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98
How the global structure of protein interaction networks evolves,”
- Proceedings of the Royal Society B,
, 2003
"... Two processes can influence the evolution of protein interaction networks: addition and elimination of interactions between proteins, and gene duplications increasing the number of proteins and interactions. The rates of these processes can be estimated from available Saccharomyces cerevisiae genom ..."
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Cited by 105 (2 self)
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Two processes can influence the evolution of protein interaction networks: addition and elimination of interactions between proteins, and gene duplications increasing the number of proteins and interactions. The rates of these processes can be estimated from available Saccharomyces cerevisiae genome data and are sufficiently high to affect network structure on short time-scales. For instance, more than 100 interactions may be added to the yeast network every million years, a fraction of which adds previously unconnected proteins to the network. Highly connected proteins show a greater rate of interaction turnover than proteins with few interactions. From these observations one can explain (without natural selection on global network structure) the evolutionary sustenance of the most prominent network feature, the distribution of the frequency P(d ) of proteins with d neighbours, which is broad-tailed and consistent with a power law, that is: P(d )~d 2g .
Evolving protein interaction networks through gene duplication
- J. Theor. Biol
"... The topology of the proteome map revealed by recent large-scale hybridization methods has shown that the distribution of protein-protein interactions is highly heterogeneous, with many proteins having few links while a few of them are heavily connected. This particular topology is shared by other ce ..."
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Cited by 86 (2 self)
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The topology of the proteome map revealed by recent large-scale hybridization methods has shown that the distribution of protein-protein interactions is highly heterogeneous, with many proteins having few links while a few of them are heavily connected. This particular topology is shared by other cellular networks, such as metabolic pathways, and it has been suggested to be responsible for the high mutational homeostasis displayed by the genome of some organisms. In this paper we explore a recent model of proteome evolution that has been shown to reproduce many of the features displayed by its real counterparts. The model is based on gene duplication plus re-wiring of the newly created genes. The statistical features displayed by the proteome of well-known organisms are reproduced, suggesting that the overall topology of the protein maps naturally emerges from the two leading mechanisms considered by the model. I.
Pairwise alignment of protein interaction networks
- Journal of Computational Biology
, 2006
"... With an ever-increasing amount of available data on protein–protein interaction (PPI) networks and research revealing that these networks evolve at a modular level, discovery of conserved patterns in these networks becomes an important problem. Although available data on protein–protein interactions ..."
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Cited by 51 (4 self)
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With an ever-increasing amount of available data on protein–protein interaction (PPI) networks and research revealing that these networks evolve at a modular level, discovery of conserved patterns in these networks becomes an important problem. Although available data on protein–protein interactions is currently limited, recently developed algorithms have been shown to convey novel biological insights through employment of elegant mathematical models. The main challenge in aligning PPI networks is to define a graph theoretical measure of similarity between graph structures that captures underlying biological phenomena accurately. In this respect, modeling of conservation and divergence of interactions, as well as the interpretation of resulting alignments, are important design parameters. In this paper, we develop a framework for comprehensive alignment of PPI networks, which is inspired by duplication/divergence models that focus on understanding the evolution of protein interactions. We propose a mathematical model that extends the concepts of match, mismatch, and gap in sequence alignment to that of match, mismatch, and duplication in network alignment and evaluates similarity between graph structures through a scoring function that accounts for evolutionary events. By relying on evolutionary models, the proposed framework facilitates interpretation of resulting alignments in terms of not only conservation but also divergence of modularity in PPI networks. Furthermore, as in the case of sequence alignment, our model allows flexibility in adjusting parameters to quantify underlying evolutionary relationships. Based on the proposed model, we formulate PPI network alignment as an optimization problem and present fast algorithms to solve this problem. Detailed experimental results from an implementation of the proposed framework show that our algorithm is able to discover conserved interaction patterns very effectively, in terms of both accuracies and computational cost. Key words: protein–protein interactions, network alignment, evolutionary models. 1.
Evolution and topology in the yeast protein interaction network,
- Genome Research
, 2004
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HotSprint: database of computational hot spots in protein interfaces
- Nucleic Acids Res
, 2008
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Molecular evolution in large genetic networks: does connectivity equal constraint
- Journal of Molecular Evolution
, 2002
"... Abstract. Genetic networks show a broad-tailed distribution of the number of interaction partners per protein, which is consistent with a power-law. It has been proposed that such broad-tailed distributions are observed because they confer robustness against mutations to the network. We evaluate thi ..."
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Cited by 24 (5 self)
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Abstract. Genetic networks show a broad-tailed distribution of the number of interaction partners per protein, which is consistent with a power-law. It has been proposed that such broad-tailed distributions are observed because they confer robustness against mutations to the network. We evaluate this hypothesis for two genetic networks, that of the E. coli core intermediary metabolism and that of the yeast protein-interaction network. Specifically, we test the hypothesis through one of its key predictions: highly connected proteins should be more important to the cell and, thus, subject to more severe selective and evolutionary constraints. We find, however, that no correlation between highly connected proteins and evolutionary rate exists in the E. coli metabolic network and that there is only a weak correlation in the yeast protein-interaction network. Furthermore, we show that the observed correlation is function-specific within the protein-interaction network: only genes involved in the cell cycle and transcription show significant correlations. Our work sheds light on conflicting results by previous researchers by comparing data from multiple types of protein-interaction datasets and by using a closely related species as a reference taxon. The finding that highly connected proteins can tolerate just as many amino acid substitutions as other proteins leads us to con-The first two authors contributed equally to the reported work. Correspondence to: Matthew W. Hahn;
ADVICE: Automated Detection and Validation of Interaction by Co-Evolution
- Nucleic Acids Res
, 2004
"... ADVICE (Automated Detection and Validation of Interaction by Co-Evolution) is a web tool for predicting and validating protein-protein interactions using the observed co-evolution between interacting proteins. Interacting proteins are known to share similar evolutionary histories since they undergo ..."
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Cited by 18 (0 self)
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ADVICE (Automated Detection and Validation of Interaction by Co-Evolution) is a web tool for predicting and validating protein-protein interactions using the observed co-evolution between interacting proteins. Interacting proteins are known to share similar evolutionary histories since they undergo coordinated evolutionary changes to preserve interactions and functionalities. The web tool automates a commonly adopted methodology to quantify the similarities in proteins ’ evolutionary histories for postulating potential protein–protein interactions. ADVICE can also be used to validate experimental data against spurious protein interactions by identifying those that have few similarities in their evolutionary histories. The web tool accepts a list of protein sequences or sequence pairs as input and retrieves orthologous sequences to compute the similarities in the proteins ’ evolutionary histories. To facilitate hypothesis generation, detected co-evolved proteins can be visualized as a network at the website. ADVICE is available at
Rapid evolution exposes the boundaries of domain structure and function in natively unfolded FG nucleoporins
- Mol. Cell Proteomics
, 2006
"... Nups) function at the nuclear pore complex (NPC) to fa-cilitate nucleocytoplasmic transport. In Saccharomyces cerevisiae, each FG Nup contains a large natively un-folded domain that is punctuated by FG repeats. These FG repeats are surrounded by hydrophilic amino acids (AAs) common to disordered pro ..."
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Cited by 17 (1 self)
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Nups) function at the nuclear pore complex (NPC) to fa-cilitate nucleocytoplasmic transport. In Saccharomyces cerevisiae, each FG Nup contains a large natively un-folded domain that is punctuated by FG repeats. These FG repeats are surrounded by hydrophilic amino acids (AAs) common to disordered protein domains. Here we show that the FG domain of Nups from human, fly, worm, and other yeast species is also enriched in these disorder-associated AAs, indicating that structural disorder is a conserved feature of FG Nups and likely serves an impor-tant role in NPC function. Despite the conservation of AA composition, FG Nup sequences from different species show extensive divergence. A comparison of the AA sub-stitution rates of proteins with syntenic orthologs in four Saccharomyces species revealed that FG Nups have
Protein networks, pleiotropy and the evolution of senescence
- Proc. Biol. Sci
, 2004
"... The number of interactions, or connectivity, among proteins in the yeast protein interaction network follows a power law. I compare patterns of connectivity for subsets of yeast proteins associated with senescence and with five other traits. I find that proteins associated with ageing have significa ..."
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Cited by 15 (0 self)
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The number of interactions, or connectivity, among proteins in the yeast protein interaction network follows a power law. I compare patterns of connectivity for subsets of yeast proteins associated with senescence and with five other traits. I find that proteins associated with ageing have significantly higher connectivity than expected by chance, a pattern not seen for most other datasets. The pattern holds even when controlling for other factors also associated with connectivity, such as localization of protein expression within the cell. I suggest that these observations are consistent with the antagonistic pleiotropy theory for the evolution of senescence. In further support of this argument, I find that a protein’s con-nectivity is positively correlated with the number of traits it influences or its degree of pleiotropy, and further show that the average degree of pleiotropy is greatest for proteins associated with senescence. I explain these results with a simple mathematical model combining assumptions of the antagonistic pleio-tropy theory for the evolution of senescence with data on network topology. These findings integrate molecular and evolutionary models of senescence, and should aid in the search for new ageing genes.
