Researchers capture a ribozyme in motion for the first time

RNA is a central biological macromolecule, now widely harnessed in medicine and nanotechnology. Like proteins, RNA function often depends on its precise three-dimensional structure. A recent study published in Nature Communications by Marcia group, has captured, for the first time, a ribozyme in motion - almost frame by frame. The researchers recorded how this tiny RNA machine folds, flexes, and assembles itself, revealing its intricate choreography in unprecedented detail.

Using integrative structural biology approach combining state-of-the-art 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 self-splicing ribozyme – an RNA molecule that can 'cut and paste' its own sequence, essentially editing itself to become operational. They captured the dynamic 'behind-the-scenes' process by which the self-splicing ribozyme folds into its functional structure. The research was led by the team of Marco Marcia, former EMBL Group Leader and currently Associate Professor and SciLifeLab Group leader at Uppsala University, Sweden.

This breakthrough was made possible thanks to the cutting-edge facilities and expert services at EMBL Grenoble, which enabled the integration of advanced structural biology methods with RNA biochemistry and enzymology. The Marcia group also benefited from close collaboration with the Centre for Structural Systems Biology (CSSB) Hamburg, 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 RNA structures is a challenging task – the inherent flexibility and negative charge make RNA a notoriously difficult target for structural studies," said Shekhar Jadhav, former predoctoral Fellow at EMBL Grenoble, now a postdoc at Uppsala University, Sweden. "Persistent efforts and extensive screening on electron microscopes ultimately led us to visualise elusive RNA dynamics."

The result is the most complete 'molecular film' to date of an RNA molecule building itself, revealing how it avoids the biological equivalent of outtakes: misfolded, non-functional states known as kinetic traps.

How one domain orchestrates the RNA storyline

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, cueing the other domains (D2, D3, D4) to enter at precisely the right moment during the folding process.

Subtle movements in key parts of the D1 molecule prompt one of its sections to open up and make way for the next. Each domain joins the scene 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 catalyse a chemical reaction, essential to the ribozyme's function.

Capturing the hidden takes

By analysing hundreds of thousands of single RNA molecules, the team reconstructed intermediate 'takes' that were invisible in static crystal structures. These fleeting frames show how the RNA explores alternative poses before settling into its final conformation.

To capture these fleeting frames, we had to develop novel cryo-EM image-processing strategies. This is a great example of how computational innovation and high-quality cryoEM data can reveal the hidden conformations of molecular machines."

Maya Topf, Group Leader at CSSB, Professor at the University Medical Centre Hamburg-Eppendorf, and a collaborator on the study

SAXS data and molecular dynamics simulations offered complementary insight into conformational plasticity, helping the scientists refine each structural frame and assemble the full storyline. The researchers found that the energy needed by the ribozyme to shift between different shapes was very small, which not only allows the RNA to move smoothly from one form to another in real life, but also makes it easier for computers to accurately simulate these natural transitions without the molecule getting stuck in unrealistic positions.

"One major strength of this work is the synergy between these cutting-edge new structural data on RNA and our advanced molecular simulations of this challenging system," said Marco De Vivo, Head of Molecular Modeling and Drug Discovery Lab and Associate Director for Computation of Institu Italiano di Technologia in Genoa, and one of the collaborators on this study. "This combined approach has clarified, at an unprecedented atomistic level of detail, the dynamic that drives the entire assembly of this RNA molecule, which now opens new avenues for drug discovery efforts targeting RNA."

From ancient scripts to modern spin-offs

Group II introns, the ribozymes featured in this molecular film, are thought to be the ancestors of the spliceosome, the complex machinery that edits 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 evolutionary lore, this work also sets the stage for RNA design and engineering – guiding how future biotechnologies might script RNA molecules to fold correctly for use in therapeutics or nanobiotechnology.

Opening the door to RNA AI

The detailed datasets and molecular mechanisms uncovered in this study offer a valuable benchmark for training and testing AI models. Some of the RNA structures resolved here have already been used in international CASP competitions - the same predictive challenge that gave rise 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 towards 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, visualise, and understand the dynamics of life's most versatile molecule.