PRINCESS, a protein interaction confidence evaluation system with multiple data sources

Advances in proteomics technologies have enabled novel protein interactions to be detected at high speed, but they come at the expense of relatively low quality. Therefore, a crucial step in utilizing the high throughput protein interaction data is evaluating their confidence and then separating the subsets of reliable interactions from the background noise for further analyses. Using Bayesian network approaches, we combine multiple heterogeneous biological evidences, including model organism protein-protein interaction, interaction domain, functional annotation, gene expression, genome context, and network topology structure, to assign reliability to the human protein-protein interactions identified by high throughput experiments. This method shows high sensitivity and specificity to predict true interactions from the human high throughput protein-protein interaction data sets. This method has been developed into an on-line confidence scoring system specifically for the human high throughput protein-protein interactions. Users may submit their protein-protein interaction data on line, and the detailed information about the supporting evidence for query interactions together with the confidence scores will be returned. The Web interface of PRINCESS (protein interaction confidence evaluation system with multiple data sources) is available at the website of China Human Proteome Organisation.     

An integrated workflow for charting the human interaction proteome: insights into the PP2A system

Protein complexes represent major functional units for the execution of biological processes. Systematic affinity purification coupled with mass spectrometry (AP‐MS) yielded a wealth of information on the compendium of protein complexes expressed in Saccharomyces cerevisiae. However, global AP‐MS analysis of human protein complexes is hampered by the low throughput, sensitivity and data robustness of existing procedures, which limit its application for systems biology research. Here, we address these limitations by a novel integrated method, which we applied and benchmarked for the human protein phosphatase 2A system. We identified a total of 197 protein interactions with high reproducibility, showing the coexistence of distinct classes of phosphatase complexes that are linked to proteins implicated in mitosis, cell signalling, DNA damage control and more. These results show that the presented analytical process will substantially advance throughput and reproducibility in future systematic AP‐MS studies on human protein complexes.     

High throughput protein-protein interaction data: clues for the architecture of protein complexes

Background

High-throughput techniques are becoming widely used to study protein-protein interactions and protein complexes on a proteome-wide scale. Here we have explored the potential of these techniques to accurately determine the constituent proteins of complexes and their architecture within the complex.

Results

Two-dimensional representations of the 19S and 20S proteasome, mediator, and SAGA complexes were generated and overlaid with high quality pairwise interaction data, core-module-attachment classifications from affinity purifications of complexes and predicted domain-domain interactions. Pairwise interaction data could accurately determine the members of each complex, but was unexpectedly poor at deciphering the topology of proteins in complexes. Core and module data from affinity purification studies were less useful for accurately defining the member proteins of these complexes. However, these data gave strong information on the spatial proximity of many proteins. Predicted domain-domain interactions provided some insight into the topology of proteins within complexes, but was affected by a lack of available structural data for the co-activator complexes and the presence of shared domains in paralogous proteins.

Conclusion

The constituent proteins of complexes are likely to be determined with accuracy by combining data from high-throughput techniques. The topology of some proteins in the complexes will be able to be clearly inferred. We finally suggest strategies that can be employed to use high throughput interaction data to define the membership and understand the architecture of proteins in novel complexes.     

Identification and characterization of histatin 1 salivary complexes by using mass spectrometry

With recent progress in the analysis of the salivary proteome, the number of salivary proteins identified has increased dramatically. However, the physiological functions of many of the newly discovered proteins remain unclear. Closely related to the study of a protein's function is the identification of its interaction partners. We investigated interactions among and functions of histatin 1 and the other proteins that are present in saliva by using high‐throughput mass spectrometric techniques. This led to the identification of 43 proteins able to interact with histatin 1. In addition, we found that these protein–protein interactions protect complex partners from proteolysis and modulate their antifungal activity. Our data contribute significantly to characterization of the salivary interactome and to understanding the biology of salivary protein complexes.     

Functional maps of protein complexes from quantitative genetic interaction data

Recently, a number of advanced screening technologies have allowed for the comprehensive quantification of aggravating and alleviating genetic interactions among gene pairs. In parallel, TAP-MS studies (tandem affinity purification followed by mass spectroscopy) have been successful at identifying physical protein interactions that can indicate proteins participating in the same molecular complex. Here, we propose a method for the joint learning of protein complexes and their functional relationships by integration of quantitative genetic interactions and TAP-MS data. Using 3 independent benchmark datasets, we demonstrate that this method is >50% more accurate at identifying functionally related protein pairs than previous approaches. Application to genes involved in yeast chromosome organization identifies a functional map of 91 multimeric complexes, a number of which are novel or have been substantially expanded by addition of new subunits. Interestingly, we find that complexes that are enriched for aggravating genetic interactions (i.e., synthetic lethality) are more likely to contain essential genes, linking each of these interactions to an underlying mechanism. These results demonstrate the importance of both large-scale genetic and physical interaction data in mapping pathway architecture and function.     

Statistically inferring protein-protein associations with affinity isolation LC-MS/MS assays

Affinity isolation of protein complexes followed by protein identification by LC-MS/MS is an increasingly popular approach for mapping protein interactions. However, systematic and random assay errors from multiple sources must be considered to confidently infer authentic protein-protein interactions. To address this issue, we developed a general, robust statistical method for inferring authentic interactions from protein prey-by-bait frequency tables using a binomial-based likelihood ratio test (LRT) coupled with Bayes' Odds estimation. We then applied our LRT-Bayes' algorithm experimentally using data from protein complexes isolated from Rhodopseudomonas palustris. Our algorithm, in conjunction with the experimental protocol, inferred with high confidence authentic interacting proteins from abundant, stable complexes, but few or no authentic interactions for lower-abundance complexes. The algorithm can discriminate against a background of prey proteins that are detected in association with a large number of baits as an artifact of the measurement. We conclude that the experimental protocol including the LRT-Bayes' algorithm produces results with high confidence but moderate sensitivity. We also found that Monte Carlo simulation is a feasible tool for checking modeling assumptions, estimating parameters, and evaluating the significance of results in protein association studies.     

Modularity and hormone sensitivity of the Drosophila melanogaster insulin receptor/target of rapamycin interaction proteome

Genetic analysis in Drosophila melanogaster has been widely used to identify a system of genes that control cell growth in response to insulin and nutrients. Many of these genes encode components of the insulin receptor/target of rapamycin (InR/TOR) pathway. However, the biochemical context of this regulatory system is still poorly characterized in Drosophila. Here, we present the first quantitative study that systematically characterizes the modularity and hormone sensitivity of the interaction proteome underlying growth control by the dInR/TOR pathway. Applying quantitative affinity purification and mass spectrometry, we identified 97 high confidence protein interactions among 58 network components. In all, 22% of the detected interactions were regulated by insulin affecting membrane proximal as well as intracellular signaling complexes. Systematic functional analysis linked a subset of network components to the control of dTORC1 and dTORC2 activity. Furthermore, our data suggest the presence of three distinct dTOR kinase complexes, including the evolutionary conserved dTTT complex (Drosophila TOR, TELO2, TTI1). Subsequent genetic studies in flies suggest a role for dTTT in controlling cell growth via a dTORC1‐ and dTORC2‐dependent mechanism.     

Mass spectrometric analysis of protein interactions

Mass spectrometry is a powerful tool for identification of interaction partners and structural characterization of protein interactions because of its high sensitivity, mass accuracy and tolerance towards sample heterogeneity. Several tools that allow studies of protein interaction are now available and recent developments that increase the confidence of studies of protein interaction by mass spectrometry include quantification of affinity-purified proteins by stable isotope labeling and reagents for surface topology studies that can be identified by mass-contributing reporters (e.g. isotope labels, cleavable cross-linkers or fragment ions. The use of mass spectrometers to study protein interactions using deuterium exchange and for analysis of intact protein complexes recently has progressed considerably.     

Fully automated protein complex prediction based on topological similarity and community structure

To understand the function of protein complexes and their association with biological processes, a lot of studies have been done towards analyzing the protein-protein interaction (PPI) networks. However, the advancement in high-throughput technology has resulted in a humongous amount of data for analysis. Moreover, high level of noise, sparseness, and skewness in degree distribution of PPI networks limits the performance of many clustering algorithms and further analysis of their interactions.In addressing and solving these problems we present a novel random walk based algorithm that converts the incomplete and binary PPI network into a protein-protein topological similarity matrix (PP-TS matrix). We believe that if two proteins share some high-order topological similarities they are likely to be interacting with each other. Using the obtained PP-TS matrix, we constructed and used weighted networks to further study and analyze the interaction among proteins. Specifically, we applied a fully automated community structure finding algorithm (Auto-HQcut) on the obtained weighted network to cluster protein complexes. We then analyzed the protein complexes for significance in biological processes. To help visualize and analyze these protein complexes we also developed an interface that displays the resulting complexes as well as the characteristics associated with each complex.Applying our approach to a yeast protein-protein interaction network, we found that the predicted protein-protein interaction pairs with high topological similarities have more significant biological relevance than the original protein-protein interactions pairs. When we compared our PPI network reconstruction algorithm with other existing algorithms using gene ontology and gene co-expression, our algorithm produced the highest similarity scores. Also, our predicted protein complexes showed higher accuracy measure compared to the other protein complex predictions.     

Molecular interaction networks for the analysis of human disease: Utility,limitations, and considerations

High‐throughput ‘‐omics’ data can be combined with large‐scale molecular interaction networks, for example, protein–protein interaction networks, to provide a unique framework for the investigation of human molecular biology. Interest in these integrative ‘‐omics’ methods is growing rapidly because of their potential to understand complexity and association with disease; such approaches have a focus on associations between phenotype and “network‐type.” The potential of this research is enticing, yet there remain a series of important considerations. Here, we discuss interaction data selection, data quality, the relative merits of using data from large high‐throughput studies versus a meta‐database of smaller literature‐curated studies, and possible issues of sociological or inspection bias in interaction data. Other work underway, especially international consortia to establish data formats, quality standards and address data redundancy, and the improvements these efforts are making to the field, is also evaluated. We present options for researchers intending to use large‐scale molecular interaction networks as a functional context for protein or gene expression data, including microRNAs, especially in the context of human disease.     

Protein complexes of the Escherichia coli cell envelope

Protein complexes are an intrinsic aspect of life in the membrane. Knowing which proteins are assembled in these complexes is therefore essential to understanding protein function(s). Unfortunately, recent high throughput protein interaction studies have failed to deliver any significant information on proteins embedded in the membrane, and many membrane protein complexes remain ill defined. In this study, we have optimized the blue native-PAGE technique for the study of membrane protein complexes in the inner and outer membranes of Escherichia coli. In combination with second dimension SDS-PAGE and mass spectrometry, we have been able to identify 43 distinct protein complexes. In addition to a number of well characterized complexes, we have identified known and orphan proteins in novel oligomeric states. For two orphan proteins, YhcB and YjdB, our findings enable a tentative functional assignment. We propose that YhcB is a hitherto unidentified additional subunit of the cytochrome bd oxidase and that YjdB, which co-localizes with the ZipA protein, is involved in cell division. Our reference two-dimensional blue native-SDS-polyacrylamide gels will facilitate future studies of the assembly and composition of E. coli membrane protein complexes during different growth conditions and in different mutant backgrounds.     

On the Importance of Polar Interactions for Complexes Containing Intrinsically Disordered Proteins

There is a growing recognition for the importance of proteins with large intrinsically disordered (ID) segments in cell signaling and regulation. ID segments in these proteins often harbor regions that mediate molecular recognition. Coupled folding and binding of the recognition regions has been proposed to confer high specificity to interactions involving ID segments. However, researchers recently questioned the origin of the interaction specificity of ID proteins because of the overrepresentation of hydrophobic residues in their interaction interfaces. Here, we focused on the role of polar and charged residues in interactions mediated by ID segments. Making use of the extended nature of most ID segments when in complex with globular proteins, we first identified large numbers of complexes between globular proteins and ID segments by using radius-of-gyration-based selection criteria. Consistent with previous studies, we found the interfaces of these complexes to be enriched in hydrophobic residues, and that these residues contribute significantly to the stability of the interaction interface. However, our analyses also show that polar interactions play a larger role in these complexes than in structured protein complexes. Computational alanine scanning and salt-bridge analysis indicate that interfaces in ID complexes are highly complementary with respect to electrostatics, more so than interfaces of globular proteins. Follow-up calculations of the electrostatic contributions to the free energy of binding uncovered significantly stronger Coulombic interactions in complexes harbouring ID segments than in structured protein complexes. However, they are counter-balanced by even higher polar-desolvation penalties. We propose that polar interactions are a key contributing factor to the observed high specificity of ID segment-mediated interactions.     

Challenges and rewards of interaction proteomics

The recent explosion of high throughput experimental technologies for characterizing protein interactions has generated large amounts of data describing interactions between thousands of proteins and producing genome scale views of protein assemblies. The systems level views afforded by these data hold great promise of leading to new knowledge but also involve many challenges. Deriving meaningful biological conclusions from these views crucially depends on our understanding of the approximation and biases that enter into deriving and interpreting the data. The challenges and rewards of interaction proteomics are reviewed here using as an example the latest comprehensive high throughput analyses of protein interactions in yeast.     

Challenges for the prediction of macromolecular interactions

Macromolecular interactions are central to most cellular processes. Experimental methods generate diverse data on these interactions ranging from high throughput protein-protein interactions (PPIs) to the crystallised structures of complexes. Despite this, only a fraction of interactions have been identified and therefore predictive methods are essential to fill in the numerous gaps. Many predictive methods use information from related proteins. Accordingly, we review the conservation of interface and ligand binding sites within protein families and their association with conserved residues and Specificity Determining Positions. We then review recent developments in predictive methods for the identification of PPIs, protein interface sites and small molecule ligand binding sites. The challenges that are still faced by the community in these areas are discussed.     

An oriented peptide array library (OPAL) strategy to study protein-protein interactions

One of the major questions in signal transduction is how the specificities of protein-protein interactions determine the assembly of distinct signaling complexes in response to stimuli. Several peptide library methods have been developed and widely used to study protein-protein interactions. These approaches primarily rely on peptide or DNA sequencing to identify the peptide or consensus motif for binding and may prove too costly or difficult to accommodate high throughput applications. We report here an oriented peptide array library (OPAL) approach that should facilitate high throughput proteomic analysis of protein-protein interactions. OPAL integrates the principles of both the oriented peptide libraries and array technologies. Hundreds of pools of oriented peptide libraries are synthesized as amino acid scan arrays. We demonstrate that these arrays can be used to map the specificities of a variety of interactions, including antibodies, protein domains such Src homology 2 domains, and protein kinases.     

In vivo analysis of proteomes and interactomes using Parallel Affinity Capture (iPAC) coupled to mass spectrometry

Affinity purification coupled to mass spectrometry provides a reliable method for identifying proteins and their binding partners. In this study we have used Drosophila melanogaster proteins triple tagged with Flag, Strep II, and Yellow fluorescent protein in vivo within affinity pull-down experiments and isolated these proteins in their native complexes from embryos. We describe a pipeline for determining interactomes by Parallel Affinity Capture (iPAC) and show its use by identifying partners of several protein baits with a range of sizes and subcellular locations. This purification protocol employs the different tags in parallel and involves detailed comparison of resulting mass spectrometry data sets, ensuring the interaction lists achieved are of high confidence. We show that this approach identifies known interactors of bait proteins as well as novel interaction partners by comparing data achieved with published interaction data sets. The high confidence in vivo protein data sets presented here add new data to the currently incomplete D. melanogaster interactome. Additionally we report contaminant proteins that are persistent with affinity purifications irrespective of the tagged bait.