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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
A novel method to detect proteins evolving at correlated rates: identifying new functional relationships between
Nathaniel L Clark1, Charles F Aquadro
1Department of Molecular Biology and Genetics, Cornell University, USA. nlc47@cornell.edu
Molecular Biology and Evolution
|January 2, 2010
Summary
Correlated protein evolution, detected using nucleotide sequences, reveals functional relationships. This novel method accurately identifies protein interactions, even among closely related species.
Area of Science:
- Evolutionary biology
- Genomics
- Biochemistry
Background:
- Interacting proteins often exhibit correlated evolutionary rates due to shared pressures or coevolution.
- Existing methods using amino acid distances can be affected by non-uniform neutral substitution rates over time.
- Detecting correlated evolution aids in understanding protein networks and inferring functional relationships.
Purpose of the Study:
- To develop and validate a novel method for detecting correlated protein evolution using nucleotide sequences.
- To address limitations of existing amino acid distance methods, particularly rate heterogeneity.
- To improve the estimation of nonsynonymous (dN) and synonymous (dS) substitution rates for evolutionary analysis.
Main Methods:
- Explored alternative methods using protein-coding nucleotide sequences.
- Developed a novel likelihood method to estimate dN/dS ratios across evolutionary branches.
- Tested the method on Drosophila nuclear pore proteins with known interactions.
Main Results:
- The novel likelihood method demonstrated robustness to realistic simulation parameters.
- Significantly correlated evolution was detected among nuclear pore proteins.
- Stable subcomplex members showed stronger correlations than transient interactors.
- The method outperformed previous approaches in detecting correlated evolution in closely related species.
Conclusions:
- Sequence-based methods offer a complementary approach to identify correlated protein evolution.
- This method can be applied genome-wide to predict protein-protein interactions and functional groups.
- Accurate estimation of dN/dS ratios is crucial for reliable evolutionary rate analysis.
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