Probabilistic RNA Designability via Interpretable Ensemble Approximation and Dynamic Decomposition

2026-02-14

AI Summary

This work addresses the lack of probabilistic assessment of target secondary structure designability in RNA inverse folding by proposing an interpretable, ensemble-based probabilistic decomposition framework. Integrating a linear-time dynamic programming algorithm, the method efficiently explores an exponentially large space of decomposition schemes to compute the tightest possible upper bound on folding probability. For the first time, it introduces an interpretable probabilistic decomposition mechanism that enables fine-grained analysis of RNA structural designability and reveals the origins of design difficulty at the motif level. Evaluated on the ArchiveII and Eterna100 benchmarks, the approach yields tighter probability bounds than existing methods while providing a structurally interpretable analytical tool for RNA design.

Technology Category

• Reasoning under Uncertainty: Probabilistic Programming
• Machine Learning: Ensemble Methods
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Motivation: RNA design aims to find RNA sequences that fold into a given target secondary structure, a problem also known as RNA inverse folding. However, not all target structures are designable. Recent advances in RNA designability have focused primarily on minimum free energy (MFE)-based criteria, while ensemble-based notions of designability remain largely underexplored. To address this gap, we introduce a theory of ensemble approximation and a probability decomposition framework for bounding the folding probabilities of RNA structures in an explainable way. We further develop a linear-time dynamic programming algorithm that efficiently searches over exponentially many decompositions and identifies the optimal one that yields the tightest probabilistic bound for a given structure. Results: Applying our methods to both native and artificial RNA structures in the ArchiveII and Eterna100 benchmarks, we obtained probability bounds that are much tighter than prior approaches. In addition, our methods further provide anatomical tools for analyzing RNA structures and understanding the sources of design difficulty at the motif level. Availability: Source code and data are available at https://github.com/shanry/RNA-Undesign. Supplementary information: Supplementary text and data are available in a separate PDF.

Problem

• RNA designability
• RNA inverse folding
• ensemble approximation
• secondary structure
• folding probability

Innovation

• ensemble approximation
• dynamic decomposition
• RNA designability
• probabilistic bound
• linear-time dynamic programming