Effect of dataset selection on the topological interpretation of protein interaction networks

BACKGROUND: Studies of the yeast protein interaction network have revealed distinct correlations between the connectivity of individual proteins within the network and the average connectivity of their neighbours. Although a number of biological mechanisms have been proposed to account for these findings, the significance and influence of the specific datasets included in these studies has not been appreciated adequately. RESULTS: We show how the use of different interaction data sets, such as those resulting from high-throughput or small-scale studies, and different modelling methodologies for the derivation pair-wise protein interactions, can dramatically change the topology of these networks. Furthermore, we show that some of the previously reported features identified in these networks may simply be the result of experimental or methodological errors and biases. CONCLUSION: When performing network-based studies, it is essential to define what is meant by the term "interaction" and this must be taken into account when interpreting the topologies of the networks generated. Consideration must be given to the type of data included and appropriate controls that take into account the idiosyncrasies of the data must be selecte

Protein Interactions from Complexes: A Structural Perspective

By combining crystallographic information with protein-interaction data obtained through traditional experimental means, this paper determines the most appropriate method for generating protein-interaction networks that incorporate data derived from protein complexes. We propose that a combined method should be considered; in which complexes composed of five chains or less are decomposed using the matrix model, whereas the spoke model is used to derive pairwise interactions for those with six chains or more. The results presented here should improve the accuracy and relevance of studies investigating the topology of protein-interaction networks

All duplicates are not equal: the difference between small-scale and genome duplication

The comparison of pairs of gene duplications generated by small-scale duplications with those created by large-scale duplications shows that they differ in quantifiable ways. It is suggested that this is directly due to biases on the paths to gene retention rather than association with different functional categories

Growth control of the eukaryote cell: a systems biology study in yeast.

BACKGROUND: Cell growth underlies many key cellular and developmental processes, yet a limited number of studies have been carried out on cell-growth regulation. Comprehensive studies at the transcriptional, proteomic and metabolic levels under defined controlled conditions are currently lacking. RESULTS: Metabolic control analysis is being exploited in a systems biology study of the eukaryotic cell. Using chemostat culture, we have measured the impact of changes in flux (growth rate) on the transcriptome, proteome, endometabolome and exometabolome of the yeast Saccharomyces cerevisiae. Each functional genomic level shows clear growth-rate-associated trends and discriminates between carbon-sufficient and carbon-limited conditions. Genes consistently and significantly upregulated with increasing growth rate are frequently essential and encode evolutionarily conserved proteins of known function that participate in many protein-protein interactions. In contrast, more unknown, and fewer essential, genes are downregulated with increasing growth rate; their protein products rarely interact with one another. A large proportion of yeast genes under positive growth-rate control share orthologs with other eukaryotes, including humans. Significantly, transcription of genes encoding components of the TOR complex (a major controller of eukaryotic cell growth) is not subject to growth-rate regulation. Moreover, integrative studies reveal the extent and importance of post-transcriptional control, patterns of control of metabolic fluxes at the level of enzyme synthesis, and the relevance of specific enzymatic reactions in the control of metabolic fluxes during cell growth. CONCLUSION: This work constitutes a first comprehensive systems biology study on growth-rate control in the eukaryotic cell. The results have direct implications for advanced studies on cell growth, in vivo regulation of metabolic fluxes for comprehensive metabolic engineering, and for the design of genome-scale systems biology models of the eukaryotic cell.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Specificity in protein interactions and its relationship with sequence diversity and coevolution

Studies of interacting proteins have found correlated evolution of the sequences of binding partners, apparently as a result of compensating mutations to maintain specificity (i.e., molecular coevolution). Here, we analyze the coevolution of interacting proteins in yeast and demonstrate correlated evolution of binding partners in eukaryotes. Detailed investigation of this apparent coevolution, focusing on the proteins' surface and binding interface, surprisingly leads to no improvement in the correlation. We conclude that true coevolution, as characterized by compensatory mutations between binding partners, is unlikely to be chiefly responsible for the apparent correlated evolution. We postulate that the correlation between sequence alignments is simply due to interacting proteins being subject to similar constraints on their evolutionary rate. Because gene expression has a strong influence on evolutionary rate, and interacting proteins will tend to have similar levels of expression, we investigated this particular constraint. We found that the absolute expression level outperformed correlated evolution for predicting interacting protein partners. A correlation between sequence alignments could also be identified not only between pairs of proteins that physically interact but also between those that are merely functionally related (i.e., within the same protein complex). This indicates that the observed correlated evolution of interacting proteins is due to similar constraints on evolutionary rate and not coevolution

Relationship between semantic distance and the proportion of pairs within each duplicate set

<p><b>Copyright information:</b></p><p>Taken from "All duplicates are not equal: the difference between small-scale and genome duplication"</p><p>http://genomebiology.com/2007/8/10/R209</p><p>Genome Biology 2007;8(10):R209-R209.</p><p>Published online 4 Oct 2007</p><p>PMCID:PMC2246283.</p><p></p> Whole-genome duplicates (WGDs) are illustrated in blue, small-scale duplicates (SSDs) in red, and random gene pairings in gray. A higher semantic distance indicates greater functional divergence

Relationship between semantic distance, duplicate set and complex membership

<p><b>Copyright information:</b></p><p>Taken from "All duplicates are not equal: the difference between small-scale and genome duplication"</p><p>http://genomebiology.com/2007/8/10/R209</p><p>Genome Biology 2007;8(10):R209-R209.</p><p>Published online 4 Oct 2007</p><p>PMCID:PMC2246283.</p><p></p> The proportion of duplicate pairs having a certain level of functional divergence as measured by semantic distance for the following: pairs of complex-forming whole-genome duplicate (WGD; dark blue), complex-forming small-scale duplicate (SSD; red), non-complex-forming WGD (light blue), and non-complex-forming SSD (pink) proteins. Significant differences in the degree of functional divergence between the pairs in the two categories (complex and non-complex) are observed. No significant difference between the semantic distances of pairs of SSDs found in complexes and complex-forming WGD pairs is observed; nor, indeed, is there any difference between SSD pairs not in complexes and WGD pairs not found within complexes