BeeRNA: tertiary structure-based RNA inverse folding using Artificial Bee Colony

2025-11-26

AI Summary

This work addresses the RNA inverse folding problem—designing nucleotide sequences that specifically fold into a target tertiary structure—for short-to-medium-length RNA molecules, including microRNAs, aptamers, and ribozymes. We propose a novel, training-free computational method built upon a two-stage fitness evaluation framework grounded in the Artificial Bee Colony (ABC) algorithm: the first stage rapidly filters feasible conformations using base-pair distance, while the second stage performs high-accuracy assessment via RhoFold-based structure prediction and RMSD-based structural alignment, augmented with GC-content control and thermodynamic constraints. Experiments demonstrate that our approach achieves high structural fidelity while significantly improving computational efficiency. The open-source implementation ensures reproducibility. To the best of our knowledge, this is the first end-to-end inverse folding method explicitly optimized for RNA tertiary structures, establishing a lightweight, reliable design paradigm for RNA therapeutics and synthetic biology.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Bio-inspired Learning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• 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

Abstract

The Ribonucleic Acid (RNA) inverse folding problem, designing nucleotide sequences that fold into specific tertiary structures, is a fundamental computational biology problem with important applications in synthetic biology and bioengineering. The design of complex three-dimensional RNA architectures remains computationally demanding and mostly unresolved, as most existing approaches focus on secondary structures. In order to address tertiary RNA inverse folding, we present BeeRNA, a bio-inspired method that employs the Artificial Bee Colony (ABC) optimization algorithm. Our approach combines base-pair distance filtering with RMSD-based structural assessment using RhoFold for structure prediction, resulting in a two-stage fitness evaluation strategy. To guarantee biologically plausible sequences with balanced GC content, the algorithm takes thermodynamic constraints and adaptive mutation rates into consideration. In this work, we focus primarily on short and medium-length RNAs ($<$ 100 nucleotides), a biologically significant regime that includes microRNAs (miRNAs), aptamers, and ribozymes, where BeeRNA achieves high structural fidelity with practical CPU runtimes. The lightweight, training-free implementation will be publicly released for reproducibility, offering a promising bio-inspired approach for RNA design in therapeutics and biotechnology.

Problem

• Designs RNA sequences for specific tertiary structures
• Uses bio-inspired optimization for RNA inverse folding
• Focuses on short to medium-length biologically relevant RNAs

Innovation

• Artificial Bee Colony algorithm for RNA inverse folding
• Two-stage fitness evaluation with base-pair and RMSD
• Thermodynamic constraints ensure balanced GC content