High confidence determination of specific protein-protein interactions using quantitative mass spectrometry

In recent years, interactions between proteins have successfully been determined by mass spectrometry. A limitation of this technology has been the need for extensive purification, which restricts throughput and implies a tradeoff between specificity and the ability to detect weak or transient interactions. Quantitative proteomics sidesteps this problem by directly comparing specific and control pull-downs. Specific interaction partners are revealed by their quantitative ratios rather than by gel-based visualization and can be retrieved from a vast excess of background proteins. This principle is revolutionizing the protein interaction field as demonstrated by recent applications in fields as diverse as tyrosine signaling pathways, cell adhesion, and chromatin biology.     

Combining different 'omics' technologies to map and validate protein-protein interactions in humans.

The mapping of protein-protein interactions is key to understanding biological processes. Many technologies have been reported to map interactions and these have been systematically applied in yeast. To date, the number of reported yeast protein interactions that have been truly validated by at least one other approach is low. The mapping of human protein interaction networks is even more complicated. Thus, it is unreasonable to try to map the human interactome; instead, interaction mapping in human cell lines should be focused along the lines of diseases or changes that can be associated with specific cells. In this paper, an approach for combining different 'omics' technologies to achieve efficient mapping and validation of protein interactions in human cell lines is presented.     

Global approaches to protein-protein interactions

The availability of complete, annotated genome sequences for a variety of eukaryotic organisms has paved the way for a paradigm shift in biomedical research from the 'one gene-one hypothesis' approach to more global, systematic strategies that analyse genes or proteins on a genome- and proteome-wide scale. One daunting task in the post-genome era is to determine how the complement of expressed cellular proteins - the proteome - is organised into functional, higher-order networks, by mapping all constitutive and dynamic protein-protein interactions. Traditionally, reductionist approaches have typically focused on a few, selected gene products and their interactions in a particular physiological context. In contrast, more holistic strategies aim at understanding complex biological systems, for example global protein-protein interaction networks on a cellular or organismal level. Several large-scale proteomics technologies have been developed to generate comprehensive, cellular protein-protein interaction maps.     

A lock-and-key model for protein-protein interactions

MOTIVATION: Protein-protein interaction networks are one of the major post-genomic data sources available to molecular biologists. They provide a comprehensive view of the global interaction structure of an organism's proteome, as well as detailed information on specific interactions. Here we suggest a physical model of protein interactions that can be used to extract additional information at an intermediate level: It enables us to identify proteins which share biological interaction motifs, and also to identify potentially missing or spurious interactions. RESULTS: Our new graph model explains observed interactions between proteins by an underlying interaction of complementary binding domains (lock-and-key model). This leads to a novel graph-theoretical algorithm to identify bipartite subgraphs within protein-protein interaction networks where the underlying data are taken from yeast two-hybrid experimental results. By testing on synthetic data, we demonstrate that under certain modelling assumptions, the algorithm will return correct domain information about each protein in the network. Tests on data from various model organisms show that the local and global patterns predicted by the model are indeed found in experimental data. Using functional and protein structure annotations, we show that bipartite subnetworks can be identified that correspond to biologically relevant interaction motifs. Some of these are novel and we discuss an example involving SH3 domains from the Saccharomyces cerevisiae interactome. AVAILABILITY: The algorithm (in Matlab format) is available (see http://www.maths.strath.ac.uk/~aas96106/lock_key.html).     

Targeting protein-protein interactions for parasite control

Finding new drug targets for pathogenic infections would be of great utility for humanity, as there is a large need to develop new drugs to fight infections due to the developing resistance and side effects of current treatments. Current drug targets for pathogen infections involve only a single protein. However, proteins rarely act in isolation, and the majority of biological processes occur via interactions with other proteins, so protein-protein interactions (PPIs) offer a realm of unexplored potential drug targets and are thought to be the next-generation of drug targets. Parasitic worms were chosen for this study because they have deleterious effects on human health, livestock, and plants, costing society billions of dollars annually and many sequenced genomes are available. In this study, we present a computational approach that utilizes whole genomes of 6 parasitic and 1 free-living worm species and 2 hosts. The species were placed in orthologous groups, then binned in species-specific orthologous groups. Proteins that are essential and conserved among species that span a phyla are of greatest value, as they provide foundations for developing broad-control strategies. Two PPI databases were used to find PPIs within the species specific bins. PPIs with unique helminth proteins and helminth proteins with unique features relative to the host, such as indels, were prioritized as drug targets. The PPIs were scored based on RNAi phenotype and homology to the PDB (Protein DataBank). EST data for the various life stages, GO annotation, and druggability were also taken into consideration. Several PPIs emerged from this study as potential drug targets. A few interactions were supported by co-localization of expression in M. incognita (plant parasite) and B. malayi (H. sapiens parasite), which have extremely different modes of parasitism. As more genomes of pathogens are sequenced and PPI databases expanded, this methodology will become increasingly applicable.     

Chromatographic methods to study protein-protein interactions.

Proteins and enzymes are now generally thought to be organized within the cell to form clusters in a dynamic and versatile way, and heterologous protein-protein interactions are believed to be involved in virtually all cellular events. Therefore we need appropriate tools to detect and study such interactions. Chromatographic techniques prove to be well suited for this kind of investigation. Real complexes formed between proteins can be studied by classic gel filtration. When enzymes are studied, active enzyme gel chromatography is a useful alternative. A variant of classic gel filtration is gel filtration equilibrium analysis, which is similar to equilibrium dialysis. When the association formed is only dynamic and equilibrates very rapidly, either the Hummel-Dryer method of equilibrium gel filtration or large-zone equilibrium filtration sometimes allows the interactions to be analyzed, both qualitatively and quantitatively. Very often, however, interactions between enzymes and proteins can only be evidenced in vitro in media that mimic the intracellular situation. Immobilized proteins are excellent tools for this type of research. Several examples are indeed known where the immobilization of an enzyme on a solid support does not affect its real properties, but rather changes its environment in such a way that the diffusion becomes limiting. Affinity chromatography using immobilized proteins allows the analysis of heterologous protein-protein interactions, both qualitatively and quantitatively. A useful alternative appears to be affinity electrophoresis. The latter technique, however, is exclusively qualitative. All these techniques are described and illustrated with examples taken from the literature.     

Effect of training datasets on support vector machine prediction of protein-protein interactions

Knowledge of protein-protein interaction is useful for elucidating protein function via the concept of 'guilt-by-association'. A statistical learning method, Support Vector Machine (SVM), has recently been explored for the prediction of protein-protein interactions using artificial shuffled sequences as hypothetical noninteracting proteins and it has shown promising results (Bock, J. R., Gough, D. A., Bioinformatics 2001, 17, 455-460). It remains unclear however, how the prediction accuracy is affected if real protein sequences are used to represent noninteracting proteins. In this work, this effect is assessed by comparison of the results derived from the use of real protein sequences with that derived from the use of shuffled sequences. The real protein sequences of hypothetical noninteracting proteins are generated from an exclusion analysis in combination with subcellular localization information of interacting proteins found in the Database of Interacting Proteins. Prediction accuracy using real protein sequences is 76.9% compared to 94.1% using artificial shuffled sequences. The discrepancy likely arises from the expected higher level of difficulty for separating two sets of real protein sequences than that for separating a set of real protein sequences from a set of artificial sequences. The use of real protein sequences for training a SVM classification system is expected to give better prediction results in practical cases. This is tested by using both SVM systems for predicting putative protein partners of a set of thioredoxin related proteins. The prediction results are consistent with observations, suggesting that real sequence is more practically useful in development of SVM classification system for facilitating protein-protein interaction prediction.     

Heterocyclic alpha-helix mimetics for targeting protein-protein interactions

The design and synthesis of alpha-helix peptidomimetics using inverse electron demand Diels-Alder reactions is described. The potency of the resulting pyridazine-based library to disrupt the Bak/Bcl-X(L) interaction was tested using an in vitro fluorescence polarization assay.     

Electrophoretic methods for studying protein-protein interactions.

Protein-protein interactions are involved in many biological processes ranging from DNA replication, to signal transduction, to metabolism control, to viral assembly. The understanding of those interactions would allow the effective design of new drugs and further manipulation of those interactions. Several useful analytical methods are available for the study of protein-protein binding, and among them, electrophoresis is commonly used. We describe two types of electrophoresis: gel electrophoresis and capillary electrophoresis. Gel electrophoresis is a well-established method used to study protein-protein interactions and includes overlay gel electrophoresis, charge shift method, band shift assay, countermigration electrophoresis, affinophoresis, affinity electrophoresis, rocket immunoelectrophoresis, and crossed immunoelectrophoresis. These techniques are briefly described along with their advantages and limitations. Capillary electrophoresis, on the other hand, is a relatively new method and affinity capillary electrophoresis has demonstrated its value in the measurement of binding constants, the estimation of kinetic rate constants, and the determination of stoichiometry of biomolecular interactions. It offers short analysis time, requires minute amounts of protein samples, usually involves no radiolabeled compounds, and, most importantly, is carried out in solution. We summarize the principles of affinity capillary electrophoresis for studying protein-protein interactions along with current limitations and describe in depth its application to the determination of stoichiometries of tight and weak binding protein-protein interactions. The protocol presented in the experimental section details the use of affinity capillary electrophoresis for the determination of stoichiometry of protein complexes.     

Learning to predict protein-protein interactions from protein sequences

In order to understand the molecular machinery of the cell, we need to know about the multitude of protein-protein interactions that allow the cell to function. High-throughput technologies provide some data about these interactions, but so far that data is fairly noisy. Therefore, computational techniques for predicting protein-protein interactions could be of significant value. One approach to predicting interactions in silico is to produce from first principles a detailed model of a candidate interaction. We take an alternative approach, employing a relatively simple model that learns dynamically from a large collection of data. In this work, we describe an attraction-repulsion model, in which the interaction between a pair of proteins is represented as the sum of attractive and repulsive forces associated with small, domain- or motif-sized features along the length of each protein. The model is discriminative, learning simultaneously from known interactions and from pairs of proteins that are known (or suspected) not to interact. The model is efficient to compute and scales well to very large collections of data. In a cross-validated comparison using known yeast interactions, the attraction-repulsion method performs better than several competing techniques.     

MaGIC: a program to generate targeted marker sets for genome-wide association studies

High-throughput genotyping technologies such as DNA pooling and DNA microarrays mean that whole-genome screens are now practical for complex disease gene discovery using association studies. Because it is currently impractical to use all available markers, a subset is typically selected on the basis of required saturation density. Restricting markers to those within annotated genomic features of interest (e.g., genes or exons) or within feature-rich regions, reduces workload and cost while retaining much information. We have designed a program (MaGIC) that exploits genome assembly data to create lists of markers correlated with other genomic features. Marker lists are generated at a user-defined spacing and can target features with a user-defined density. Maps are in base pairs or linkage disequilibrium units (LDUs) as derived from the International HapMap data, which is useful for association studies and fine-mapping. Markers may be selected on the basis of heterozygosity and source database, and single nucleotide polymorphism (SNP) markers may additionally be selected on the basis of validation status. The import function means the method can be used for any genomic features such as housekeeping genes, long interspersed elements (LINES), or Alu repeats in humans, and is also functional for other species with equivalent data. The program and source code is freely available at http://cogent.iop.kcl.ac.uk/MaGIC.cogx.     

Global analysis of protein-protein interactions reveals multiple CYP2E1-reductase complexes

Although a single binary functional complex between cytochrome P450 (P450 or CYP for a specific isoform) and cytochrome P450 reductase (CPR) has been generally accepted in the literature, this simple model failed to explain the experimentally observed catalytic activity of recombinant CYP2E1 in dependence on the total concentration of the added CPR-K56Q mutant. Our rejection of the simplest 1:1 binding model was based on two independent lines of experimental evidence. First, under the assumption of the 1:1 binding model, separate analyses of titration curves obtained while varying either P450 or CPR concentrations individually produced contradictory results. Second, an asymmetric Job plot suggested the existence of higher order molecular complexes. To identify the most probable complexation mechanism, we generated a comprehensive data set where the concentrations of both P450 and P450 were varied simultaneously, rather than one at a time. The resulting two-dimensional data were globally fit to 32 candidate mechanistic models, involving the formation of binary, ternary, and quaternary P450.CPR complexes, in the absence or presence or P450 and CPR homodimers. Of the 32 candidate models (mechanisms), two models were approximately equally successful in explaining our experimental data. The first plausible model involves the binary complex P450.CPR, the quaternary complex (P450)2.(CPR)2, and the homodimer (P450)2. The second plausible model additionally involves a weakly bound ternary complex (P450)2.CPR. Importantly, only the binary complex P450.CPR seems catalytically active in either of the two most probable mechanisms.     

Using indirect protein-protein interactions for protein complex prediction

Protein complexes are fundamental for understanding principles of cellular organizations. As the sizes of protein-protein interaction (PPI) networks are increasing, accurate and fast protein complex prediction from these PPI networks can serve as a guide for biological experiments to discover novel protein complexes. However, it is not easy to predict protein complexes from PPI networks, especially in situations where the PPI network is noisy and still incomplete. Here, we study the use of indirect interactions between level-2 neighbors (level-2 interactions) for protein complex prediction. We know from previous work that proteins which do not interact but share interaction partners (level-2 neighbors) often share biological functions. We have proposed a method in which all direct and indirect interactions are first weighted using topological weight (FS-Weight), which estimates the strength of functional association. Interactions with low weight are removed from the network, while level-2 interactions with high weight are introduced into the interaction network. Existing clustering algorithms can then be applied to this modified network. We have also proposed a novel algorithm that searches for cliques in the modified network, and merge cliques to form clusters using a "partial clique merging" method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein-protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes.     

Refined phylogenetic profiles method for predicting protein-protein interactions

MOTIVATION: The increasing availability of complete genome sequences provides excellent opportunity for the further development of tools for functional studies in proteomics. Several experimental approaches and in silico algorithms have been developed to cluster proteins into networks of biological significance that may provide new biological insights, especially into understanding the functions of many uncharacterized proteins. Among these methods, the phylogenetic profiles method has been widely used to predict protein-protein interactions. It involves the selection of reference organisms and identification of homologous proteins. Up to now, no published report has systematically studied the effects of the reference genome selection and the identification of homologous proteins upon the accuracy of this method. RESULTS: In this study, we optimized the phylogenetic profiles method by integrating phylogenetic relationships among reference organisms and sequence homology information to improve prediction accuracy. Our results revealed that the selection of the reference organisms set and the criteria for homology identification significantly are two critical factors for the prediction accuracy of this method. Our refined phylogenetic profiles method shows greater performance and potentially provides more reliable functional linkages compared with previous methods.     

Analysis on multi-domain cooperation for predicting protein-protein interactions

Background  

Domains are the basic functional units of proteins. It is believed that protein-protein interactions are realized through domain interactions. Revealing multi-domain cooperation can provide deep insights into the essential mechanism of protein-protein interactions at the domain level and be further exploited to improve the accuracy of protein interaction prediction.     

In-cell NMR for protein-protein interactions (STINT-NMR)

We describe an in-cell NMR-based method for mapping the structural interactions (STINT-NMR) that underlie protein-protein complex formation. This method entails sequentially expressing two (or more) proteins within a single bacterial cell in a time-controlled manner and monitoring their interactions using in-cell NMR spectroscopy. The resulting NMR data provide a complete titration of the interaction and define structural details of the interacting surfaces at atomic resolution. Unlike the case where interacting proteins are simultaneously overexpressed in the labeled medium, in STINT-NMR the spectral complexity is minimized because only the target protein is labeled with NMR-active nuclei, which leaves the interactor protein(s) cryptic. This method can be combined with genetic and molecular screens to provide a structural foundation for proteomic studies. The protocol takes 4 d from the initial transformation of the bacterial cells to the acquisition of the NMR spectra.