In search of the biological significance of modular structures in protein networks

Many complex networks such as computer and social networks exhibit modular structures, where links between nodes are much denser within modules than between modules. It is widely believed that cellular networks are also modular, reflecting the relative independence and coherence of different functional units in a cell. While many authors have claimed that observations from the yeast protein–protein interaction (PPI) network support the above hypothesis, the observed structural modularity may be an artifact because the current PPI data include interactions inferred from protein complexes through approaches that create modules (e.g., assigning pairwise interactions among all proteins in a complex). Here we analyze the yeast PPI network including protein complexes (PIC network) and excluding complexes (PEC network). We find that both PIC and PEC networks show a significantly greater structural modularity than that of randomly rewired networks. Nonetheless, there is little evidence that the structural modules correspond to functional units, particularly in the PEC network. More disturbingly, there is no evolutionary conservation among yeast, fly, and nematode modules at either the whole-module or protein-pair level. Neither is there a correlation between the evolutionary or phylogenetic conservation of a protein and the extent of its participation in various modules. Using computer simulation, we demonstrate that a higher-than-expected modularity can arise during network growth through a simple model of gene duplication, without natural selection for modularity. Taken together, our results suggest the intriguing possibility that the structural modules in the PPI network originated as an evolutionary byproduct without biological significance.     

DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions

The Database of Interacting Proteins (DIP: http://dip.doe-mbi.ucla.edu) is a database that documents experimentally determined protein–protein interactions. It provides the scientific community with an integrated set of tools for browsing and extracting information about protein interaction networks. As of September 2001, the DIP catalogs ~11 000 unique interactions among 5900 proteins from >80 organisms; the vast majority from yeast, Helicobacter pylori and human. Tools have been developed that allow users to analyze, visualize and integrate their own experimental data with the information about protein–protein interactions available in the DIP database.     

Amino acid–base interactions: a three-dimensional analysis of protein–DNA interactions at an atomic level

To assess whether there are universal rules that govern amino acid–base recognition, we investigate hydrogen bonds, van der Waals contacts and water-mediated bonds in 129 protein–DNA complex structures. DNA–backbone interactions are the most numerous, providing stability rather than specificity. For base interactions, there are significant base–amino acid type correlations, which can be rationalised by considering the stereochemistry of protein side chains and the base edges exposed in the DNA structure. Nearly two-thirds of the direct read-out of DNA sequences involves complex networks of hydrogen bonds, which enhance specificity. Two-thirds of all protein–DNA interactions comprise van der Waals contacts, compared to about one-sixth each of hydrogen and water-mediated bonds. This highlights the central importance of these contacts for complex formation, which have previously been relegated to a secondary role. Although common, water-mediated bonds are usually non-specific, acting as space-fillers at the protein–DNA interface. In conclusion, the majority of amino acid–base interactions observed follow general principles that apply across all protein–DNA complexes, although there are individual exceptions. Therefore, we distinguish between interactions whose specificities are ‘universal’ and ‘context-dependent’. An interactive Web-based atlas of side chain–base contacts provides access to the collected data, including analyses and visualisation of the three-dimensional geometry of the interactions.     

Frequent Assembly of Chimeric Complexes in the Protein Interaction Network of an Interspecies Yeast Hybrid

Hybrids between species often show extreme phenotypes, including some that take place at the molecular level. In this study, we investigated the phenotypes of an interspecies diploid hybrid in terms of protein–protein interactions inferred from protein correlation profiling. We used two yeast species, Saccharomyces cerevisiae and Saccharomyces uvarum, which are interfertile, but yet have proteins diverged enough to be differentiated using mass spectrometry. Most of the protein–protein interactions are similar between hybrid and parents, and are consistent with the assembly of chimeric complexes, which we validated using an orthogonal approach for the prefoldin complex. We also identified instances of altered protein–protein interactions in the hybrid, for instance, in complexes related to proteostasis and in mitochondrial protein complexes. Overall, this study uncovers the likely frequent occurrence of chimeric protein complexes with few exceptions, which may result from incompatibilities or imbalances between the parental proteomes.     

Using genome-wide measurements for computational prediction of SH2–peptide interactions

Peptide-recognition modules (PRMs) are used throughout biology to mediate protein–protein interactions, and many PRMs are members of large protein domain families. Recent genome-wide measurements describe networks of peptide–PRM interactions. In these networks, very similar PRMs recognize distinct sets of peptides, raising the question of how peptide-recognition specificity is achieved using similar protein domains. The analysis of individual protein complex structures often gives answers that are not easily applicable to other members of the same PRM family. Bioinformatics-based approaches, one the other hand, may be difficult to interpret physically. Here we integrate structural information with a large, quantitative data set of SH2 domain–peptide interactions to study the physical origin of domain–peptide specificity. We develop an energy model, inspired by protein folding, based on interactions between the amino-acid positions in the domain and peptide. We use this model to successfully predict which SH2 domains and peptides interact and uncover the positions in each that are important for specificity. The energy model is general enough that it can be applied to other members of the SH2 family or to new peptides, and the cross-validation results suggest that these energy calculations will be useful for predicting binding interactions. It can also be adapted to study other PRM families, predict optimal peptides for a given SH2 domain, or study other biological interactions, e.g. protein–DNA interactions.     

Dissecting protein-RNA recognition sites

We analyze the protein–RNA interfaces in 81 transient binary complexes taken from the Protein Data Bank. Those with tRNA or duplex RNA are larger than with single-stranded RNA, and comparable in size to protein–DNA interfaces. The protein side bears a strong positive electrostatic potential and resembles protein–DNA interfaces in its amino acid composition. On the RNA side, the phosphate contributes less, and the sugar much more, to the interaction than in protein–DNA complexes. On average, protein–RNA interfaces contain 20 hydrogen bonds, 7 that involve the phosphates, 5 the sugar 2′OH, and 6 the bases, and 32 water molecules. The average H-bond density per unit buried surface area is less with tRNA or single-stranded RNA than with duplex RNA. The atomic packing is also less compact in interfaces with tRNA. On the protein side, the main chain NH and Arg/Lys side chains account for nearly half of all H-bonds to RNA; the main chain CO and side chain acceptor groups, for a quarter. The 2′OH is a major player in protein–RNA recognition, and shape complementarity an important determinant, whereas electrostatics and direct base–protein interactions play a lesser part than in protein–DNA recognition.     

Prediction of yeast protein-protein interaction network: insights from the Gene Ontology and annotations

A map of protein–protein interactions provides valuable insight into the cellular function and machinery of a proteome. By measuring the similarity between two Gene Ontology (GO) terms with a relative specificity semantic relation, here, we proposed a new method of reconstructing a yeast protein–protein interaction map that is solely based on the GO annotations. The method was validated using high-quality interaction datasets for its effectiveness. Based on a Z-score analysis, a positive dataset and a negative dataset for protein–protein interactions were derived. Moreover, a gold standard positive (GSP) dataset with the highest level of confidence that covered 78% of the high-quality interaction dataset and a gold standard negative (GSN) dataset with the lowest level of confidence were derived. In addition, we assessed four high-throughput experimental interaction datasets using the positives and the negatives as well as GSPs and GSNs. Our predicted network reconstructed from GSPs consists of 40753 interactions among 2259 proteins, and forms 16 connected components. We mapped all of the MIPS complexes except for homodimers onto the predicted network. As a result, ~35% of complexes were identified interconnected. For seven complexes, we also identified some nonmember proteins that may be functionally related to the complexes concerned. This analysis is expected to provide a new approach for predicting the protein–protein interaction maps from other completely sequenced genomes with high-quality GO-based annotations.     

Purification of MAP–kinase protein complexes and identification of candidate components by XL–TAP–MS

The purification of low-abundance protein complexes and detection of in vivo protein–protein interactions in complex biological samples remains a challenging task. Here, we devised crosslinking and tandem affinity purification coupled to mass spectrometry (XL–TAP–MS), a quantitative proteomics approach for analyzing tandem affinity-purified, crosslinked protein complexes from plant tissues. We exemplarily applied XL–TAP–MS to study the MKK2–Mitogen-activated protein kinase (MPK4) signaling module in Arabidopsis thaliana. A tandem affinity tag consisting of an in vivo-biotinylated protein domain flanked by two hexahistidine sequences was adopted to allow for the affinity-based isolation of formaldehyde–crosslinked protein complexes under fully denaturing conditions. Combined with ¹⁵N stable isotopic labeling and tandem MS we captured and identified a total of 107 MKK2–MPK4 module-interacting proteins. Consistent with the role of the MPK signaling module in plant immunity, many of the module-interacting proteins are involved in the biotic and abiotic stress response of Arabidopsis. Validation of binary protein–protein interactions by in planta split-luciferase assays and in vitro kinase assays disclosed several direct phosphorylation targets of MPK4. Together, the XL–TAP–MS approach purifies low abundance protein complexes from biological samples and discovers previously unknown protein–protein interactions.

XL–TAP–MS: a novel technique that allows purification of crosslinked, low abundant protein complexes from plant tissues under denatured conditions and detection of in vivo protein–protein interactions.     

Protein-RNA interactions: a structural analysis

A detailed computational analysis of 32 protein–RNA complexes is presented. A number of physical and chemical properties of the intermolecular interfaces are calculated and compared with those observed in protein–double-stranded DNA and protein–single-stranded DNA complexes. The interface properties of the protein–RNA complexes reveal the diverse nature of the binding sites. van der Waals contacts played a more prevalent role than hydrogen bond contacts, and preferential binding to guanine and uracil was observed. The positively charged residue, arginine, and the single aromatic residues, phenylalanine and tyrosine, all played key roles in the RNA binding sites. A comparison between protein–RNA and protein–DNA complexes showed that whilst base and backbone contacts (both hydrogen bonding and van der Waals) were observed with equal frequency in the protein–RNA complexes, backbone contacts were more dominant in the protein–DNA complexes. Although similar modes of secondary structure interactions have been observed in RNA and DNA binding proteins, the current analysis emphasises the differences that exist between the two types of nucleic acid binding protein at the atomic contact level.     

In vitro selection of Jun-associated proteins using mRNA display

Although yeast two-hybrid assay and biochemical methods combined with mass spectrometry have been successfully employed for the analyses of protein–protein interactions in the field of proteomics, these methods encounter various difficulties arising from the usage of living cells, including inability to analyze toxic proteins and restriction of testable interaction conditions. Totally in vitro display technologies such as ribosome display and mRNA display are expected to circumvent these difficulties. In this study, we applied an mRNA display technique to screening for interactions of a basic leucine zipper domain of Jun protein in a mouse brain cDNA library. By performing iterative affinity selection and sequence analyses, we selected 16 novel Jun-associated protein candidates in addition to four known interactors. By means of real-time PCR and pull-down assay, 10 of the 16 newly discovered candidates were confirmed to be direct interactors with Jun in vitro. Furthermore, interaction of 6 of the 10 proteins with Jun was observed in cultured cells by means of co-immunoprecipitation and observation of subcellular localization. These results demonstrate that this in vitro display technology is effective for the discovery of novel protein–protein interactions and can contribute to the comprehensive mapping of protein–protein interactions.     

Integration of New Genes into Cellular Networks,and Their Structural Maturation

It has been recently discovered that new genes can originate de novo from noncoding DNA, and several biological traits including expression or sequence composition form a continuum from noncoding sequences to conserved genes. In this article, using yeast genes I test whether the integration of new genes into cellular networks and their structural maturation shows such a continuum by analyzing their changes with gene age. I show that 1) The number of regulatory, protein–protein, and genetic interactions increases continuously with gene age, although with very different rates. New regulatory interactions emerge rapidly within a few million years, while the number of protein–protein and genetic interactions increases slowly, with a rate of 2–2.25 × 10⁻⁸/year and 4.8 × 10⁻⁸/year, respectively. 2) Gene essentiality evolves relatively quickly: the youngest essential genes appear in proto-genes ∼14 MY old. 3) In contrast to interactions, the secondary structure of proteins and their robustness to mutations indicate that new genes face a bottleneck in their evolution: proto-genes are characterized by high β-strand content, high aggregation propensity, and low robustness against mutations, while conserved genes are characterized by lower strand content and higher stability, most likely due to the higher probability of gene loss among young genes and accumulation of neutral mutations.     

The role of disorder in RNA binding affinity and specificity

Most RNA-binding modules are small and bind few nucleotides. RNA-binding proteins typically attain the physiological specificity and affinity for their RNA targets by combining several RNA-binding modules. Here, we review how disordered linkers connecting RNA-binding modules govern the specificity and affinity of RNA–protein interactions by regulating the effective concentration of these modules and their relative orientation. RNA-binding proteins also often contain extended intrinsically disordered regions that mediate protein–protein and RNA–protein interactions with multiple partners. We discuss how these regions can connect proteins and RNA resulting in heterogeneous higher-order assemblies such as membrane-less compartments and amyloid-like structures that have the characteristics of multi-modular entities. The assembled state generates additional RNA-binding specificity and affinity properties that contribute to further the function of RNA-binding proteins within the cellular environment.     

Interaction generality, a measurement to assess the reliability of a protein-protein interaction

Here we introduce the ‘interaction generality’ measure, a new method for computationally assessing the reliability of protein–protein interactions obtained in biological experiments. This measure is basically the number of proteins involved in a given interaction and also adopts the idea that interactions observed in a complicated interaction network are likely to be true positives. Using a group of yeast protein–protein interactions identified in various biological experiments, we show that interactions with low generalities are more likely to be reproducible in other independent assays. We constructed more reliable networks by eliminating interactions whose generalities were above a particular threshold. The rate of interactions with common cellular roles increased from 63% in the unadjusted estimates to 79% in the refined networks. As a result, the rate of cross-talk between proteins with different cellular roles decreased, enabling very clear predictions of the functions of some unknown proteins. The results suggest that the interaction generality measure will make interaction data more useful in all organisms and may yield insights into the biological roles of the proteins studied.     

Cavities in protein–DNA and protein–RNA interfaces

An analysis of cavities present in protein–DNA and protein–RNA complexes is presented. In terms of the number of cavities and their total volume, the interfaces formed in these complexes are akin to those in transient protein–protein heterocomplexes. With homodimeric proteins protein–DNA interfaces may contain cavities involving both the protein subunits and DNA, and these are more than twice as large as cavities involving a single protein subunit and DNA. A parameter, cavity index, measuring the degree of surface complementarity, indicates that the packing of atoms in protein–protein/DNA/RNA is very similar, but it is about two times less efficient in the permanent interfaces formed between subunits in homodimers. As within the tertiary structure and protein–protein interfaces, protein–DNA interfaces have a higher inclination to be lined by β-sheet residues; from the DNA side, base atoms, in particular those in minor grooves, have a higher tendency to be located in cavities. The larger cavities tend to be less spherical and solvated. A small fraction of water molecules are found to mediate hydrogen-bond interactions with both the components, suggesting their primary role is to fill in the void left due to the local non-complementary nature of the surface patches.     

CANVS: an easy-to-use application for the analysis and visualization of mass spectrometry–based protein–protein interaction/association data

The elucidation of a protein’s interaction/association network is important for defining its biological function. Mass spectrometry–based proteomic approaches have emerged as powerful tools for identifying protein–protein interactions (PPIs) and protein–protein associations (PPAs). However, interactome/association experiments are difficult to interpret, considering the complexity and abundance of data that are generated. Although tools have been developed to identify protein interactions/associations quantitatively, there is still a pressing need for easy-to-use tools that allow users to contextualize their results. To address this, we developed CANVS, a computational pipeline that cleans, analyzes, and visualizes mass spectrometry–based interactome/association data. CANVS is wrapped as an interactive Shiny dashboard with simple requirements, allowing users to interface easily with the pipeline, analyze complex experimental data, and create PPI/A networks. The application integrates systems biology databases such as BioGRID and CORUM to contextualize the results. Furthermore, CANVS features a Gene Ontology tool that allows users to identify relevant GO terms in their results and create visual networks with proteins associated with relevant GO terms. Overall, CANVS is an easy-to-use application that benefits all researchers, especially those who lack an established bioinformatic pipeline and are interested in studying interactome/association data.     

Direct measurement of protein–protein interactions by FLIM-FRET at UV laser-induced DNA damage sites in living cells

Protein–protein interactions are essential to ensure timely and precise recruitment of chromatin remodellers and repair factors to DNA damage sites. Conventional analyses of protein–protein interactions at a population level may mask the complexity of interaction dynamics, highlighting the need for a method that enables quantification of DNA damage-dependent interactions at a single-cell level. To this end, we integrated a pulsed UV laser on a confocal fluorescence lifetime imaging (FLIM) microscope to induce localized DNA damage. To quantify protein–protein interactions in live cells, we measured Förster resonance energy transfer (FRET) between mEGFP- and mCherry-tagged proteins, based on the fluorescence lifetime reduction of the mEGFP donor protein. The UV-FLIM-FRET system offers a unique combination of real-time and single-cell quantification of DNA damage-dependent interactions, and can distinguish between direct protein–protein interactions, as opposed to those mediated by chromatin proximity. Using the UV-FLIM-FRET system, we show the dynamic changes in the interaction between poly(ADP-ribose) polymerase 1, amplified in liver cancer 1, X-ray repair cross-complementing protein 1 and tripartite motif containing 33 after DNA damage. This new set-up complements the toolset for studying DNA damage response by providing single-cell quantitative and dynamic information about protein–protein interactions at DNA damage sites.