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.     

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.     

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.     

Compacting local protein folds with a “hybrid protein model”

 The “hybrid protein model” is a fuzzy model for compacting local protein structures. It learns a nonredundant database encoded in a previously defined structural alphabet composed of 16 protein blocks (PBs). The hybrid protein is composed of a series of distributions of the probability of observing the PBs. The training is an iterative unsupervised process that for every fold to be learnt consists of looking for the most similar pattern present in the hybrid protein and modifying it slightly. Finally each position of the hybrid protein corresponds to a set of similar local structures. Superimposing those local structures yields an average root mean square of 3.14 ?. The significant amino acid characteristics related to the local structures are determined. The use of this model is illustrated by finding the most similar folds between two cytochromes P450. Received: 13 June 2000 / Accepted: 18 September 2000 / Published online: 19 January 2001     

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.     

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.     

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.     

Protein function prediction using neighbor relativity in protein–protein interaction network

There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called “Neighbor Relativity Coefficient” (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network.     

Prediction of protein–protein complexes using replica exchange with repulsive scaling

The realistic prediction of protein–protein complex structures is import to ultimately model the interaction of all proteins in a cell and for the design of new protein–protein interactions. In principle, molecular dynamics (MD) simulations allow one to follow the association process under realistic conditions including full partner flexibility and surrounding solvent. However, due to the many local binding energy minima at the surface of protein partners, MD simulations are frequently trapped for long times in transient association states. We have designed a replica-exchange based scheme employing different levels of a repulsive biasing between partners in each replica simulation. The bias acts only on intermolecular interactions based on an increase in effective pairwise van der Waals radii (repulsive scaling (RS)-REMD) without affecting interactions within each protein or with the solvent. For a set of five protein test cases (out of six) the RS-REMD technique allowed the sampling of near-native complex structures even when starting from the opposide site with respect to the native binding site for one partner. Using the same start structures and same computational demand regular MD simulations sampled near native complex structures only for one case. The method showed also improved results for the refinement of docked structures in the vicinity of the native binding geometry compared to regular MD refinement.     

Novel pyrrolopyrimidine-based α-helix mimetics: cell-permeable inhibitors of protein−protein interactions

There is considerable interest in developing non-peptidic, small-molecule α-helix mimetics to disrupt α-helix-mediated protein?protein interactions. Herein, we report the design of a novel pyrrolopyrimidine-based scaffold for such α-helix mimetics with increased conformational rigidity. We also developed a facile solid-phase synthetic route that is amenable to divergent synthesis of a large library. Using a fluorescence polarization-based assay, we identified cell-permeable, dual MDMX/MDM2 inhibitors, demonstrating that the designed molecules can act as α-helix mimetics.     

Membrane protein–surfactant complexes

Transmembrane proteins expose to the surrounding membrane a belt of mainly hydrophobic amino acid residues, which makes them insoluble in water. Solubilizing them and handling them in vitro generally relies on the use of dissociating surfactants (detergents). Exposing membrane proteins to detergents, however, adversely affects their stability, which is a major hindrance in their study. After briefly recalling relevant aspects of membrane protein structure, the modus operandi of detergents and the problems they raise, we describe alternative approaches such as insertion into bicelles or lipid cubic phases, or association with non-detergent amphiphiles such as peptitergents, hemifluorinated surfactants and amphipols. These novel supramolecular assemblies offer a fascinating playground for collaborative studies between organic chemists, physical chemists and biologists, and they have spurred imaginative works in each of these fields.     

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.     

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.     

Scoring protein–protein docked structures based on the balance and tightness of binding

One main issue in protein-protein docking is to filter or score the putative docked structures. Unlike many popular scoring functions that are based on geometric and energetic complementarity, we present a set of scoring functions that are based on the consideration of local balance and tightness of binding of the docked structures. These scoring functions include the force and moment acting on one component (ligand) imposed by the other (receptor) and the second order spatial derivatives of protein-protein interaction potential. The scoring functions were applied to the docked structures of 19 test targets including enzyme/inhibitor, antibody/antigen and other classes of protein complexes. The results indicate that these scoring functions are also discriminative for the near-native conformation. For some cases, such as antibody/antigen, they show more discriminative efficiency than some other scoring functions, such as desolvation free energy (deltaG(des)) based on pairwise atom-atom contact energy (ACE). The correlation analyses between present scoring functions and the energetic functions also show that there is no clear correlation between them; therefore, the present scoring functions are not essentially the same as energy functions.     

Calcium-binding ability of soy protein hydrolysates

This present study investigated the ability of various soy protein hydrolysates(SPHs)in binding calcium.It was demonstrated that the amount of Ca-bound depended greatly on the SPHs obtained using different proteases,which included:neutrase, flavourzyme,protease M and pepsin.The maximum level of Ca-bound(66.9 mg/g)occurred when protease M was used to hydrolyze soy protein.Peptide fragments exhibiting high Ca-binding capacity had molecular weights of either 14.4 or 8-9 kDa.The level of Ca-bound increased linearly with the increment of carboxyl content in SPHs,and further deamidation on SPHs from protease M improved Ca-binding of the hydrolysate.     

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.