ArticleBMC evolutionary biology2003
Apparent dependence of protein evolutionary rate on number of interactions is linked to biases in protein-protein interactions data sets.
Article in BMC evolutionary biology, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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64 citing papers in PubMed, 139 citations in OpenAlex.
- Cell Compartment is a Predictor of Protein Rate of Evolution, but not in the Manner Expected: Evidence Against the Extended Complexity Hypothesis.Genome biology and evolution · 2025Article
- The loci of environmental adaptation in a model eukaryote.Nature communications · 2024Article
- Re-examining Correlations Between Synonymous Codon Usage and Protein Bond Angles in Escherichia coli.Genome biology and evolution · 2024Article
- Number of human protein interactions correlates with structural, but not regulatory conservation of the respective genes.Frontiers in genetics · 2024Article
- Speciation and the developmental alarm clock.eLife · 2020Review
- Aligning functional network constraint to evolutionary outcomes.BMC evolutionary biology · 2020Article
- Evolving knowledge graph similarity for supervised learning in complex biomedical domains.BMC bioinformatics · 2020Article
- Identifying genuine protein-protein interactions within communities of gene co-expression networks using a deconvolution method.IET systems biology · 2019Article
- Dependency Between Protein-Protein Interactions and Protein Variability and Evolutionary Rates in Vertebrates: Observed Relationships and Stochastic Modeling.Journal of molecular evolution · 2019Article
- Metabolic Determinants of Enzyme Evolution in a Genome-Scale Bacterial Metabolic Network.Genome biology and evolution · 2018Article
- ProteomeVis: a web app for exploration of protein properties from structure to sequence evolution across organisms' proteomes.Bioinformatics (Oxford, England) · 2018Article
- Patterns of Population Variation in Two Paleopolyploid Eudicot Lineages Suggest That Dosage-Based Selection on Homeologs Is Long-Lived.Genome biology and evolution · 2018Article
- Position Matters: Network Centrality Considerably Impacts Rates of Protein Evolution in the Human Protein-Protein Interaction Network.Genome biology and evolution · 2017Article
- Positive Selection and Centrality in the Yeast and Fly Protein-Protein Interaction Networks.BioMed research international · 2016Article
- Determinants of the rate of protein sequence evolution.Nature reviews. Genetics · 2015Review
- Hsp90 promotes kinase evolution.Molecular biology and evolution · 2015Article
- Gene pleiotropy constrains gene expression changes in fish adapted to different thermal conditions.Nature communications · 2014Article
- Experimental evolution of protein-protein interaction networks.The Biochemical journal · 2013Review
- The impact of trans-regulation on the evolutionary rates of metazoan proteins.Nucleic acids research · 2013Article
- Three independent determinants of protein evolutionary rate.Journal of molecular evolution · 2013Review
4 more citing papers are in PubMed but not listed here.
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2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral studies have suggested that proteins that interact with more partners evolve more slowly. The strength and validity of this association has been called into question. Here we investigate how biases in high-throughput protein-protein interaction studies could lead to a spurious correlation.
resultsWe examined the correlation between evolutionary rate and the number of protein-protein interactions for sets of interactions determined by seven different high-throughput methods in Saccharomyces cerevisiae. Some methods have been shown to be biased towards counting more interactions for abundant proteins, a fact that could be important since abundant proteins are known to evolve more slowly. We show that the apparent tendency for interactive proteins to evolve more slowly varies directly with the bias towards counting more interactions for abundant proteins. Interactions studies with no bias show no correlation between evolutionary rate and the number of interactions, and the one study biased towards counting fewer interactions for abundant proteins actually suggests that interactive proteins evolve more rapidly. In all cases, controlling for protein abundance significantly decreases the observed correlation between interactions and evolutionary rate. Finally, we disprove the hypothesis that small data set size accounts for the failure of some interactions studies to show a correlation between evolutionary rate and the number of interactions.
conclusionsThe only correlation supported by a careful analysis of the data is between evolutionary rate and protein abundance. The reported correlation between evolutionary rate and protein-protein interactions cannot be separated from the biases of some protein-protein interactions studies to count more interactions for abundant proteins.
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