A network-based analysis of polyanion-binding proteins utilizing yeast protein arrays

The high affinity of certain cellular polyanions for many proteins (polyanion-binding proteins (PABPs)) has been demonstrated previously. It has been hypothesized that such polyanions may be involved in protein structure stabilization, stimulation of folding through chaperone-like activity, and intra- and extracellular protein transport as well as intracellular organization. The purpose of the proteomics studies reported here was to seek evidence for the idea that the nonspecific but high affinity interactions of PABPs with polyanions have a functional role in intracellular processes. Utilizing yeast protein arrays and five biotinylated cellular polyanion probes (actin, tubulin, heparin, heparan sulfate, and DNA), we identified proteins that interact with these probes and analyzed their structural and amino acid sequence requirements as well as their predicted functions in the yeast proteome. We also provide evidence for the existence of a network-like system for PABPs and their potential roles as critical hubs in intracellular behavior. This investigation takes a first step toward achieving a better understanding of the nature of polyanion-protein interactions within cells and introduces an alternative way of thinking about intracellular organization.     

Polyanions and the proteome

The behavior of the proteome reflects spatial and temporal organization both within and without cells. We propose that various macromolecular entities possessing polyanionic character such as proteoglycans, lipid bilayer surfaces, microtubules, microfilaments, and polynucleotides may provide a functional network that mediates a variety of cellular phenomena. The interaction of proteins with this array of polyanions is characterized by a lower degree of specificity than seen with most commonly recognized macromolecular interactions. In this commentary, potential roles for this polyanion network in diverse functions such as protein/protein interactions, protein folding and stabilization, macromolecular transport, and various disease processes are all considered, as well as the use of polyanions as therapeutic agents. The role of small polyanions in the regulation of protein/polyanion interactions is also postulated. We provide preliminary experimental analysis of the extent to which proteins interact with polyanions inside cells using a combination of two-dimensional chromatographic and electrophoretic methods and antibody arrays. We conclude that many hundreds to thousands of such interactions are present in cells and argue that future understanding of the proteome will require that the "polyanion world" be taken into account.     

Kinetic recognition of the retinoblastoma tumor suppressor by a specific protein target

The retinoblastoma tumor suppressor (Rb) plays a key role in cell cycle control and is linked to various types of human cancer. Rb binds to the LxCxE motif, present in a number of cellular and viral proteins such as AdE1A, SV40 large T-antigen and human papillomavirus (HPV) E7, all instrumental in revealing fundamental mechanisms of tumor suppression, cell cycle control and gene expression. A detailed kinetic study of RbAB binding to the HPV E7 oncoprotein shows that an LxCxE-containing E7 fragment binds through a fast two-state reaction strongly favored by electrostatic interactions. Conversely, full-length E7 binds through a multistep process involving a pre-equilibrium between E7 conformers, a fast electrostatically driven association step guided by the LxCxE motif and a slow conformational rearrangement. This kinetic complexity arises from the conformational plasticity and intrinsically disordered nature of E7 and from multiple interaction surfaces present in both proteins. Affinity differences between E7N domains from high- and low-risk types are explained by their dissociation rates. In fact, since Rb is at the center of a large protein interaction network, fast and tight recognition provides an advantage for disruption by the viral proteins, where the balance of physiological and pathological interactions is dictated by kinetic ligand competition. The localization of the LxCxE motif within an intrinsically disordered domain provides the fast, diffusion-controlled interaction that allows viral proteins to outcompete physiological targets. We describe the interaction mechanism of Rb with a protein ligand, at the same time an LxCxE-containing model target, and a paradigmatic intrinsically disordered viral oncoprotein.     

Interaction between Intrinsically Disordered Proteins Frequently Occurs in a Human Protein-Protein Interaction Network

Intrinsic protein disorder is a widespread phenomenon characterised by a lack of stable three-dimensional structures and is considered to play an important role in protein-protein interactions (PPIs). This study examined the genome-wide preference of disorder in PPIs by using exhaustive disorder prediction in human PPIs. We categorised the PPIs into three types (interaction between disordered proteins, interaction between structured proteins, and interaction between a disordered protein and a structured protein) with regard to the flexibility of molecular recognition and compared these three interaction types in an existing human PPI network with those in a randomised network. Although the structured regions were expected to become the identifiers for binding recognition, this comparative analysis revealed unexpected results. The occurrence of interactions between disordered proteins was significantly frequent, and that between a disordered protein and a structured protein was significantly infrequent. We found that this propensity was much stronger in interactions between nonhub proteins. We also analysed the interaction types from a functional standpoint by using GO, which revealed that the interaction between disordered proteins frequently occurred in cellular processes, regulation, and metabolic processes. The number of interactions, especially in metabolic processes between disordered proteins, was 1.8 times as large as that in the randomised network. Another analysis conducted by using KEGG pathways provided results where several signaling pathways and disease-related pathways included many interactions between disordered proteins. All of these analyses suggest that human PPIs preferably occur between disordered proteins and that the flexibility of the interacting protein pairs may play an important role in human PPI networks.     

A human protein-protein interaction network: a resource for annotating the proteome

Protein-protein interaction maps provide a valuable framework for a better understanding of the functional organization of the proteome. To detect interacting pairs of human proteins systematically, a protein matrix of 4456 baits and 5632 preys was screened by automated yeast two-hybrid (Y2H) interaction mating. We identified 3186 mostly novel interactions among 1705 proteins, resulting in a large, highly connected network. Independent pull-down and co-immunoprecipitation assays validated the overall quality of the Y2H interactions. Using topological and GO criteria, a scoring system was developed to define 911 high-confidence interactions among 401 proteins. Furthermore, the network was searched for interactions linking uncharacterized gene products and human disease proteins to regulatory cellular pathways. Two novel Axin-1 interactions were validated experimentally, characterizing ANP32A and CRMP1 as modulators of Wnt signaling. Systematic human protein interaction screens can lead to a more comprehensive understanding of protein function and cellular processes.     

Virtual identification of essential proteins within the protein interaction network of yeast

Topological analysis of large scale protein-protein interaction networks (PINs) is important for understanding the organizational and functional principles of individual proteins. The number of interactions that a protein has in a PIN has been observed to be correlated with its indispensability. Essential proteins generally have more interactions than the nonessential ones. We show here that the lethality associated with removal of a protein from the yeast proteome correlates with different centrality measures of the nodes in the PIN, such as the closeness of a protein to many other proteins, or the number of pairs of proteins which need a specific protein as an intermediary in their communications, or the participation of a protein in different protein clusters in the PIN. These measures are significantly better than random selection in identifying essential proteins in a PIN. Centrality measures based on graph spectral properties of the network, in particular the subgraph centrality, show the best performance in identifying essential proteins in the yeast PIN. Subgraph centrality gives important structural information about the role of individual proteins, and permits the selection of possible targets for rational drug discovery through the identification of essential proteins in the PIN.     

Functional topology in a network of protein interactions

MOTIVATION: The building blocks of biological networks are individual protein-protein interactions (PPIs). The cumulative PPI data set in Saccharomyces cerevisiae now exceeds 78 000. Studying the network of these interactions will provide valuable insight into the inner workings of cells. RESULTS: We performed a systematic graph theory-based analysis of this PPI network to construct computational models for describing and predicting the properties of lethal mutations and proteins participating in genetic interactions, functional groups, protein complexes and signaling pathways. Our analysis suggests that lethal mutations are not only highly connected within the network, but they also satisfy an additional property: their removal causes a disruption in network structure. We also provide evidence for the existence of alternate paths that bypass viable proteins in PPI networks, while such paths do not exist for lethal mutations. In addition, we show that distinct functional classes of proteins have differing network properties. We also demonstrate a way to extract and iteratively predict protein complexes and signaling pathways. We evaluate the power of predictions by comparing them with a random model, and assess accuracy of predictions by analyzing their overlap with MIPS database. CONCLUSIONS: Our models provide a means for understanding the complex wiring underlying cellular function, and enable us to predict essentiality, genetic interaction, function, protein complexes and cellular pathways. This analysis uncovers structure-function relationships observable in a large PPI network.     

Categorizing biases in high-confidence high-throughput protein-protein interaction data sets

We characterized and evaluated the functional attributes of three yeast high-confidence protein-protein interaction data sets derived from affinity purification/mass spectrometry, protein-fragment complementation assay, and yeast two-hybrid experiments. The interacting proteins retrieved from these data sets formed distinct, partially overlapping sets with different protein-protein interaction characteristics. These differences were primarily a function of the deployed experimental technologies used to recover these interactions. This affected the total coverage of interactions and was especially evident in the recovery of interactions among different functional classes of proteins. We found that the interaction data obtained by the yeast two-hybrid method was the least biased toward any particular functional characterization. In contrast, interacting proteins in the affinity purification/mass spectrometry and protein-fragment complementation assay data sets were over- and under-represented among distinct and different functional categories. We delineated how these differences affected protein complex organization in the network of interactions, in particular for strongly interacting complexes (e.g. RNA and protein synthesis) versus weak and transient interacting complexes (e.g. protein transport). We quantified methodological differences in detecting protein interactions from larger protein complexes, in the correlation of protein abundance among interacting proteins, and in their connectivity of essential proteins. In the latter case, we showed that minimizing inherent methodology biases removed many of the ambiguous conclusions about protein essentiality and protein connectivity. We used these findings to rationalize how biological insights obtained by analyzing data sets originating from different sources sometimes do not agree or may even contradict each other. An important corollary of this work was that discrepancies in biological insights did not necessarily imply that one detection methodology was better or worse, but rather that, to a large extent, the insights reflected the methodological biases themselves. Consequently, interpreting the protein interaction data within their experimental or cellular context provided the best avenue for overcoming biases and inferring biological knowledge.     

Analyzing protein lists with large networks: edge-count probabilities in random graphs with given expected degrees.

We present an analytical framework to analyze lists of proteins with large undirected graphs representing their known functional relationships. We consider edge-count variables such as the number of interactions between a protein and a list, the size of a subgraph induced by a list, and the number of interactions bridging two lists. We derive approximate analytical expressions for the probability distributions of these variables in a model of a random graph with given expected degrees. Probabilities obtained with the analytical expressions are used to mine a protein interaction network for functional modules, characterize the connectedness of protein functional categories, and measure the strength of relations between modules.     

Ontological visualization of protein-protein interactions

Background  

Cellular processes require the interaction of many proteins across several cellular compartments. Determining the collective network of such interactions is an important aspect of understanding the role and regulation of individual proteins. The Gene Ontology (GO) is used by model organism databases and other bioinformatics resources to provide functional annotation of proteins. The annotation process provides a mechanism to document the binding of one protein with another. We have constructed protein interaction networks for mouse proteins utilizing the information encoded in the GO annotations. The work reported here presents a methodology for integrating and visualizing information on protein-protein interactions.     

Mapping the Polarity Interactome

Interactions between proteins are an essential part of biology, and the desire to identify these interactions has led to the development of numerous technologies to systematically map protein–protein interactions at a large scale. As in most cellular processes, protein interactions are central to the control of cell polarity, and a full understanding of polarity will require comprehensive knowledge of the protein interactions involved. At its core, cell polarity is established through carefully regulated mutually inhibitory interactions between several groups of cortical proteins. While several interactions have been identified, the dynamics and molecular mechanisms that control these interactions are not well understood. Cell polarity also needs to be integrated with cellular processes including junction formation, cytoskeletal organization, organelle positioning, protein trafficking, and functional specialization of membrane domains. Moreover, polarized cells need to respond to external cues that coordinate polarity at the tissue level. Identifying the protein–protein interactions responsible for integrating polarity with all of these processes remains a major challenge, in part because the mechanisms of polarity control vary in different contexts and with developmental times. Because of their unbiased nature, systematic large-scale protein–protein interaction mapping approaches can be particularly helpful to identify such mechanisms. Here, we discuss methods commonly used to generate proteome-wide interactome maps, with an emphasis on advances in our understanding of cell polarity that have been achieved through application of such methods.     

Evolution and function of CAG/polyglutamine repeats in protein-protein interaction networks

Expanded runs of consecutive trinucleotide CAG repeats encoding polyglutamine (polyQ) stretches are observed in the genes of a large number of patients with different genetic diseases such as Huntington's and several Ataxias. Protein aggregation, which is a key feature of most of these diseases, is thought to be triggered by these expanded polyQ sequences in disease-related proteins. However, polyQ tracts are a normal feature of many human proteins, suggesting that they have an important cellular function. To clarify the potential function of polyQ repeats in biological systems, we systematically analyzed available information stored in sequence and protein interaction databases. By integrating genomic, phylogenetic, protein interaction network and functional information, we obtained evidence that polyQ tracts in proteins stabilize protein interactions. This happens most likely through structural changes whereby the polyQ sequence extends a neighboring coiled-coil region to facilitate its interaction with a coiled-coil region in another protein. Alteration of this important biological function due to polyQ expansion results in gain of abnormal interactions, leading to pathological effects like protein aggregation. Our analyses suggest that research on polyQ proteins should shift focus from expanded polyQ proteins into the characterization of the influence of the wild-type polyQ on protein interactions.     

Linking structural features of protein complexes and biological function

Protein–protein interaction (PPI) establishes the central basis for complex cellular networks in a biological cell. Association of proteins with other proteins occurs at varying affinities, yet with a high degree of specificity. PPIs lead to diverse functionality such as catalysis, regulation, signaling, immunity, and inhibition, playing a crucial role in functional genomics. The molecular principle of such interactions is often elusive in nature. Therefore, a comprehensive analysis of known protein complexes from the Protein Data Bank (PDB) is essential for the characterization of structural interface features to determine structure–function relationship. Thus, we analyzed a nonredundant dataset of 278 heterodimer protein complexes, categorized into major functional classes, for distinguishing features. Interestingly, our analysis has identified five key features (interface area, interface polar residue abundance, hydrogen bonds, solvation free energy gain from interface formation, and binding energy) that are discriminatory among the functional classes using Kruskal-Wallis rank sum test. Significant correlations between these PPI interface features amongst functional categories are also documented. Salt bridges correlate with interface area in regulator-inhibitors (r = 0.75). These representative features have implications for the prediction of potential function of novel protein complexes. The results provide molecular insights for better understanding of PPIs and their relation to biological functions.     

Finding biologically relevant protein domain interactions: conserved binding mode analysis

Proteins evolved through the shuffling of functional domains, and therefore, the same domain can be found in different proteins and species. Interactions between such conserved domains often involve specific, well-determined binding surfaces reflecting their important biological role in a cell. To find biologically relevant interactions we developed a method of systematically comparing and classifying protein domain interactions from the structural data. As a result, a set of conserved binding modes (CBMs) was created using the atomic detail of structure alignment data and the protein domain classification of the Conserved Domain Database. A conserved binding mode is inferred when different members of interacting domain families dock in the same way, such that their structural complexes superimpose well. Such domain interactions with recurring structural themes have greater significance to be biologically relevant, unlike spurious crystal packing interactions. Consequently, this study gives lower and upper bounds on the number of different types of interacting domain pairs in the structure database on the order of 1000-2000. We use CBMs to create domain interaction networks, which highlight functionally significant connections by avoiding many infrequent links between highly connected nodes. The CBMs also constitute a library of docking templates that may be used in molecular modeling to infer the characteristics of an unknown binding surface, just as conserved domains may be used to infer the structure of an unknown protein. The method's ability to sort through and classify large numbers of putative interacting domain pairs is demonstrated on the oligomeric interactions of globins.     

Delineating the Tes Interaction Site in Zyxin and Studying Cellular Effects of Its Disruption

Focal adhesions are integrin-based structures that link the actin cytoskeleton and the extracellular matrix. They play an important role in various cellular functions such as cell signaling, cell motility and cell shape. To ensure and fine tune these different cellular functions, adhesions are regulated by a large number of proteins. The LIM domain protein zyxin localizes to focal adhesions where it participates in the regulation of the actin cytoskeleton. Because of its interactions with a variety of binding partners, zyxin has been proposed to act as a molecular scaffold. Here, we studied the interaction of zyxin with such a partner: Tes. Similar to zyxin, Tes harbors three highly conserved LIM domains of which the LIM1 domain directly interacts with zyxin. Using different zyxin variants in pull-down assays and ectopic recruitment experiments, we identified the Tes binding site in zyxin and showed that four highly conserved amino acids are crucial for its interaction with Tes. Based upon these findings, we used a zyxin mutant defective in Tes-binding to assess the functional consequences of abrogating the zyxin-Tes interaction in focal adhesions. Performing fluorescence recovery after photobleaching, we showed that zyxin recruits Tes to focal adhesions and modulates its turnover in these structures. However, we also provide evidence for zyxin-independent localization of Tes to focal adhesions. Zyxin increases focal adhesion numbers and reduces focal adhesion lifetimes, but does so independent of Tes. Quantitative analysis showed that the loss of interaction between zyxin and Tes affects the process of cell spreading. We conclude that zyxin influences focal adhesion dynamics, that it recruits Tes and that this interaction is functional in regulating cell spreading.     

The Balancing Act of Intrinsically Disordered Proteins: Enabling Functional Diversity while Minimizing Promiscuity

Intrinsically disordered proteins (IDPs) or regions (IDRs) perform diverse cellular functions, but are also prone to forming promiscuous and potentially deleterious interactions. We investigate the extent to which the properties of, and content in, IDRs have adapted to enable functional diversity while limiting interference from promiscuous interactions in the crowded cellular environment. Information on protein sequences, their predicted intrinsic disorder, and 3D structure contents is related to data on protein cellular concentrations, gene co-expression, and protein–protein interactions in the well-studied yeast Saccharomyces cerevisiae. Results reveal that both the protein IDR content and the frequency of “sticky” amino acids in IDRs (those more frequently involved in protein interfaces) decrease with increasing protein cellular concentration. This implies that the IDR content and the amino acid composition of IDRs experience negative selection as the protein concentration increases. In the S. cerevisiae protein–protein interaction network, the higher a protein's IDR content, the more frequently it interacts with IDR-containing partners, and the more functionally diverse the partners are. Employing a clustering analysis of Gene Ontology terms, we newly identify ~ 600 putative multifunctional proteins in S. cerevisiae. Strikingly, these proteins are enriched in IDRs and contribute significantly to all the observed trends. In particular, IDRs of multi-functional proteins feature more sticky amino acids than IDRs of their non-multifunctional counterparts, or the surfaces of structured yeast proteins. This property likely affords sufficient binding affinity for the functional interactions, commonly mediated by short IDR segments, thereby counterbalancing the loss in overall IDR conformational entropy upon binding.