Constructing structural networks of signaling pathways on the proteome scale

Proteins function through their interactions, and the availability of protein interaction networks could help in understanding cellular processes. However, the known structural data are limited and the classical network node-and-edge representation, where proteins are nodes and interactions are edges, shows only which proteins interact; not how they interact. Structural networks provide this information. Protein-protein interface structures can also indicate which binding partners can interact simultaneously and which are competitive, and can help forecasting potentially harmful drug side effects. Here, we use a powerful protein-protein interactions prediction tool which is able to carry out accurate predictions on the proteome scale to construct the structural network of the extracellular signal-regulated kinases (ERK) in the mitogen-activated protein kinase (MAPK) signaling pathway. This knowledge-based method, PRISM, is motif-based, and is combined with flexible refinement and energy scoring. PRISM predicts protein interactions based on structural and evolutionary similarity to known protein interfaces.     

Using indirect protein-protein interactions for protein complex prediction

Protein complexes are fundamental for understanding principles of cellular organizations. As the sizes of protein-protein interaction (PPI) networks are increasing, accurate and fast protein complex prediction from these PPI networks can serve as a guide for biological experiments to discover novel protein complexes. However, it is not easy to predict protein complexes from PPI networks, especially in situations where the PPI network is noisy and still incomplete. Here, we study the use of indirect interactions between level-2 neighbors (level-2 interactions) for protein complex prediction. We know from previous work that proteins which do not interact but share interaction partners (level-2 neighbors) often share biological functions. We have proposed a method in which all direct and indirect interactions are first weighted using topological weight (FS-Weight), which estimates the strength of functional association. Interactions with low weight are removed from the network, while level-2 interactions with high weight are introduced into the interaction network. Existing clustering algorithms can then be applied to this modified network. We have also proposed a novel algorithm that searches for cliques in the modified network, and merge cliques to form clusters using a "partial clique merging" method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein-protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes.     

Cluster conservation as a novel tool for studying protein-protein interactions evolution

Protein-protein interactions networks has come to be a buzzword associated with nets containing edges that represent a pair of interacting proteins (e.g. hormone-receptor, enzyme-inhibitor, antigen-antibody, and a subset of multichain biological machines). Yet, each such interaction composes its own unique network, in which vertices represent amino acid residues, and edges represent atomic contacts. Recent studies have shown that analyses of the data encapsulated in these detailed networks may impact predictions of structure-function correlation. Here, we study homologous families of protein-protein interfaces, which share the same fold but vary in sequence. In this context, we address what properties of the network are shared among relatives with different sequences (and hence different atomic interactions) and which are not. Herein, we develop the general mathematical framework needed to compare the modularity of homologous networks. We then apply this analysis to the structural data of a few interface families, including hemoglobin alpha-beta, growth hormone-receptor, and Serine protease-inhibitor. Our results suggest that interface modularity is an evolutionarily conserved property. Hence, protein-protein interfaces can be clustered down to a few modules, with the boundaries being evolutionarily conserved along homologous complexes. This suggests that protein engineering of protein-protein binding sites may be simplified by varying each module, but retaining the overall modularity of the interface.     

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

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

Dynamic proteomics in modeling of the living cell. Protein-protein interactions

This review is devoted to describing, summarizing, and analyzing of dynamic proteomics data obtained over the last few years and concerning the role of protein-protein interactions in modeling of the living cell. Principles of modern high-throughput experimental methods for investigation of protein-protein interactions are described. Systems biology approaches based on integrative view on cellular processes are used to analyze organization of protein interaction networks. It is proposed that finding of some proteins in different protein complexes can be explained by their multi-modular and polyfunctional properties; the different protein modules can be located in the nodes of protein interaction networks. Mathematical and computational approaches to modeling of the living cell with emphasis on molecular dynamics simulation are provided. The role of the network analysis in fundamental medicine is also briefly reviewed.     

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.     

Specificity of molecular interactions in transient protein-protein interaction interfaces

In this study, we investigate what types of interactions are specific to their biological function, and what types of interactions are persistent regardless of their functional category in transient protein-protein heterocomplexes. This is the first approach to analyze protein-protein interfaces systematically at the molecular interaction level in the context of protein functions. We perform systematic analysis at the molecular interaction level using classification and feature subset selection technique prevalent in the field of pattern recognition. To represent the physicochemical properties of protein-protein interfaces, we design 18 molecular interaction types using canonical and noncanonical interactions. Then, we construct input vector using the frequency of each interaction type in protein-protein interface. We analyze the 131 interfaces of transient protein-protein heterocomplexes in PDB: 33 protease-inhibitors, 52 antibody-antigens, 46 signaling proteins including 4 cyclin dependent kinase and 26 G-protein. Using kNN classification and feature subset selection technique, we show that there are specific interaction types based on their functional category, and such interaction types are conserved through the common binding mechanism, rather than through the sequence or structure conservation. The extracted interaction types are C(alpha)-- H...O==C interaction, cation...anion interaction, amine...amine interaction, and amine...cation interaction. With these four interaction types, we achieve the classification success rate up to 83.2% with leave-one-out cross-validation at k = 15. Of these four interaction types, C(alpha)--H...O==C shows binding specificity for protease-inhibitor complexes, while cation-anion interaction is predominant in signaling complexes. The amine ... amine and amine...cation interaction give a minor contribution to the classification accuracy. When combined with these two interactions, they increase the accuracy by 3.8%. In the case of antibody-antigen complexes, the sign is somewhat ambiguous. From the evolutionary perspective, while protease-inhibitors and sig-naling proteins have optimized their interfaces to suit their biological functions, antibody-antigen interactions are the happenstance, implying that antibody-antigen complexes do not show distinctive interaction types. Persistent interaction types such as pi...pi, amide-carbonyl, and hydroxyl-carbonyl interaction, are also investigated. Analyzing the structural orientations of the pi...pi stacking interactions, we find that herringbone shape is a major configuration in transient protein-protein interfaces. This result is different from that of protein core, where parallel-displaced configurations are the major configuration. We also analyze overall trend of amide-carbonyl and hydroxyl-carbonyl interactions. It is noticeable that nearly 82% of the interfaces have at least one hydroxyl-carbonyl interactions.     

Structural similarity and classification of protein interaction interfaces

Interactions between proteins play a key role in many cellular processes. Studying protein-protein interactions that share similar interaction interfaces may shed light on their evolution and could be helpful in elucidating the mechanisms behind stability and dynamics of the protein complexes. When two complexes share structurally similar subunits, the similarity of the interaction interfaces can be found through a structural superposition of the subunits. However, an accurate detection of similarity between the protein complexes containing subunits of unrelated structure remains an open problem. Here, we present an alignment-free machine learning approach to measure interface similarity. The approach relies on the feature-based representation of protein interfaces and does not depend on the superposition of the interacting subunit pairs. Specifically, we develop an SVM classifier of similar and dissimilar interfaces and derive a feature-based interface similarity measure. Next, the similarity measure is applied to a set of 2,806×2,806 binary complex pairs to build a hierarchical classification of protein-protein interactions. Finally, we explore case studies of similar interfaces from each level of the hierarchy, considering cases when the subunits forming interactions are either homologous or structurally unrelated. The analysis has suggested that the positions of charged residues in the homologous interfaces are not necessarily conserved and may exhibit more complex conservation patterns.     

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.     

A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory

Background

The study of biological interaction networks is a central theme of systems biology. Here, we investigate the relationships between two distinct types of interaction networks: the metabolic pathway map and the protein-protein interaction network (PIN). It has long been established that successive enzymatic steps are often catalyzed by physically interacting proteins forming permanent or transient multi-enzymes complexes. Inspecting high-throughput PIN data, it was shown recently that, indeed, enzymes involved in successive reactions are generally more likely to interact than other protein pairs. In our study, we expanded this line of research to include comparisons of the underlying respective network topologies as well as to investigate whether the spatial organization of enzyme interactions correlates with metabolic efficiency.

Results

Analyzing yeast data, we detected long-range correlations between shortest paths between proteins in both network types suggesting a mutual correspondence of both network architectures. We discovered that the organizing principles of physical interactions between metabolic enzymes differ from the general PIN of all proteins. While physical interactions between proteins are generally dissortative, enzyme interactions were observed to be assortative. Thus, enzymes frequently interact with other enzymes of similar rather than different degree. Enzymes carrying high flux loads are more likely to physically interact than enzymes with lower metabolic throughput. In particular, enzymes associated with catabolic pathways as well as enzymes involved in the biosynthesis of complex molecules were found to exhibit high degrees of physical clustering. Single proteins were identified that connect major components of the cellular metabolism and may thus be essential for the structural integrity of several biosynthetic systems.

Conclusion

Our results reveal topological equivalences between the protein interaction network and the metabolic pathway network. Evolved protein interactions may contribute significantly towards increasing the efficiency of metabolic processes by permitting higher metabolic fluxes. Thus, our results shed further light on the unifying principles shaping the evolution of both the functional (metabolic) as well as the physical interaction network.     

Predicting interactions in protein networks by completing defective cliques

Datasets obtained by large-scale, high-throughput methods for detecting protein-protein interactions typically suffer from a relatively high level of noise. We describe a novel method for improving the quality of these datasets by predicting missed protein-protein interactions, using only the topology of the protein interaction network observed by the large-scale experiment. The central idea of the method is to search the protein interaction network for defective cliques (nearly complete complexes of pairwise interacting proteins), and predict the interactions that complete them. We formulate an algorithm for applying this method to large-scale networks, and show that in practice it is efficient and has good predictive performance. More information can be found on our website http://topnet.gersteinlab.org/clique/ CONTACT: Mark.Gerstein@yale.edu SUPPLEMENTARY INFORMATION: Supplementary Materials are available at Bioinformatics online.     

Structural interpretation of protein-protein interaction network

Background

Currently a huge amount of protein-protein interaction data is available from high throughput experimental methods. In a large network of protein-protein interactions, groups of proteins can be identified as functional clusters having related functions where a single protein can occur in multiple clusters. However experimental methods are error-prone and thus the interactions in a functional cluster may include false positives or there may be unreported interactions. Therefore correctly identifying a functional cluster of proteins requires the knowledge of whether any two proteins in a cluster interact, whether an interaction can exclude other interactions, or how strong the affinity between two interacting proteins is.

Methods

In the present work the yeast protein-protein interaction network is clustered using a spectral clustering method proposed by us in 2006 and the individual clusters are investigated for functional relationships among the member proteins. 3D structural models of the proteins in one cluster have been built – the protein structures are retrieved from the Protein Data Bank or predicted using a comparative modeling approach. A rigid body protein docking method (Cluspro) is used to predict the protein-protein interaction complexes. Binding sites of the docked complexes are characterized by their buried surface areas in the docked complexes, as a measure of the strength of an interaction.

Results

The clustering method yields functionally coherent clusters. Some of the interactions in a cluster exclude other interactions because of shared binding sites. New interactions among the interacting proteins are uncovered, and thus higher order protein complexes in the cluster are proposed. Also the relative stability of each of the protein complexes in the cluster is reported.

Conclusions

Although the methods used are computationally expensive and require human intervention and judgment, they can identify the interactions that could occur together or ones that are mutually exclusive. In addition indirect interactions through another intermediate protein can be identified. These theoretical predictions might be useful for crystallographers to select targets for the X-ray crystallographic determination of protein complexes.

     

How many protein-protein interactions types exist in nature?

"Protein quaternary structure universe" refers to the ensemble of all protein-protein complexes across all organisms in nature. The number of quaternary folds thus corresponds to the number of ways proteins physically interact with other proteins. This study focuses on answering two basic questions: Whether the number of protein-protein interactions is limited and, if yes, how many different quaternary folds exist in nature. By all-to-all sequence and structure comparisons, we grouped the protein complexes in the protein data bank (PDB) into 3,629 families and 1,761 folds. A statistical model was introduced to obtain the quantitative relation between the numbers of quaternary families and quaternary folds in nature. The total number of possible protein-protein interactions was estimated around 4,000, which indicates that the current protein repository contains only 42% of quaternary folds in nature and a full coverage needs approximately a quarter century of experimental effort. The results have important implications to the protein complex structural modeling and the structure genomics of protein-protein interactions.     

Target selection for complex structural genomics

Most cellular processes are carried out by macromolecular assemblies and regulated through a complex network of transient protein-protein interactions. Genome-wide interaction discovery experiments are already delivering the first drafts of whole organism interactomes and, thus, depicting the limits of the interaction space. However, a complete understanding of molecular interactions can only come from high-resolution three-dimensional structures, as they provide key atomic details about the binding interfaces. The launch of structural genomics initiatives focused on protein interactions and complexes could quickly fill up the interaction space with structural details, offering a new perspective on how cell networks operate at atomic level. Clear target selection strategies that rationally identify the key interactions and complexes that should be first tackled are fundamental to maximize the return, minimize the costs and prevent experimental difficulties.     

Charged residues at protein interaction interfaces: unexpected conservation and orchestrated divergence

Protein-protein interactions play an essential role in the functioning of cell. The importance of charged residues and their diverse role in protein-protein interactions have been well studied using experimental and computational methods. Often, charged residues located in protein interaction interfaces are conserved across the families of homologous proteins and protein complexes. However, on a large scale, it has been recently shown that charged residues are significantly less conserved than other residue types in protein interaction interfaces. The goal of this work is to understand the role of charged residues in the protein interaction interfaces through their conservation patterns. Here, we propose a simple approach where the structural conservation of the charged residue pairs is analyzed among the pairs of homologous binary complexes. Specifically, we determine a large set of homologous interactions using an interaction interface similarity measure and catalog the basic types of conservation patterns among the charged residue pairs. We find an unexpected conservation pattern, which we call the correlated reappearance, occurring among the pairs of homologous interfaces more frequently than the fully conserved pairs of charged residues. Furthermore, the analysis of the conservation patterns across different superkingdoms as well as structural classes of proteins has revealed that the correlated reappearance of charged residues is by far the most prevalent conservation pattern, often occurring more frequently than the unconserved charged residues. We discuss a possible role that the new conservation pattern may play in the long-range electrostatic steering effect.     

Integration of protein motions with molecular networks reveals different mechanisms for permanent and transient interactions

The integration of molecular networks with other types of data, such as changing levels of gene expression or protein-structural features, can provide richer information about interactions than the simple node-and-edge representations commonly used in the network community. For example, the mapping of 3D-structural data onto networks enables classification of proteins into singlish- or multi-interface hubs (depending on whether they have >2 interfaces). Similarly, interactions can be classified as permanent or transient, depending on whether their interface is used by only one or by multiple partners. Here, we incorporate an additional dimension into molecular networks: dynamic conformational changes. We parse the entire PDB structural databank for alternate conformations of proteins and map these onto the protein interaction network, to compile a first version of the Dynamic Structural Interaction Network (DynaSIN). We make this network available as a readily downloadable resource file, and we then use it to address a variety of downstream questions. In particular, we show that multi-interface hubs display a greater degree of conformational change than do singlish-interface ones; thus, they show more plasticity which perhaps enables them to utilize more interfaces for interactions. We also find that transient associations involve smaller conformational changes than permanent ones. Although this may appear counterintuitive, it is understandable in the following framework: as proteins involved in transient interactions shuttle between interchangeable associations, they interact with domains that are similar to each other and so do not require drastic structural changes for their activity. We provide evidence for this hypothesis through showing that interfaces involved in transient interactions bind fewer classes of domains than those in a control set.     

Molecular principles of the interactions of disordered proteins

Thorough knowledge of the molecular principles of protein-protein recognition is essential to our understanding of protein function at the cellular level. Whereas interactions of ordered proteins have been analyzed in great detail, complexes of intrinsically unstructured/disordered proteins (IUPs) have hardly been addressed so far. Here, we have collected a database of 39 complexes of experimentally verified IUPs, and compared their interfaces with those of 72 complexes of ordered, globular proteins. The characteristic differences found between the two types of complexes suggest that IUPs represent a distinct molecular implementation of the principles of protein-protein recognition. The interfaces do not differ in size, but those of IUPs cover a much larger part of the surface of the protein than for their ordered counterparts. Moreover, IUP interfaces are significantly more hydrophobic relative to their overall amino acid composition, but also in absolute terms. They rely more on hydrophobic-hydrophobic than on polar-polar interactions. Their amino acids in the interface realize more intermolecular contacts, which suggests a better fit with the partner due to induced folding upon binding that results in a better adaptation to the partner. The two modes of interaction also differ in that IUPs usually use only a single continuous segment for partner binding, whereas the binding sites of ordered proteins are more segmented. Probably, all these features contribute to the increased evolutionary conservation of IUP interface residues. These noted molecular differences are also manifested in the interaction energies of IUPs. Our approximation of these by low-resolution force-fields shows that IUPs gain much more stabilization energy from intermolecular contacts, than from folding, i.e. they use their binding energy for folding. Overall, our findings provide a structural rationale to the prior suggestions that many IUPs are specialized for functions realized by protein-protein interactions.