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Published on: July 14, 2015
Evolutionary dynamics on protein bi-stability landscapes can potentially resolve adaptive conflicts
Tobias Sikosek1, Erich Bornberg-Bauer, Hue Sun Chan
1Evolutionary Bioinformatics Group, Institute for Evolution and Biodiversity, University of Münster, Münster, Germany. sikosek@wwu.de
Plos Computational Biology
|October 3, 2012
Summary
Bi-stable proteins, capable of adopting two native structures, act as evolutionary bridges. These proteins are crucial for navigating adaptive conflict and enhancing protein sequence evolution.
Area of Science:
- Protein biophysics
- Evolutionary biology
- Computational biology
Background:
- Some proteins exhibit two distinct native-state conformations, termed bi-stable proteins.
- Bi-stable proteins are hypothesized to bridge neutral networks in protein sequence space.
- Adaptive conflict scenarios may favor bi-stable proteins offering dual biological functions.
Purpose of the Study:
- To identify bi-stable or multi-stable proteins using a biophysical model.
- To investigate the role of bi-stability in enhancing phenotype connectivity.
- To analyze the evolutionary success of bi-stable proteins under adaptive conflict.
Main Methods:
- Developed a biophysical model to analyze protein sequence space for bi-stability.
- Simulated protein evolution under adaptive conflict with relaxed selection pressures.
- Created a method to identify bridge-like proteins in the Protein Data Bank (PDB).
Main Results:
- Bi-stable proteins enhance connectivity between neutral networks in sequence space.
- Bi-stability exhibits a gradient, decreasing with mutations away from exact bi-stability.
- Bi-stable proteins demonstrated high success rates in simulated adaptive conflict scenarios.
- Identified real proteins with bridge-like properties and verified bi-stability gradients.
Conclusions:
- Bi-stable proteins are significant for protein evolution, particularly under adaptive conflict.
- The identified bi-stability gradient provides a framework for future studies.
- New testable predictions for protein bi-stability and evolution were proposed.
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