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The Boltzmann distributions of molecular structures predict likely changes through random mutations.

Nora S Martin1, Sebastian E Ahnert2

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New molecular structures evolve via mutations. This study reveals that the likelihood of a mutation creating a new structure depends on its alternative structures with high Boltzmann frequency, applicable to RNA and protein models.

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Area of Science:

  • Molecular evolution
  • Biophysics
  • Computational biology

Background:

  • Molecular structures evolve through mutations, connecting genotypes to phenotypes.
  • The probability of a mutation yielding a new structure varies significantly, impacting evolutionary outcomes.
  • Understanding phenotypic mutation probabilities (φqp) is crucial for modeling molecular evolution.

Purpose of the Study:

  • To investigate the biophysical principles governing phenotypic mutation probabilities (φqp).
  • To explain and predict how the likelihood of a mutation changing structure p to structure q depends on the target structure q.
  • To generalize the concept of plastogenetic congruence to entire neutral spaces of structures.

Main Methods:

  • Analysis of genotype-phenotype maps for RNA secondary structures and the HP protein model.
  • Focus on phenotypic mutation probabilities (φqp), the likelihood of a random mutation changing structure p to structure q.
  • Examination of the relationship between φqp and the Boltzmann frequency of alternative structures.

Main Results:

  • A simple biophysical principle explains and predicts φqp based on the target structure q.
  • High φqp occurs when sequences folding to structure p are likely to also fold to structure q with high Boltzmann frequency.
  • This principle generalizes plastogenetic congruence from individual sequences to neutral spaces of structures.

Conclusions:

  • Phenotypic mutation probabilities are predictable via a simple biophysical principle.
  • The likelihood of structural changes depends on the prevalence of alternative structures with high Boltzmann frequency.
  • Findings offer insights into evolutionary pathways and may aid in estimating mutation likelihoods.