Abstract
BackgroundWhether or not a protein's number of physical interactions with other proteins plays a role in determining its rate of evolution has been a contentious issue. A recent analysis suggested that the observed correlation between number of interactions and evolutionary rate may be due to experimental biases in high-throughput protein interaction data sets.DiscussionThe number of interactions per protein, as measured by some protein interaction data sets, shows no correlation with evolutionary rate. Other data sets, however, do reveal a relationship. Furthermore, even when experimental biases of these data sets are taken into account, a real correlation between number of interactions and evolutionary rate appears to exist.SummaryA strong and significant correlation between a protein's number of interactions and evolutionary rate is apparent for interaction data from some studies. The extremely low agreement between different protein interaction data sets indicates that interaction data are still of low coverage and/or quality. These limitations may explain why some data sets reveal no correlation with evolutionary rates.
Highlights
Whether or not a protein's number of physical interactions with other proteins plays a role in determining its rate of evolution has been a contentious issue
Summary: A strong and significant correlation between a protein's number of interactions and evolutionary rate is apparent for interaction data from some studies
These limitations may explain why some data sets reveal no correlation with evolutionary rates
Summary
Critique of Bloom and Adami Bloom and Adami [7] tested protein interaction data from seven methods (two experimental and five computational) individually for correlations between the number of protein interactions and protein evolutionary rates, while statistically controlling for gene expression levels. Additional analysis of the data A simple statistical method for examining the relationship between two variables (e.g., number of interactions, I and rate of evolution, E), while partially controlling for a third, potentially related variable (e.g., gene expression, A), is to divide the dataset into quantiles according to the controlled variable. In order for inaccurate expression data to explain this result, the expression data in those three bins would have to be noisy – they would have to be negatively correlated with the true expression levels of those genes Since this is quite unlikely to be the case, we believe the most parsimonious explanation is that the number of interaction partners a protein has is correlated with its evolutionary rate independently of its expression level
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