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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
Neutrality and evolvability of designed protein sequences
Arnab Bhattacherjee1, Parbati Biswas
1Department of Chemistry, University of Delhi, Delhi 110007, India.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|September 28, 2010
Summary
This study reveals that a specific foldability criterion enhances protein sequence robustness against mutations. This method proves more effective than simulations, improving protein engineering and de novo design by better shaping evolutionary sequence space.
Area of Science:
- Computational Biology
- Protein Engineering
- Biophysics
Background:
- Understanding protein foldability is crucial for predicting evolutionary trajectories.
- Protein sequences must balance stability with the ability to evolve.
Purpose of the Study:
- To analyze the impact of foldability on protein evolvability using theoretical and simulation approaches.
- To develop and test a generalized foldability criterion for designing robust protein sequences.
Main Methods:
- A self-consistent mean-field theory and Monte Carlo simulations were employed.
- Protein sequences were modeled with binary amino acid patterns based on a foldability criterion derived from free energy approximations.
- The robustness of designed sequences against cumulative point mutations under neutral conditions was assessed using stability (ΔΔG) as selective pressure.
Main Results:
- The foldability criterion effectively selects viable protein sequences, outperforming standard Monte Carlo methods.
- Sequences designed using the theoretical foldability criterion exhibited greater robustness to mutations compared to simulation-generated sequences.
- The foldability criterion significantly influences how selective pressures shape the evolutionary sequence space.
Conclusions:
- A novel foldability criterion enhances protein sequence robustness and evolutionary potential.
- This approach offers a more effective strategy for de novo protein design and engineering applications.
- The findings provide insights into the interplay between protein structure, stability, and evolution.
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Overview