Guest Column | April 9, 2026

Can Making mRNA More "Rigid" Unlock The Next Leap In RNA Medicines?

By Riddhi Banik, Center for Biotechnology and Interdisciplinary Studies, RPI

Molecular structure for medical, technology, chemistry, science-GettyImages-2228457760

Over the past decade, mRNA has rapidly evolved from a fragile biological molecule into a powerful therapeutic platform.1 Yet, despite its success, one fundamental challenge remains largely unresolved: mRNA is structurally dynamic, constantly shifting between multiple conformations.2 This structural variability influences how efficiently proteins are produced to how easily the molecule can be purified and manufactured at scale.3

And this vast structural ensemble raises the question: Rather than trying to optimize mRNA within this fluctuating landscape, what if we could constrain it, making it more structurally defined, or “rigid”? Interestingly, several recent studies, though seemingly different in approach, are converging on this idea. Some engineer the 5′ end of the molecule with multiple caps to strengthen interactions with the cellular translation machinery.4 Others redesign the poly(A) tail to introduce loop-like structures that resist degradation.5 And more recently, metal ions have been used to physically compact mRNA into tighter, more ordered conformations.6

At their core, these approaches are not just about improving expression, they are about reducing the number of structural possibilities an mRNA molecule can adopt.7

The Hidden Problem: mRNA Does Not Have A Single Structure

To understand why this matters, it is important to look at how mRNA structure is typically predicted. Most computational tools widely used across academia and industry attempt to identify a “minimum free energy” structure, essentially the most stable folding pattern under a given set of conditions.8, 9 However, this is an approximation. In reality, mRNA molecules do not settle into a single structure; they exist as an ensemble of competing conformations, many of which have very similar energies.10,11

What complicates this further is that these predictions are highly sensitive to the environment. A structure that appears optimal under one buffer condition may not be favored under another. The conditions during in vitro transcription, purification, storage, and delivery into cells are all different. Inside the cell, additional factors such as RNA-binding proteins, molecular crowding, ionic conditions, and oxidative stress further reshape the folding landscape.12

This means that the same mRNA sequence can behave differently depending on where it is in its life cycle. For scientists and engineers, this creates a frustrating gap: We design based on predicted structures, but the molecule behaves according to a shifting reality. This disconnect limits our ability to confidently link sequence, structure, and function.13

A more rigid or constrained mRNA structure could help bridge this gap. By narrowing the range of accessible conformations, it may be possible to create molecules that behave more consistently across different environments, effectively making them less dependent on buffer conditions and more predictable in biological systems.

Three Paths Toward Structural Constraint

The multicapped mRNA approach focuses on the 5′ end, which plays a central role in initiating protein synthesis. By adding additional cap structures and modifying the surrounding region, this strategy strengthens the interaction between mRNA and the cell’s translation machinery. The result is a significant increase in protein production, driven not by global structural rigidity but by enhanced control over a key functional site. This is a highly targeted way of reducing variability, ensuring that the initiation step behaves reliably, even if the rest of the molecule remains flexible.14

The poly(A)-loop strategy, in contrast, operates at the 3′ end. By introducing complementary sequences that form a loop within the poly(A) tail, the structure becomes more compact and resistant to degradation processes such as deadenylation. This approach suggests that even modest localized structural features can influence the lifetime of mRNA. However, the underlying mechanism is still inferred rather than directly measured, leaving open questions about how exactly these structures interact with cellular decay pathways.5

The metal ion-assisted folding strategy is the most direct attempt to impose global structural rigidity. By introducing divalent metal ions, the negatively charged RNA backbone is neutralized, allowing the molecule to collapse into a more compact folded state. What makes this approach particularly interesting is that its effects extend beyond the RNA itself. The compacted mRNA alters the physical properties of the lipid nanoparticles used for delivery, making them more rigid and, in turn, affecting how they interact with cell membranes.6 This has been linked to improved intracellular processing and more efficient release of mRNA into the cytoplasm, highlighting a broader principle: RNA structure can influence not just biology but also delivery physics.

From Structural Control To Manufacturing Advantage

While much of the discussion around mRNA optimization focuses on biological performance, structural rigidity could also have important implications for manufacturing. One of the persistent challenges in mRNA production is the formation of impurities, particularly high molecular weight species that arise from interactions between mRNA molecules.15, 16 These can include dimers, aggregates, and other complex structures formed through base pairing or transient associations.17

Such impurities complicate purification and can reduce product consistency. Their formation is, in part, a consequence of structural flexibility; when mRNA molecules have multiple accessible conformations, they are more likely to interact with each other in unintended ways.18

A more structurally constrained mRNA could, in principle, reduce these interactions.3,19 If the molecule adopts a well-defined conformation, it may be less prone to forming aggregates, leading to cleaner separation during purification. This could translate into simpler downstream processing, higher yields, and ultimately lower production costs.

At the same time, a more predictable structure could improve analytical characterization. Techniques that measure size, folding, or hydrodynamic behavior often struggle with heterogeneous samples. A more uniform structural population could make it easier to distinguish between desired products and impurities, improving both quality control and regulatory confidence.

The Trade-Off: Innovation Vs. Practicality

Despite these promising possibilities, each strategy comes with its own set of challenges.

Highly engineered solutions, such as multicapped mRNA, rely on complex chemistry and specialized reagents. These can be expensive and are often sourced from a limited number of suppliers, raising concerns about scalability and supply chain resilience. As mRNA therapeutics move toward global deployment, dependence on niche reagents could become a bottleneck.

Simpler sequence-based approaches, like poly(A) looping, may be easier to manufacture but still need to demonstrate consistent benefits across different applications and conditions.

Metal ion-assisted folding offers an intriguing balance; it may be relatively low-cost to implement but introduces new variables that must be carefully controlled. The concentration and type of metal ions must be optimized, as excessive amounts can damage RNA or interfere with cellular processes. This introduces uncertainty not just in performance but also in safety.

Regulatory Realities And Long-Term Outlook

Ultimately, the success of any of these approaches will depend not only on their scientific merit but also on their compatibility with regulatory expectations. Introducing new structural features, whether chemical modifications or metal complexes, requires thorough evaluation of safety, consistency, and long-term effects.20

Regulatory agencies are likely to scrutinize these innovations through multiple lenses: how reproducible the manufacturing process is, whether new impurities are introduced, how the modified mRNA behaves in vivo over time, and whether repeated dosing poses risks.21

This creates a tension between innovation and adoption. The most sophisticated designs may offer the greatest performance gains but also face the highest barriers to approval. Conversely, simpler modifications may be easier to implement but deliver more modest improvements.

Toward A More Predictive Future For mRNA Design

What makes the concept of structural rigidity particularly exciting is its potential to improve not just performance but also understanding. By reducing structural variability, we may be able to better connect computational predictions with experimental outcomes. This could enable a more integrated approach to mRNA design, where simulations guide experiments more effectively, reducing trial and error and accelerating development.

In this sense, structural constraint is not just a design feature; it is a pathway toward greater predictability in RNA engineering.

The field is still in its early stages, and no single strategy has yet emerged as the definitive solution. But together, these approaches suggest a compelling direction: By carefully controlling the structural landscape of mRNA, we may be able to create molecules that are not only more potent but also more manufacturable, more predictable, and ultimately more accessible.

The challenge now is to determine how far we can push this idea without tipping the balance between innovation, cost, and real-world feasibility.

References

  1. Shi, Y.; Shi, M.; Wang, Y.; You, J. Progress and prospects of mRNA-based drugs in preclinical and clinical applications. Signal Transduction and Targeted Therapy 2024, 9 (1), 322. DOI: 10.1038/s41392-024-02002-z.
  2. Hofacker, I. L.; Fontana, W.; Stadler, P. F.; Bonhoeffer, L. S.; Tacker, M.; Schuster, P. Fast folding and comparison of RNA secondary structures. Monatshefte für Chemie / Chemical Monthly 1994, 125 (2), 167–188. DOI: 10.1007/BF00818163.
  3. Jarand, C. W.; Deng, Z.; Brader, M. L.; Reed, W. F. Consequences of mRNA Secondary Structure on Stability against both Hydrolysis and Aggregation: The Role of Electrostatic, π–π Stacking, and Thermal Effects. ACS Omega 2026, 11 (3), 4440–4456. DOI: 10.1021/acsomega.5c10266.
  4. Chen, H.; Liu, D.; Aditham, A.; Guo, J.; Huang, J.; Kostas, F.; Maher, K.; Friedrich, M. J.; Xavier, R. J.; Zhang, F.; et al. Chemical and topological design of multicapped mRNA and capped circular RNA to augment translation. Nature Biotechnology 2025, 43 (7), 1128–1143. DOI: 10.1038/s41587-024-02393-y.
  5. Oh, A.; Lee, S.; Park, H.-J.; Yoon, S.; Ha, D.; Lee, J.; Lee, S.; Roh, G.; Cho, Y.; Lee, S.-Y.; et al. Loop structure in poly(A) tail of mRNA vaccine enhances antigen translation efficiency and mRNA stability. npj Vaccines 2025, 10 (1), 234. DOI: 10.1038/s41541-025-01287-7.
  6. Yang, B.; Li, B.; Zhu, Y.; Zhao, M.; Cheng, Y.; Zhao, X.; En-Jie, D. T.; Wang, Y.; Zhang, M.; Tang, X.; et al. Rational design of rigid mRNA folding architecture to enhance intracellular processing and protein production. Nature Nanotechnology 2026, 21 (3), 407–418. DOI: 10.1038/s41565-025-02114-9.
  7. Dai, N.; Zhou, T.; Tang, W. Y.; Mathews, D. H.; Huang, L. EnsembleDesign: messenger RNA design minimizing ensemble free energy via probabilistic lattice parsing. Bioinformatics 2025, 41 (Supplement_1), i391–i400. DOI: 10.1093/bioinformatics/btaf245 (accessed 4/4/2026).
  8. Zuker, M.; Stiegler, P. Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Research 1981, 9 (1), 133–148. DOI: 10.1093/nar/9.1.133 (accessed 4/4/2026).
  9. Mathews, D. H.; Turner, D. H. Prediction of RNA secondary structure by free energy minimization. Current Opinion in Structural Biology 2006, 16 (3), 270–278. DOI: https://doi.org/10.1016/j.sbi.2006.05.010.
  10. Mathews, D. H.; Turner, D. H.; Watson, R. M. RNA Secondary Structure Prediction. Current Protocols in Nucleic Acid Chemistry 2016, 67 (1), 11.12.11–11.12.19. DOI: https://doi.org/10.1002/cpnc.19 (accessed 2026/04/04).
  11. Lorenz, R.; Bernhart, S. H.; Höner zu Siederdissen, C.; Tafer, H.; Flamm, C.; Stadler, P. F.; Hofacker, I. L. ViennaRNA Package 2.0. Algorithms for Molecular Biology 2011, 6 (1), 26. DOI: 10.1186/1748-7188-6-26.
  12. Hou, X.; Shi, J.; Xiao, Y. mRNA medicine: Recent progresses in chemical modification, design, and engineering. Nano Research 2024, 17 (10), 9015–9030. DOI: 10.1007/s12274024-6978-6.
  13. Badura, J.; Rybarczyk, A.; Zok, T. Comprehensive datasets for RNA design, machine learning, and beyond. Scientific Reports 2025, 15 (1), 21417. DOI: 10.1038/s41598-02507041-2.
  14. Chen, H., Liu, D., Aditham, A. et al. Chemical and topological design of multicapped mRNA and capped circular RNA to augment translation. Nat Biotechnol 2025, 43 (8), 1128– 1143.
  15. Pons Royo, M. d. C.; Arnold, T.; Perez Rodriguez, I.; Ostrovsky, N.; Al-Jazrawe, M.; Hatas, A.; Tenberg, V.; Myerson, A. S.; Braatz, R. D. Purification of messenger RNA directly from crude IVT using polyethylene glycol and NaCl precipitation. Process Biochemistry 2025, 156, 263–273. DOI: https://doi.org/10.1016/j.procbio.2025.06.002.
  16. Pons Royo, M. d. C.; Arnold, T.; Rodriguez, I. P.; Ostrovsky, N.; Al-Jazrawe, M.; Hatas, A.; Myerson, A. S.; Braatz, R. D. Continuous purification of mRNA by precipitation and sequential TFF. Separation and Purification Technology 2025, 379, 134837. DOI: https://doi.org/10.1016/j.seppur.2025.134837.
  17. Camperi, J.; Roper, B.; Freund, E.; Leylek, R.; Nissenbaum, A.; Galan, C.; Caothien, R.; Hu, Z.; Ko, P.; Lee, A.; et al. Exploring the Impact of In Vitro-Transcribed mRNA Impurities on Cellular Responses. Analytical Chemistry 2024, 96 (44), 17789–17799. DOI: 10.1021/acs.analchem.4c04162.
  18. Camperi, J.; Lippold, S.; Ayalew, L.; Roper, B.; Shao, S.; Freund, E.; Nissenbaum, A.; Galan, C.; Cao, Q.; Yang, F.; et al. Comprehensive Impurity Profiling of mRNA: Evaluating Current Technologies and Advanced Analytical Techniques. Analytical Chemistry 2024, 96 (9), 3886–3897. DOI: 10.1021/acs.analchem.3c05539.
  19. Webb, A. L. J.; Welbourne, E. N.; Minshull, T. C.; Loveday, K. A.; Bora, P.; Kis, Z.; Nilsson, G. A.; Murthy, A.; Örnskov, E.; Dickman, M. J. Analysis of mRNA multimerisation (aggregation) using non-denaturing ion-pair reversed-phase liquid chromatography. Journal of Chromatography A 2026, 1769, 466653. DOI: https://doi.org/10.1016/j.chroma.2025.466653.
  20. Luan, X.; Wang, L.; Song, G.; Zhou, W. Innate immune responses to RNA: sensing and signaling. Frontiers in Immunology 2024, Volume 15 - 2024, Review. DOI: 10.3389/fimmu.2024.1287940.
  21. Saxena, T.; Sharvani, A.; Stavika, A.; Rohini, M. Regulatory considerations for messenger RNA vaccines: A comprehensive review on World Health Organization guidelines on development, production, and clinical evaluation. Perspectives in Clinical Research 9900.

About The Author

Riddhi Banik is a Ph.D. candidate in Chemical and Biological Engineering at Rensselaer Polytechnic Institute, working at the interface of biomolecular engineering, surface chemistry, and RNA therapeutics. Her research focuses on affinity membrane platforms for mRNA purification, emphasizing control of ligand density, architecture, and spatial presentation to improve binding efficiency and selectivity. She has utilized site-specific bioconjugation strategies, such as thiol–maleimide click, azide–alkyne reactions, and aza-Michael additions to engineer linker architectures that enable multivalency and avidity-enhanced interactions. She also develops mechanistic models to understand how steric effects and transport phenomena influence binding under process-relevant conditions. Banik’s work extends to peptide-based systems for selective removal of impurities such as dsRNA and uncapped RNA. She is broadly interested in scalable and cost-effective solutions for RNA manufacturing.