RNA Structure Prediction and Analysis

Ribonucleic acid (RNA) is central to cellular information flow, performing roles that range from genetic messaging to regulation of gene expression and catalysis. Unlike DNA, RNA is single-stranded and folds into diverse architectures that govern its interactions and functions. Structure prediction and analysis aim to reveal these conformations virtually and experimentally, offering insight into molecular mechanisms and facilitating the design of therapeutics and diagnostics. Computational approaches employ thermodynamic models, comparative sequence analysis and, increasingly, machine-learning frameworks to infer secondary structure elements—helices, loops and junctions—before attempting three-dimensional (3D) reconstructions. Experimental methods, including chemical probing, crosslinking and high-throughput sequencing, validate in silico models and capture dynamic ensembles of conformations under physiological conditions. Integration of data-driven algorithms with biophysical parameters has transformed the field, yielding improved accuracy in base-pair prediction, detection of non-canonical interactions and higher-order assembly mapping. These advances underpin applications such as RNA-based drug design, synthetic biology and the characterisation of viral genomes, underscoring the global importance of understanding RNA folding landscapes.

Research from Nature Portfolio

Innovative crosslinking techniques have elucidated RNA–RNA contacts within living cells. A novel method employs multifunctional chemical probes to immobilise intermolecular interfaces, enabling reconstruction of higher-order structures and revealing how cellular stress modulates mRNA compaction and virus–host RNA networks. On the computational front, motif-aware pretraining of a transformer-based language model has delivered a unified framework that excels across classification, interaction and structure prediction tasks. By encoding known RNA motifs and tokenising molecule types, the model achieves enhanced accuracy and adaptability without task-specific retraining. Complementing these developments, integration of deep neural network-derived folding scores with established nearest-neighbour thermodynamic parameters has produced robust secondary-structure predictors. Thermodynamic regularisation bridges data-driven and physics-based approaches, reducing overfitting and delivering consistent performance on novel non-coding RNAs while preserving computational efficiency.

RNA Structure Prediction and Analysis publication trend

The graph below shows the total number of articles in rna structure prediction and analysis across all publications each year (not limited to Nature Index journals).

Technical terms

Secondary structure: The two-dimensional pattern of base pairing and unpaired regions that determines the basic fold of an RNA strand.

Tertiary structure: The three-dimensional arrangement of the RNA backbone and side-chain interactions beyond local helices and loops.

Pseudoknot: A non-nested base-pairing interaction in which nucleotides in a loop pair with sequences outside the loop, forming complex topologies.

Transformer architecture: A neural-network framework based on self-attention mechanisms, enabling context-aware encoding of sequential data such as nucleotide sequences.

Thermodynamic parameters: Experimental free-energy values for local RNA motifs, used in dynamic-programming algorithms to predict the most stable structures.

References

1. KARR-seq reveals cellular higher-order RNA structures and RNA–RNA interactions. Nature Biotechnology (2024).
2. Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning. Nature Machine Intelligence (2024).
3. Deep dive into RNA: a systematic literature review on RNA structure prediction using machine learning methods. Artificial Intelligence Review (2024).
4. RNA secondary structure prediction using deep learning with thermodynamic integration. Nature Communications (2021).
5. ViennaRNA Package 2.0. Algorithms for Molecular Biology (2011).