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.     

PPIDomainMiner: Inferring domain-domain interactions from multiple sources of protein-protein interactions

Many biological processes are mediated by protein-protein interactions (PPIs). Because protein domains are the building blocks of proteins, PPIs likely rely on domain-domain interactions (DDIs). Several attempts exist to infer DDIs from PPI networks but the produced datasets are heterogeneous and sometimes not accessible, while the PPI interactome data keeps growing.We describe a new computational approach called “PPIDM” (Protein-Protein Interactions Domain Miner) for inferring DDIs using multiple sources of PPIs. The approach is an extension of our previously described “CODAC” (Computational Discovery of Direct Associations using Common neighbors) method for inferring new edges in a tripartite graph. The PPIDM method has been applied to seven widely used PPI resources, using as “Gold-Standard” a set of DDIs extracted from 3D structural databases. Overall, PPIDM has produced a dataset of 84,552 non-redundant DDIs. Statistical significance (p-value) is calculated for each source of PPI and used to classify the PPIDM DDIs in Gold (9,175 DDIs), Silver (24,934 DDIs) and Bronze (50,443 DDIs) categories. Dataset comparison reveals that PPIDM has inferred from the 2017 releases of PPI sources about 46% of the DDIs present in the 2020 release of the 3did database, not counting the DDIs present in the Gold-Standard. The PPIDM dataset contains 10,229 DDIs that are consistent with more than 13,300 PPIs extracted from the IMEx database, and nearly 23,300 DDIs (27.5%) that are consistent with more than 214,000 human PPIs extracted from the STRING database. Examples of newly inferred DDIs covering more than 10 PPIs in the IMEx database are provided.Further exploitation of the PPIDM DDI reservoir includes the inventory of possible partners of a protein of interest and characterization of protein interactions at the domain level in combination with other methods. The result is publicly available at http://ppidm.loria.fr/.     

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.     

Mining literature for protein-protein interactions

MOTIVATION: A central problem in bioinformatics is how to capture information from the vast current scientific literature in a form suitable for analysis by computer. We address the special case of information on protein-protein interactions, and show that the frequencies of words in Medline abstracts can be used to determine whether or not a given paper discusses protein-protein interactions. For those papers determined to discuss this topic, the relevant information can be captured for the Database of Interacting PROTEINS: Furthermore, suitable gene annotations can also be captured. RESULTS: Our Bayesian approach scores Medline abstracts for probability of discussing the topic of interest according to the frequencies of discriminating words found in the abstract. More than 80 discriminating words (e.g. complex, interaction, two-hybrid) were determined from a training set of 260 Medline abstracts corresponding to previously validated entries in the Database of Interacting Proteins. Using these words and a log likelihood scoring function, approximately 2000 Medline abstracts were identified as describing interactions between yeast proteins. This approach now forms the basis for the rapid expansion of the Database of Interacting Proteins.     

Combining protein-protein interactions information with support vector machine to identify chronic obstructive pulmonary disease related genes

Chronic obstructive pulmonary disease (COPD) is a complex human disease with a high mortality rate. So far, the studies of COPD have not been well organized despite the well-documented role of cigarette smoking in the genesis of COPD. In the recent years, microarray analyses have helped to identify some potential disease related genes. However, the low reproducibility of many published gene signatures has been criticized. It therefore suggested that incorporation of network or pathway information into prognostic biomarker discovery might improve the prediction performance. In this analysis, we combined protein-protein interactions (PPI) information with the support vector machine (SVM) method to identify potential COPD-related genes that would allow one to distinguish accurately severe emphysema from non-/mildly emphysematous lung tissue. We identified 8 COPD-related feature genes. When compared with another SVM method which did not use the prior PPI information, the prediction accuracy was significantly enhanced (AUC was increased from 0.513 to 0.909). On the base of results obtained one can suppose that incorporating network of prior knowledge into gene selection methods significantly improves classification accuracy. Consequently, the gene expression profiles from human emphysematous lung tissue may provide insight into the pathogenesis, and a good classification prediction algorithm based on prior biological knowledge can further strengthen this performance.     

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).     

Biochemical approaches for discovering protein-protein interactions

Protein–protein interactions or protein complexes are integral in nearly all cellular processes, ranging from metabolism to structure. Elucidating both individual protein associations and complex protein interaction networks, while challenging, is an essential goal of functional genomics. For example, discovering interacting partners for a 'protein of unknown function' can provide insight into actual function far beyond what is possible with sequence-based predictions, and provide a platform for future research. Synthetic genetic approaches such as two-hybrid screening often reveal a perplexing array of potential interacting partners for any given target protein. It is now known, however, that this type of anonymous screening approach can yield high levels of false-positive results, and therefore putative interactors must be confirmed by independent methods. In vitro biochemical strategies for identifying interacting proteins are varied and time-honored, some being as old as the field of protein chemistry itself. Herein we discuss five biochemical approaches for isolating and characterizing protein–protein interactions in vitro : co-immunoprecipitation, blue native gel electrophoresis, in vitro binding assays, protein cross-linking, and rate-zonal centrifugation. A perspective is provided for each method, and where appropriate specific, trial-tested methods are included.     

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.     

Simultaneous confidence sets for several effective doses

Construction of simultaneous confidence sets for several effective doses currently relies on inverting the Scheffé type simultaneous confidence band, which is known to be conservative. We develop novel methodology to make the simultaneous coverage closer to its nominal level, for both two‐sided and one‐sided simultaneous confidence sets. Our approach is shown to be considerably less conservative than the current method, and is illustrated with an example on modeling the effect of smoking status and serum triglyceride level on the probability of the recurrence of a myocardial infarction.     

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.     

Analysis of sequence-reactivity space for protein-protein interactions

Sequence–reactivity space is defined by the relationships between amino acid type choices at some residue positions in a protein and the reactivities of the resulting variants. We are studying Kazal superfamily serine proteinase inhibitors, under substitution of any combination of residue types at 10 binding‐region positions. Reactivities are defined by the standard free energy of association for an inhibitor against an enzyme, and we are interested in both the strength (the free energy value) and specificity (relative free energy values for one inhibitor against different enzymes). Characterizing the structure of such a space poses several interesting questions: (1) How many sequences achieve particular strength and specificity characteristics? (2) What is the best such sequence? (3) What are some nearly‐as‐good alternatives? (4) What are their common residue type characteristics (e.g., conservation and correlation)? Although these problems are all highly combinatorial in nature, this article develops an efficient, integrated mechanism to address them under a data‐driven model that predicts reactivity for given sequences. We employ sampling and a novel deterministic distribution propagation algorithm, in order to determine both the reactivity distribution and sequence composition statistics; integer programming and a novel branch‐and‐bound search algorithm, in order to optimize sequences and enumerate near‐optimal sequences; and correlation‐based sequence decomposition, in order to identify sequence motifs. We demonstrate the value of our mechanism in analyzing the Kazal superfamily sequence–reactivity space, providing insights into the underlying biochemistry and suggesting hypotheses for further experimental consideration. In general, our mechanism offers a valuable tool for investigating the available degrees of freedom in protein design within a combined computational–experimental framework. Proteins 2005. © 2004 Wiley‐Liss, Inc.     

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.