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Molecular information theory reveals evolved proteins contain information close to that needed for folding. Protein folding achieves ~50% energy-to-information conversion efficiency, linking sequence, entropy, and energetics.

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Area of Science:

  • Protein Science
  • Molecular Biology
  • Biophysics

Background:

  • Protein folding is crucial for biological function.
  • Understanding the relationship between protein sequence, structure, and stability is a key challenge.
  • Molecular information theory offers a novel framework for analyzing complex biological systems.

Purpose of the Study:

  • To apply molecular information theory to analyze single-domain protein folding.
  • To investigate the information content of protein sequences in relation to folding requirements.
  • To quantify the energy-to-information conversion efficiency during protein folding.

Main Methods:

  • Analysis of sequence-based potentials and reduced amino acid alphabets.
  • Calculation of backbone configurational entropy and secondary structure content.
  • Integration of residue burial layers and mutational studies of protein stability changes.

Main Results:

  • Evolved proteins contain information content (∼2.2 ± 0.3 bits/site·operation) close to that required to specify a fold.
  • The effective alphabet size in evolved proteins matches the number of residue conformations in the unfolded state (~5).
  • Protein folding exhibits an energy-to-information conversion efficiency of approximately 50%.

Conclusions:

  • A quantitative link exists between molecular information theory and energy landscape theory for protein folding.
  • Sequence evolution, configurational entropy, and folding energetics are interconnected.
  • The findings provide insights into the efficiency of biological processes compared to artificial machines.