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EnsembleDesign: messenger RNA design minimizing ensemble free energy via probabilistic lattice parsing

Ning Dai1, Tianshuo Zhou1, Wei Yu Tang1

  • 1School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97330, United States.

Bioinformatics (Oxford, England)
|July 15, 2025
PubMed
Abstract

Insights

Optimizing messenger RNA (mRNA) sequences requires considering all possible structures, not just one. Our new algorithm, EnsembleDesign, minimizes ensemble free energy for more flexible and stable mRNA designs.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Molecular Biology

Background:

  • Messenger RNA (mRNA) sequence design is crucial for applications like vaccines.
  • Previous methods focused on minimizing minimum free energy (MFE), neglecting alternative mRNA conformations.
  • Optimal mRNA function requires flexibility among multiple stable structures during translation.

Purpose of the Study:

  • To develop a novel algorithm for optimizing mRNA sequences by minimizing ensemble free energy.
  • To address the computational complexity of optimizing the entire Boltzmann ensemble of mRNA structures.

Main Methods:

  • Introduced EnsembleDesign, a novel algorithm using continuous relaxation.
  • Extended existing lattice representation and dynamic programming to probabilistic approaches.
  • Optimized expected ensemble free energy over a distribution of candidate sequences.

Main Results:

  • EnsembleDesign outperforms LinearDesign in minimizing ensemble free energy, particularly for longer sequences.
  • Ensemble designs exhibit lower average unpaired probabilities, reducing degradation.
  • Generated mRNA sequences demonstrate increased flexibility with flatter Boltzmann ensembles.

Conclusions:

  • Minimizing ensemble free energy is a more effective objective for mRNA sequence design.
  • EnsembleDesign provides a robust method for generating optimized mRNA sequences with improved stability and flexibility.

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