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Area of Science:

  • Biophysics
  • Computational Biology
  • Molecular Virology

Background:

  • Current RNA folding algorithms do not fully account for interactions within secondary structures.
  • Understanding viral RNA (vRNA) encapsidation is crucial for viral assembly and function.

Purpose of the Study:

  • To develop a theoretical framework, Flory mean-field theory, for vRNA molecules.
  • To investigate sequence-selective vRNA encapsidation and the factors influencing it.

Main Methods:

  • Extended RNA folding algorithms to include inter-structural section interactions.
  • Applied Flory mean-field theory to analyze vRNA encapsidation.
  • Utilized a single parameter, the largest eigenvalue of the Kramers matrix, to represent sequence dependence.

Main Results:

  • Identified a free energy bias of approximately 20 kBT favoring vRNA encapsidation over randomized isomers.
  • Demonstrated a substantial bias favoring the encapsidation of a single large vRNA molecule over two smaller RNA molecules with the same nucleotide sequence.
  • Observed that dividing a large vRNA releases stored elastic energy, contributing to preferential packaging.

Conclusions:

  • The Flory method provides a nonspecific mechanism for preferential encapsidation of larger vRNA molecules.
  • Results align with recent experimental findings on RNA packaging competition.
  • Copackaged RNA molecules are predicted to remain segregated within the capsid.