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Register a new userIs the intrinsic disorder of proteins the cause of the scale-free architecture of protein-protein interaction networks?
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In protein-protein interaction networks certain topological properties appear to be recurrent: networks maps are considered scale-free. It is possible that this topology is reflected in the protein structure. In this paper we investigate the role of protein disorder in the network topology. We find that the disorder of a protein (or of its neighbors) is independent of its number of protein-protein interactions. This result suggests that protein disorder does not play a role in the scale-free architecture of protein networks.
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Motivation: High-throughput experimental techniques have been producing more and more protein-protein interaction (PPI) data. PPI network alignment greatly benefits the understanding of evolutionary relationship among species, helps identify conserved sub-networks and provides extra information for functional annotations. Although a few methods have been developed for multiple PPI network alignment, the alignment quality is still far away from perfect and thus, new network alignment methods are needed. Result: In this paper, we present a novel method, denoted as ConvexAlign, for joint alignment of multiple PPI networks by convex optimization of a scoring function composed of sequence similarity, topological score and interaction conservation score. In contrast to existing methods that generate multiple alignments in a greedy or progressive manner, our convex method optimizes alignments globally and enforces consistency among all pairwise alignments, resulting in much better alignment quality. Tested on both synthetic and real data, our experimental results show that ConvexAlign outperforms several popular methods in producing functionally coherent alignments. ConvexAlign even has a larger advantage over the others in aligning real PPI networks. ConvexAlign also finds a few conserved complexes among 5 species which cannot be detected by the other methods.
The evolution, regulation and sustenance of biological complexity is determined by protein-protein interaction network that is filled with dynamic events. Recent experimental evidences point out that clustering of proteins has a vital role in many cellular processes. Upsurge in fluorescence imaging methods has given a new spin to our ability to probe protein interactions in cellular and sub-cellular compartments. Despite the increasing detection sensitivity, quantitative information that can be obtained from these imaging methods is limited. This is primarily due to (i) the difficulty in tracking the problem analytically and (ii) limitations in spatio-temporal resolution that can be achieved in interrogating living cells in real time. A novel point of view based on diffusion-driven percolative clustering is proposed here that can plausibly shed more light on the complex issues of protein-protein interactions. Since this model is open to computational analysis, it is quantitative in its premise. Besides being able to analyze the phenomenon, the power of any model is gauged by its ability to predict interesting and novel features of the phenomenon itself, which can subsequently be tested by additional experiments. To this end, an experimental assay based on fluorescence lifetime imaging is proposed to verify the validity of the percolation model.
Complexes of physically interacting proteins are one of the fundamental functional units responsible for driving key biological mechanisms within the cell. Their identification is therefore necessary not only to understand complex formation but also the higher level organization of the cell. With the advent of high-throughput techniques in molecular biology, significant amount of physical interaction data has been cataloged from organisms such as yeast, which has in turn fueled computational approaches to systematically mine complexes from the network of physical interactions among proteins (PPI network). In this survey, we review, classify and evaluate some of the key computational methods developed till date for the identification of protein complexes from PPI networks. We present two insightful taxonomies that reflect how these methods have evolved over the years towards improving automated complex prediction. We also discuss some open challenges facing accurate reconstruction of complexes, the crucial ones being presence of high proportion of errors and noise in current high-throughput datasets and some key aspects overlooked by current complex detection methods. We hope this review will not only help to condense the history of computational complex detection for easy reference, but also provide valuable insights to drive further research in this area.
Recently, the structural controllability theory has been introduced to analyze the Protein-Protein Interaction (PPI) network. The indispensable nodes, which their removal increase the number of driver nodes to control the network, are found essential in PPI network. However, the PPI network is far from complete and there may exist many false-positive or false-negative interactions, which promotes us to question: are these indispensable nodes robust to structural change? Here we systematically investigate the robustness of indispensable nodes of PPI network by removing and adding possible interactions. We found that the indispensable nodes are sensitive to the structural change and very few edges can change the type of many indispensable nodes. The finding may promote our understanding to the control principle of PPI network.
Network of packages with regulatory interactions (dependences and conflicts) from Debian GNU/Linux operating system is compiled and used as analogy of a gene regulatory network. Using a trace-back algorithm we assembly networks from the potential pool of packages for both scale-free and exponential topology from real and a null model data, respectively. We calculate the maximum number of packages that can be functionally installed in the system (i.e., the active network size). We show that scale-free regulatory networks allow a larger active network size than random ones. Small genomes with scale-free regulatory topology could allow much more functionality than large genomes with an exponential one, with implications on its dynamics, robustness and evolution.
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