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A model for protein sequence evolution based on selective pressure for protein stability: application to hemoglobins
1Department of Biology, Long Island University, Brooklyn, NY 11201, USA. lmarsh@liu.edu
Evolutionary Bioinformatics Online
|October 9, 2009
Summary
Protein evolution is driven by selection against instability, maintaining protein stability over time. A new model shows sites with destabilizing residues change faster, supporting stability-based selection in protein evolution.
Area of Science:
- Evolutionary biology
- Molecular evolution
- Biochemistry
Background:
- Protein stability is crucial for function and is maintained throughout evolution.
- Sequence changes occur, yet overall protein structure and stability persist.
- Understanding the evolutionary pressures on protein sequences is key.
Purpose of the Study:
- To develop and test an empirical, all-site, stability-based model of protein evolution.
- To investigate the role of residue stability contributions in driving evolutionary rates.
- To determine if stability-based selection explains evolutionary patterns in diverse proteins.
Main Methods:
- Developed an empirical model incorporating residue stability contributions at all sites.
- Utilized a structure-based method to predict residue stationary frequencies based on stability.
- Applied maximum likelihood analyses to compare the stability-based model with Poisson models using hemoglobin data.
Main Results:
- Sites with destabilizing residues exhibited significantly faster evolutionary rates in hemoglobins compared to sites with stabilizing residues.
- The stability-based model accurately predicted evolutionary patterns across diverse protein families.
- Maximum likelihood studies favored the stability-based model over simpler methods for hemoglobin evolution.
Conclusions:
- Purifying selection to maintain protein structural stability is a dominant force in protein evolution.
- The contribution of individual residues to protein stability directly influences their evolutionary rates.
- Stability-based selection provides a unifying framework for understanding protein sequence evolution.
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