Cyclic voltammetric studies of carbohydrate–protein interactions on gold surface

A simple electrochemical method for the determination of association constants between carbohydrates and carbohydrate-binding proteins using cyclic voltammetry (CV) is described. The binding of concanavalin A (Con A) and cholera toxin (CT) to their specific α-mannose and β-galactose derivatives self-assembled on gold electrodes is electrochemically monitored with a redox probe of K₃Fe(CN)₆/K₄Fe(CN)₆. Upon binding of the proteins to the carbohydrate-modified electrodes, the redox current in CV decreases. The binding-induced change in electrochemical signal is thus used to construct Langmuir adsorption isotherm for the carbohydrate–protein interactions and to obtain the association constants. The association constants of carbohydrate–protein interactions determined by CV ((5.8 ± 1.2) × 10⁷ M^− 1 for mannose–Con A, (2.6 ± 0.5) × 10⁸ M^− 1 for galactose-CT) were in good agreement with those measured with electrochemical impedance spectroscopy and quartz crystal microbalance.     

Influence of protein–protein interactions on bulk mass transport during ultrafiltration

Bulk mass transfer limitations can have a significant effect on the flux and selectivity during membrane ultrafiltration. Most previous studies of these phenomena have employed the simple stagnant film analysis, but this model is unable to account for the effects of solute–solute interactions on mass transport. We have developed a generalized framework for multicomponent mass transfer that includes both thermodynamic and hydrodynamic (frictional) interactions. Thermodynamic (virial) coefficients were evaluated from osmotic pressure data for albumin (BSA) and immunoglobulins (IgG), while hydrodynamic interaction parameters were determined from filtrate flux data obtained in a stirred cell using fully retentive membranes. The protein concentration profiles in the bulk solution were evaluated by numerical solution of the governing continuity equations incorporating the multicomponent diffusive flux. This model was used to analyze flux and protein transmission data obtained for the filtration of BSA and IgG mixtures through partially permeable membranes. The model accurately predicted the large reduction in flux and BSA transmission upon addition of IgG. These effects were due to the coupling between BSA and IgG mass transfer caused by protein–protein interactions.     

Using ensemble methods to deal with imbalanced data in predicting protein–protein interactions

In proteins, the number of interacting pairs is usually much smaller than the number of non-interacting ones. So the imbalanced data problem will arise in the field of protein–protein interactions (PPIs) prediction. In this article, we introduce two ensemble methods to solve the imbalanced data problem. These ensemble methods combine the based-cluster under-sampling technique and the fusion classifiers. And then we evaluate the ensemble methods using a dataset from Database of Interacting Proteins (DIP) with 10-fold cross validation. All the prediction models achieve area under the receiver operating characteristic curve (AUC) value about 95%. Our results show that the ensemble classifiers are quite effective in predicting PPIs; we also gain some valuable conclusions on the performance of ensemble methods for PPIs in imbalanced data. The prediction software and all dataset employed in the work can be obtained for free at http://cic.scu.edu.cn/bioinformatics/Ensemble_PPIs/index.html.     

NMR-based analysis of protein–ligand interactions

Physiological processes are mainly controlled by intermolecular recognition mechanisms involving protein–protein and protein–ligand (low molecular weight molecules) interactions. One of the most important tools for probing these interactions is high-field solution nuclear magnetic resonance (NMR) through protein-observed and ligand-observed experiments, where the protein receptor or the organic compounds are selectively detected. NMR binding experiments rely on comparison of NMR parameters of the free and bound states of the molecules. Ligand-observed methods are not limited by the protein molecular size and therefore have great applicability for analysing protein–ligand interactions. The use of these NMR techniques has considerably expanded in recent years, both in chemical biology and in drug discovery. We review here three major ligand-observed NMR methods that depend on the nuclear Overhauser effect—transferred nuclear Overhauser effect spectroscopy, saturation transfer difference spectroscopy and water–ligand interactions observed via gradient spectroscopy experiments—with the aim of reporting recent developments and applications for the characterization of protein–ligand complexes, including affinity measurements and structural determination.     

Studying protein–protein affinity and immobilized ligand–protein affinity interactions using MS-based methods

This review discusses the most important current methods employing mass spectrometry (MS) analysis for the study of protein affinity interactions. The methods are discussed in depth with particular reference to MS-based approaches for analyzing protein–protein and protein–immobilized ligand interactions, analyzed either directly or indirectly. First, we introduce MS methods for the study of intact protein complexes in the gas phase. Next, pull-down methods for affinity-based analysis of protein–protein and protein–immobilized ligand interactions are discussed. Presently, this field of research is often called interactomics or interaction proteomics. A slightly different approach that will be discussed, chemical proteomics, allows one to analyze selectivity profiles of ligands for multiple drug targets and off-targets. Additionally, of particular interest is the use of surface plasmon resonance technologies coupled with MS for the study of protein interactions. The review addresses the principle of each of the methods with a focus on recent developments and the applicability to lead compound generation in drug discovery as well as the elucidation of protein interactions involved in cellular processes. The review focuses on the analysis of bioaffinity interactions of proteins with other proteins and with ligands, where the proteins are considered as the bioactives analyzed by MS.     

Investigation of protein–protein interactions in living cells by chemical crosslinking and mass spectrometry

The identification of protein–protein interactions within their physiological environment is the key to understanding biological processes at the molecular level. However, the artificial nature of in vitro experiments, with their lack of other cellular components, may obstruct observations of specific cellular processes. In vivo analyses can provide information on the processes within a cell that might not be observed in vitro. Chemical crosslinking combined with mass spectrometric analysis of the covalently connected binding partners allows us to identify interacting proteins and to map their interface regions directly in the cell. In this paper, different in vivo crosslinking strategies for deriving information on protein–protein interactions in their physiological environment are described.     

Development of small molecules designed to modulate protein–protein interactions

Summary Protein–protein interactions are ubiquitous, essential to almost all known biological processes, and offer attractive opportunities for therapeutic intervention. Developing small molecules that modulate protein–protein interactions is challenging, owing to the large size of protein-complex interface, the lack of well-defined binding pockets, etc. We describe a general approach based on the “privileged-structure hypothesis” [Che, Ph.D. Thesis, Washington University, 2003] – that any organic templates capable of mimicking surfaces of protein-recognition motifs are potential privileged scaffolds as protein-complex antagonists – to address the challenges inherent in the discovery of small-molecule inhibitors of protein–protein interactions.This paper is adapted from a presentation at the 230th National Meeting of the American Chemical Society, Washington DC, August 28 – September 1, 2005, Abstract COMP-136.     

Carbohydrate–protein interactions and their biosensing applications

Carbohydrate recognition is clearly present throughout nature, playing a major role in the initial attachment of one biological entity to another. The important question is whether these prevalent interactions could provide a real suitable alternative to the use of antibodies or nucleic acid for detection and identification. Currently, examples of carbohydrates being employed in biological detection systems are limited. The challenges of using carbohydrate recognition for detection mainly come from the weak affinity of carbohydrate–protein interactions, the lack of versatile carbohydrate scaffolds with well-defined structures, and the less developed high-information-content, real-time, and label-free assay technology. In this review, we focus on discussing the characteristics of carbohydrate–protein interactions in nature and the methods for carbohydrate immobilization based on surface coupling chemistry in terms of their general applicability for developing carbohydrate- and lectin-based label-free sensors. Furthermore, examples of innovative design of multivalent carbohydrate–protein interactions for sensor applications are given. We limit our review to show the feasibility of carbohydrate and lectin as recognition elements for label-free sensor development in several representative cases to formulate a flexible platform for their use as recognition elements for real-world biosensor applications.     

Label-free bioelectronic detection of aptamer–protein interactions

We demonstrate for the first time the utility of nucleic acid aptamers for electrochemical detection of proteins. Highly specific and sensitive label-free detection of the target protein is achieved by combining aptamer-coated magnetic beads and chronopotentiometric stripping measurements of the captured protein (in connection to the intrinsic electroactivity of the protein). Lysozyme has thus been detected selectively in a mixture containing a large excess of six proteins and amino acids (both electroactive and non-electroactive), with a detection limit of 350 fmol (7 nM). While aptamer-based electronic sensors are in their infancy, such devices offer attractive opportunities for electrochemical detection of proteins and for developing proteomic chips.     

Liposomal glyco-microarray for studying glycolipid–protein interactions

A microarray enables high-throughput interaction screening of numerous biomolecules; however, fabrication of a microarray composed of cellular membrane components has proven difficult. We report fabrication of a liposomal glyco-microarray by using an azide-reactive liposome that carries synthetic and natural glycolipids via chemically selective and biocompatible liposome immobilization chemistry. Briefly, liposomes carrying anchor lipid dipalmitoylphosphatidylethanolamine (DPPE)-PEG(2000)-triphenylphosphine and ganglioside (GM1 or GM3) were prepared first and were then printed onto an azide-modified glass slide so as to afford a liposomal glyco-microarray via Staudinger ligation. Fluorescent dye release kinetics and fluorescence imaging confirmed successful liposome immobilization and specific protein binding to the intact arrayed glycoliposomes. The liposomal glyco-microarray with different gangliosides showed their specific lectin and toxin binding with different binding affinity. The azide-reactive liposome provides a facile strategy for fabrication of either a natural or a synthetic glycolipid-based membrane-mimetic glycoarray. This liposomal glyco-microarray is simple and broadly applicable and thus will find important biomedical applications, such as studying glycolipid-protein interactions and toxin screening applications.     

Microarray of DNA–protein complexes on poly-3-hydroxybutyrate surface for pathogen detection

A novel strategy was developed for the specific immobilization of DNA probes on poly-3-hydroxybutyrate (PHB) surface by using the substrate-binding domain (SBD) of PHB depolymerase as an active binding motif. To demonstrate whether this method can be used for the detection of clinical pathogens, the pathogen-specific biotin-labeled DNA probes were immobilized via core streptavidin (cSA) fused to the SBD. The pathogen-specific 15-mer oligonucleotide probes were designed for four model pathogens, while the target DNAs were prepared by PCR using universal primers. The complex of pathogen-specific probes and cSA-SBD fusion protein was immobilized on the PHB-coated slide by microspotting. This DNA–protein complex microarray was able to successfully diagnose Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa. Furthermore, the specific pathogens could be diagnosed in the presence of other microorganisms. Thus, the DNA–protein complex microarray platform technology employing PHB and the SBD reported here can be widely used for the detection of DNA–DNA and DNA–biomolecule interactions without synthetic or chemical modification of biomolecules or solid surface.     

A functional feature analysis on diverse protein–protein interactions: application for the prediction of binding affinity

Protein–protein interactions (PPIs) play crucial roles in diverse cellular processes. There are different types of PPIs based on the composition, affinity and whether the association is permanent or transient. Analyzing the diversity of PPIs at the atomic level is crucial for uncovering the key features governing the interactions involved in PPI. A systematic physico-chemical and conformational studies were implemented on interfaces involved in different PPIs, including crystal packing, weak transient heterodimers, weak transient homodimers, strong transient heterodimers and homodimers. The comparative analysis shows that the interfaces tend to be larger, less planar, and more tightly packed with the increase of the interaction strength. Meanwhile the strong interactions undergo greater conformational changes than the weak ones involving main chains as well as side chains. Finally, using 18 features derived from our analysis, we developed a support vector regression model to predict the binding affinity with a promising result, which further demonstrate the reliability of our studies. We believe this study will provide great help in more thorough understanding the mechanism of diverse PPIs.     

Transition metal atoms grafted on the nanodiamonds surface: Identification and guest–host spin–spin interactions

This survey describes recent achievements in creating a new type of materials – nanodiamonds grafted with atoms of transition metals. Structural features of some selected chelate complexes studied by density functional theory, their scope and limitations as well as possible applications are discussed. Using the example of copper ions, their location relative to subsurface defects of detonation diamond is investigated by the method of electron paramagnetic resonance (EPR).     

Inhibition of protein interactions: co-crystalized protein–protein interfaces are nearly as good as holo proteins in rigid-body ligand docking

Modulating protein interaction pathways may lead to the cure of many diseases. Known protein–protein inhibitors bind to large pockets on the protein–protein interface. Such large pockets are detected also in the protein–protein complexes without known inhibitors, making such complexes potentially druggable. The inhibitor-binding site is primary defined by the side chains that form the largest pocket in the protein-bound conformation. Low-resolution ligand docking shows that the success rate for the protein-bound conformation is close to the one for the ligand-bound conformation, and significantly higher than for the apo conformation. The conformational change on the protein interface upon binding to the other protein results in a pocket employed by the ligand when it binds to that interface. This proof-of-concept study suggests that rather than using computational pocket-opening procedures, one can opt for an experimentally determined structure of the target co-crystallized protein–protein complex as a starting point for drug design.     

Biophysical and electrochemical studies of protein–nucleic acid interactions

Monatshefte für Chemie - Chemical Monthly - This review is devoted to biophysical and electrochemical methods used for studying protein–nucleic acid (NA) interactions. The importance of...     

Plasmonics for the study of metal ion–protein interactions

The study of metal–protein interactions is an expanding field of research investigated by bioinorganic chemists as it has wide applications in biological systems. Very recently, it has been reported that it is possible to study metal–protein interactions by immobilizing biomolecules on metal surfaces and applying experimental approaches based on plasmonics which have usually been used to investigate protein–protein interactions. This is possible because the electronic structure of metals generates plasmons whose properties can be exploited to obtain information from biomolecules that interact not only with other molecules but also with ions in solution. One major challenge of such approaches is to immobilize the protein to be studied on a metal surface with preserved native structure. This review reports and discusses all the works that deal with such an expanding new field of application of plasmonics with specific attention to surface plasmon resonance, highlighting the advantages and drawbacks of such approaches in comparison with other experimental techniques traditionally used to study metal–protein interactions.

Assessment of high-confidence protein–protein interactome in yeast

The identification of protein–protein interactions (PPIs) and their networks is vitally important to systemically define and understand the roles of proteins in biological systems. In spite of development of numerous experimental systems to detect PPIs and diverse research on assessment of the quality of the obtained data, a consensus – highly reliable, almost complete – interactome of Saccharomyces cerevisiae is not presented yet. In this work, we proposed an unsupervised statistical approach to create a high-confidence yeast PPI network. For this, we assembled databases of interacting protein pairs for yeast and obtained an extremely large PPI dataset which comprises of 135 154 non-redundant interactions between 6191 yeast proteins. A scoring scheme considering eight heterogeneous biological features resulted with a broad score distribution and a highly reliable network consisting of 29 046 physical interactions with scores higher than the threshold value of 0.85, for which sensitivity, specificity and coverage were 86%, 68%, and 72%, respectively. We evaluated our method by comparing it with other scoring schemes and showed that reducing the noise inherent in experimental PPIs via our scoring scheme further increased the accuracy. Current study is expected to increase the efficiency of the methodologies in biological research which make use of protein interaction networks.