RNA is a central biological macromolecule, now widely used in medicine and nanotechnology. Like proteins, the function of RNA often depends on its precise three-dimensional structure. A recent study published in Nature communications from Marcia’s team, has captured, for the first time, a ribozyme in motion – almost frame-by-frame. Researchers have documented how this tiny RNA machine folds, bends and assembles, revealing its intricate choreography in unprecedented detail.
Using an integrated structural biology approach that combines cutting-edge techniques – cryo-electron microscopy (cryo-EM), small-angle X-ray scattering (SAXS), RNA biochemistry and enzymology, image processing and molecular simulations – the scientists observed the assembly of a molecular molecule that can ‘cut and paste’ its own sequence, essentially editing itself to become functional. They captured the dynamic “behind the scenes” process by which the self-assembling ribozyme folds into its functional structure. The research was led by the team of Marco Marcia, former head of the EMBL Group and currently Associate Professor and head of the SciLifeLab Group at Uppsala University, Sweden.
This innovation was made possible by EMBL Grenoble’s state-of-the-art facilities and specialized services, which enabled the integration of advanced structural biology methods with RNA biochemistry and enzymology. The Marcia group also benefited from close collaboration with the Hamburg Center for Structural Systems Biology (CSSB), where innovative cryo-EM image processing approaches tailored for this specific project were developed, and the Istituto Italiano di Tecnologia (IIT), which provided high-level molecular simulation expertise.
“Determining the structures of RNA is a challenge – the inherent flexibility and negative charge make RNA an extremely difficult target for structural studies,” said Shekhar Jadhav, a former predoctoral fellow at EMBL Grenoble, now a postdoctoral fellow at Uppsala University in Sweden. “Persistent efforts and extensive examination in electron microscopes finally led us to visualize elusive RNA dynamics.”
The result is the most complete “molecular film” to date of an RNA molecule itself, revealing how it avoids the biological equivalent of outputs: misfolded, dysfunctional states known as kinetic traps.
How a domain orchestrates RNA history
At the heart of this production is Domain 1 (D1), the ribozyme’s central scaffold and, as it turns out, its director. This domain acts as a molecular gate, prompting the other domains (D2, D3, D4) to enter at precisely the right time during the folding process.
Subtle movements in key parts of the D1 molecule prompt one of its segments to open and make way for the next. Each domain joins the stage only when the previous one is correctly in place, creating a seamless sequence of molecular choreography that prevents structural errors and ensures a flawless finale: the formation of a structure that can catalyze a chemical reaction essential for the ribozyme to function.
Recording of hidden downloads
By analyzing hundreds of thousands of individual RNA molecules, the team reconstructed intermediate “handles” that were invisible in static crystal structures. These fleeting frames show how RNA explores alternative positions before settling into its final conformation.
To capture these fleeting frames, we had to develop new cryo-EM image processing strategies. This is an excellent example of how computational innovation and high-quality cryoEM data can reveal the hidden configurations of molecular machines.”
Maya Topf, group leader at the CSSB, Professor at the University Medical Center Hamburg-Eppendorf and collaborator in the study
SAXS data and molecular dynamics simulations provided complementary insight into conformational plasticity, helping scientists refine each structural framework and piece together the complete story. The researchers discovered that the energy required by the ribozyme to shift between different shapes was very small, which not only allows the RNA to move smoothly from one shape to another in real life, but also makes it easier for computers to accurately simulate these physical transitions without the molecule getting stuck in unrealistic positions.
“A major strength of this work is the synergy between these new state-of-the-art structural data for RNA and our advanced molecular simulations of this challenging system,” said Marco De Vivo, Head of the Molecular Modeling and Drug Discovery Laboratory and Deputy Director of Computation at the Institu Italiano di Technologia in Genoa and one of the study’s investigators. “This combined approach has elucidated, to an unprecedented individualistic level of detail, the dynamics driving the entire assembly of this RNA molecule, which now opens new avenues for RNA-targeting drug discovery efforts.”
From ancient scripts to modern spin-offs
Group II introns, the ribozymes that appear in this molecular film, are thought to be the ancestors of splicing, the complex machinery that processes RNA in human cells.
By revealing how these molecules fold efficiently and avoid kinetic traps, the study provides new insight into how early RNA-based life may have evolved its RNA editing tools. Beyond the evolutionary tradition, this work also lays the groundwork for RNA design and engineering – guiding how future biotechnologies might write RNA molecules to fold correctly for use in therapeutics or nanobiotechnology.
Opening the door to RNA AI
The detailed datasets and molecular mechanisms revealed in this study offer a valuable benchmark for training and testing artificial intelligence models. Some of the RNA structures solved here have already been used in international CASP competitions—the same predictive challenge that led to AlphaFold—as recently described in the journal Proteins.
“This work is expected to play a key role in shaping artificial intelligence approaches to RNA structure prediction, paving the way to a new ‘AlphaFold for RNA,'” said Marcia.
This convergence of experimental precision and machine learning marks a new phase for RNA structural biology, where AI and cryo-EM and complementary experimental approaches can learn from each other to predict, visualize and understand the dynamics of life’s most versatile molecule.
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