ArticleGenome biology and evolution2017
Position Matters: Network Centrality Considerably Impacts Rates of Protein Evolution in the Human Protein-Protein Interaction Network.
Article in Genome biology and evolution, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- An integrative methodology based on protein-protein interaction networks for identification and functional annotation of disease-relevant genes applied to channelopathies.BMC bioinformatics · 2019Pooled it
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- Protein-protein interaction network architecture of human polygenic traits reveals domain-spanning connectivity and evolutionary pressures.Genome biology · 2026Article
- Signatures of selection in pleiotropic genes involved in insect neuronal and immune systems.bioRxiv : the preprint server for biology · 2026Article
- Global genetic rewiring during compensatory evolution in the yeast polarity network.EMBO reports · 2026Article
- NOODAI: a webserver for network-oriented multi-omics data analysis and integration pipeline.Bioinformatics (Oxford, England) · 2025Article
- Network architecture of transcriptomic stress responses in zebrafish embryos.PLoS computational biology · 2025Article
- Tubulin sequence divergence is associated with the use of distinct microtubule regulators.Current biology : CB · 2025Article
- Asymmetric Evolution of Protein Domains in the Leucine-Rich Repeat Receptor-Like Kinase Family of Plant Signaling Proteins.Molecular biology and evolution · 2023Article
- Weaker selection on genes with treatment-specific expression consistent with a limit on plasticity evolution in Arabidopsis thaliana.Genetics · 2023Article
- The Effect of Developmental Pleiotropy on the Evolution of Insect Immune Genes.Genome biology and evolution · 2023Article
- Developmental phenomics suggests that H3K4 monomethylation confers multi-level phenotypic robustness.Cell reports · 2022Article
- Review
- Variables Influencing Differences in Sequence Conservation in the Fission Yeast Schizosaccharomyces pombe.Journal of molecular evolution · 2021Article
- Evolutionary mode for the functional preservation of fast-evolvingOpen biology · 2021Article
- Integrative proteomic network analyses support depot-specific roles for leucine rich repeat LGI family member 3 in adipose tissues.Experimental and therapeutic medicine · 2021Article
- Universal Constraints on Protein Evolution in the Long-Term Evolution Experiment with Escherichia coli.Genome biology and evolution · 2021Article
- Richard Dickerson, Molecular Clocks, and Rates of Protein Evolution.Journal of molecular evolution · 2021Article
- Elucidating the network features and evolutionary attributes of intra- and interspecific protein-protein interactions between human and pathogenic bacteria.Scientific reports · 2021Article
- Network Medicine-Based Unbiased Disease Modules for Drug and Diagnostic Target Identification in ROSopathies.Handbook of experimental pharmacology · 2021Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The proteins of any organism evolve at disparate rates. A long list of factors affecting rates of protein evolution have been identified. However, the relative importance of each factor in determining rates of protein evolution remains unresolved. The prevailing view is that evolutionary rates are dominantly determined by gene expression, and that other factors such as network centrality have only a marginal effect, if any. However, this view is largely based on analyses in yeasts, and accurately measuring the importance of the determinants of rates of protein evolution is complicated by the fact that the different factors are often correlated with each other, and by the relatively poor quality of available functional genomics data sets. Here, we use correlation, partial correlation and principal component regression analyses to measure the contributions of several factors to the variability of the rates of evolution of human proteins. For this purpose, we analyzed the entire human protein-protein interaction data set and the human signal transduction network-a network data set of exceptionally high quality, obtained by manual curation, which is expected to be virtually free from false positives. In contrast with the prevailing view, we observe that network centrality (measured as the number of physical and nonphysical interactions, betweenness, and closeness) has a considerable impact on rates of protein evolution. Surprisingly, the impact of centrality on rates of protein evolution seems to be comparable, or even superior according to some analyses, to that of gene expression. Our observations seem to be independent of potentially confounding factors and from the limitations (biases and errors) of interactomic data sets.
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