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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
Protein protein interactions, evolutionary rate, abundance and age
Ramazan Saeed1, Charlotte M Deane
1Department of Statistics, Oxford OX1 3TG, UK. saeed@stats.ox.ac.uk
BMC Bioinformatics
|March 15, 2006
Summary
A protein's evolutionary rate and interaction count are linked, but dataset accuracy is key. Protein interaction numbers are more influenced by protein age than evolutionary rate, independent of gene expression.
Area of Science:
- Molecular Biology
- Evolutionary Biology
- Bioinformatics
Background:
- The relationship between protein evolutionary rate and interaction number is debated.
- A common hypothesis suggests highly interacting proteins evolve slower due to functional constraints.
- Previous studies show conflicting results regarding this correlation.
Purpose of the Study:
- To investigate the correlation between protein evolutionary rate and interaction number.
- To identify reasons for conflicting results in prior research.
- To explore factors influencing protein evolution and interaction networks.
Main Methods:
- Large-scale analysis across multiple datasets.
- Statistical examination of evolutionary rates and protein interaction data.
- Assessment of dataset error rates and their impact on correlation.
Main Results:
- Dataset accuracy significantly impacts the observed correlation between evolutionary rate and interaction number.
- The correlation is not an artifact of underlying data distributions and is likely biologically relevant.
- Protein age shows a strong positive correlation with interaction number, independent of gene expression levels.
Conclusions:
- The correlation between protein interactions and evolutionary rate is dataset-dependent.
- Protein age is a stronger determinant of interaction number than evolutionary rate.
- Dataset accuracy is crucial for reliable conclusions in evolutionary biology studies.
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