Topology of small-world networks of protein-protein complex structures

Sol, Antonio del; Fujihashi, Hirotomo; O’Meara, Paul

doi:10.1093/bioinformatics/bti167

Cited by 110 publications

(67 citation statements)

References 19 publications

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“…Common features of these networks appear as for example the small-world structure found in various fields of application like peer-to-peer networks (Latapy and Magnien 2008), lexical network (Gaume 2004), networks between firm directors (Davis et. al 2003) or protein networks (del Sol et al, 2004). The characteristic of a small-world are that the average distance between two nodes of the network is low (also known as tight global connectivity) and secondly that the probability of existence of an edge between two nodes is higher if these two nodes are linked to a third one (we also say that the network has a strong local connectivity) (Watts, 2003).…”

Section: Analyzing a Dense Networkmentioning

confidence: 99%

A network approach to the French system of legal codes—part I: analysis of a dense network

2011

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We explore one aspect of the structure of a codified legal system at the national level using a new type of representation to understand the strong or weak dependencies between the various fields of law. In Part I of this study, we analyze the graph associated with the network in which each French legal code is a vertex and an edge is produced between two vertices when a code cites another code at least one time. We show that this network distinguishes from many other real networks from a high density, giving it a particular structure that we call concentrated world and that differentiates a national legal system (as considered with a resolution at the code level) from small-world graphs identified in many social networks. Our analysis then shows that a few communities (groups of highly wired vertices) of codes covering large domains of regulation are structuring the whole system. Indeed we mainly find a central group of influent codes, a group of codes related to social issues and a group of codes dealing with territories and natural resources. The study of this codified legal system is also of interest in the field of the analysis of real networks. In particular we examine the impact of the high density on the structural characteristics of the graph and on the ways communities are searched for. Finally we provide an original visualization of this graph on an hemicyle-like plot, this representation being based on a statistical reduction of dissimilarity measures between vertices.In Part II (a following paper) we show how the consideration of the weights attributed to each edge in the network in proportion to the number of citations between two vertices (codes) allows deepening the analysis of the French legal system.

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Section: Analyzing a Dense Networkmentioning

confidence: 99%

A network approach to the French system of legal codes—part I: analysis of a dense network

2011

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“…Therefore, the scoring terms are designed to differentiate the two incorrect docking structures from the correct docking results (see methods). By analyzing the co-crystallized structures, previous researches [14,24,25] found that the centrally conserved residues or hot spot residues are key in preserving characteristic path lengths of protein complexes. However, by comparing correct docking complexes with those incorrect ones, our research shows that the correct binding complexes have lower characteristic path lengths and better connectivities of the complex network than the incorrect ones.…”

Section: Analysis Of Residue Network Propertiesmentioning

confidence: 99%

“…Some academic studies found that the protein contact networks are small world [13][14][15][16][17], which have large values of the clustering coefficient and small values of the characteristic path length. Some application studies have focused on monomer proteins, such as the prediction of key residues in protein folding [13,18] or identification of functional residues in protein structures [19], the correlation between the residue fluctuations and the average shortest path lengths [20] and the mechanisms of protein folding [21][22][23].…”

mentioning

confidence: 99%

“…Some application studies have focused on monomer proteins, such as the prediction of key residues in protein folding [13,18] or identification of functional residues in protein structures [19], the correlation between the residue fluctuations and the average shortest path lengths [20] and the mechanisms of protein folding [21][22][23]. Additionally, some groups have studied the protein-protein complexes to identify key residues or hot spots in proteinprotein interactions [14,[24][25][26], to reveal the ligand binding network-bridging effects [27] and to analyze the allosteric coupling mechanisms of protein-protein binding [28]. The investigations of residue network have provided systematic and deeper understanding for the structure of proteins.…”

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confidence: 99%

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Network analysis of protein-protein interaction

et al. 2010

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Residue networks are constructed by defining the residues as the vertices and atom contacts between them as the edges. The residue network of a protein complex is divided into two types of networks, i.e. the hydrophobic and the hydrophilic residue networks. By analyzing the network parameters, it is found that the correct binding complex conformations are of both higher sum of the interface degree values and lower characteristic path length than those incorrect ones. These features reflect that the correct binding complex conformations have better geometric and/or residue type complementarity, and the correct binding modes are very important for preserving the characteristic path lengths of native protein complexes. In addition, two scoring terms are proposed based on the network parameters, in which the characteristics of the entire complex shape and residue type complementarity are taken into account. These network-based scoring terms have also been used in conjunction with other scoring terms, and the new multi-term scoring HPNCscore is devised in this work. It can improve the discrimination of the combined scoring function of RosettaDock more than 12%. This work might enhance our knowledge of the mechanisms of protein-protein interactions and recognition.interface, characteristic path length, residue network, scoring function Citation:Chang S, Gong X Q, Jiao X, et al. Network analysis of protein-protein interaction.

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“…Of these indices, betweenness has been extensively used in recent years for the analysis of social-interaction networks, as well as other large-scale complex networks. Some applications include lethality in biological networks [5,6,7], study of sexual networks and AIDS [8], identifying key actors in terrorist networks [9,10], organizational behavior [11], and supply chain management processes [12]. Betweenness is also used as the primary routine in popular algorithms for clustering and community identification [13] in real-world networks.…”

Section: Introductionmentioning

confidence: 99%

Approximating Betweenness Centrality

Bader

Kintali

Madduri

et al.

Lecture Notes in Computer Science

263

191

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Abstract. Betweenness is a centrality measure based on shortest paths, widely used in complex network analysis. It is computationally-expensive to exactly determine betweenness; currently the fastest-known algorithm by Brandes requires O(nm) time for unweighted graphs and O(nm + n 2 log n) time for weighted graphs, where n is the number of vertices and m is the number of edges in the network. These are also the worstcase time bounds for computing the betweenness score of a single vertex. In this paper, we present a novel approximation algorithm for computing betweenness centrality of a given vertex, for both weighted and unweighted graphs. Our approximation algorithm is based on an adaptive sampling technique that significantly reduces the number of single-source shortest path computations for vertices with high centrality. We conduct an extensive experimental study on real-world graph instances, and observe that our random sampling algorithm gives very good betweenness approximations for biological networks, road networks and web crawls.

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Topology of small-world networks of protein-protein complex structures

Abstract: http://www.fujirebio.co.jp/support/index.php (under construction).

Cited by 110 publications

References 19 publications

A network approach to the French system of legal codes—part I: analysis of a dense network

A network approach to the French system of legal codes—part I: analysis of a dense network

Network analysis of protein-protein interaction

Approximating Betweenness Centrality

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