RNA Therapeutics After the Vaccine Boom: What Works, What Is Next, and What Still Blocks the Field

RNA Therapeutics: What Works, What Is Next, and What Still Blocks the Field

 

RNA medicines are now a real product class, but the next decade depends on delivery, manufacturing, and platform-aware regulation.

RNA Therapeutics in 2026: From Platform Promise to Delivery Reality

Summary

RNA-centered therapeutics have moved from a "promising platform" story to a differentiated product class with real regulatory, commercial, and clinical traction. The strongest proof points now sit in three clusters: vaccines built on translatable RNA, liver-directed RNA silencing using GalNAc conjugates or lipid nanoparticles, and antisense medicines for rare or genetically defined disease. The last three years were especially important: the FDA approved mRESVIA in 2024 as the first mRNA vaccine for a non-COVID indication, the EU approved the self-amplifying RNA vaccine Kostaive in 2025 after Japan's 2023 authorization, and 2025 alone brought three new oligonucleotide approvals in the U.S. - fitusiran, donidalorsen, and plozasiran - signaling that RNA medicines are broadening beyond niche neurology and transthyretin disease. At the same time, the field remains uneven: miRNA therapeutics still have no phase III successes or approvals, CRISPR-based RNA editing has only just entered early human testing, and circular RNA remains a platform bet rather than a validated product class.

Analytically, the field's central challenge is no longer whether RNA can work, but where and how reliably it can work. Delivery beyond liver and locally accessible tissues remains the dominant bottleneck; endosomal escape, tissue biodistribution, repeat-dose immunogenicity, chemistry-dependent toxicity, and scalable manufacturing still constrain the jump from rare disease to common disease. The regulatory environment is becoming more favorable, however: FDA finalized clinical pharmacology guidance for oligonucleotide therapeutics in 2024, issued draft nonclinical safety guidance in late 2024, and launched a 2026 framework for individualized ultra-rare therapies; EMA in parallel published synthetic oligonucleotide manufacturing guidance in 2024 and mRNA-vaccine quality guidance in 2025. In practice, this means the next wave of winners will likely be companies that treat delivery, analytics, and regulatory design as an integrated platform rather than as separate workstreams.

For an industry audience, the biggest opportunity is clear: RNA offers the fastest route from target validation to drug candidate for many classes of disease biology, especially where the target is genetically defined, intracellular, or "undruggable" by classical small molecules and antibodies. But the platform is fragmenting. There is no single "RNA market"; instead there are several operating models: chronic liver-directed RNAi for prevalent cardiometabolic disease, personalized or semi-personalized cancer vaccination, splice modulation by ASOs or small molecules, locally delivered ocular and CNS medicines, and now an emerging frontier of transient RNA editing. The most credible near-term strategy is to build on validated chemistries and delivery routes while selectively investing in extrahepatic targeting, AI-guided sequence and nanoparticle design, and manufacturing systems that can handle both precision and scale.

RNA Modalities And Mechanisms

"RNA-centered therapeutics" is best understood as two related families: medicines made of RNA or oligonucleotides, and medicines that target RNA as a substrate. Within that umbrella, mechanism matters more than modality labels. mRNA and circRNA deliver coding information for protein production; siRNA exploits RNA interference through Ago2/RISC-mediated cleavage; antisense oligonucleotides can trigger RNase H1 degradation, sterically block translation, or switch splicing; miRNA therapeutics either replace lost regulatory microRNAs or inhibit pathogenic ones; aptamers use folded nucleic acids as ligands; RNA-targeting small molecules bind structured RNA or splice-regulatory motifs; and CRISPR/Cas13-style RNA editors offer transient, programmable RNA knockdown or base editing without permanent DNA changes. The platform lesson from the last decade is that "RNA" is not a single drug class but a family of pharmacologies, chemistries, and delivery logics.

The strategic takeaway from this comparison is that mechanism-specific fit is decisive. If the disease biology is hepatocyte-centric and chronic, siRNA or GalNAc-ASO often has the best benefit-risk and manufacturing logic. If rapid protein expression is needed, mRNA or saRNA is attractive, particularly in vaccines and oncology. If the therapeutic goal is splice correction, ASOs and RNA-binding small molecules remain the leaders. And if transient reversibility matters - a compelling argument in retina or other tissues where permanent genomic editing may be too risky - RNA editing is conceptually powerful but still clinically immature.

Breakthroughs And Clinical Translation

A helpful way to read the current landscape is by asking which modalities have crossed the "platform credibility" threshold. By mid-2026, that threshold has clearly been crossed by mRNA vaccines, multiple ASO subclasses, and liver-directed siRNA. The more recent approvals matter because they show breadth expansion: from COVID to RSV and saRNA vaccines; from hATTR and rare liver diseases to hypercholesterolemia, hemophilia, familial chylomicronemia syndrome, and hereditary angioedema; and from gene suppression alone to splice modulation and biomarker-driven accelerated approval. By contrast, the modalities still waiting for a definitive translation signal are miRNA, circRNA, and CRISPR-based RNA editing.

Two breakthrough patterns stand out. First, liver delivery is no longer just a rare-disease story. Inclisiran moved RNAi into high-volume cardiovascular prevention; plozasiran and olezarsen positioned RNA medicines against severe triglyceride disorders; and fitusiran extended RNAi toward hematology with a mechanism defined by antithrombin silencing rather than target replacement. Second, regulators have become comfortable with mechanism-matched evidence packages, even when those packages are unconventional: tofersen's accelerated approval based on plasma neurofilament reduction is the clearest recent case.

The unresolved story is therapeutic mRNA outside vaccines. The Merck-Moderna melanoma program remains the most advanced signal: five-year Phase 2b KEYNOTE-942 data presented on June 1, 2026 showed sustained recurrence-free and distant metastasis-free survival improvements for intismeran autogene (mRNA-4157/V940) plus pembrolizumab, but the product remains investigational and unapproved. Meanwhile, recent corporate behavior suggests that big pharma believes the next upside may come from "RNA-plus-delivery" platform combinations rather than naked modality bets - a logic visible in Lilly's 2026 move for Orna's circular RNA plus LNP platform and Novo Nordisk's 2024 acquisition of Cardior's cardiovascular RNA assets.

Core Technical Bottlenecks

Delivery remains the field's most consequential bottleneck. The liver is the best-served organ because both GalNAc conjugation and many LNP compositions naturally favor hepatocyte uptake. GalNAc conjugates exploit the asialoglycoprotein receptor and have enabled the durable, infrequent subcutaneous dosing seen with agents such as inclisiran and plozasiran. Outside the liver, however, the problem becomes much harder: serum protein binding, nanoparticle corona formation, endothelial barriers, endosomal escape, and cellular heterogeneity all degrade effective delivery. Recent reviews continue to describe extrahepatic delivery as the major translational limitation for oligonucleotides and LNP-RNA systems.

Stability and immunogenicity cut both ways. For therapeutic RNAs, chemical modification is usually essential, not optional. Phosphorothioate backbones, 2'-O-methyl, 2'-MOE, LNA, and related modifications improve nuclease resistance, protein binding, and potency for oligonucleotides; N1-methylpseudouridine, optimized caps, and poly(A) architecture improved translatable RNA performance and were central to the COVID vaccine era. But each gain brings tradeoffs: backbone chemistry can drive protein-binding toxicities, PEG-bearing formulations raise complement and anti-PEG questions, and innate immune activation must be minimized for chronic therapeutics while being harnessed, not erased, in vaccines. FDA's 2024 clinical pharmacology guidance explicitly treats immunogenicity risk assessment as a core development task for oligonucleotide therapeutics, and FDA in 2025 required updated myocarditis/pericarditis warnings for mRNA COVID-19 vaccines - a reminder that platform safety liabilities can evolve after launch.

Specificity is also more complicated than "Watson-Crick matching" suggests. siRNA can produce seed-mediated off-target repression; ASOs can create hybridization-dependent and hybridization-independent toxicities; splice correction can reveal cryptic or tissue-specific biology; and miRNA therapies face the hardest problem of all because one miRNA often regulates many transcripts across multiple tissues. This is a major reason the miRNA field has lagged: recent analyses still conclude that the space has generated intriguing biology but no phase III winners or marketed products. By contrast, tofersen shows that when genetic causality is unusually strong and biomarkers are mechanistically coherent, regulators may tolerate residual uncertainty.

Manufacturing is now a strategic differentiator. Traditional solid-phase oligonucleotide synthesis works for rare diseases, but broad-population RNA medicines require cleaner impurity control, lower solvent intensity, better analytics, and eventually higher-throughput or alternative synthesis routes. EMA's 2024 oligonucleotide guideline explicitly addresses characterization, specifications, analytical control, conjugation, and product development. On the mRNA side, the key CMC pain points are template quality, in vitro transcription consistency, capping, dsRNA impurities, purification, formulation, sterile fill-finish, and comparability when platforms are updated. The fact that EMA issued a dedicated 2025 guideline on mRNA-vaccine quality is itself evidence that RNA CMC has become specialized enough to require modality-specific regulation.

CNS and tissue targeting remain the hardest frontier. The clinical successes in CNS RNA medicine - from nusinersen to tofersen - relied on local intrathecal delivery, not systemic blood-brain barrier penetration. Reviews in 2025 continue to emphasize receptor-mediated transport, peptide targeting, focused ultrasound, and locally delivered nanoparticles as the most credible routes to broader CNS translation. Retina, lung, muscle, and immune cells are all active targets; but compared with hepatocytes, none yet has a universally accepted delivery standard equivalent to GalNAc. That imbalance explains why so much platform innovation is now aimed at barcoded in vivo screening, organ-specific lipid design, peptides, antibody-oligo conjugates, and hybrid local/systemic strategies.

Enabling Technologies And Innovation Engines

The enabling-technology story is no longer just "LNPs got better." It is an ecosystem of chemistry, screening, computation, and manufacturing.

Novel delivery systems

Extrahepatic LNP engineering is the clearest active frontier. High-impact 2024-2025 work used barcoded in vivo screens to identify lipid formulations with lung and immune-cell tropism, while a 2025 Nature Biotechnology paper described AI-guided LNP design for pulmonary gene therapy. More broadly, recent reviews of LNP fate emphasize that composition alone is not enough: corona biology, endosomal escape, particle morphology, and tissue microenvironment all influence performance. If first-generation RNA delivery was "make a stable particle," second-generation delivery is "engineer the whole in vivo journey."

Chemical modification and scaffold innovation

For oligonucleotides, the foundational playbook remains backbone and sugar modification plus targeted conjugation. For mRNA and saRNA, the differentiators are now optimized UTRs, codon architecture, caps, modified nucleosides, dsRNA impurity control, and formulations matched to route and indication. Circular RNA adds another engineering layer: ribosome entry, circularization chemistry, purity, and translational control. Recent big-pharma interest in Orna suggests that industry increasingly values circRNA not just for longer expression, but for the possibility of combining durable translation with in vivo cell engineering.

In vivo selection, next-generation SELEX, and high-throughput biology

RNA discovery is becoming more empirical and more multiplexed. Discovery platforms for RNA therapeutics now pair computational design with ex vivo functional assays, organoid systems, barcoded in vivo screening, and improved aptamer-selection workflows. In aptamers specifically, advances in SELEX and post-selection modification aim to solve historical liabilities in affinity, degradation, and tissue specificity. The common industry pattern is clear: library-scale experimentation is replacing the older, serial "candidate-by-candidate" optimization model.

AI and ML design

AI is becoming useful precisely where the design space is combinatorial: RNA sequence design, secondary-structure optimization, codon choice, untranslated regions, and nanoparticle formulation. The most credible near-term use case is not fully autonomous drug design, but constrained optimization - using ML to triage huge sequence or lipid spaces before wet-lab selection. The strongest evidence so far is in delivery-system design and screening acceleration, not in replacing biology-led target selection.

Manufacturing innovation

RNA manufacturing is moving toward three priorities: higher-fidelity synthesis, better real-time analytics, and more scalable process architectures. End-to-end continuous mRNA production was demonstrated earlier, but recent work is making the workflow more industrially relevant through in-process analytics and platform-scale control. On the oligonucleotide side, enzymatic synthesis is becoming a serious long-term alternative to conventional phosphoramidite chemistry, including a 2025 Nature Biotechnology report of template-independent enzymatic RNA oligo synthesis. These advances matter commercially because RNA's next growth phase depends on moving from kilogram-scale rare-disease supply to much larger and more sustainable production systems.

Business, Policy, And Access

The most successful business models in RNA therapeutics now share one principle: monetize the platform by narrowing the technical risk. Merck and Moderna's V940 collaboration is a classic shared-development/shared-profit model, with the companies publicly stating equal cost and profit sharing. Novo Nordisk's acquisition of Cardior for up to EUR1.025 billion shows the value placed on mechanistically differentiated extrahepatic RNA assets in cardiovascular disease. Lilly's February 2026 agreement to acquire Orna - reported by Lilly as an acquisition to advance cell therapies through circular RNA plus LNPs, and by Reuters as worth up to $2.4 billion - reflects a second pattern: big pharma is willing to pay for enabling platforms even before late-stage proof, if the platform plausibly opens a new therapeutic category such as in vivo CAR-T.

A second business model is regional commercialization and specialization. Ionis has repeatedly used this model - for example in eplontersen with AstraZeneca and in Asia-Pacific expansion for donidalorsen with Otsuka - to reduce launch burden while preserving platform value. This model fits RNA especially well because disease-area expertise, route-specific clinical operations, and reimbursement strategy differ sharply across neurology, cardiometabolic disease, rare immunology, and vaccines. RNA companies that try to be both platform innovators and fully integrated commercial organizations often end up overextended.

Policy is becoming more important, not less. The FDA's 2024-2026 actions - final oligonucleotide clinical pharmacology guidance, draft nonclinical ONT guidance, platform technology designation, and a framework for individualized ultra-rare therapies - collectively indicate a more platform-aware regulatory posture. EMA's 2024 synthetic oligonucleotide manufacturing guideline and 2025 mRNA-vaccine quality guideline show the same shift in Europe. These are not bureaucratic footnotes: for RNA developers, regulatory alignment on CMC, biodistribution, biomarkers, and platform comparability is now a source of competitive advantage.

Korea is relevant here as both a policy test case and a manufacturing node. In May 2025, the Korean government announced a four-year mRNA vaccine self-sufficiency project supporting development from nonclinical work through phase III. The Ministry of Health and Welfare's 2025 Korean ARPA-H call also included a personalized cancer-vaccine optimization platform. In parallel, WHO and Korean partners continued to build the Republic of Korea's role as a global biomanufacturing training hub for vaccine and biologics capacity. For RNA therapeutics, this combination - domestic platform ambition plus global training and manufacturing policy - is exactly the kind of ecosystem strategy that can matter as much as any single asset.

Safety, ethics, and access remain structural issues. RNA medicines often target rare diseases with high per-patient prices and complex lifelong dosing; outside vaccines, global manufacturing remains geographically concentrated; and individualized approaches raise fairness questions that classical blockbuster models do not. FDA's 2022 guidance for individualized investigational ASOs and its 2026 individualized-therapy framework are important because they implicitly recognize these tensions: how much evidence is enough for a mutation-specific or N-of-1 therapy, and who will pay for it? Vaccine history also matters. WHO's mRNA technology-transfer program and the lessons of COVAX underscore that rapid RNA innovation does not automatically produce equitable access unless manufacturing know-how, training, and procurement mechanisms are deliberately distributed.

Actionable Recommendations And Outlook

For the short term, the best opportunities are highly target-validated, route-matched programs. That means liver-directed cardiometabolic RNAi, ASOs or small molecules for splicing disorders, and improved local-delivery programs in eye and CNS. Companies should prioritize mechanisms with measurable biomarkers, accepted clinical endpoints, and a delivery route that already has regulatory precedent. In parallel, teams should build CMC and bioanalytical sophistication early - especially impurity profiling, biodistribution strategy, and comparability planning - because those are now frequent rate-limiting steps, not back-end chores.

For the medium term, the field should focus on extrahepatic delivery and selective platform generalization. The most important technical investments are organ- and cell-selective LNPs, conjugates for muscle/immune/CNS targeting, endosomal-escape engineering, and barcoded in vivo discovery systems tied to AI-guided optimization. Therapeutic mRNA beyond vaccines is likely to succeed first where manufacturing speed and personalization matter most - oncology, immunotherapy, and possibly select protein-replacement settings with local or repeatable dosing. Regulators are signaling openness to platform approaches, so companies should seek development programs that let them reuse validated chemistry, analytics, and formulation knowledge across multiple assets.

For the long term, the highest upside sits in transient cell engineering and programmable RNA repair. CRISPR-based RNA editing could become attractive in settings where reversibility is a feature, not a bug, but only if delivery becomes substantially better and long-term safety packages become clearer. Circular RNA also remains a meaningful long-term opportunity, especially if it proves superior for durable but non-permanent protein expression in immune reprogramming or regenerative contexts. The caution is that both areas are still pre-validation. Strategic capital should therefore favor platform options and milestone-based partnerships rather than premature commercialization assumptions.

The most realistic future outlook is therefore selective expansion, not universal platform dominance. RNA therapeutics will likely keep winning first where biology is genetically sharp, tissue exposure is solvable, and biomarkers allow rapid iteration. That set already includes vaccines, liver disease, some neurologic disease, and parts of immunology and hematology. The next decade's real breakthrough will be the first broadly reproducible extrahepatic delivery platform. If that arrives, RNA therapeutics could move from a successful specialty class to a central pillar of mainstream drug development. If it does not, the field will still grow - but as several highly successful niches rather than one all-conquering modality.

Open Questions And Limitations

This review prioritizes official and primary sources, but several emerging areas remain fluid as of 3 June 2026. Therapeutic mRNA outside vaccines is still late-stage rather than approved in the sources reviewed here; miRNA and circRNA lack major-market approvals; and CRISPR-based RNA editing is only just entering early human trials. Some company pipeline claims - especially in preclinical circRNA and extrahepatic delivery - remain ahead of peer-reviewed clinical validation and should be treated as directional rather than settled.

References 

Regulatory guidance and product approvals

Clinical Pharmacology Considerations for the Development of Oligonucleotide Therapeutics. U.S. FDA, 2024. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-pharmacology-considerations-development-oligonucleotide-therapeutics

Nonclinical Safety Assessment of Oligonucleotide-Based Therapeutics. U.S. FDA, 2024 draft. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/nonclinical-safety-assessment-oligonucleotide-based-therapeutics

Considerations for the use of the Plausible Mechanism Framework to Develop Individualized Therapies that Target Specific Genetic Conditions with Known Biological Cause. U.S. FDA, 2026 draft. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-plausible-mechanism-framework-develop-individualized-therapies-target-specific

Development and manufacture of oligonucleotides - Scientific guideline. European Medicines Agency, 2024 draft. https://www.ema.europa.eu/en/development-manufacture-oligonucleotides-scientific-guideline

Draft guideline on quality aspects of mRNA vaccines. European Medicines Agency, 2025. https://www.ema.europa.eu/en/documents/scientific-guideline/draft-guideline-quality-aspects-mrna-vaccines_en.pdf

MRESVIA. U.S. FDA, 2024. https://www.fda.gov/vaccines-blood-biologics/vaccines/mresvia

Kostaive. European Medicines Agency EPAR, 2025. https://www.ema.europa.eu/en/medicines/human/EPAR/kostaive

Report on the Deliberation Results: Kostaive. PMDA, 2023. https://www.pmda.go.jp/files/000269813.pdf

Novel Drug Approvals for 2025. U.S. FDA, 2026. https://www.fda.gov/drugs/novel-drug-approvals-fda/novel-drug-approvals-2025

FDA Approves Novel Treatment for Hemophilia A or B, with or without Factor Inhibitors. U.S. FDA, 2025. https://www.fda.gov/news-events/press-announcements/fda-approves-novel-treatment-hemophilia-or-b-or-without-factor-inhibitors

Drug Trials Snapshots: DAWNZERA. U.S. FDA, 2025. https://www.fda.gov/drugs/drug-trials-snapshots/drug-trials-snapshots-dawnzera

FDA approves drug to reduce triglycerides in adults with familial chylomicronemia syndrome. U.S. FDA, 2025. https://www.fda.gov/drugs/news-events-human-drugs/fda-approves-drug-reduce-triglycerides-adults-familial-chylomicronemia-syndrome

FDA approves add-on therapy to lower cholesterol among certain high-risk adults. U.S. FDA, 2021. https://www.fda.gov/drugs/news-events-human-drugs/fda-approves-add-therapy-lower-cholesterol-among-certain-high-risk-adults

FDA approves treatment of amyotrophic lateral sclerosis associated with a mutation in the SOD1 gene. U.S. FDA, 2023. https://www.fda.gov/drugs/news-events-human-drugs/fda-approves-treatment-amyotrophic-lateral-sclerosis-associated-mutation-sod1-gene

Drug Trials Snapshots: IZERVAY. U.S. FDA, 2023. https://www.fda.gov/drugs/drug-approvals-and-databases/drug-trials-snapshots-izervay

FDA Approves Required Updated Warning in Labeling of mRNA COVID-19 Vaccines Regarding Myocarditis and Pericarditis Following Vaccination. U.S. FDA, 2025. https://www.fda.gov/vaccines-blood-biologics/safety-availability-biologics/fda-approves-required-updated-warning-labeling-mrna-covid-19-vaccines-regarding-myocarditis-and?hl=en-US

In-depth reviews

Advances in oligonucleotide drug delivery. Nature Reviews Drug Discovery, 2020. https://www.nature.com/articles/s41573-020-0075-7

Drug delivery systems for RNA therapeutics. Nature Reviews Genetics, 2022. https://www.nature.com/articles/s41576-021-00439-4

Chemistry, structure, and function of approved oligonucleotide therapeutics. Nucleic Acids Research, 2023. https://academic.oup.com/nar/article/51/6/2529/7070965

Advancements in clinical RNA therapeutics: Present developments and prospective outlooks. Cell Reports Medicine, 2024. https://pmc.ncbi.nlm.nih.gov/articles/PMC11148805/

What will it take to get miRNA therapies to market?. Nature Biotechnology, 2024. https://www.nature.com/articles/s41587-024-02480-0

Trial of Antisense Oligonucleotide Tofersen for SOD1 ALS. New England Journal of Medicine, 2022. https://www.nejm.org/doi/full/10.1056/NEJMoa2204705

Plozasiran for Managing Persistent Chylomicronemia and Pancreatitis Risk. New England Journal of Medicine, 2024. https://www.nejm.org/doi/10.1056/NEJMoa2409368

Delivery, manufacturing, and platform technologies

High-throughput barcoding of nanoparticles identifies cationic, degradable lipid-like materials for mRNA delivery to the lungs in female preclinical models. Nature Communications, 2024. https://www.nature.com/articles/s41467-024-45422-9

Artificial intelligence-guided design of lipid nanoparticles for pulmonary gene therapy. Nature Biotechnology, 2025. https://www.nature.com/articles/s41587-024-02490-y

Template-independent enzymatic synthesis of RNA oligonucleotides. Nature Biotechnology, 2025. https://www.nature.com/articles/s41587-024-02244-w

Nanoparticulate delivery and targeting of RNA to the brain. Biochimica et Biophysica Acta - Cancer Reviews, 2025. https://www.sciencedirect.com/science/article/pii/S0304419X25002227

A First-in-Human Clinical Trial to Evaluate the Safety, Tolerability, and Efficacy of a Novel CRISPR RNA-editing Therapy in Patients with Mecp2 Duplication Syndrome. ClinicalTrials.gov, 2024-2026. https://clinicaltrials.gov/study/NCT06615206

Business, policy, and access

Moderna and Merck Present 5-Year Data for Intismeran Autogene in Combination With KEYTRUDA in Patients With High-Risk Stage III/IV Melanoma Following Complete Resection at the 2026 ASCO Annual Meeting. Merck, 2026. https://www.merck.com/news/moderna-and-merck-present-5-year-data-for-intismeran-autogene-in-combination-with-keytruda-pembrolizumab-in-patients-with-high-risk-stage-iii-iv-melanoma-following-complete-resection-at-the-20/

Lilly to acquire Orna Therapeutics to advance cell therapies. Eli Lilly and Company, 2026. https://investor.lilly.com/news-releases/news-release-details/lilly-acquire-orna-therapeutics-advance-cell-therapies

Novo Nordisk to acquire Cardior Pharmaceuticals and strengthen pipeline in cardiovascular disease. Novo Nordisk and Cardior Pharmaceuticals, 2024. https://cardior.de/wp-content/uploads/2024/03/PR240325_Cardior_Final.pdf

Press Release by the Korea Disease Control and Prevention Agency: mRNA Vaccine Development Support Project. KDCA, 2025. https://www.kdca.go.kr/bbs/eng/189/225954/download.do

Call for applications - 2025 Hands-on training for mRNA vaccine manufacturing organised by the Global Training Hub for Biomanufacturing in the Republic of Korea, supported by the World Health Organization. World Health Organization, 2025. https://www.who.int/news-room/articles-detail/call-for-applications-2025-hands-on-training-for-mrna-vaccine-manufacturing-organised-by-the-global-training-hub-for-biomanufacturing-in-the-republic-of-korea--supported-by-the-world-health-organization

RNA Is Not a Flat Message. It Is a Shape-Shifting Machine

 

Graphical Abstract

For decades, biology students have been taught a clean story: DNA stores information, RNA carries the message, and proteins do the work.

That story is useful. It is also incomplete.

RNA is not simply a courier moving genetic instructions from one place to another. It folds. It bends. It hides some regions and exposes others. It can adopt more than one structure, sometimes within the same population of molecules. These alternative shapes can influence whether an RNA is translated, degraded, stabilized, or ignored.

A new study in Nature Methods pushes this idea further by showing how individual RNA molecules can be read not only as sequences, but as structural objects. The authors developed a method called sm-PORE-cupine, which combines chemical RNA structure probing with nanopore direct RNA sequencing to detect RNA structure ensembles in single molecules. In simpler terms, they built a way to ask: what shapes are different copies of the same RNA molecule actually taking inside a cell?

Why RNA Structure Is Hard to See

RNA structure is usually measured as an average. Scientists treat many copies of an RNA molecule with a chemical probe, sequence the result, and infer which bases are paired or unpaired. This is powerful, but it hides variation.

Imagine taking a photograph of a crowd and averaging all the faces into one image. You would get a blurry “average person,” but you would lose the actual individuals.

RNA has the same problem.

One transcript may not exist as a single structure. Some molecules may fold one way, others another way. These different structural states are called RNA structure ensembles. The biological meaning may lie not in the average structure, but in the minority conformation that appears only under certain conditions.

That is the central challenge this study addresses.

The Core Idea: Read RNA Directly, Then Recover Its Shape

The method builds on nanopore direct RNA sequencing. Unlike many sequencing methods that first convert RNA into cDNA, direct RNA sequencing pulls native RNA molecules through a nanopore and measures current changes as the molecule passes through.

The authors combined this with SHAPE chemical probing using NAI-N3, a reagent that preferentially modifies flexible, single-stranded RNA regions. Modified bases alter the nanopore signal. By detecting those altered signals along each molecule, the researchers could infer which parts of that individual RNA molecule were structurally exposed.

This sounds straightforward, but there was a technical trap. Higher chemical modification rates improve structural information, but heavily modified RNA reads become harder to basecall and map. Many reads that contain valuable structure information are lost because standard alignment struggles with them.

The clever solution was to stop relying only on basecalled sequence alignment. The authors used direct signal alignment with dynamic time warping, allowing them to recover reads that conventional mapping would miss. In benchmark RNAs, this rescued a substantial fraction of otherwise failed reads and increased the usable data for downstream structure analysis.

That detail matters. The reads most likely to be thrown away are often the ones carrying rich modification signals. Recovering them improves the ability to distinguish structural populations.

Sorting RNA Molecules Into Structural Populations

After detecting modification patterns on individual molecules, the next problem was clustering: how do you separate one RNA shape from another?

The authors tested several clustering approaches and found that a Bernoulli mixture model performed well for separating RNA structural populations. They validated this using known riboswitches, including the adenosine riboswitch.

Riboswitches are useful test cases because they change structure when bound to specific ligands. The method could distinguish ligand-bound and unbound populations and even detect intermediate or minority conformations. Importantly, it could identify alternative structure populations even when one state represented only about 10% of the molecules.

This is the biological payoff: not merely “RNA has this structure,” but “this RNA population contains multiple structural states, and their proportions change.”

SARS-CoV-2: One Genome, Many Structural Possibilities

The authors then applied sm-PORE-cupine to SARS-CoV-2 RNA. Viral RNAs are especially interesting because structure can regulate replication, translation, packaging, and immune evasion.

The study found that the 3′ end of the SARS-CoV-2 genome is highly structurally heterogeneous. This region contains several subgenomic RNAs, and the authors showed that different subgenomic RNAs, including nucleocapsid, ORF7a, and ORF8, display different levels of structural heterogeneity. The nucleocapsid RNA was especially heterogeneous among the tested subgenomic RNAs.

This suggests that viral RNA structure is not a fixed map. It is more like a set of competing layouts, with different viral transcripts folding into distinct structural populations.

That has major implications. If RNA structure affects viral gene expression, then drugs or antisense strategies targeting viral RNA may need to account for structural diversity, not just sequence.

Candida albicans: RNA Structure During a Cellular Identity Shift

The most biologically interesting part of the study may be its work in Candida albicans, a fungal pathogen that can shift from yeast-like growth at 30 °C to hyphal growth at 37 °C.

This transition matters because the hyphal form is associated with pathogenicity. The authors asked whether RNA structural ensembles change during this temperature-dependent transition.

They performed structure probing in vivo and in vitro at both temperatures and found several important patterns.

First, RNA structures were generally more homogeneous in vitro than in vivo. That means the cellular environment introduces structural complexity that purified RNA does not fully capture.

Second, RNA structures became modestly more homogeneous at higher temperature.

Third, coding sequences were more structurally heterogeneous than 3′ untranslated regions, while highly translated transcripts tended to have more homogeneous 3′ UTR structures at 37 °C.

This points toward a regulatory role for 3′ UTR structure. The 3′ UTR is often treated as a control panel for RNA stability, localization, and translation. This study adds another layer: the structure of that control panel may shift with temperature.

RNA Thermometers Beyond Bacteria?

The authors identified 95 regions in C. albicans 3′ UTRs that changed structural heterogeneity between 30 °C and 37 °C. They focused on two transcripts, RPS19A and RPL29, and showed that their 3′ UTR structural changes were linked to changes in translation using luciferase reporter assays.

This is a striking result because it suggests that some fungal mRNAs may behave like RNA thermometers. Their structures respond to temperature, and those structural changes affect protein production.

The phrase “RNA thermometer” is familiar in bacterial gene regulation, but this study suggests a broader principle: eukaryotic mRNAs may also use temperature-sensitive structure ensembles to tune expression.

Why This Study Matters

The real advance here is not just another RNA probing method. It is a change in resolution.

Older approaches often asked:

What is the average structure of this RNA?

This study asks:

How many structural states does this RNA population contain, and how do those states change across conditions?

That distinction matters for RNA biology, virology, fungal pathogenesis, and therapeutic targeting. If an RNA exists in multiple structural states, then the biologically relevant state may not be the dominant one. A low-abundance conformation could control translation, expose a regulatory motif, recruit a protein, or create a druggable structural pocket.

The study also highlights a broader lesson for transcriptomics. RNA sequencing has become extremely good at counting molecules and identifying isoforms. But RNA molecules are not linear strings floating passively in the cell. Their folding creates another layer of information—one that may explain why two RNAs with similar abundance can behave differently.

The Bigger Picture

Biology is moving from sequence to structure, from averages to single molecules, and from static models to ensembles.

sm-PORE-cupine fits directly into that transition. It gives researchers a way to observe RNA structural diversity molecule by molecule, transcript by transcript, and condition by condition.

The work also reminds us that the cell is not a test tube. RNA folding in vivo is shaped by temperature, proteins, translation, decay machinery, molecular crowding, and local cellular context. A structure predicted on a computer or measured in purified RNA may capture only part of the story.

RNA is not just a message.

It is a molecule with memory, movement, and choice. It can fold into different futures. This study gives us a sharper way to watch those futures form.

CRISPR’s New Kill Switch: How Cas12a2 Turns a Cell’s Own RNA Against It

 

Graphical Abstract

CRISPR has usually been described as a molecular scalpel. That metaphor is useful, but it is also a little too polite. A scalpel cuts where it is told. It edits. It repairs. It leaves behind a changed genome and, in many cases, a living cell.

The new study “RNA-triggered cell killing with CRISPR–Cas12a2” pushes CRISPR into a different role. Here, CRISPR is not merely an editor. It becomes a programmable execution system. The enzyme Cas12a2 can be guided to recognize a specific RNA molecule inside a cell. Once it finds that RNA, it unleashes widespread DNA damage, pushing the cell toward death. In simple terms: the cell’s own transcript becomes the trigger for its destruction.

That is why this work feels important. It does not just ask whether CRISPR can change a cell. It asks whether CRISPR can decide which cells should survive.

The old CRISPR problem: editing is easier than killing

In bacteria, CRISPR-based killing is relatively straightforward. If a CRISPR nuclease cuts an essential DNA sequence, the bacterium often cannot recover. But eukaryotic cells, including human cells, are much better at surviving DNA damage. They have repair systems such as non-homologous end joining and homology-directed repair. A conventional Cas9 or Cas12a cut may produce an edit rather than death.

That is useful for genome engineering, but frustrating if the goal is to eliminate a dangerous cell.

Cas13, another CRISPR system, targets RNA. It can degrade RNA transcripts, but in mammalian cells this does not always translate into robust cell death. The cell may lose a transcript, slow down, or compensate. The authors of this study frame Cas12a2 as a different kind of tool: an RNA-sensing nuclease that responds to transcript recognition by shredding DNA in trans.

This distinction matters. Cas12a2 is not simply cutting the target RNA. It is using the RNA as a molecular tripwire.

How Cas12a2 works

Cas12a2 is guided by a small RNA sequence. When the guide finds a matching target RNA, especially near an adenine-rich protospacer-flanking sequence, the enzyme becomes activated. Once activated, Cas12a2 does not politely cut one defined locus. It begins collateral cleavage of nucleic acids, including double-stranded DNA.

That sounds dangerous, and biologically it is. But the danger is also the point.

The researchers tested two related versions, SuCas12a2 and GeCas12a2, and showed that they can be programmed against specific transcripts. In yeast, targeting the ADE2 transcript with GeCas12a2 caused a dramatic reduction in surviving transformants. The system worked even when cells were given a repair template, suggesting that Cas12a2 killing was not easily escaped by standard local DNA repair.

Then came the more important test: human cells.

Killing human cells by recognizing a transcript

The team first used HeLa cells engineered to express GFP. GFP is useful because it gives researchers a clean, visible target. When they delivered GeCas12a2 with a guide aimed at the GFP transcript, the GFP-expressing cells failed to grow and were strongly depleted. The study reports about 86% cell depletion after targeting GFP in HeLa-GFP cells, while non-targeting controls continued to proliferate.

The result was not limited to an artificial GFP transcript. The authors also targeted endogenous transcripts across several cancer-derived cell lines, including transcripts with different abundance levels. Cas12a2 could deplete cells even when some target transcripts were relatively poorly expressed, although transcript abundance still mattered. The tool also worked when delivered as Cas12a2 mRNA and guide RNA packaged in lipid nanoparticles, an important delivery format for future therapeutic development.

The central idea is straightforward but powerful: if a cell expresses the RNA that Cas12a2 has been programmed to recognize, it can be eliminated. If it does not express that RNA, it should be spared.

What actually kills the cell?

The authors did not stop at showing cell loss. They asked what was happening inside the cell.

Activated Cas12a2 produced extensive double-stranded DNA breaks. The team measured this using 53BP1 foci, a marker of DNA double-strand break repair. Cas12a2 targeting GFP or GAPDH caused at least a 5.2-fold increase in 53BP1 foci compared with non-targeting or vehicle controls. The DNA damage level was comparable to that caused by established DNA-damaging anti-cancer drugs such as cisplatin and etoposide.

But there is a crucial difference. Cisplatin and etoposide damage DNA broadly. Cas12a2 is activated only when the chosen transcript is present.

The downstream consequences looked like a cell in serious trouble: abnormal DNA-content profiles, reduced G1 cell population, signs of mitotic catastrophe, apoptosis markers such as annexin V and caspase-3/7 activity, and inflammatory gene-expression signatures. The authors conclude that RNA-triggered Cas12a2 eliminates human cells mainly through extensive DNA damage followed principally by apoptosis, with other death pathways also contributing.

The specificity question

A programmable cell-killing system is only useful if it does not kill the wrong cells.

This is the most obvious concern. If Cas12a2 tolerates mismatches too easily, a guide intended for one RNA might accidentally recognize a related transcript and kill healthy cells. The authors therefore tested guides against transcripts absent from human cells, looked for DNA damage, examined barcode integration as a readout of double-strand breaks, and tested predicted off-target RNA candidates.

Under the tested conditions, they found no measurable off-target activation in human cells. Mismatched guides generally failed to trigger depletion, and non-targeting guides did not induce the transcriptomic disruption seen with true on-target activation.

This is encouraging, but it should not be overread. The system is still early. Specificity will need to be tested across more cell types, transcriptomes, delivery contexts, disease models, and guide designs. For a cell-killing technology, “mostly specific” is not enough. The safety threshold will be much higher than for ordinary gene perturbation.

Application 1: killing HPV-positive cells

The first major application was viral infection.

Cells infected with high-risk human papillomavirus express viral transcripts that are absent from normal human cells. That makes HPV an attractive test case. The researchers designed guides against HPV E6 and E7 transcripts, two viral oncogenes central to HPV-driven cancers.

In HPV18-positive HeLa-GFP cells, Cas12a2 guides targeting E6 or E7 produced about 94% cell reduction. The same guides did not significantly deplete HPV-negative HEK293-GFP cells.

The team also moved into an in vivo model. In a patient-derived xenograft model of HPV16-positive head and neck squamous cell carcinoma, intratumoral administration of lipid nanoparticle-packaged GeCas12a2 mRNA plus an HPV16 E6 guide significantly reduced tumour growth compared with buffer control. Histology showed Cas12a2 expression and apoptotic markers after treatment.

This is not yet a therapy. It is a proof of concept. But it demonstrates why RNA-triggered killing is interesting: viral transcripts can act as highly specific molecular flags.

Application 2: enriching successfully edited cells

Genome editing often produces mixed populations. Some cells receive the intended edit; others remain unedited. Researchers usually need selection markers, sorting, cloning, or laborious screening to enrich the edited cells.

Cas12a2 offers a clever alternative. Program it to recognize the unedited transcript. Cells that failed editing still express the original RNA and are killed. Cells carrying the desired edit disrupt the guide-recognition site and survive.

The authors first showed that Cas12a2 could remove one cell type from a mixed culture. In a co-culture of GFP-expressing and RFP-expressing HeLa cells, GFP-targeting GeCas12a2 caused about 93% reduction of GFP-positive cells while RFP-positive cells continued growing.

They then used the method to enrich genome edits. After FnCas12a editing of a GFP locus, GeCas12a2 targeting the unedited GFP transcript increased indel frequency by 3.1-fold. For prime editing of GAPDH, Cas12a2 counterselection enriched precise edits by up to 4.3-fold compared with non-targeting controls.

This could become valuable in editing workflows, especially where edited cells are rare and difficult to isolate.

Application 3: targeting a cancer mutation

The most dramatic part of the study involves KRASG12C, a clinically important oncogenic mutation. The challenge is severe: the mutant RNA differs from wild-type KRAS by a single nucleotide. A useful system must kill cells expressing mutant KRAS while sparing cells expressing wild-type KRAS.

The researchers empirically selected a guide that activated Cas12a2 with the KRASG12C transcript but not the wild-type transcript. In engineered U2OS cells overexpressing either wild-type KRAS or KRASG12C, the KRASG12C-targeting Cas12a2 RNP depleted mutant cells by about 62% without measurably depleting wild-type KRAS-overexpressing cells.

They then tested NCI-H23 cells, which naturally carry heterozygous KRASG12C. Cas12a2 targeting KRASG12C caused about 50% depletion and increased DNA damage markers. When combined with sotorasib, an FDA-approved KRASG12C inhibitor, cell depletion exceeded 85% in the tested setting. Importantly, sotorasib-resistant cells were still depleted by Cas12a2, suggesting a possible complementary strategy for resistant cancer cells.

Again, this is early-stage biology, not a ready clinical intervention. But the concept is striking: a single mutated RNA base can potentially become the trigger for selective cell destruction.

Why this paper matters

The larger significance is not simply that Cas12a2 kills cells. Many things kill cells. The significance is that Cas12a2 can connect cell identity to cell death through RNA recognition.

That opens a broad design space. In principle, one could imagine targeting cells based on viral RNAs, fusion transcripts, cancer mutations, aberrant splice junctions, circular RNAs, edited RNAs, or other disease-associated transcript signatures. The authors explicitly suggest applications across basic research, medicine, biotechnology, biomanufacturing, and agriculture.

It also changes how we think about CRISPR. Traditional CRISPR editing asks: “What sequence do we want to change?” Cas12a2 asks a harsher question: “Which transcript marks a cell that should not remain alive?”

The hard problems ahead

Several barriers remain.

Delivery is the largest one. Getting Cas12a2 and its guide into the right cells, at the right dose, without unacceptable toxicity, will be difficult. Local injection into a tumour is very different from systemic delivery in a patient.

Guide design also needs maturation. The enzyme requires suitable target features, including the appropriate sequence context. High-throughput screens and machine-learning models may be needed to predict which guides kill efficiently and which are safest.

Another issue is survival. Some cells may escape Cas12a2-triggered damage. Understanding what those surviving cells look like genetically, epigenetically, and functionally will be essential. A system that damages DNA but fails to kill every target cell could create complicated risks.

Finally, immune effects may be double-edged. Cas12a2-induced damage and inflammatory signalling might help expose tumours to the immune system, but uncontrolled inflammation could also create toxicity.

A new kind of programmable biology

The study presents Cas12a2 as a programmable RNA-triggered cell-killing platform. It is not merely another CRISPR editor. It is closer to a molecular trap: quiet until it hears the right RNA, destructive once activated.

That makes it both exciting and dangerous in the productive scientific sense. It is exciting because it gives researchers a way to remove cells based on transcriptional identity. It is dangerous because any technology built to kill cells must earn trust through rigorous specificity, delivery, and safety testing.

Still, the conceptual advance is clear. If Cas9 taught us to rewrite genomes, and Cas13 taught us to manipulate RNA, Cas12a2 may teach us how to eliminate cells by listening to what they express.

For cancer biology, virology, genome engineering, and synthetic biology, that is a serious new possibility.

When a Fungus Uses RNA as a Weapon: The Hidden RNA Battle Inside Rice Cells

 

Thernablog.blogspot.com 

How a fungal long non-coding RNA hijacks a rice microRNA and opens a new chapter in cross-kingdom RNA warfare

For years, plant immunity was explained mostly through proteins.

Plants detect pathogens using receptor proteins. Pathogens fight back using effector proteins. The battle was imagined as a molecular arms race between host immune receptors and pathogen-secreted protein weapons.

That picture is still true.

But it is no longer complete.

A new Nature study, “A pathogen lncRNA secreted into rice sequesters a host miRNA for virulence,” shows that a fungal pathogen can use a long non-coding RNA as an effector. Not a protein. Not a toxin. Not an enzyme. An RNA molecule.

The pathogen is Magnaporthe oryzae, the devastating fungus that causes rice blast disease. The host is rice. The weapon is a fungal long non-coding RNA called lnc117761. The target is a rice microRNA called miR5827. The outcome is suppressed rice immunity and enhanced fungal infection.

This is not just another plant pathology paper.

It changes how we think about host–pathogen communication.

The fungus is not only sending proteins into the plant. It is sending regulatory RNA.

The Old Model: Pathogens Attack With Proteins

Most classical models of plant immunity begin with recognition.

Plants detect conserved pathogen signals using pattern-recognition receptors at the cell surface. They also use intracellular immune receptors to recognize pathogen effectors. Pathogens, in response, secrete effectors to suppress immunity and create a more favorable environment for infection.

This protein-centered view has shaped decades of plant pathology.

But RNA biology has been quietly complicating the story.

Small RNAs are already known to move across kingdoms. Plants can send small RNAs into pathogens to silence virulence genes. Pathogens can send small RNAs into plants to suppress host immunity. This cross-kingdom RNA interference has become one of the most exciting areas in plant–microbe interaction research.

The new study goes further.

It suggests that long non-coding RNAs can also act as cross-kingdom pathogenic effectors.

That is a much bigger conceptual jump.

The Fungal RNA: lnc117761

The researchers searched for fungal long non-coding RNAs associated with rice infection by M. oryzae. They examined different fungal stages, including mycelium, conidiophore, appressorium, and invasive hyphae.

One RNA stood out.

lnc117761, a 1,589-nucleotide long non-coding RNA, was highly expressed during infection-related stages. When the researchers deleted lnc117761 from the fungus, pathogenicity dropped sharply. The mutant fungus could still grow and form appressoria, but it was defective in invasive hyphal development inside rice tissue.

That distinction matters.

The RNA was not simply helping the fungus grow. It was helping the fungus infect.

When rice plants were engineered to express lnc117761, they became more susceptible to blast disease, showing longer lesions and higher fungal biomass. This strongly suggested that lnc117761 behaves like an effector — except its active form is RNA.

The Rice Defense RNA: miR5827

The next question was obvious: what does lnc117761 do inside the host?

Long non-coding RNAs often act through base-pairing with other RNAs. So the researchers searched for rice RNAs that could pair with lnc117761. The strongest candidate was a rice microRNA called miR5827.

This rice miRNA normally promotes disease resistance.

Its job is to repress PKR1, a gene encoding a serine/threonine-protein kinase receptor that functions as a negative regulator of rice immunity. In simpler terms, PKR1 holds back defense. miR5827 suppresses PKR1, allowing rice immunity to work more effectively.

When miR5827 was knocked out, rice became more susceptible to blast disease. When miR5827 was overexpressed, rice became more resistant. Conversely, PKR1 knockout increased resistance, while PKR1 overexpression reduced resistance.

So the host pathway is clear:

miR5827 suppresses PKR1 → PKR1 repression strengthens immunity → rice resists fungal invasion.

The fungus has evolved a countermeasure.

The Trick: RNA Sponging

lnc117761 contains a binding site that pairs with rice miR5827. The researchers showed that miR5827 binds to the miR5827-binding site in lnc117761, but not to mutated versions of that site. They validated this interaction through multiple assays, including EMSA, rice protoplast assays, RNA pull-down, LIGR coupled with RT–qPCR, and ITC. The reported dissociation constant for miR5827–lnc117761 binding was 3.44 μM.

The biological meaning is elegant and unpleasant.

The fungal lncRNA acts as a sponge.

It enters the rice cell and sequesters miR5827. Once miR5827 is trapped, it can no longer efficiently repress PKR1. PKR1 expression rises. Rice immunity is weakened. The fungus gains ground.

The pathway becomes:

fungal lnc117761 binds rice miR5827 → miR5827 is neutralized → PKR1 is released → immunity is suppressed → fungal infection increases.

That is RNA warfare at the level of molecular decoys.

The fungus does not need to destroy the host RNA. It only needs to distract it.

The RNA Crosses Into Rice Cells

One of the most important parts of the study is that lnc117761 does not simply act inside the fungus. It is transported into rice cells during infection.

The researchers detected lnc117761 in infected and nearby uninfected rice cells. They used in situ hybridization to show lnc117761 signals in rice tissue, while control fungal mRNAs remained restricted to fungal cells. They also found lnc117761 enriched in extracellular vesicles from infection-related fungal hyphae, supporting an extracellular-vesicle-mediated secretion route.

They went further by tagging lnc117761 with a Pepper RNA fluorescence system. Red fluorescence from lnc117761–4×Pepper appeared in rice cells distinct from fungal GFP signal, supporting direct visualization of RNA delivery into host tissue.

This is where the paper becomes especially important.

It suggests that fungal extracellular vesicles may carry long non-coding RNAs into plant cells, where they manipulate host RNA regulatory circuits.

That is a remarkable form of infection biology.

A Tiny Binding Site With Big Consequences

The interaction depends on a short complementary region.

The lnc117761 sequence contains a 21-nucleotide miR5827-binding site. Within that region, the authors identified a 9-nucleotide core motif, GUUGCAACA, that is essential for binding and virulence. Mutations in this motif weakened or abolished miR5827 binding and reduced the ability of lnc117761 to promote disease.

This is biologically satisfying because it connects sequence, binding, and pathogenicity.

The paper does not merely show that the RNA is present. It shows that a defined RNA–RNA interaction matters.

Remove the binding site, and the effect is lost.

That is the difference between correlation and mechanism.

The Bigger Evolutionary Clue

The authors also examined whether the miR5827–lnc117761 binding motif is a one-off curiosity or part of a broader regulatory pattern.

They found related sequence features across microorganisms and plants. The study reports that portions of the 21-nucleotide binding site are commonly found in hundreds of microbial species and dozens of plant species. Similar miR5827-like RNAs were detected in several plants, including barley, Arabidopsis, potato, Brachypodium, and wheat.

This raises an intriguing possibility.

Some conserved non-coding DNA regions may encode regulatory RNAs that mediate biological interactions across species. In other words, parts of the genome once dismissed as “dark matter” may participate in host–pathogen recognition, defense, and counter-defense.

That does not mean every similar motif is functional. But it does mean the field should look more carefully.

The next effector may not be a protein.

It may be an RNA hiding in plain sight.

Can This Be Used for Disease Control?

The practical implications are significant.

The study extended the concept beyond rice blast. The researchers identified a related non-coding RNA in Rhizoctonia solani, the causal agent of rice sheath blight. Knocking down this RNA by exogenous small interfering RNA reduced pathogenicity. They also showed that miR5827 contributes to resistance against sheath blight.

The idea was also tested in wheat against Fusarium graminearum, the causal agent of Fusarium head blight. A synthetic wheat miR5827 mimic, called TamiR5827, improved resistance in wheat seedlings and spikes.

This makes the study especially relevant for RNA-based crop protection.

A few possible applications emerge:

Synthetic miRNA mimics could be developed as protective molecules.
Host miRNA alleles with stronger expression could be used in breeding.
Pathogen lncRNA sequences could become targets for spray-induced gene silencing.
Genome editing could be used to reduce negative immune regulators such as PKR1.
RNA delivery systems could be designed to reinforce host defense pathways.

For researchers working on dsRNA sprays, SIGS, RNAi biopesticides, and RNA-guided plant immunity, this paper opens a new direction. It suggests that we should not only target pathogen protein-coding genes. We should also look for pathogen non-coding RNAs that act as virulence factors.

Why This Study Matters for RNA Biology

This paper matters because it expands the definition of an effector.

In classical pathology, an effector is usually a pathogen-secreted protein that manipulates the host. Here, the effector is a long non-coding RNA that manipulates a host microRNA.

That is a different layer of biology.

It means host–pathogen interactions can be governed by RNA–RNA recognition, not only protein–protein or protein–DNA interactions. It also means that non-coding genomic regions can encode active molecules that directly influence disease outcomes.

For RNA biology, the lesson is even broader.

RNA is not merely a message.
RNA is not merely a target.
RNA can be a weapon.
RNA can be a decoy.
RNA can be a mobile effector.
RNA can decide whether immunity holds or collapses.

This is exactly why the RNA field is becoming so important.

The functional genome is larger than the protein-coding genome. And many of its most interesting instructions may be written in regulatory RNAs that we have not yet learned how to read.

The Necessary Caution

This is a powerful study, but it should not be overgeneralized too quickly.

The authors themselves note that the miR5827–PKR1 axis is a major mechanism but probably not the only mechanism by which lnc117761 promotes virulence. lnc117761 may sponge other plant miRNAs, interact with plant proteins, or regulate additional fungal processes.

It is also not yet clear how common this kind of long non-coding RNA effector mechanism is across pathogen–host systems. The paper provides an important example and a strong framework, but future comparative genomics, infection biology, RNA interactome mapping, and functional validation will be needed.

That is the correct way to read this study.

Not as the final answer.

As a door opening.

A New View of Plant–Pathogen Warfare

The image is striking.

A fungal pathogen enters rice tissue. It does not only push in with mechanical force. It does not only secrete enzymes or protein effectors. It sends an RNA molecule ahead, packaged through a secretion route, delivered into host cells, where it binds and neutralizes a host microRNA.

The host miRNA normally keeps an immune brake under control. The fungal RNA releases that brake. Disease follows.

This is not random molecular noise.

This is regulatory combat.

And it reminds us that the RNA world did not end when proteins evolved. It is still operating inside modern cells, inside pathogens, inside crops, and inside the invisible negotiations that decide whether a plant survives infection.

The next generation of plant disease control may come from understanding these RNA conversations.

Not only which genes are expressed.
Not only which proteins are secreted.
But which RNAs move, which RNAs bind, and which RNAs win.

---

Reference

He, M., et al., “A pathogen lncRNA secreted into rice sequesters a host miRNA for virulence.” Nature, 2026. https://www.nature.com/articles/s41586-026-10572-x

RNA Was Never Just a Messenger. Now It Looks Like an Architect.

 

Graphical Abstract

For years, RNA has lived in the public imagination as biology’s courier: DNA writes the instructions, RNA carries them, proteins do the work. That story was always too clean. Ribozymes, riboswitches, long non-coding RNAs, guide RNAs and viral RNA genomes have already shown that RNA can sense, catalyse, regulate and remember molecular states. But a new Nature study pushes the argument further. Some natural RNAs do not merely fold into useful shapes. They assemble into large, symmetrical, protein-free molecular architectures that look closer to engineered nanostructures than ordinary cellular transcripts.

The study, titled “Naturally ornate RNA-only complexes revealed by cryo-EM,” reports high-resolution structures of three unusually large bacterial RNAs: OLE, ROOL and GOLLD. These RNAs were already known from earlier comparative genomics as strange, elaborate non-coding RNAs with unusually ornate predicted secondary structures. Their biological functions, however, remained mostly mysterious. What Kretsch and colleagues found is striking: each RNA can assemble with copies of itself into a defined higher-order structure without requiring proteins as construction scaffolds.

The surprise hidden inside “non-coding” RNA

The phrase “non-coding RNA” often sounds like a negative definition: RNA that does not encode protein. But that label hides a more interesting possibility. If an RNA is not translated, it may instead act through its own shape. Its loops, stems, bulges, pockets and junctions may become the functional surface.

The problem is that most natural RNA structures are still unknown. The paper notes that although the RFAM database contains more than 4,000 RNA families, only a small fraction have experimentally solved tertiary structures. This gap matters because sequence alone rarely tells us what a large RNA can physically do. A long RNA may look like a tangled transcript on paper, yet in three dimensions it can become a pocket, a scaffold, a sensor or a cage.

That is where cryo-electron microscopy changed the story. Instead of treating these RNAs as abstract secondary-structure diagrams, the authors visualized them as physical objects.

OLE RNA: a dimer shaped like bundled pipes

The first RNA, OLE — short for Ornate Large Extremophilic RNA — comes from extremophilic bacteria and has been linked to stress adaptation, metal ion homeostasis, energy availability and drug-related responses. Earlier work suggested that OLE interacts with several proteins, which made it reasonable to suspect that proteins might be needed to stabilize its structure.

But the cryo-EM data showed that OLE can form a defined RNA-only dimer. Two RNA molecules come together into a compact structure shaped like parallel co-axial pipes. The interface is not casual. It is held by multiple RNA–RNA contacts, including unusual base-pairing and loop interactions. One particularly notable feature is an unusual symmetric A–A interaction between the two RNA chains.

This is important because it changes how we think about OLE-associated proteins. Instead of proteins forcing the RNA into shape, the RNA may already form a structured platform that proteins bind afterward. The paper also discusses possible binding sites for OapA, OapC and RpsU, suggesting that OLE may organize protein partners rather than simply being organized by them.

ROOL RNA: an eight-piece RNA nanocage

The second RNA, ROOL, is even more visually surprising. ROOL RNAs are found in bacterial prophages and phages, often near tRNA islands. Their function remains unknown, but they had been predicted to contain complex secondary structures and pseudoknots.

In the cryo-EM reconstruction, ROOL forms an octameric nanocage: eight copies of the RNA assemble into a closed, hollow structure with dihedral symmetry. Its diameter is about 280 Å, larger than the maximal dimension of a bacterial ribosome. Inside, the cage is largely empty except for a disordered linker region.

The ROOL structure is not just eight RNAs stuck together. Each RNA chain forms multiple contacts with neighboring chains. These contacts include A-minor interactions, kissing loops and other non-canonical RNA motifs. In other words, ROOL behaves like a self-assembling RNA object with repeated architectural rules. It looks less like a transcript and more like a molecular container.

GOLLD RNA: a larger, fourteen-part cage

The third RNA, GOLLD, goes further. GOLLD RNAs are among the largest members of the set studied here, with many examples exceeding 800 nucleotides. Like ROOL, they are associated with bacterial phages and prophages, but their sequences and predicted secondary structures are distinct.

The solved GOLLD structure forms a 14-subunit nanocage with D7 symmetry. Its diameter is about 380 Å, making it even larger than the ROOL cage. The structure resembles a closed shell assembled from RNA alone, with an empty interior except for a disordered linker. The authors describe the 5′ and 3′ regions as forming separate structural domains, helping explain why different regions of the RNA may evolve with different constraints.

This is not a minor structural curiosity. Natural RNA molecules forming large, symmetric, hollow cages without proteins is a remarkable biological design principle. Protein cages are familiar in biology: viral capsids, bacterial microcompartments, ferritin-like assemblies and other proteinaceous shells. RNA-only cages, especially natural ones of this scale and order, are much less expected.

Why this is probably not a laboratory artefact

Whenever a molecule forms an impressive structure in vitro, the skeptical question is obvious: does this happen in real biological contexts, or only under experimental conditions?

The authors address that concern directly. They solved another large RNA, the raiA motif, under similar cryo-EM conditions and found it as a monomer, arguing against the idea that any large RNA would automatically multimerize in their setup. They also tested another RNA, HEARO, which was disordered without its protein partner. These controls suggest that the OLE, ROOL and GOLLD assemblies are specific, not generic cryo-EM artefacts.

Additional biophysical evidence strengthens the case. Mass photometry confirmed the expected stoichiometries: OLE as a dimer, ROOL as an octamer and GOLLD as a 14-mer, even at RNA concentrations as low as 12.5 nM. Dynamic light scattering further showed that ROOL and GOLLD form thermostable multimers up to 55 °C, with no detectable monomer fraction under tested conditions.

The evolutionary evidence matters too. Sequence covariation analysis showed conservation not only of internal stems but also of sites involved in intermolecular interactions. That means evolution appears to preserve the very contacts that allow these RNAs to assemble with one another.

The old view of RNA is getting too small

This study does not claim that all large non-coding RNAs form nanocages. Nor does it fully solve what OLE, ROOL and GOLLD do in cells. The authors are careful on that point. Function remains the next hard question.

But the structural message is already clear: RNA can form natural quaternary architectures that rival proteins in symmetry and scale. OLE creates a defined dimeric platform. ROOL and GOLLD form hollow cages. Their architectures are stabilized by RNA-specific structural logic: kissing loops, A-minor motifs, pseudoknots, non-canonical base pairs and conserved intermolecular bridges.

The most exciting possibility is that these cages are not decorative. They may encapsulate or organize other molecules. The authors suggest that ROOL and GOLLD nanocages could potentially contain linkers, metabolites, proteins, tRNAs, ribosomal components or other macromolecules, although these ideas still need experimental proof.

Why RNA nanotechnology should pay attention

Synthetic biologists and RNA nanotechnologists have long tried to design RNA cages, rings, tiles and scaffolds. This paper shows that nature may already have evolved such objects, using motifs that can now be studied at near-atomic detail. These natural RNAs offer a library of structural strategies for building large RNA assemblies without proteins.

That matters for design. If researchers want RNA molecules that self-assemble, carry cargo, organize enzymes, sense conditions or build intracellular compartments, natural RNA cages may provide design rules that are more robust than purely artificial constructs. The same study also provides useful data for RNA structure prediction, because large RNA quaternary structures remain difficult for computational models.

The unfinished question

The most honest ending is not that “RNA has rewritten biology.” That would be too easy. Biology is not rewritten by one structure paper. But this study does something valuable: it widens the imaginable.

RNA is not only a messenger, regulator or catalytic relic from an ancient RNA world. In some organisms, it may be an architect. It can fold, pair, brace, dock, cage and assemble. It can build large molecular objects from nothing more than copies of itself.

The next question is no longer whether RNA can make ornate structures. It clearly can. The harder question is what these structures are doing inside the cell — and how many more are still hiding in genomic databases, mislabelled by our limited imagination as merely “non-coding.”

How experiments and AI are finally beginning to reveal the hidden architecture of RNA

  

Graphical Abstract

RNA Structure Is Entering Its Integration Era

RNA is not just a messenger. It is a negotiator, a sensor, a scaffold, a switch, a catalyst, and sometimes the quiet organizer of entire molecular conversations inside the cell.

For decades, biology treated RNA mainly as a carrier of genetic information: DNA made the instructions, RNA delivered them, and proteins did the work. That older view now feels almost naïve. Many RNAs do not merely transmit information. They perform it. They fold into shapes, bind proteins, sense metabolites, control gene expression, regulate translation, catalyze reactions, and help determine whether a cell responds, adapts, survives, or fails.

The catch is simple: RNA function depends heavily on RNA structure.

And RNA structure is difficult.

A recent Nature Biotechnology Perspective, “Integrated experimental and AI innovations for RNA structure determination,” captures where the field is now heading. Published in 2026, the article argues that RNA structure determination is being reshaped by two forces moving together: improved experimental technologies and rapidly advancing artificial intelligence-based modeling tools.  

The important word is together.

Experiments alone are powerful but limited. AI alone is fast but not always biologically grounded. The real breakthrough is emerging where the two meet.

RNA Is a Shape-Shifting Molecule

RNA structure is not a decorative detail. It is often the mechanism.

A messenger RNA may contain structural elements that influence stability or translation. A riboswitch changes shape when it binds a metabolite. A viral RNA genome can use folded regions to regulate replication or translation. A long noncoding RNA may act through structural modules that organize protein complexes. A guide RNA must adopt the correct form to work efficiently with CRISPR-associated proteins.

In each case, sequence matters, but sequence is not enough.

The same RNA sequence can fold into different conformations depending on magnesium ions, proteins, temperature, chemical modifications, ligand binding, cellular crowding, or stress conditions. RNA is also highly charged and flexible, which makes it harder to capture structurally than many more rigid biomolecules.

This is why RNA structure determination is so demanding. Researchers are not just trying to find a single beautiful 3D model. They are trying to understand a moving structural ensemble.

That movement is not noise.

It is biology.

Why Classical Experimental Methods Still Matter

Before AI can predict RNA structure confidently, experimental data must continue to define what is real.

Classical structural biology has given RNA researchers several powerful methods. X-ray crystallography can reveal atomic detail, but many RNAs resist crystallization because they are flexible and heterogeneous. NMR spectroscopy can capture local structure and dynamics, especially for smaller RNAs, but it becomes harder with large and complex molecules. Cryo-electron microscopy, or cryo-EM, has become increasingly important for large RNA-containing complexes and is now pushing into RNA-only structures with improved scaffolding and modeling approaches. The Perspective highlights multiple recent cryo-EM-related advances for RNA structure determination, including scaffold-enabled and high-resolution approaches. (Nature)

Cryo-EM has changed the mood of structural biology. Molecules that were once too large, too flexible, or too difficult to crystallize can now be visualized in new ways. For RNA, this is especially important because many functional RNAs do not behave like rigid objects. They fold, flex, bind, release, and remodel.

Still, cryo-EM is not magic. Small RNAs remain difficult. Flexible regions may blur. Model building can be challenging. Interpreting density maps requires skill, prior knowledge, and increasingly, computational assistance.

That is where AI begins to enter the story.

AI Is Not Replacing Experiments. It Is Extending Them

AI-based structure prediction has transformed protein biology, and RNA researchers are now asking whether RNA can have a similar moment. The answer is complicated.

Protein structure prediction benefited from massive structural datasets and strong evolutionary signals. RNA has less experimentally solved 3D structural data, fewer large training datasets, and more troublesome conformational behavior. RNA also depends strongly on noncanonical base pairs, ion-mediated interactions, electrostatics, and alternative folding states.

So RNA cannot simply borrow protein AI and call the problem solved.

But the field is moving fast.

Recent RNA modeling tools are using geometric deep learning, language-model-inspired strategies, transformer networks, end-to-end learning, and hybrid physics-plus-machine-learning methods. The Nature Biotechnology Perspective lists several recent computational advances, including trRosettaRNA, RhoFold, NuFold, RoseTTAFoldNA, AlphaFold 3, and other RNA or biomolecular modeling frameworks. (Nature)

These tools are not all doing the same thing. Some predict RNA tertiary structures from sequence. Some work better with secondary structure inputs. Some model protein–RNA complexes. Some help rank predicted structures. Some assist with cryo-EM map interpretation. Together, they show a field in rapid expansion.

The key lesson is this: AI is becoming useful not because it replaces structural biology, but because it can generate hypotheses faster than experiments alone.

A predicted structure can guide mutagenesis.
A predicted fold can help interpret chemical probing data.
A predicted RNA–protein interface can direct biochemical validation.
A predicted conformer can suggest what state a molecule might adopt under specific conditions.

The model is not the answer. It is the beginning of a better experiment.

The Power of Integrative RNA Structure Determination

The strongest future for RNA structure biology is not “experiment versus computation.” It is integrative modeling.

In an integrative workflow, researchers may combine:

RNA sequence
secondary structure prediction
chemical probing data
evolutionary conservation
cryo-EM density maps
NMR constraints
known RNA motifs
deep learning models
physics-based refinement
molecular dynamics simulations

Each method contributes a different kind of truth.

Chemical probing can reveal which nucleotides are exposed or constrained. Cryo-EM can provide global architecture. AI can propose plausible folds. Evolutionary information can identify conserved interactions. Physics-based refinement can improve stereochemistry. Functional assays can test whether the proposed structure actually matters.

This integration is especially valuable because RNA is dynamic. A single method may capture one state, while another reveals a different one. A computational model may predict a possible conformer, while experimental probing can test whether that conformer exists under biological conditions.

For RNA, structure determination must increasingly become ensemble-aware.

The question is not only:

What does this RNA look like?

It is also:

What shapes can this RNA adopt, and which shape controls function?

Cryo-EM and AI Are Becoming Partners

One of the most exciting developments is the pairing of cryo-EM with AI-assisted model building.

Cryo-EM can generate density maps, but interpreting those maps for RNA is not always straightforward. RNA bases, sugar-phosphate backbones, modified nucleotides, and flexible regions can be difficult to assign correctly, especially when resolution varies across the molecule.

AI tools can help automate parts of this process. The Perspective cites recent methods such as CryoREAD for de novo nucleic acid modeling from cryo-EM maps and other automated approaches for detecting and modeling DNA/RNA in cryo-EM density. (Nature)

This matters because scalability has been a bottleneck.

If every RNA structure requires slow, highly specialized manual interpretation, progress remains limited. If AI-assisted pipelines can accelerate model building while maintaining accuracy, RNA structural biology becomes more accessible to more laboratories.

That could change the field.

Not every lab can spend years solving one RNA structure. But more labs may be able to combine probing, cryo-EM, prediction, and validation into a practical workflow.

Why This Matters for Biotechnology and Medicine

RNA structure is not only an academic puzzle. It is becoming a design problem.

RNA therapeutics are expanding rapidly: mRNA vaccines, siRNAs, antisense oligonucleotides, guide RNAs, aptamers, ribozymes, circular RNAs, self-amplifying RNAs, and RNA-based sensors all depend on folding behavior. A therapeutic RNA may fail not because its sequence is wrong, but because its structure is unstable, inaccessible, immunogenic, or incompatible with delivery.

For RNA drug discovery, structure matters even more.

Small molecules do not bind “RNA” in the abstract. They bind pockets, grooves, motifs, exposed bases, dynamic states, and structured regions. If we cannot see or predict those structural features, rational RNA-targeted drug design remains limited.

Better RNA structure determination could help researchers:

design more stable therapeutic RNAs
optimize guide RNA folding
identify druggable RNA motifs
predict RNA–protein interactions
engineer riboswitches and aptamers
design RNA nanostructures
understand viral RNA regulatory elements
improve RNA delivery and expression systems

This is where RNA structure becomes translational.

The future of RNA medicine will not depend only on writing better sequences. It will depend on designing molecules that fold into the right structures at the right time in the right cellular environment.

The Remaining Challenges Are Serious

There is reason for excitement, but not for hype.

RNA structure prediction still faces major limitations. Experimental RNA 3D structure datasets remain smaller than protein datasets. Many RNAs are flexible and context-dependent. Chemical modifications can alter folding. Cellular environments are difficult to reproduce. Protein binding can remodel RNA architecture. Magnesium and other ions can determine whether a predicted fold is realistic.

AI models may also produce confident-looking structures that are wrong.

That danger is not trivial. A beautiful model can mislead if it is not tested. RNA structural biology cannot afford blind trust in prediction tools, especially when the results are used for therapeutic design or mechanistic claims.

The correct attitude is not skepticism for its own sake.

It is disciplined validation.

Predictions should be challenged by experiments. Experiments should be interpreted with computational help. Models should be treated as hypotheses, not final truth.

The Field Is Moving Toward Structure-Guided RNA Biology

The deepest shift is conceptual.

RNA biology is moving from sequence annotation toward structure-guided understanding. Researchers increasingly want to know not only where an RNA is expressed, but how it folds, what it binds, how it changes shape, and whether that shape can be engineered.

This shift will influence many fields: virology, cancer biology, neuroscience, plant biology, synthetic biology, diagnostics, and therapeutic design.

For RNA researchers, the opportunity is enormous.

We can now imagine a future where an RNA sequence is analyzed not only for open reading frames, motifs, or target sites, but also for structural states, ligandable pockets, conformational switches, protein-binding surfaces, and engineering potential.

That future will not come from AI alone.

It will come from the integration of experimental structure determination, biochemical probing, deep learning, physical modeling, and functional validation.

RNA is a social molecule inside the cell because it does not act alone. It interacts, responds, recruits, folds, and refolds.

Perhaps the tools we use to study it must behave the same way.

Not one method.
Not one model.
Not one structure.

A network of evidence.

That is where RNA structure determination is going.

And that is why this field now feels less like a technical subdiscipline and more like one of the central engines of modern RNA biotechnology.

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References / Sources

1. Wang, W., Su, B., Peng, Z., & Yang, J. “Integrated experimental and AI innovations for RNA structure determination.” Nature Biotechnology, 44, 205–214, 2026. Published 5 January 2026. DOI: 10.1038/s41587-025-02974-5. (Nature)

Why AlphaFold transformed protein biology, while RNA structure prediction remains one of biology’s most stubborn frontiers

 

RNA Function Follows Form — But RNA Refuses to Sit Still

At a virtual conference in November 2020, structural biology changed.

The winner of the CASP14 protein-structure-prediction challenge was announced: AlphaFold, developed by Google DeepMind. The result was not merely better than previous tools. It was dramatically better. AlphaFold showed that artificial intelligence could predict many protein structures with near-experimental accuracy, solving a problem researchers had been chasing for decades.

Protein biology had its revolution.

RNA biology is still waiting for its equivalent moment.

That is the central tension behind Diana Kwon’s Nature Technology Feature, “RNA function follows form – why is it so hard to predict?” The article captures a frustrating truth: RNA is biologically essential, structurally fascinating, and increasingly important for medicine — but predicting its shape remains far harder than many people expected. 

RNA Is Not Just a Messenger

For decades, RNA was introduced in textbooks as a middleman: DNA stores genetic information, RNA carries the message, and proteins do the real work.

That explanation is now painfully incomplete.

RNA can regulate genes, catalyze reactions, guide protein complexes, control splicing, sense metabolites, organize cellular machinery, and influence disease. Ribozymes, riboswitches, long noncoding RNAs, microRNAs, guide RNAs, viral RNAs, circular RNAs, and therapeutic RNAs all remind us that RNA is not passive.

RNA does things.

But RNA does those things because it folds.

Its biological function depends on stems, loops, bulges, pseudoknots, junctions, long-range contacts, base stacking, ion coordination, and interactions with proteins or small molecules. In RNA biology, structure is not decoration. Structure is often the mechanism.

That is why the phrase “function follows form” matters.

An RNA molecule’s sequence tells us what it could become. Its structure tells us what it is actually capable of doing.

Why Proteins Were Easier for AI

AlphaFold’s success in protein structure prediction was built on several advantages.

Proteins have been studied structurally for a long time. Thousands of high-quality protein structures were already available in the Protein Data Bank. Protein sequences also carry rich evolutionary information: if two residues change together across evolution, they may physically interact in the folded protein. AlphaFold and related tools learned from these patterns at massive scale.

RNA does not offer the same easy path.

There are far fewer experimentally solved RNA structures than protein structures. Many RNAs are small, flexible, chemically sensitive, and structurally heterogeneous. RNA often depends on magnesium ions, cellular proteins, modifications, ligand binding, and environmental conditions to fold correctly. A sequence may not point to one stable structure. It may point to a shifting ensemble.

That makes RNA a much harder target for machine learning.

A protein often behaves like a molecule trying to reach a stable folded state.

RNA often behaves like a molecule negotiating among several states.

RNA’s Flexibility Is the Problem — and the Biology

The biggest mistake is to think RNA structure prediction is simply protein structure prediction with different building blocks.

RNA has its own grammar.

It has only four standard bases, but those bases can form canonical and noncanonical interactions. Its phosphate backbone is highly charged. Its folding can depend strongly on ions. It can form alternative secondary structures. It can switch conformations when binding a metabolite or protein. It can expose or hide regulatory regions depending on context.

This is not just a computational nuisance.

RNA flexibility is often exactly how RNA works.

A riboswitch must change shape to regulate gene expression. A viral RNA may remodel itself during infection. A guide RNA must fold into a form compatible with its protein partner. An mRNA may contain structural elements that affect translation, degradation, or immune recognition.

So the goal is not always to predict one “correct” RNA structure.

The goal may be to predict a population of possible structures, then understand which one matters under a specific biological condition.

That is much harder.

AlphaFold 3 Helps, But It Does Not End the RNA Problem

AlphaFold 3 expanded the modeling landscape by predicting biomolecular complexes involving proteins, DNA, RNA, small molecules, ions, and modified residues. That is a major advance because biology rarely happens molecule by molecule in isolation. Cells are crowded with interacting systems. 

But RNA structure prediction is still not solved.

AlphaFold 3 can model RNA-containing complexes, but RNA-only folding and RNA conformational ensembles remain difficult. Many RNA structures depend on experimental constraints, secondary structure priors, or specialized RNA-focused modeling approaches. The RNA field is therefore not simply waiting for one universal model to solve everything.

It is building a different toolkit.

The New RNA AI Toolkit

Several AI-based methods are now pushing RNA structure prediction forward.

Tools such as RhoFold+ use RNA language models and deep learning to predict RNA 3D structures from sequence. RhoFold+ was trained with large-scale RNA sequence information and designed to address the data scarcity that limits RNA modeling. 

Other methods, including trRosettaRNA and trRosettaRNA2, use RNA-specific structural logic, secondary structure information, and deep learning to improve 3D prediction. Recent work on trRosettaRNA2 emphasizes the value of secondary-structure-aware modeling and conformer prediction — a crucial feature because RNA often exists in multiple structural states rather than one fixed architecture. 

These methods suggest an important principle:

RNA prediction will probably not be solved by sequence alone.

It will require secondary structure priors, evolutionary signals, experimental probing data, cryo-EM maps, chemical constraints, molecular simulations, and AI models working together.

Experiments Still Matter

The rise of AI does not make RNA experiments obsolete.

It makes them more important.

Chemical probing methods such as SHAPE and DMS-based approaches can reveal which nucleotides are flexible, paired, exposed, or protected. Cryo-electron microscopy can capture larger RNA-containing complexes. NMR can reveal dynamics and local structure. X-ray crystallography can still provide atomic detail when crystals are available.

Each method sees a different part of the RNA story.

AI can propose models quickly. Experiments can test whether those models are real.

This is where RNA structure biology is heading: not toward purely computational prediction, but toward integrative structure determination.

A model becomes more trustworthy when it agrees with probing data, mutational analysis, cryo-EM density, biochemical function, and evolutionary conservation.

For RNA, evidence must converge.

Why This Matters for Medicine

RNA structure prediction is not just a technical problem for structural biologists. It is becoming central to biotechnology and medicine.

mRNA vaccines, siRNA drugs, antisense oligonucleotides, CRISPR guide RNAs, aptamers, ribozymes, circular RNAs, and self-amplifying RNAs all depend on folding behavior. A therapeutic RNA may fail because it folds incorrectly, exposes the wrong region, activates unwanted immune sensors, degrades too quickly, or binds inefficiently.

RNA-targeted small-molecule drugs are another major frontier. For a drug to bind RNA selectively, the RNA must present a recognizable structural pocket or motif. Without structural knowledge, RNA drug discovery becomes guesswork.

Better RNA structure prediction could help researchers design more stable RNAs, improve guide RNA performance, identify druggable RNA motifs, engineer riboswitches, and understand viral RNA elements.

In short, RNA structure prediction is not only about seeing molecules.

It is about designing them.

The Real Lesson from AlphaFold

AlphaFold changed protein biology because it made high-quality structural models widely accessible. It did not eliminate experiments, but it changed where experiments begin.

RNA needs a similar shift.

But the RNA version of that revolution may look different. It may not be a single model that predicts one final structure from sequence. It may be a network of tools that predicts secondary structures, tertiary folds, alternative conformers, RNA–protein complexes, RNA–ligand interactions, and experimentally testable structural hypotheses.

RNA does not sit still.

So RNA structure prediction must become comfortable with motion.

The next breakthrough will not simply tell us, “Here is the RNA structure.”

It will tell us:

Here are the structures this RNA can adopt. Here is when they appear. Here is what they bind. Here is how they regulate biology. Here is how we can redesign them.

That is the future RNA biology is moving toward.

Protein structure prediction had its AlphaFold moment.

RNA’s moment may be harder, slower, and messier.

But it may also be more interesting — because RNA is not merely a molecule with a shape.

It is a molecule with possibilities.

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References / Sources

1. Kwon, D. “RNA function follows form – why is it so hard to predict?” Nature, 2025. (Nature)

2. Jumper, J. et al. “Highly accurate protein structure prediction with AlphaFold.” Nature, 2021. (Nature)

3. Abramson, J. et al. “Accurate structure prediction of biomolecular interactions with AlphaFold 3.” Nature, 2024. (Nature)

4. Shen, T. et al. “Accurate RNA 3D structure prediction using a language model-based deep learning approach.” Nature Methods, 2024. (Nature)

5. Wang, W. et al. “The trRosettaRNA server for RNA structure prediction.” Nature Protocols, 2026. (Yang Lab)

6. CASP16 RNA structure prediction assessment and Yang-Server/trRosettaRNA2 reporting. (Wiley Online Library)