Topology of molecular interaction networks

Abstract Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks. Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs. Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network 1 topology to generic properties such as robustness, it is used to predict specific functions or phenotypes. Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further

Differential evolutionary conservation of motif modes in the yeast protein interaction network

BACKGROUND: The importance of a network motif (a recurring interconnected pattern of special topology which is over-represented in a biological network) lies in its position in the hierarchy between the protein molecule and the module in a protein-protein interaction network. Until now, however, the methods available have greatly restricted the scope of research. While they have focused on the analysis in the resolution of a motif topology, they have not been able to distinguish particular motifs of the same topology in a protein-protein interaction network. RESULTS: We have been able to assign the molecular function annotations of Gene Ontology to each protein in the protein-protein interactions of Saccharomyces cerevisiae. For various motif topologies, we have developed an algorithm, enabling us to unveil one million "motif modes", each of which features a unique topological combination of molecular functions. To our surprise, the conservation ratio, i.e., the extent of the evolutionary constraints upon the motif modes of the same motif topology, varies significantly, clearly indicative of distinct differences in the evolutionary constraints upon motifs of the same motif topology. Equally important, for all motif modes, we have found a power-law distribution of the motif counts on each motif mode. We postulate that motif modes may very well represent the evolutionary-conserved topological units of a protein interaction network. CONCLUSION: For the first time, the motifs of a protein interaction network have been investigated beyond the scope of motif topology. The motif modes determined in this study have not only enabled us to differentiate among different evolutionary constraints on motifs of the same topology but have also opened up new avenues through which protein interaction networks can be analyzed

The G protein-coupled receptor heterodimer network (GPCR-HetNet) and its hub components

G protein-coupled receptors (GPCRs) oligomerization has emerged as a vital characteristic of receptor structure. Substantial experimental evidence supports the existence of GPCR-GPCR interactions in a coordinated and cooperative manner. However, despite the current development of experimental techniques for large-scale detection of GPCR heteromers, in order to understand their connectivity it is necessary to develop novel tools to study the global heteroreceptor networks. To provide insight into the overall topology of the GPCR heteromers and identify key players, a collective interaction network was constructed. Experimental interaction data for each of the individual human GPCR protomers was obtained manually from the STRING and SCOPUS databases. The interaction data were used to build and analyze the network using Cytoscape software. The network was treated as undirected throughout the study. It is comprised of 156 nodes, 260 edges and has a scale-free topology. Connectivity analysis reveals a significant dominance of intrafamily versus interfamily connections. Most of the receptors within the network are linked to each other by a small number of edges. DRD2, OPRM, ADRB2, AA2AR, AA1R, OPRK, OPRD and GHSR are identified as hubs. In a network representation 10 modules/clusters also appear as a highly interconnected group of nodes. Information on this GPCR network can improve our understanding of molecular integration. GPCR-HetNet has been implemented in Java and is freely available at http://www.iiia.csic.es/similar to ismel/GPCR-Nets/index.html

TopoGSA: network topological gene set analysis

Summary: TopoGSA (Topology-based Gene Set Analysis) is a web-application dedicated to the computation and visualization of network topological properties for gene and protein sets in molecular interaction networks. Different topological characteristics, such as the centrality of nodes in the network or their tendency to form clusters, can be computed and compared with those of known cellular pathways and processes

Ferrogels cross-linked by magnetic particles: Field-driven deformation and elasticity studied using computer simulations

Ferrogels, i.e. swollen polymer networks into which magnetic particles are immersed, can be considered as "smart materials" since their shape and elasticity can be controlled by an external magnetic field. Using molecular dynamics simulations on the coarse-grained level we study a ferrogel in which the magnetic particles act as the cross-linkers of the polymer network. In a homogeneous external magnetic field the direct coupling between the orientation of the magnetic moments and the polymers by means of covalent bonds gives rise to a deformation of the gel, independent of the interparticle dipole-dipole interaction. In this paper we report quantitative measurements of this deformation, the gel's elastic moduli and its magnetic response. Our results demonstrate that these properties depend significantly on the topology of the polymer network

Architecture of transcriptional regulatory circuits is knitted over the topology of bio-molecular interaction networks

Background: Uncovering the operating principles underlying cellular processes by using 'omics' data is often a difficult task due to the high-dimensionality of the solution space that spans all interactions among the bio-molecules under consideration. A rational way to overcome this problem is to use the topology of bio-molecular interaction networks in order to constrain the solution space. Such approaches systematically integrate the existing biological knowledge with the 'omics' data. Results: Here we introduce a hypothesis-driven method that integrates bio-molecular network topology with transcriptome data, thereby allowing the identification of key biological features (Reporter Features) around which transcriptional changes are significantly concentrated. We have combined transcriptome data with different biological networks in order to identify Reporter Gene Ontologies, Reporter Transcription Factors, Reporter Proteins and Reporter Complexes, and use this to decipher the logic of regulatory circuits playing a key role in yeast glucose repression and human diabetes. Conclusion: Reporter Features offer the opportunity to identify regulatory hot-spots in bio-molecular interaction networks that are significantly affected between or across conditions. Results of the Reporter Feature analysis not only provide a snapshot of the transcriptional regulatory program but also are biologically easy to interpret and provide a powerful way to generate new hypotheses. Our Reporter Features analyses of yeast glucose repression and human diabetes data brings hints towards the understanding of the principles of transcriptional regulation controlling these two important and potentially closely related systems

Conformational Change and Topological Stability of Proteins

The conformation and topology of a protein changes when stabilizing forces are absent, but the mechanisms by which these changes occur remains elusive. This dissertation aims to broaden the understandings. On the conformational level, the M20 loop conformers of E. coli dihydrofolate reductase are interrogated to identify factors responsible for their stability as well as to determine how one conformer might change into another. Molecular dynamics is used to simulate the open, closed and occluded conformers (observed in X-ray crystal structures) under a series of different single ligand conditions. Analysis shows that all open conformers move to a similar new conformation. Free energy methods examine the stability of the new loop conformer relative to the others. External perturbation molecular dynamics simulations and normal mode analysis methods examine possible M20 loop pathways occurring either when one loop conformer is forced to change into another or when a ligand is pulled out of its binding site. On the topological level, conserved residue-residue interaction networks found among three different protein superfamilies (the all α-helix death domains, the α/β-plaits and the all β-sheet immunoglobulins), each of different secondary structure but sharing the Greek-Key topology, are assessed for any inherent stability they might contain relative to randomly selected interaction networks. This assessment is achieved by simulating one protein from each family at different temperatures, ranging from 300 to 600 K, and observing that adding thermal energy to the system causes the random interaction networks to fall apart more easily than the conserved networks. When considered together, the conformational and topological projects, although very different from each other, both demonstrate the same idea - that regardless of scale, instability causes change and vice versa. This dissertation is divided into five chapters: Introduction, Theoretical Background, M20 Loop Conformers of Dihydrofolate Reductase, Conserved Contact Networks of Greek-Key Proteins and Summary

Molecular Principles of Gene Fusion Mediated Rewiring of Protein Interaction Networks in Cancer

Gene fusions are common cancer-causing mutations, but the molecular principles by which fusion protein products affect interaction networks and cause disease are not well understood. Here, we perform an integrative analysis of the structural, interactomic, and regulatory properties of thousands of putative fusion proteins. We demonstrate that genes that form fusions (i.e., parent genes) tend to be highly connected hub genes, whose protein products are enriched in structured and disordered interaction-mediating features. Fusion often results in the loss of these parental features and the depletion of regulatory sites such as post-translational modifications. Fusion products disproportionately connect proteins that did not previously interact in the protein interaction network. In this manner, fusion products can escape cellular regulation and constitutively rewire protein interaction networks. We suggest that the deregulation of central, interaction-prone proteins may represent a widespread mechanism by which fusion proteins alter the topology of cellular signaling pathways and promote cancer