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.     

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.     

Droplet microfluidics with magnetic beads: a new tool to investigate drug–protein interactions

In this study, we give the proof of concept for a method to determine binding constants of compounds in solution. By implementing a technique based on magnetic beads with a microfluidic device for segmented flow generation, we demonstrate, for individual droplets, fast, robust and complete separation of the magnetic beads. The beads are used as a carrier for one binding partner and hence, any bound molecule is separated likewise, while the segmentation into small microdroplets ensures fast mixing, and opens future prospects for droplet-wise analysis of drug candidate libraries. We employ the method for characterization of drug–protein binding, here warfarin to human serum albumin. The approach lays the basis for a microfluidic droplet-based screening device aimed at investigating the interactions of drugs with specific targets including enzymes and cells. Furthermore, the continuous method could be employed for various applications, such as binding assays, kinetic studies, and single cell analysis, in which rapid removal of a reactive component is required.     

What induces pocket openings on protein surface patches involved in protein–protein interactions?

We previously showed for the proteins BCL-X_L, IL-2, and MDM2 that transient pockets at their protein–protein binding interfaces can be identified by applying the PASS algorithm to molecular dynamics (MD) snapshots. We now investigated which aspects of the natural conformational dynamics of proteins induce the formation of such pockets. The pocket detection protocol was applied to three different conformational ensembles for the same proteins that were extracted from MD simulations of the inhibitor bound crystal conformation in water and the free crystal/NMR structure in water and in methanol. Additional MD simulations studied the impact of backbone mobility. The more efficient CONCOORD or normal mode analysis (NMA) techniques gave significantly smaller pockets than MD simulations, whereas tCONCOORD generated pockets comparable to those observed in MD simulations for two of the three systems. Our findings emphasize the influence of solvent polarity and backbone rearrangements on the formation of pockets on protein surfaces and should be helpful in future generation of transient pockets as putative ligand binding sites at protein–protein interfaces. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Intermolecular interactions in methyl formate-ethanol mixtures at 303–313 K according to ultrasonic data

Density (ρ), viscosity (η), and ultrasonic velocity (U) have been measured for a binary mixture composed of methyl formate and ethanol at 303, 308, and 313 K. The adiabatic compressibility (β), acoustic impedance (Z), free length (L _f ), free volume (V _f ), internal pressure (π _i ), viscous relaxation time (τ), and Gibbs free energy (ΔG) were calculated from the experimental data. The excess values of these parameters (β ^E , Z ^E , L _f ^E , V _f ^E , π _i ^E , τ ^E , and ΔG ^E ) have also been calculated using the determined parameters and interpreted in terms of molecular interactions. The deviations in the sign and values of these excess parameters from the ideal mixing reveal the nature of intermolecular interactions between components of the mixture.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

Weighted edge based clustering to identify protein complexes in protein–protein interaction networks incorporating gene expression profile

Protein complex detection from protein–protein interaction (PPI) network has received a lot of focus in recent years. A number of methods identify protein complexes as dense sub-graphs using network information while several other methods detect protein complexes based on topological information. While the methods based on identifying dense sub-graphs are more effective in identifying protein complexes, not all protein complexes have high density. Moreover, existing methods focus more on static PPI networks and usually overlook the dynamic nature of protein complexes. Here, we propose a new method, Weighted Edge based Clustering (WEC), to identify protein complexes based on the weight of the edge between two interacting proteins, where the weight is defined by the edge clustering coefficient and the gene expression correlation between the interacting proteins. Our WEC method is capable of detecting highly inter-connected and co-expressed protein complexes. The experimental results of WEC on three real life data shows that our method can detect protein complexes effectively in comparison with other highly cited existing methods.Availability: The WEC tool is available at http://agnigarh.tezu.ernet.in/~rosy8/shared.html.     

Using water–solvent systems to estimate in vivo blood–tissue partition coefficients

Background

Blood–tissue partition coefficients indicate how a chemical will distribute throughout the body and are an important part of any pharmacokinetic study. They can be used to assess potential toxicological effects from exposure to chemicals and the efficacy of potential novel drugs designed to target certain organs or the central nervous system. In vivo measurement of blood–tissue partition coefficients is often complicated, time-consuming, and relatively expensive, so developing in vitro systems that approximate in vivo ones is desirable. We have determined such systems for tissues such as brain, muscle, liver, lung, kidney, heart, skin, and fat.

Results

Several good (p < 0.05) blood–tissue partition coefficient models were developed using a single water–solvent system. These include blood–brain, blood–lung, blood–heart, blood–fat, blood–skin, water–skin, and skin permeation. Many of these partition coefficients have multiple water–solvent systems that can be used as models. Several solvents—methylcyclohexane, 1,9-decadiene, and 2,2,2-trifluoroethanol—were common to multiple models and thus a single measurement can be used to estimate multiple blood–tissue partition coefficients. A few blood–tissue systems require a combination of two water–solvent partition coefficient measurements to model well (p < 0.01), namely: blood–muscle: chloroform and dibutyl ether, blood–liver: N-methyl-2-piperidone and ethanol/water (60:40) volume, and blood–kidney: DMSO and ethanol/water (20:80) volume.

Conclusion

In vivo blood–tissue partition coefficients can be easily estimated through water–solvent partition coefficient measurements.

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Graphical abstract: Predicted blood-brain barrier partition coefficients coloured by measured log BB value

     

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.

Synthesis of an electronically-tuned minimally interfering alkynyl photo-affinity label to measure small molecule–protein interactions

We report the synthesis of an electronically-tuned minimally interfering photo-affinity label (MI-PAL), a compact five-carbon tag functionalized with an alkyl diazirine and alkyne handle. MI-PAL is compatible with protein photo-conjugation, click chemistry and mass spectrometry and readily installed to complex molecules for biological target identification.     

Surface plasmon resonance imaging for affinity analysis of aptamer–protein interactions with PDMS microfluidic chips

We report on the use of PDMS multichannels for affinity studies of DNA aptamer–human Immunoglobulin E (IgE) interactions by surface plasmon resonance imaging (SPRi). The sensing surface was prepared with thiol-terminated aptamers through a self-assembling process in the PDMS channels defined on a gold substrate. Cysteamine was codeposited with the thiol aptamers to promote proper spatial arrangement of the aptamers and thus maintain their optimal binding efficiencies. Four aptamers with different nucleic acid sequences were studied to test their interaction affinity toward IgE, and the results confirmed that aptamer I (5′-SH-GGG GCA CGT TTA TCC GTC CCT CCT AGT GGC GTG CCC C-3′) has the strongest binding affinity. Control experiments were conducted with a PEG-functionalized surface and IgG was used to replace IgE in order to verify the selective binding of aptamer I to the IgE molecules. A linear concentration-dependent relationship between IgE and aptamer I was obtained, and a 2-nM detection limit was achieved. SPRi data were further analyzed by global fitting, and the dissociation constant of aptamer I–IgE complex was found to be 2.7 × 10⁻⁷ M, which agrees relatively well with the values reported in the literature. Aptamer affinity screening by SPR imaging demonstrates marked advantages over competing methods because it does not require labeling, can be used in real-time, and is potentially high-throughput. The ability to provide both qualitative and quantitative results on a multichannel chip further establishes SPRi as a powerful tool for the study of biological interactions in a multiplexed format. Figure The SPRi sensograms and thier global fits for aptamer I and IgE interactions. Insert in the difference image obtained with the PDMS microchannel flow cell for aptamer IV, III, and I (from left to right     

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.