Flexible nets. The roles of intrinsic disorder in protein interaction networks

Proteins participate in complex sets of interactions that represent the mechanistic foundation for much of the physiology and function of the cell. These protein-protein interactions are organized into exquisitely complex networks. The architecture of protein-protein interaction networks was recently proposed to be scale-free, with most of the proteins having only one or two connections but with relatively fewer 'hubs' possessing tens, hundreds or more links. The high level of hub connectivity must somehow be reflected in protein structure. What structural quality of hub proteins enables them to interact with large numbers of diverse targets? One possibility would be to employ binding regions that have the ability to bind multiple, structurally diverse partners. This trait can be imparted by the incorporation of intrinsic disorder in one or both partners. To illustrate the value of such contributions, this review examines the roles of intrinsic disorder in protein network architecture. We show that there are three general ways that intrinsic disorder can contribute: First, intrinsic disorder can serve as the structural basis for hub protein promiscuity; secondly, intrinsically disordered proteins can bind to structured hub proteins; and thirdly, intrinsic disorder can provide flexible linkers between functional domains with the linkers enabling mechanisms that facilitate binding diversity. An important research direction will be to determine what fraction of protein-protein interaction in regulatory networks relies on intrinsic disorder.     

Examination of the relationship between essential genes in PPI network and hub proteins in reverse nearest neighbor topology

Background  

In many protein-protein interaction (PPI) networks, densely connected hub proteins are more likely to be essential proteins. This is referred to as the "centrality-lethality rule", which indicates that the topological placement of a protein in PPI network is connected with its biological essentiality. Though such connections are observed in many PPI networks, the underlying topological properties for these connections are not yet clearly understood. Some suggested putative connections are the involvement of essential proteins in the maintenance of overall network connections, or that they play a role in essential protein clusters. In this work, we have attempted to examine the placement of essential proteins and the network topology from a different perspective by determining the correlation of protein essentiality and reverse nearest neighbor topology (RNN).     

Stratus not altocumulus: a new view of the yeast protein interaction network

Systems biology approaches can reveal intermediary levels of organization between genotype and phenotype that often underlie biological phenomena such as polygenic effects and protein dispensability. An important conceptualization is the module, which is loosely defined as a cohort of proteins that perform a dedicated cellular task. Based on a computational analysis of limited interaction datasets in the budding yeast Saccharomyces cerevisiae, it has been suggested that the global protein interaction network is segregated such that highly connected proteins, called hubs, tend not to link to each other. Moreover, it has been suggested that hubs fall into two distinct classes: "party" hubs are co-expressed and co-localized with their partners, whereas "date" hubs interact with incoherently expressed and diversely localized partners, and thereby cohere disparate parts of the global network. This structure may be compared with altocumulus clouds, i.e., cotton ball-like structures sparsely connected by thin wisps. However, this organization might reflect a small and/or biased sample set of interactions. In a multi-validated high-confidence (HC) interaction network, assembled from all extant S. cerevisiae interaction data, including recently available proteome-wide interaction data and a large set of reliable literature-derived interactions, we find that hub-hub interactions are not suppressed. In fact, the number of interactions a hub has with other hubs is a good predictor of whether a hub protein is essential or not. We find that date hubs are neither required for network tolerance to node deletion, nor do date hubs have distinct biological attributes compared to other hubs. Date and party hubs do not, for example, evolve at different rates. Our analysis suggests that the organization of global protein interaction network is highly interconnected and hence interdependent, more like the continuous dense aggregations of stratus clouds than the segregated configuration of altocumulus clouds. If the network is configured in a stratus format, cross-talk between proteins is potentially a major source of noise. In turn, control of the activity of the most highly connected proteins may be vital. Indeed, we find that a fluctuation in steady-state levels of the most connected proteins is minimized.     

Simple Topological Features Reflect Dynamics and Modularity in Protein Interaction Networks

The availability of large-scale protein-protein interaction networks for numerous organisms provides an opportunity to comprehensively analyze whether simple properties of proteins are predictive of the roles they play in the functional organization of the cell. We begin by re-examining an influential but controversial characterization of the dynamic modularity of the S. cerevisiae interactome that incorporated gene expression data into network analysis. We analyse the protein-protein interaction networks of five organisms, S. cerevisiae, H. sapiens, D. melanogaster, A. thaliana, and E. coli, and confirm significant and consistent functional and structural differences between hub proteins that are co-expressed with their interacting partners and those that are not, and support the view that the former tend to be intramodular whereas the latter tend to be intermodular. However, we also demonstrate that in each of these organisms, simple topological measures are significantly correlated with the average co-expression of a hub with its partners, independent of any classification, and therefore also reflect protein intra- and inter- modularity. Further, cross-interactomic analysis demonstrates that these simple topological characteristics of hub proteins tend to be conserved across organisms. Overall, we give evidence that purely topological features of static interaction networks reflect aspects of the dynamics and modularity of interactomes as well as previous measures incorporating expression data, and are a powerful means for understanding the dynamic roles of hubs in interactomes.     

Protein interaction networks in medicine and disease

The physical interaction of proteins is subject to intense investigation that has revealed that proteins are assembled into large densely connected networks. In this review, we will examine how signaling pathways can be combined to form higher order protein interaction networks. By using network graph theory, these interaction networks can be further analyzed for global organization, which has revealed unique aspects of the relationships between protein networks and complex biological phenotypes. Moreover, several studies have shown that the structure and dynamics of protein networks are disturbed in complex diseases such as cancer progression. These relationships suggest a novel paradigm for treatment of complex multigenic disease where the protein interaction network is the target of therapy more so than individual molecules within the network.     

On the functional and structural characterization of hubs in protein–protein interaction networks

A number of interesting issues have been addressed on biological networks about their global and local properties. The connection between the topological properties of proteins in Protein–Protein Interaction (PPI) networks and their biological relevance has been investigated focusing on hubs, i.e. proteins with a large number of interacting partners. We will survey the literature trying to answer the following questions: Do hub proteins have special biological properties? Do they tend to be more essential than non-hub proteins? Are they more evolutionarily conserved? Do they play a central role in modular organization of the protein interaction network? Are there structural properties that characterize hub proteins?     

Role of intrinsic disorder in transient interactions of hub proteins

Hubs in the protein-protein interaction network have been classified as "party" hubs, which are highly correlated in their mRNA expression with their partners while "date" hubs show lesser correlation. In this study, we explored the role of intrinsic disorder in date and party hub interactions. The data reveals that intrinsic disorder is significantly enriched in date hub proteins when compared with party hub proteins. Intrinsic disorder has been largely implicated in transient binding interactions. The disorder to order transition, which occurs during binding interactions in disordered regions, renders the interaction highly reversible while maintaining the high specificity. The enrichment of intrinsic disorder in date hubs may facilitate transient interactions, which might be required for date hubs to interact with different partners at different times.     

Identification of hub proteins from sequence

Identification of hub proteins from sequence is a challenge in molecular biology. Therefore, it is of interest to predict protein hubs in networks. We describe the prediction of protein "hub" using physiochemical, thermodynamic and conformational properties of amino acid residues in sequence. We have used twenty sequence based features to identify hub behaviour. Linear discriminant analysis and normalised Bayesian approach were utilized for identifying hub proteins solely using these sequence features in E. coli/H. sapiens datasets with accuracies of 99.5/98.6, 87.8/89.6 and 90.1/92.6, respectively.     

Stability and rigidity/flexibility—Two sides of the same coin?

Protein molecules require both flexibility and rigidity for functioning. The fast and accurate prediction of protein rigidity/flexibility is one of the important problems in protein science. We have determined flexible regions for four homologous pairs from thermophilic and mesophilic organisms by two methods: the fast FoldUnfold which uses amino acid sequence and the time consuming MDFirst which uses three-dimensional structures. We demonstrate that both methods allow determining flexible regions in protein structure. For three of the four thermophile–mesophile pairs of proteins, FoldUnfold predicts practically the same flexible regions which have been found by the MD/First method. As expected, molecular dynamics simulations show that thermophilic proteins are more rigid in comparison to their mesophilic homologues. Analysis of rigid clusters and their decomposition provides new insights into protein stability. It has been found that the local networks of salt bridges and hydrogen bonds in thermophiles render their structure more stable with respect to fluctuations of individual contacts. Such network includes salt bridge triads Agr-Glu-Lys and Arg-Glu-Arg, or salt bridges (such as Arg-Glu) connected with hydrogen bonds. This ionic network connects alpha helices and rigidifies the structure. Mesophiles can be characterized by stand alone salt bridges and hydrogen bonds or small ionic clusters. Such difference in the network of salt bridges results in different flexibility of homologous proteins. Combining both approaches allows characterizing structural features in atomic detail that determine the rigidity/flexibility of a protein structure. This article is a part of a Special Issue entitled: The emerging dynamic view of proteins: Protein plasticity in allostery, evolution and self-assembly.     

Negatively Charged Disordered Regions are Prevalent and Functionally Important Across Proteomes

Intrinsically disordered regions (IDRs) of proteins are often characterized by a high fraction of charged residues, but differ in their overall net charge and in the organization of the charged residues. The function-encoding information stored via IDR charge composition and organization remains elusive. Here, we aim to decipher the sequence–function relationship in IDRs by presenting a comprehensive bioinformatic analysis of the charge properties of IDRs in the human, mouse, and yeast proteomes. About 50% of the proteins comprise at least a single IDR, which is either positively or negatively charged. Highly negatively charged IDRs are longer and possess greater net charge per residue compared with highly positively charged IDRs. A striking difference between positively and negatively charged IDRs is the characteristics of the repeated units, specifically, of consecutive Lys or Arg residues (K/R repeats) and Asp or Glu (D/E repeats) residues. D/E repeats are found to be about five times longer than K/R repeats, with the longest found containing 49 residues. Long stretches of consecutive D and E are found to be more prevalent in nucleic acid-related proteins. They are less common in prokaryotes, and in eukaryotes their abundance increases with genome size. The functional role of D/E repeats and the profound differences between them and K/R repeats are discussed.     

Network Propagation with Dual Flow for Gene Prioritization

Based on the hypothesis that the neighbors of disease genes trend to cause similar diseases, network-based methods for disease prediction have received increasing attention. Taking full advantage of network structure, the performance of global distance measurements is generally superior to local distance measurements. However, some problems exist in the global distance measurements. For example, global distance measurements may mistake non-disease hub proteins that have dense interactions with known disease proteins for potential disease proteins. To find a new method to avoid the aforementioned problem, we analyzed the differences between disease proteins and other proteins by using essential proteins (proteins encoded by essential genes) as references. We find that disease proteins are not well connected with essential proteins in the protein interaction networks. Based on this new finding, we proposed a novel strategy for gene prioritization based on protein interaction networks. We allocated positive flow to disease genes and negative flow to essential genes, and adopted network propagation for gene prioritization. Experimental results on 110 diseases verified the effectiveness and potential of the proposed method.     

Cliques in mitotic spindle network bring kinetochore‐associated complexes to form dependence pathway

The mitotic spindle is an essential molecular machine for chromosome segregation during mitosis. Achieving a better understanding of its organization at the topological level remains a daunting task. To determine the functional connections among 137 mitotic spindle proteins, a protein–protein interaction network among queries was constructed. Many hub proteins, which connect more than one query and serve as highly plausible candidates for expanding the mitotic spindle proteome, are ranked by conventional degree centrality and a new subnetwork specificity score. Evaluation of the ranking results by literature reviews and empirical verification of SEPT6, a novel top‐ranked hub, suggests that the subnetwork specificity score could enrich for putative spindle‐related proteins. Topological analysis of this expanded network shows the presence of 30 3‐cliques and six 4‐cliques (fully connected subgraphs) that, respectively, reside in eight kinetochore‐associated complexes, of which seven are evolution conserved. Notably, these complexes strikingly form dependence pathways for the assembly of the kinetochore complex. These analyses indicate the feasibility of using network topology, i.e. cliques, to uncover novel pathways to accelerate our understanding of potential biological processes.     

Evolutionary innovations and the organization of protein functions in genotype space

The organization of protein structures in protein genotype space is well studied. The same does not hold for protein functions, whose organization is important to understand how novel protein functions can arise through blind evolutionary searches of sequence space. In systems other than proteins, two organizational features of genotype space facilitate phenotypic innovation. The first is that genotypes with the same phenotype form vast and connected genotype networks. The second is that different neighborhoods in this space contain different novel phenotypes. We here characterize the organization of enzymatic functions in protein genotype space, using a data set of more than 30,000 proteins with known structure and function. We show that different neighborhoods of genotype space contain proteins with very different functions. This property both facilitates evolutionary innovation through exploration of a genotype network, and it constrains the evolution of novel phenotypes. The phenotypic diversity of different neighborhoods is caused by the fact that some functions can be carried out by multiple structures. We show that the space of protein functions is not homogeneous, and different genotype neighborhoods tend to contain a different spectrum of functions, whose diversity increases with increasing distance of these neighborhoods in sequence space. Whether a protein with a given function can evolve specific new functions is thus determined by the protein's location in sequence space.     

Mapping the protein interaction network in methicillin-resistant Staphylococcus aureus

Mortality attributable to infection with methicillin-resistant Staphylococcus aureus (MRSA) has now overtaken the death rate for AIDS in the United States, and advances in research are urgently needed to address this challenge. We report the results of the systematic identification of protein-protein interactions for the hospital-acquired strain MRSA-252. Using a high-throughput pull-down strategy combined with quantitative proteomics to distinguish specific from nonspecific interactors, we identified 13,219 interactions involving 608 MRSA proteins. Consecutive analyses revealed that this protein interaction network (PIN) exhibits scale-free organization with the characteristic presence of highly connected hub proteins. When clinical and experimental antimicrobial targets were queried in the network, they were generally found to occupy peripheral positions in the PIN with relatively few interacting partners. In contrast, the hub proteins identified in this MRSA PIN that are essential for network integrity and stability have largely been overlooked as drug targets. Thus, this empirical MRSA-252 PIN provides a rich source for identifying critical proteins essential for network stability, many of which can be considered as prospective antimicrobial drug targets.     

Four-dimensional visualisation and analysis of protein-protein interaction networks

Protein-protein interaction networks are typically built with interactions collated from many experiments. These networks are thus composite and show all interactions that are currently known to occur in a cell. However, these representations are static and ignore the constant changes in protein-protein interactions. Here we present software for the generation and analysis of dynamic, four-dimensional (4-D) protein interaction networks. In this, time-course-derived abundance data are mapped onto three-dimensional networks to generate network movies. These networks can be navigated, manipulated and queried in real time. Two types of dynamic networks can be generated: a 4-D network that maps expression data onto protein nodes and one that employs 'real-time rendering' by which protein nodes and their interactions appear and disappear in association with temporal changes in expression data. We illustrate the utility of this software by the analysis of singlish interface date hub interactions during the yeast cell cycle. In this, we show that proteins MLC1 and YPT52 show strict temporal control of when their interaction partners are expressed. Since these proteins have one and two interaction interfaces, respectively, it suggests that temporal control of gene expression may be used to limit competition at the interaction interfaces of some hub proteins. The software and movies of the 4-D networks are available at http://www.systemsbiology.org.au/downloads_geomi.html.     

Assessing significance of connectivity and conservation in protein interaction networks.

Comparative analyses of cellular interaction networks enable understanding of the cell's modular organization through identification of functional modules and complexes. These techniques often rely on topological features such as connectedness and density, based on the premise that functionally related proteins are likely to interact densely and that these interactions follow similar evolutionary trajectories. Significant recent work has focused on efficient algorithms for identification of such functional modules and their conservation. In spite of algorithmic advances, development of a comprehensive infrastructure for interaction databases is in relative infancy compared to corresponding sequence analysis tools. One critical, and as yet unresolved aspect of this infrastructure is a measure of the statistical significance of a match, or a dense subcomponent. In the absence of analytical measures, conventional methods rely on computationally expensive simulations based on ad-hoc models for quantifying significance. In this paper, we present techniques for analytically quantifying statistical significance of dense components in reference model graphs. We consider two reference models--a G(n, p) model in which each pair of nodes in a graph has an identical likelihood, p, of sharing an edge, and a two-level G(n, p) model, which accounts for high-degree hub nodes generally observed in interaction networks. Experiments performed on a rich collection of protein interaction (PPI) networks show that the proposed model provides a reliable means of evaluating statistical significance of dense patterns in these networks. We also adapt existing state-of-the-art network clustering algorithms by using our statistical significance measure as an optimization criterion. Comparison of the resulting module identification algorithm, SIDES, with existing methods shows that SIDES outperforms existing algorithms in terms of sensitivity and specificity of identified clusters with respect to available GO annotations.     

The role of structural disorder in the rewiring of protein interactions through evolution

Structurally disordered regions play a key role in protein-protein interaction networks and the evolution of highly connected proteins, enabling the molecular mechanisms for multiple binding. However, the role of protein disorder in the evolution of interaction networks has only been investigated through the analysis of individual proteins, making it impossible to distinguish its specific impact in the (re)shaping of their interaction environments. Now, the availability of large interactomes for several model organisms permits exploration of the role of disorder in protein interaction networks not only at the level of the interacting proteins but of the interactions themselves. By comparing the interactomes of human, fly, and yeast, we discovered that, despite being much more abundant, disordered interactions are significantly less conserved than their ordered counterparts. Furthermore, our analyses provide evidence that this happens not only because disordered proteins are less conserved but also because they display a higher capacity to rewire their interaction neighborhood through evolution. Overall, our results support the hypothesis that conservation of disorder gives a clear evolutionary advantage, facilitating the change of interaction partners during evolution. Moreover, this mechanism is not exclusive of a few anecdotal cases but a global feature present in the interactome networks of entire organisms.     

Modularity in the evolution of yeast protein interaction network

Protein interaction networks are known to exhibit remarkable structures: scale-free and small-world and modular structures. To explain the evolutionary processes of protein interaction networks possessing scale-free and small-world structures, preferential attachment and duplication-divergence models have been proposed as mathematical models. Protein interaction networks are also known to exhibit another remarkable structural characteristic, modular structure. How the protein interaction networks became to exhibit modularity in their evolution? Here, we propose a hypothesis of modularity in the evolution of yeast protein interaction network based on molecular evolutionary evidence. We assigned yeast proteins into six evolutionary ages by constructing a phylogenetic profile. We found that all the almost half of hub proteins are evolutionarily new. Examining the evolutionary processes of protein complexes, functional modules and topological modules, we also found that member proteins of these modules tend to appear in one or two evolutionary ages. Moreover, proteins in protein complexes and topological modules show significantly low evolutionary rates than those not in these modules. Our results suggest a hypothesis of modularity in the evolution of yeast protein interaction network as systems evolution.