RNARedPrint

RNARedPrint generates RNA sequences that fold into multiple target structures, including pseudoknots, by performing Boltzmann-weighted sampling over constraint networks to achieve specified free energies.

Key Features:

• Boltzmann-Weighted Sampling: Performs Boltzmann-weighted sampling using constraint networks to enable positive design of sequences with target free energies, including support for pseudoknotted configurations.
• GC-Content Control: Enforces GC-content constraints during sequence generation to influence RNA stability and function.
• Empirical Validation: Validated on established benchmarks using biologically relevant multi-target Boltzmann-weighted designs.
• Comparison with Existing Methods: Demonstrates advantages over previous multi-target sampling strategies, particularly for negative design of multi-stable RNAs.
• Theoretical Foundation: Grounded on a theoretical result establishing the #P-hardness of counting designs.

Scientific Applications:

• Synthetic Biology: Design of multi-stable RNAs with precise energy profiles for synthetic biology applications.
• In‑silico Screening: Generation and screening of candidate sequences to reduce experimental validation burden.
• Multi-stability and Negative Design Studies: Support for negative design strategies and research on RNA multi-stability.
• Broad Research Use: Applicable across research domains requiring control of RNA folding and energetic profiles.

Methodology:

Applies constraint networks to perform Boltzmann-weighted sampling that targets specified free energies and optionally enforces GC-content constraints.