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.     

Predicting the Binding Patterns of Hub Proteins: A Study Using Yeast Protein Interaction Networks

Background

Protein-protein interactions are critical to elucidating the role played by individual proteins in important biological pathways. Of particular interest are hub proteins that can interact with large numbers of partners and often play essential roles in cellular control. Depending on the number of binding sites, protein hubs can be classified at a structural level as singlish-interface hubs (SIH) with one or two binding sites, or multiple-interface hubs (MIH) with three or more binding sites. In terms of kinetics, hub proteins can be classified as date hubs (i.e., interact with different partners at different times or locations) or party hubs (i.e., simultaneously interact with multiple partners).

Methodology

Our approach works in 3 phases: Phase I classifies if a protein is likely to bind with another protein. Phase II determines if a protein-binding (PB) protein is a hub. Phase III classifies PB proteins as singlish-interface versus multiple-interface hubs and date versus party hubs. At each stage, we use sequence-based predictors trained using several standard machine learning techniques.

Conclusions

Our method is able to predict whether a protein is a protein-binding protein with an accuracy of 94% and a correlation coefficient of 0.87; identify hubs from non-hubs with 100% accuracy for 30% of the data; distinguish date hubs/party hubs with 69% accuracy and area under ROC curve of 0.68; and SIH/MIH with 89% accuracy and area under ROC curve of 0.84. Because our method is based on sequence information alone, it can be used even in settings where reliable protein-protein interaction data or structures of protein-protein complexes are unavailable to obtain useful insights into the functional and evolutionary characteristics of proteins and their interactions.

Availability

We provide a web server for our three-phase approach: http://hybsvm.gdcb.iastate.edu.     

Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks

The idea of “date” and “party” hubs has been influential in the study of protein–protein interaction networks. Date hubs display low co-expression with their partners, whilst party hubs have high co-expression. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. Here, we show that the reported importance of date hubs to network connectivity can in fact be attributed to a tiny subset of them. Crucially, these few, extremely central, hubs do not display particularly low expression correlation, undermining the idea of a link between this quantity and hub function. The date/party distinction was originally motivated by an approximately bimodal distribution of hub co-expression; we show that this feature is not always robust to methodological changes. Additionally, topological properties of hubs do not in general correlate with co-expression. However, we find significant correlations between interaction centrality and the functional similarity of the interacting proteins. We suggest that thinking in terms of a date/party dichotomy for hubs in protein interaction networks is not meaningful, and it might be more useful to conceive of roles for protein-protein interactions rather than for individual proteins.     

The role of disorder in interaction networks: a structural analysis

Recent studies have emphasized the value of including structural information into the topological analysis of protein networks. Here, we utilized structural information to investigate the role of intrinsic disorder in these networks. Hub proteins tend to be more disordered than other proteins (i.e. the proteome average); however, we find this only true for those with one or two binding interfaces (‘single’‐interface hubs). In contrast, the distribution of disordered residues in multi‐interface hubs is indistinguishable from the overall proteome. Surprisingly, we find that the binding interfaces in single‐interface hubs are highly structured, as is the case for multi‐interface hubs. However, the binding partners of single‐interface hubs tend to have a higher level of disorder than the proteome average, suggesting that their binding promiscuity is related to the disorder of their binding partners. In turn, the higher level of disorder of single‐interface hubs can be partly explained by their tendency to bind to each other in a cascade. A good illustration of this trend can be found in signaling pathways and, more specifically, in kinase cascades. Finally, our findings have implications for the current controversy related to party and date‐hubs.     

Identification of transient hub proteins and the possible structural basis for their multiple interactions

Proteins that can interact with multiple partners play central roles in the network of protein-protein interactions. They are called hub proteins, and recently it was suggested that an abundance of intrinsically disordered regions on their surfaces facilitates their binding to multiple partners. However, in those studies, the hub proteins were identified as proteins with multiple partners, regardless of whether the interactions were transient or permanent. As a result, a certain number of hub proteins are subunits of stable multi-subunit proteins, such as supramolecules. It is well known that stable complexes and transient complexes have different structural features, and thus the statistics based on the current definition of hub proteins will hide the true nature of hub proteins. Therefore, in this paper, we first describe a new approach to identify proteins with multiple partners dynamically, using the Protein Data Bank, and then we performed statistical analyses of the structural features of these proteins. We refer to the proteins as transient hub proteins or sociable proteins, to clarify the difference with hub proteins. As a result, we found that the main difference between sociable and nonsociable proteins is not the abundance of disordered regions, in contrast to the previous studies, but rather the structural flexibility of the entire protein. We also found greater predominance of charged and polar residues in sociable proteins than previously reported.     

Hubs with network motifs organize modularity dynamically in the protein-protein interaction network of yeast

Background

It has been recognized that modular organization pervades biological complexity. Based on network analysis, ‘party hubs’ and ‘date hubs’ were proposed to understand the basic principle of module organization of biomolecular networks. However, recent study on hubs has suggested that there is no clear evidence for coexistence of ‘party hubs’ and ‘date hubs’. Thus, an open question has been raised as to whether or not ‘party hubs’ and ‘date hubs’ truly exist in yeast interactome.

Methodology

In contrast to previous studies focusing on the partners of a hub or the individual proteins around the hub, our work aims to study the network motifs of a hub or interactions among individual proteins including the hub and its neighbors. Depending on the relationship between a hub''s network motifs and protein complexes, we define two new types of hubs, ‘motif party hubs’ and ‘motif date hubs’, which have the same characteristics as the original ‘party hubs’ and ‘date hubs’ respectively. The network motifs of these two types of hubs display significantly different features in spatial distribution (or cellular localizations), co-expression in microarray data, controlling topological structure of network, and organizing modularity.

Conclusion

By virtue of network motifs, we basically solved the open question about ‘party hubs’ and ‘date hubs’ which was raised by previous studies. Specifically, at the level of network motifs instead of individual proteins, we found two types of hubs, motif party hubs (mPHs) and motif date hubs (mDHs), whose network motifs display distinct characteristics on biological functions. In addition, in this paper we studied network motifs from a different viewpoint. That is, we show that a network motif should not be merely considered as an interaction pattern but be considered as an essential function unit in organizing modules of networks.     

Distinct roles of overlapping and non-overlapping regions of hub protein interfaces in recognition of multiple partners

Cellular functions of an organism are maintained by protein-protein interactions. Those proteins that bind multiple partners asynchronously (date hub proteins) are important to make the interaction network coordinated. It is known that many date hub proteins bind different partners at overlapping (OV) interfaces. To understand how OV interfaces of date hub proteins can recognize multiple partners, we analyzed the difference between OV and non-overlapping (Non-OV) regions of interfaces involved in the binding of different partners. By using the structures of 16 date hub proteins with various interaction partners (ranging from 5 to 33), we compared buried surface area, compositions of amino acid residues and secondary structures, and side-chain orientations. It was found that buried interface residues are important for recognizing multiple partners, while exposed interface residues are important for determining specificity to a particular ligand. In addition, our analyses reveal that residue compositions in OV and Non-OV regions are different and that residues in OV region show diverse side-chain torsion angles to accommodate binding to multiple targets.     

Similar binding sites and different partners: implications to shared proteins in cellular pathways

We studied a data set of structurally similar interfaces that bind to proteins with different binding-site structures and different functions. Our multipartner protein interface clusters enable us to address questions like: What makes a given site bind different proteins? How similar/different are the interactions? And, what drives the apparently less-specific association? We find that proteins with common binding-site motifs preferentially use conserved interactions at similar interface locations, despite the different partners. Helices are major vehicles for binding different partners, allowing alternate ways to achieve favorable association. The binding sites are characterized by imperfect packing, planar architectures, bridging water molecules, and, on average, smaller size. Interestingly, analysis of the connectivity of these proteins illustrates that they have more interactions with other proteins. These findings are important in predicting "date hubs," if we assume that "date hubs" are shared proteins with binding sites capable of transient binding to multipartners, linking higher-order networks.     

Dynamic Hubs Show Competitive and Static Hubs Non-Competitive Regulation of Their Interaction Partners

Date hub proteins have 1 or 2 interaction interfaces but many interaction partners. This raises the question of whether all partner proteins compete for the interaction interface of the hub or if the cell carefully regulates aspects of this process? Here, we have used real-time rendering of protein interaction networks to analyse the interactions of all the 1 or 2 interface hubs of Saccharomyces cerevisiae during the cell cycle. By integrating previously determined structural and gene expression data, and visually hiding the nodes (proteins) and their edges (interactions) during their troughs of expression, we predict when interactions of hubs and their partners are likely to exist. This revealed that 20 out of all 36 one- or two- interface hubs in the yeast interactome fell within two main groups. The first was dynamic hubs with static partners, which can be considered as ‘competitive hubs’. Their interaction partners will compete for the interaction interface of the hub and the success of any interaction will be dictated by the kinetics of interaction (abundance and affinity) and subcellular localisation. The second was static hubs with dynamic partners, which we term ‘non-competitive hubs’. Regulatory mechanisms are finely tuned to lessen the presence and/or effects of competition between the interaction partners of the hub. It is possible that these regulatory processes may also be used by the cell for the regulation of other, non-cell cycle processes.     

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.     

Characterization of protein hubs by inferring interacting motifs from protein interactions

The characterization of protein interactions is essential for understanding biological systems. While genome-scale methods are available for identifying interacting proteins, they do not pinpoint the interacting motifs (e.g., a domain, sequence segments, a binding site, or a set of residues). Here, we develop and apply a method for delineating the interacting motifs of hub proteins (i.e., highly connected proteins). The method relies on the observation that proteins with common interaction partners tend to interact with these partners through a common interacting motif. The sole input for the method are binary protein interactions; neither sequence nor structure information is needed. The approach is evaluated by comparing the inferred interacting motifs with domain families defined for 368 proteins in the Structural Classification of Proteins (SCOP). The positive predictive value of the method for detecting proteins with common SCOP families is 75% at sensitivity of 10%. Most of the inferred interacting motifs were significantly associated with sequence patterns, which could be responsible for the common interactions. We find that yeast hubs with multiple interacting motifs are more likely to be essential than hubs with one or two interacting motifs, thus rationalizing the previously observed correlation between essentiality and the number of interacting partners of a protein. We also find that yeast hubs with multiple interacting motifs evolve slower than the average protein, contrary to the hubs with one or two interacting motifs. The proposed method will help us discover unknown interacting motifs and provide biological insights about protein hubs and their roles in interaction networks.     

Features analysis for identification of date and party hubs in protein interaction network of <Emphasis Type="Italic">Saccharomyces Cerevisiae</Emphasis>

Background  

It has been understood that biological networks have modular organizations which are the sources of their observed complexity. Analysis of networks and motifs has shown that two types of hubs, party hubs and date hubs, are responsible for this complexity. Party hubs are local coordinators because of their high co-expressions with their partners, whereas date hubs display low co-expressions and are assumed as global connectors. However there is no mutual agreement on these concepts in related literature with different studies reporting their results on different data sets. We investigated whether there is a relation between the biological features of Saccharomyces Cerevisiae 's proteins and their roles as non-hubs, intermediately connected, party hubs, and date hubs. We propose a classifier that separates these four classes.     

What properties characterize the hub proteins of the protein-protein interaction network of Saccharomyces cerevisiae?

Background  

Most proteins interact with only a few other proteins while a small number of proteins (hubs) have many interaction partners. Hub proteins and non-hub proteins differ in several respects; however, understanding is not complete about what properties characterize the hubs and set them apart from proteins of low connectivity. Therefore, we have investigated what differentiates hubs from non-hubs and static hubs (party hubs) from dynamic hubs (date hubs) in the protein-protein interaction network of Saccharomyces cerevisiae.     

Disorder and sequence repeats in hub proteins and their implications for network evolution

Protein interaction networks display approximate scale-free topology, in which hub proteins that interact with a large number of other proteins determine the overall organization of the network. In this study, we aim to determine whether hubs are distinguishable from other networked proteins by specific sequence features. Proteins of different connectednesses were compared in the interaction networks of Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, and Homo sapienswith respect to the distribution of predicted structural disorder, sequence repeats, low complexity regions, and chain length. Highly connected proteins ("hub proteins") contained significantly more of, and greater proportion of, these sequence features and tended to be longer overall as compared to less connected proteins. These sequence features provide two different functional means for realizing multiple interactions: (1) extended interaction surface and (2) flexibility and adaptability, providing a mechanism for the same region to bind distinct partners. Our view contradicts the prevailing view that scaling in protein interactomes arose from gene duplication and preferential attachment of equivalent proteins. We propose an alternative evolutionary network specialization process, in which certain components of the protein interactome improved their fitness for binding by becoming longer or accruing regions of disorder and/or internal repeats and have therefore become specialized in network organization.     

Functional dissection of an intrinsically disordered protein: Understanding the roles of different domains of Knr4 protein in protein�Cprotein interactions

Knr4, recently characterized as an intrinsically disordered Saccharomyces cerevisiae protein, participates in cell wall formation and cell cycle regulation. It is constituted of a functional central globular core flanked by a poorly structured N‐terminal and large natively unfolded C‐terminal domains. Up to now, about 30 different proteins have been reported to physically interact with Knr4. Here, we used an in vivo two‐hybrid system approach and an in vitro surface plasmon resonance (BIAcore) technique to compare the interaction level of different Knr4 deletion variants with given protein partners. We demonstrate the indispensability of the N‐terminal domain of Knr4 for the interactions. On the other hand, presence of the unstructured C‐terminal domain has a negative effect on the interaction strength. In protein interactions networks, the most highly connected proteins or “hubs” are significantly enriched in unstructured regions, and among them the transient hub proteins contain the largest and most highly flexible regions. The results presented here of our analysis of Knr4 protein suggest that these large disordered regions are not always involved in promoting the protein–protein interactions of hub proteins, but in some cases, might rather inhibit them. We propose that this type of regions could prevent unspecific protein interactions, or ensure the correct timing of occurrence of transient interactions, which may be of crucial importance for different signaling and regulation processes.     

Pairwise binding competition experiments for sorting hub-protein/effector interaction hierarchy and simultaneous equilibria

NMR experiments on proteins in simultaneous equilibria with multiple binding partners can provide a tool to understand complex biological interaction networks. Competition among proteins for binding to signaling hubs is often at the basis of the information transmission across signaling networks in every organism. Changes in affinity towards one or more partners, as well as changes of the relative concentration of the competing partners, can determine pathways alterations that lead to pathological consequences. Overall, the knowledge of the interaction hierarchy of the multiple partners to a single signaling hub can lead to new therapeutic strategies. Smith and Ikura (Nat Chem Biol 10:223–230, 2014) have recently proposed pairwise competition NMR experiments to determine the binding hierarchy in network interactions. We have taken the moves from their approach to show how from pairwise competition NMR experiments the ratios between the equilibrium constants for multiple binding partners can be determined, and thus, given their concentration in solution, the concentrations of all the possible complexes can be obtained.     

Evolution of Specificity in Protein-Protein Interactions

Hub proteins are proteins that maintain promiscuous molecular recognition. Because they are reported to play essential roles in cellular control, there has been a special interest in the study of their structural and functional properties, yet the mechanisms by which they evolve to maintain functional interactions are poorly understood. By combining biophysical simulations of coarse-grained proteins and analysis of proteins-complex crystallographic structures, we seek to elucidate those mechanisms. We focus on two types of hub proteins: Multi hubs, which interact with their partners through different interfaces, and Singlish hubs, which do so through a single interface. We show that loss of structural stability is required for the evolution of protein-protein-interaction (PPI) networks, and it is more profound in Singlish hub systems. In addition, different ratios of hydrophobic to electrostatic interfacial amino acids are shown to support distinct network topologies (i.e., Singlish and Multi systems), and therefore underlie a fundamental design principle of PPI in a crowded environment. We argue that the physical nature of hydrophobic and electrostatic interactions, in particular, their favoring of either same-type interactions (hydrophobic-hydrophobic), or opposite-type interactions (negatively-positively charged) plays a key role in maintaining the network topology while allowing the protein amino acid sequence to evolve.     

Evolution of Specificity in Protein-Protein Interactions

Hub proteins are proteins that maintain promiscuous molecular recognition. Because they are reported to play essential roles in cellular control, there has been a special interest in the study of their structural and functional properties, yet the mechanisms by which they evolve to maintain functional interactions are poorly understood. By combining biophysical simulations of coarse-grained proteins and analysis of proteins-complex crystallographic structures, we seek to elucidate those mechanisms. We focus on two types of hub proteins: Multi hubs, which interact with their partners through different interfaces, and Singlish hubs, which do so through a single interface. We show that loss of structural stability is required for the evolution of protein-protein-interaction (PPI) networks, and it is more profound in Singlish hub systems. In addition, different ratios of hydrophobic to electrostatic interfacial amino acids are shown to support distinct network topologies (i.e., Singlish and Multi systems), and therefore underlie a fundamental design principle of PPI in a crowded environment. We argue that the physical nature of hydrophobic and electrostatic interactions, in particular, their favoring of either same-type interactions (hydrophobic-hydrophobic), or opposite-type interactions (negatively-positively charged) plays a key role in maintaining the network topology while allowing the protein amino acid sequence to evolve.