How experiments, AI, and integrative modeling are changing the way we understand RNA folding, function, and therapeutics
RNA Structure
For a long time, RNA was treated as biology’s messenger: DNA made the plan, RNA carried the instructions, and proteins did the real work.
That view is now far too small.
RNA is not just a passive courier of genetic information. It can fold into complex structures, catalyze biochemical reactions, regulate gene expression, control splicing, bind proteins, sense metabolites, and participate in disease processes. In many cases, RNA’s function depends not only on its sequence but on its shape.
That is why RNA structure has become one of the most important frontiers in molecular biology.
A review published in Nature Methods titled “Advances and opportunities in RNA structure experimental determination and computational modeling” captures this shift clearly: if we want to understand what RNA does, we must understand how it folds. The authors summarize modern experimental and computational tools for RNA secondary and tertiary structure analysis and argue that the growing volume of RNA structural data is opening the door to more powerful integrative modeling approaches. (Nature)
The message is simple but profound:
RNA biology is becoming structural biology.
Why RNA Structure Matters
RNA structure begins with base pairing.
Adenine pairs with uracil. Guanine pairs with cytosine. Sometimes guanine pairs with uracil. These interactions create stems, loops, bulges, junctions, pseudoknots, and long-range contacts. Together, they form what we call RNA secondary structure.
But RNA does not stop there.
Secondary structure folds into three-dimensional structure, where distant regions of the molecule come together in space. This 3D architecture determines whether an RNA can bind a protein, recognize a small molecule, act as an enzyme, regulate translation, or switch between active and inactive states.
This is where RNA becomes difficult.
Unlike many proteins, RNA is often highly flexible. It may not have one fixed structure. Instead, the same RNA molecule can exist as a population of conformers—different structural states that shift depending on ions, temperature, binding partners, chemical modifications, or cellular context.
That flexibility is not a technical nuisance. It is often the biology itself.
A riboswitch functions because it changes shape. Viral RNA genomes rely on structural elements for replication and translation. Long noncoding RNAs may act through modular structural domains. Therapeutic RNAs must fold correctly to remain stable and functional.
So, asking “What is the RNA sequence?” is no longer enough.
The better question is:
What structures can this RNA form, and which of those structures matter?
The Experimental Toolbox Is Expanding
RNA structure determination has traditionally relied on powerful but demanding experimental methods.
X-ray crystallography can provide high-resolution structures, but RNA molecules are often hard to crystallize. Their flexibility, charge, and conformational heterogeneity make crystal formation difficult.
Nuclear magnetic resonance spectroscopy, or NMR, is useful for studying RNA dynamics and smaller RNA structures, but it becomes more challenging as RNA size and complexity increase.
Cryo-electron microscopy, or cryo-EM, has become increasingly valuable for larger RNA-containing complexes, especially ribonucleoproteins. It allows researchers to visualize molecular assemblies that were once extremely difficult to capture structurally.
Alongside these classical approaches, high-throughput structure-probing methods have transformed the field. Techniques based on chemical probing, such as SHAPE and DMS-based approaches, can reveal which nucleotides are flexible, paired, exposed, or protected. These methods do not always give a full atomic structure, but they provide crucial experimental constraints for modeling RNA folding.
This is one of the most important changes in the field.
RNA structure determination is no longer limited to solving one purified molecule at a time. Researchers can now collect structural information across many RNAs, under different conditions, sometimes even inside cells.
That matters because RNA structure is context-dependent.
An RNA may fold differently in vitro and in vivo. It may change structure when bound to proteins. It may respond to stress, metabolites, ions, or chemical modifications. Experimental probing helps reveal these dynamic states rather than forcing RNA into a single static model.
Computational Modeling Is Catching Up
For decades, RNA secondary structure prediction relied heavily on thermodynamic models. These approaches estimate the most favorable base-pairing arrangement based on energy rules. They remain useful, especially for shorter RNAs and well-behaved structures.
But RNA folding is not always clean.
Long-range interactions, pseudoknots, tertiary contacts, alternative conformers, and cellular constraints can make structure prediction difficult. That is where computational modeling has had to evolve.
Modern RNA modeling now combines several layers of information:
Sequence conservation
Thermodynamic folding
Experimental probing data
Known structural motifs
Machine learning
Deep learning
3D geometry prediction
Molecular dynamics simulations
The strongest approaches are often integrative. They do not rely on only one source of information. Instead, they combine experimental constraints with computational prediction to generate more biologically realistic models.
This is exactly where the field is moving.
A major theme of the Nature Methods review is that experimental data and computational modeling are becoming increasingly connected. More experimental structural data improves computational tools. Better computational tools help interpret experimental data. Together, they create a feedback loop that can accelerate RNA structure discovery.
AI Is Changing the RNA Structure Landscape
Protein structure prediction had its AlphaFold moment. RNA structure prediction is still more difficult, but it is rapidly advancing.
Recent AI-based methods are beginning to predict RNA secondary structure, tertiary structure, RNA–protein interactions, RNA–small molecule binding, and conformational states. These tools are helped by the rise of RNA language models, deep learning architectures, and larger biological datasets.
A 2023 review in Briefings in Bioinformatics highlighted how machine learning and deep learning are being applied to RNA secondary structure prediction, RNA aptamers, RNA–protein interactions, and RNA drug discovery.
More recently, models such as trRosettaRNA2 have shown how secondary structure priors can guide RNA 3D structure and conformer prediction. The 2026 Nature Machine Intelligence article describing trRosettaRNA2 emphasizes that RNA 3D prediction remains difficult because experimental RNA structure data are scarce and RNA molecules are flexible, but structure-aware deep learning can improve prediction performance.
This is important because RNA models cannot simply copy protein-prediction logic.
RNA has its own grammar.
Its folding depends heavily on base pairing, electrostatics, magnesium ions, noncanonical interactions, and alternative conformations. AI tools that understand or incorporate these features may be better suited for RNA than generic structure predictors.
Why This Matters for RNA Therapeutics
RNA structure is not just an academic problem.
It is becoming central to biomedical research.
mRNA vaccines, siRNAs, antisense oligonucleotides, guide RNAs, ribozymes, aptamers, circular RNAs, and self-amplifying RNAs all depend on RNA behavior. Stability, translation efficiency, immune recognition, intracellular delivery, target binding, and degradation can all be influenced by structure.
For therapeutic RNA design, structure prediction can help answer practical questions:
Will this RNA fold into the intended form?
Will it expose the right binding region?
Will it hide important motifs?
Will it form unwanted secondary structures?
Will it remain stable long enough to function?
Will chemical modifications disrupt or improve folding?
RNA structure also matters for RNA-targeted small-molecule drugs.
For many years, drug discovery focused mainly on proteins. RNA was often considered too flexible, too charged, or too difficult to target selectively. That view is changing. RNA can form structured pockets, motifs, and conformational states that small molecules may recognize.
A 2026 Nature Biotechnology article notes that RNA has emerged as an attractive target for drug discovery because its structures can be modulated by small molecules to influence processes such as splicing, translation, RNA–protein interactions, noncoding RNA processing, and RNA virus replication.
This is where RNA structure becomes directly translational.
If we can map RNA structures accurately, we may be able to design drugs that bind RNA with the same strategic precision once reserved for proteins.
The Challenge: RNA Is Not Still
The biggest challenge is that RNA is dynamic.
Many methods still try to report one dominant structure. But biological RNA may exist as an ensemble. The “minor” conformer may be the functional one. A transient structure may control splicing. A rare state may expose a druggable pocket. A ligand may shift the entire folding landscape.
This creates a major challenge for both experiments and AI.
The future of RNA structure biology will not be only about predicting one beautiful model. It will be about predicting structural ensembles.
That means researchers will need tools that can capture alternative folds, kinetic traps, folding intermediates, ligand-bound states, protein-bound states, and cellular context.
This is also why experimental validation remains essential.
AI can generate models. Chemical probing can test accessibility. Cryo-EM can visualize large complexes. NMR can capture dynamics. Mutational analysis can test function. No single method is enough.
The best RNA structural biology will be hybrid.
The Future: Structure-Guided RNA Biology
The next phase of RNA research will likely be defined by integration.
Experimental probing will feed computational models. AI will generate structural hypotheses. High-throughput datasets will refine prediction tools. Molecular simulations will explore conformational flexibility. Drug discovery platforms will search for ligandable RNA motifs. Synthetic biologists will design RNA switches, sensors, scaffolds, and therapeutic constructs with structural logic built in from the beginning.
For plant biology, virology, cancer biology, neuroscience, and RNA therapeutics, this shift is significant.
RNA structure is no longer a specialist detail.
It is becoming a design principle.
We are entering a period where researchers will not only ask what an RNA sequence encodes, but what it folds into, how it moves, what it binds, and how that structure can be engineered.
That is the real opportunity.
The future of RNA biology will belong not only to those who can read RNA sequences, but to those who can understand RNA shapes.
And increasingly, those shapes will be discovered by experiments and predicted by machines working together.
References / Sources
Zhang, J., Fei, Y., Sun, L., & Zhang, Q. C. “Advances and opportunities in RNA structure experimental determination and computational modeling.” Nature Methods, 19, 1193–1207, 2022.
PubMed entry for Zhang et al., “Advances and opportunities in RNA structure experimental determination and computational modeling.”
Sato, K. “Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure, aptamers and drug discovery.” Briefings in Bioinformatics, 2023.
Wang, W., Peng, Z., & Yang, J. “Predicting RNA 3D structure and conformers using a pre-trained secondary structure model and structure-aware attention.” Nature Machine Intelligence, 2026.
Fei, Y. et al. “Predicting small molecule–RNA interactions without experimentally resolved RNA structures.” Nature Biotechnology, 2026.
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