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Sequence variability of proteins evolutionarily constrained by solution-thermodynamic function.

F N Braun1

  • 1Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, 10691 Stockholm, Sweden.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|March 5, 2004
PubMed
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This study models protein homolog dispersal using silk fibroin and hemoglobin as templates, revealing how sequence properties influence protein self-assembly and evolution. Findings align with existing bioinformatic data, enhancing our understanding of protein sequence-fitness landscapes.

Area of Science:

  • Biophysics
  • Computational Biology
  • Protein Science

Background:

  • Understanding protein sequence-fitness landscapes is crucial for predicting protein behavior and evolution.
  • Thermodynamic principles govern protein self-assembly, influencing their functional properties.

Purpose of the Study:

  • To model protein homolog dispersal across sequence-fitness landscapes.
  • To investigate the role of solution thermodynamics and sequence properties in protein self-assembly.
  • To connect theoretical models with empirical bioinformatic data.

Main Methods:

  • Utilizing silk fibroin and hemoglobin as model protein systems.
  • Constructing theoretical phase topology landscapes based on sequence length and hydrophobic-polar composition.
  • Applying mutation-selection dynamics to determine steady states of homolog distribution in sequence space.

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Main Results:

  • Protein homolog dispersal is modeled across landscapes defined by solution thermodynamics.
  • Self-assembly phenomena like liquid-liquid phase separation, gelation, and liquid crystalline states were considered.
  • The calculated distributions of protein homologs in sequence space are consistent with Swiss-Prot bioinformatic data.

Conclusions:

  • The study provides a theoretical framework for understanding protein homolog dispersal.
  • Thermodynamic properties and sequence composition significantly shape protein sequence-fitness landscapes.
  • The model successfully integrates theoretical predictions with real-world protein data.