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Large scale statistical prediction of protein-protein interaction by potentially interacting domain pair
- Genome Inform
, 2002
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Architecture of basic building blocks in protein and domain structural interaction networks
, 2005
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S: Scaling law in sizes of protein sequence families: from super-families to orphan genes
- Proteins
"... ABSTRACT It has been observed that the size of protein sequence families is unevenly distrib-uted, with few super families with a large number of members and many “orphan ” proteins that do not belong to any family. Here it is shown that the distribution of sizes of protein families in different dat ..."
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ABSTRACT It has been observed that the size of protein sequence families is unevenly distrib-uted, with few super families with a large number of members and many “orphan ” proteins that do not belong to any family. Here it is shown that the distribution of sizes of protein families in different databases and classifications (Protomap, Prodom, Cog) follows a power-law behavior with similar scaling exponents, which is characteristic of self-organizing systems. Since large databases are used in this study, a more detailed analysis of the data than in previous studies was possible. Hence, it is shown that the size distribution is governed by two exponents, different for the super families and the orphan proteins. A simple model of protein evolu-tion is proposed, in which proteins are dynamically generated and clustered into families. The model yields a scaling behavior very similar to the distribu-tion observed in the actual sequence databases, including the two distinct regimes for the large and small families, and thus suggests that the existence of “super families ” of proteins and “orphan ” pro-teins are two manifestations of the same evolution-ary process. Proteins 2003;51:569–576. © 2003Wiley-Liss, Inc. Key words: protein families; size distribution; scal-ing; power-law; evolution
Metabolome Based Reaction Graphs of M. tuberculosis and M. leprae: A Comparative Network Analysis
"... Background. Several types of networks, such as transcriptional, metabolic or protein-protein interaction networks of various organisms have been constructed, that have provided a variety of insights into metabolism and regulation. Here, we seek to exploit the reaction-based networks of three organis ..."
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Background. Several types of networks, such as transcriptional, metabolic or protein-protein interaction networks of various organisms have been constructed, that have provided a variety of insights into metabolism and regulation. Here, we seek to exploit the reaction-based networks of three organisms for comparative genomics. We use concepts from spectral graph theory to systematically determine how differences in basic metabolism of organisms are reflected at the systems level and in the overall topological structures of their metabolic networks. Methodology/Principal Findings. Metabolome-based reaction networks of Mycobacterium tuberculosis, Mycobacterium leprae and Escherichia coli have been constructed based on the KEGG LIGAND database, followed by graph spectral analysis of the network to identify hubs as well as the sub-clustering of reactions. The shortest and alternate paths in the reaction networks have also been examined. Sub-cluster profiling demonstrates that reactions of the mycolic acid pathway in mycobacteria form a tightly connected sub-cluster. Identification of hubs reveals reactions involving glutamate to be central to mycobacterial metabolism, and pyruvate to be at the centre of the E. coli metabolome. The analysis of shortest paths between reactions has revealed several paths that are shorter than well established pathways. Conclusions. We conclude that severe downsizing of the leprae genome has not significantly altered the global structure of its reaction network but has reduced the total number of alternate paths between its reactions while keeping the shortest paths between them intact. The hubs in the mycobacterial networks that are absent in the human metabolome can be explored as potential drug targets. This work demonstrates the usefulness of constructing metabolome
Large scale statistical prediction of protein-protein interaction by potentially interacting domain (PID) pair
- In Genome Inform Ser Workshop Genome Inform
, 2002
"... Protein-protein interaction plays a critical role in biological processes. The identification of interacting proteins by computational methods can provide new leads in functional studies of uncharacterized proteins without performing extensive experiments. We developed a database for the potentially ..."
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Protein-protein interaction plays a critical role in biological processes. The identification of interacting proteins by computational methods can provide new leads in functional studies of uncharacterized proteins without performing extensive experiments. We developed a database for the potentially interacting domain pairs (PID) extracted from a dataset of experimentally identified interacting protein pairs (DIP: database of interacting proteins) with InterPro, an integrated database of protein families, domains and functional sites. In developing protein interaction databases and predictive methods, sensitive statistical scoring systems is critical to provide a reliability index for accurate functional analysis of interaction networks. We present a statistical scoring system, named “PID matrix score ” as a measure of the interaction probability (interactability) between domains. This system provided a valuable tool for functional prediction of unknown proteins. For the evaluation of PID matrix, cross validation was performed with subsets of DIP data. The prediction system gives about 50 % sensitivity and more than 98 % specificity, which implies that the information for interacting proteins pairs could be enriched about 30 fold with the PID matrix. It is demonstrated that mapping of the genome-wide interaction network can be achieved by using the PID matrix.
Systematic Assessment of Protein Interaction Data using Graph Topology Approaches
, 2006
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ProDGe
, 2011
"... investigating protein-protein interactions at the domain level ..."
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BIOINFORMATICS Architecture of Basic Building Blocks in Protein and Domain Structural Interaction Networks
"... Motivation: The structural interaction of proteins and their domains in networks is one of the most basic molecular mechanisms for biological cells. Topological analysis of such networks can provide an understanding of and solutions for predicting properties of proteins and their evolution in terms ..."
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Motivation: The structural interaction of proteins and their domains in networks is one of the most basic molecular mechanisms for biological cells. Topological analysis of such networks can provide an understanding of and solutions for predicting properties of proteins and their evolution in terms of domains. A single paradigm for the analysis of interacti-ons at different layers, such as domain and protein layers, is needed. Results: Applying a colored vertex graph model, we integra-ted two basic interaction layers under a unified model: (1) structural domains, and (2) their protein/complex networks. We identified four basic and distinct elements in the model that explains protein interactions at the domain level. We searched for motifs in the networks to detect their topologi-cal characteristics using a pruning strategy and a hash table for rapid detection. We obtained the following results: First, compared with a random distribution, a substantial part of the protein interactions could be explained by domain-level struc-tural interaction information. Second, there were distinct kinds of protein interaction patterns classified by specific and distin-guishable numbers of domains. The intermolecular domain interaction was the most dominant protein interaction pattern. Third, despite the coverage of the protein interaction informa-tion differing among species, the similarity of their networks indicated shared architectures of protein interaction network in living organisms. Remarkably, there were only a few basic architectures in the model (fewer than ten for a four-node network topology), and we propose that most biological com-binations of domains into proteins and complexes can be explained by a small number of key topological motifs.
