEG of yeast Saccharomyces cerevisiae were mined to objectively evaluate the above method and attenuate the bias. The SVM model combined with CC transformation yielded the best performance with a high accuracy of 87.94% using gold standard positives and gold standard negatives datasets. The source code of MATLAB and the datasets are available on request under smgsmg@mail.ustc.edu.cn.     

Fluorometric detection of protein kinase Cα activity based on phosphorylation-induced dissociation of a polyion complex

Here, we report the fluorometric detection of protein kinase Cα (PKCα) activity in a cancerous cell lysate using a polyion complex (PIC) composed of a quencher (BHQ3)-modified chondroitin sulfate [CS(X)] and a dendrimer modified with a cationic peptide substrate (FKKQGSFAKKK-NH₂) and a near infrared (NIR) fluorophore (Cy5.5) (polymer 1). When polymer 1 formed the PIC with CS(X) through electrostatic interactions, the NIR fluorescence was quenched effectively via Förster resonance energy transfer (FRET) between Cy5.5 and BHQ3. However, this quenched fluorescence was recovered when the pendant peptides in polymer 1 were phosphorylated with PKCα due to the dissociation of the PIC. When PKCα was added to the PIC dispersion, a significant increase in fluorescence intensity was observed, whereas its fluorescence increase was inhibited with a PKCα inhibitor in a concentration-dependent manner. Furthermore, our PIC was robust enough to measure PKCα activity in a cancerous cellular lysate without purification. The PKCα-responsive PIC offers a simple, rapid, sensitive, and robust approach to detect PKCα activity in crude cellular lysates that would be suitable for drug screening formats and cancer diagnosis using crude cellular lysates.     

Evaluation of hydrogen bonds of ecdysteroids in the ligand–receptor interactions using a protein modeling system

The insect molting hormone, 20-hydroxyecdysone (20E) and its analogs (ecdysteroids) specifically bind to the ecdysone receptor. Previously, we synthesized various ecdysteroids containing the side chain moiety of ponasterone A (PonA), and measured the binding activity against Drosophila Kc cells to study the structure–activity relationship. Here we quantitatively analyzed the structure–activity relationship for the ligand binding of ecdysteroids including 20E and PonA. Since the hydrogen bonding (HB) is one of the important physicochemical properties for ligand binding to the ecdysteroid receptor, the number of possible HBs between the ligand molecule and the receptor was manually counted in the modeled ligand–receptor complex for all compounds. The construction of the ligand–receptor model was executed by the full-automatic modeling system (FAMS) in which calculation was done by simulated annealing. The binding potency of 15 ecdysteroids to Kc-cells were linearly correlated (r² = 0.63) with the number of HBs which are observed between ligand and receptor molecule. Contribution of steric and electrostatic effects on the ligand–receptor binding was also examined using a three-dimensional quantitative structure–activity relationship (3-D QSAR), comparative molecular field analysis (CoMFA).     

Increased precision for analysis of protein–ligand dissociation constants determined from chemical shift titrations

NMR is ideally suited for the analysis of protein-protein and protein ligand interactions with dissociation constants ranging from ~2 μM to ~1 mM, and with kinetics in the fast exchange regime on the NMR timescale. For the determination of dissociation constants (K ( D )) of 1:1 protein-protein or protein-ligand interactions using NMR, the protein and ligand concentrations must necessarily be similar in magnitude to the K ( D ), and nonlinear least squares analysis of chemical shift changes as a function of ligand concentration is employed to determine estimates for the parameters K ( D ) and the maximum chemical shift change (Δδ(max)). During a typical NMR titration, the initial protein concentration, [P (0)], is held nearly constant. For this condition, to determine the most accurate parameters for K ( D ) and Δδ(max) from nonlinear least squares analyses requires initial protein concentrations that are ~0.5 × K ( D ), and a maximum concentration for the ligand, or titrant, of ~10 × [P (0)]. From a practical standpoint, these requirements are often difficult to achieve. Using Monte Carlo simulations, we demonstrate that co-variation of the ligand and protein concentrations during a titration leads to an increase in the precision of the fitted K ( D ) and Δδ(max) values when [P (0)] > K ( D ). Importantly, judicious choice of protein and ligand concentrations for a given NMR titration, combined with nonlinear least squares analyses using two independent variables (ligand and protein concentrations) and two parameters (K ( D ) and Δδ(max)) is a straightforward approach to increasing the accuracy of measured dissociation constants for 1:1 protein-ligand interactions.     

Development of a novel PPARγ ligand screening system using pinpoint fluorescence-probed protein

The activation of peroxisome-proliferator-activated receptor-γ (PPARγ), which plays a central role in adipocyte differentiation, depends on ligand-dependent co-activator recruitment. In this study, we developed a novel method of PPARγ ligand screening by measuring the increase in fluorescent polarization accompanied by the interaction of a fluorescent co-activator and PPARγ. Sterol receptor co-activator-1 (SRC-1), a major PPARγ co-activator, was probed by fluorescent TAMRA by the Amber codon fluorescence probe method. Polarization was increased by adding PPARγ ligands to a solution containing labeled SRC-1 (designated TAMRA-SRC-S) and PPARγ. The disassociation constants (Kd) of the PPARγ synthesized ligands, pioglitazone (221 nM), troglitazone (83.0 nM), and 15-deoxy-Δ12,14-prostaglandin J(2) (15d-ΔPGJ(2)) (156 nM), were determined by this method. Farnesol (2.89 μM) and bixin (21.1 μM), which we have reported to be PPARγ ligands, increased the fluorescent polarization. Their Kd values were in agreement with the ED(50) values obtained in the luciferase assay. The results indicate that the method is valuable for screening natural PPARγ ligands.     

High-efficiency recovery of target cells using improved yeast display system for detection of protein–protein interactions

We constructed a high-throughput screening (HTS) system for target cells based on the detection of protein–protein interactions by flow cytometric sorting due to the improvement in the yeast cell surface display system. Interaction model proteins, which are the ZZ domain derived from Staphylococcus aureus and the Fc part of human immunoglobulin G (IgG), were displayed on the yeast cell surface. We achieved a rapid and enhanced expression of these proteins as a result of adopting an appropriate yeast strain and a suitable promoter. The displayed ZZ domain had an ability to bind to rabbit IgG and the displayed Fc part to protein A. These were confirmed by flow cytometry and fluorescence microscopy. Furthermore, the cells displaying the ZZ domain or Fc part were isolated from the model libraries constructed by mixing the control yeast cells with the target yeast cells. The ratio of the target cells was increased from 0.0001% to more than 70% by two cycles of cell sorting. These results indicate that we can achieve a rapid and highly efficient isolation method for the target cells with FACSCalibur and that this method will further extend the application of flow cytometric sorting to library selections.     

Phenotypic characterization of tannin–protein complex degrading bacteria from faeces of goat

Tannin–protein complex degrading bacteria after enrichment were isolated from unadapted goat faecal samples. Based on the morphological, hemolytic and biochemical characters, the isolates were categorized in two groups comprising GF1–GF4 and GF5–GF6. All the isolates were gram-positive cocci, catalase negative belonging to different strains of Group D streptococci, Enterococcus faecalis. Among six isolates, GF1 was the most resistant that could tolerate up to 4% of tannic acid in the medium with no significant change in the morphology. Tannase activity was detected in all the isolates, indicating their tannin degrading potential while gallate decarboxylase activity was detected only in three isolates GF1, GF2 and GF6.     

Continuous production of ethanol in a two-stage fermentation process using a protein — Phospholipid complex as a protective agent

A two-stage continuous fermentation process, using a continuous stirred tank fermenter (CSTF) and a tower fermenter (TF) connected in series, has been studied for ethanol production from d-glucose. The addition of a protein-phospholipid complex as a protective agent (PA) in the TF led to a three-fold increase in ethanol productivity and a 23.63% increase in final ethanol concentration in the tower effluent. The results are consistent with our previous findings on a cascade operation of CSTFs, namely, that the addition of PA to the tower increases cell tolerance towards ethanol at ethanol concentrations up to 70 gl^?1. Both studies indicate that beyond this experimentally determined critical ethanol concentration, ethanol production is significantly inhibited. Mathematical modelling of the behaviour of a single flocculated yeast floc suggested that, for yeast flocs up to 1 mm diameter, neither internal nor external diffusion of substrate is limiting. Therefore, a simplified mathematical method was developed for the analysis of the TF system. By plotting the calculated values from the derived performance equation and the experimental values of substrate conversion vs. residence time, good agreement was obtained between these two for the addition of PA. However, a small deviation was observed for the PA-free system.     

Solvated protein–DNA docking using HADDOCK

Interfacial water molecules play an important role in many aspects of protein–DNA specificity and recognition. Yet they have been mostly neglected in the computational modeling of these complexes. We present here a solvated docking protocol that allows explicit inclusion of water molecules in the docking of protein–DNA complexes and demonstrate its feasibility on a benchmark of 30 high-resolution protein–DNA complexes containing crystallographically-determined water molecules at their interfaces. Our protocol is capable of reproducing the solvation pattern at the interface and recovers hydrogen-bonded water-mediated contacts in many of the benchmark cases. Solvated docking leads to an overall improvement in the quality of the generated protein–DNA models for cases with limited conformational change of the partners upon complex formation. The applicability of this approach is demonstrated on real cases by docking a representative set of 6 complexes using unbound protein coordinates, model-built DNA and knowledge-based restraints. As HADDOCK supports the inclusion of a variety of NMR restraints, solvated docking is also applicable for NMR-based structure calculations of protein–DNA complexes.     

Computation of temperature-dependent dissociation rates of metastable protein–ligand complexes

Molecular simulations are often used to analyse the stability of protein–ligand complexes. The stability can be characterised by exit rates or using the exit time approach, i.e. by computing the expected holding time of the complex before its dissociation. However determining exit rates by straightforward molecular dynamics methods can be challenging for stochastic processes in which the exit event occurs very rarely. Finding a low variance procedure for collecting rare event statistics is still an open problem. In this work we discuss a novel method for computing exit rates which uses results of Robust Perron Cluster Analysis (PCCA+). This clustering method gives the possibility to define a fuzzy set by a membership function, which provides additional information of the kind ‘the process is being about to leave the set’. Thus, the derived approach is not based on the exit event occurrence and, therefore, is also applicable in case of rare events. The novel method can be used to analyse the temperature effect of protein–ligand systems through the differences in exit rates, and, thus, open up new drug design strategies and therapeutic applications.     

Purification and functional analysis of protein kinase G-1α using a bacterial expression system

3',5' Cyclic guanosine monophosphate (cGMP)-dependent protein kinase G-1α (PKG-1α) is an enzyme that is a target of several anti-hypertensive and erectile dysfunction drugs. Binding of cGMP to PKG-1α produces a conformational change that leads to enzyme activation. Activated PKG-1α performs important roles both in blood vessel vasodilation and in maintaining the smooth muscle cell in a differentiated contractile state. Recombinant PKG-1α has been expressed and purified using Sf9-insect cells. However, attempts at purifying full length protein in a soluble and active form in prokaryotes have thus far been unsuccessful. These attempts have been hampered by the lack of proper eukaryotic protein folding machinery in bacteria. In this study, we report the successful expression and purification of PKG-1α using a genetically engineered Escherichia coli strain, Rosetta-gami 2(DE3), transduced with full-length human PKG-1α cDNA containing a C-terminal histidine tag. PKG-1α was purified to homogeneity using sequential nickel affinity chromatography, gel filtration and ion exchange MonoQ columns. Protein identity was confirmed by immunoblot analysis. N-terminal sequencing using Edman degradation demonstrated that the purified protein was full length. Analysis of enzyme kinetics, using a nonlinear regression curve, identified that, at constant cGMP levels (10μM) and varying ATP concentrations, PKG-1α had a maximal velocity (V(max)) of 5.02±0.25pmol/min/μg and a Michaelis-Menten constant (K(m)) of 11.78±2.68μM ATP. Recent studies have suggested that endothelial function can be attenuated by oxidative and/or nitrosative stress but the role of PKG-1α under these conditions is unclear. We found that PKG-1α enzyme activity was attenuated by exposure to the NO donor, spermine NONOate, hydrogen peroxide, and peroxynitrite but not by superoxide, suggesting that the attenuation of PKG-1α activity may be an under-appreciated mechanism underlying the development of endothelial dysfunction in a number of cardiovascular diseases.     

Will a large complex system be productive?

While the relationship between food web complexity and stability has been well documented, how complexity affects productivity remains elusive. In this study, we combine food web theory and a data set of 149 aquatic food webs to investigate the effect of complexity (i.e. species richness, connectance, and average interaction strength) on ecosystem productivity. We find that more complex ecosystems tend to be more productive, although different facets of complexity have contrasting effects. A higher species richness and/or average interaction strength increases productivity, whereas a higher connectance often decreases it. These patterns hold not only between realized complexity and productivity, but also characterize responses of productivity to simulated declines of complexity. Our model also predicts a negative association between productivity and stability along gradients of complexity. Empirical analyses support our predictions on positive complexity-productivity relationships and negative productivity-stability relationships. Our study provides a step forward towards reconciling ecosystem complexity, productivity and stability.     

Helix-mediated protein–protein interactions as targets for intervention using foldamers

Protein–protein interactions (PPIs) play a central role in virtually all biological processes and have been the focus of intense investigation from structural molecular biology to cell biology for the majority of the last two decades and, more recently, are emerging as important targets for pharmaceutical intervention. A common motif found at the interface of PPIs is the α-helix, suggesting that, in the same way as the “lock and key” model has evolved for competitive inhibition of enzymes, it should be possible to elaborate “rule-based” approaches for inhibition of helix-mediated PPIs. This review will describe the biological function and structural features of a series of representative helix-mediated PPIs and discuss approaches that are being developed to target these interactions with small molecules that employ non-natural amino acids.     

Modeling EphB4-EphrinB2 protein–protein interaction using flexible docking of a short linear motif

Background

Many protein–protein interactions are mediated by a short linear motif. Usually, amino acid sequences of those motifs are known or can be predicted. It is much harder to experimentally characterize or predict their structure in the bound form. In this work, we test a possibility of using flexible docking of a short linear motif to predict the interaction interface of the EphB4-EphrinB2 complex (a system extensively studied for its significance in tumor progression).

Methods

In the modeling, we only use knowledge about the motif sequence and experimental structures of EphB4-EphrinB2 complex partners. The proposed protocol enables efficient modeling of significant conformational changes in the short linear motif fragment during molecular docking simulation. For the docking simulations, we use the CABS-dock method for docking fully flexible peptides to flexible protein receptors (available as a server at http://biocomp.chem.uw.edu.pl/CABSdock/). Based on the docking result, the protein–protein complex is reconstructed and refined.

Results

Using this novel protocol, we obtained an accurate EphB4-EphrinB2 interaction model.

Conclusions

The results show that the CABS-dock method may be useful as the primary docking tool in specific protein–protein docking cases similar to EphB4-EphrinB2 complex—that is, where a short linear motif fragment can be identified.