Lights, camera, action: molecular film captures an RNA molecule building itself
Original story from the European Molecular Biology Laboratory (EMBL; Grenoble, France).
Researchers have visualized how a large RNA molecule assembles itself into a functional machine.
RNA is a central biological macromolecule, now widely harnessed in medicine and nanotechnology. Like proteins, RNA function often depends on its precise 3D structure. A recent study led by Marco Marcia – former EMBL Grenoble (France) Group Leader and current Associate Professor and SciLifeLab Group leader at Uppsala University (Sweden) – 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 an integrative structural biology approach combining state-of-the-art techniques – cryogenic 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, which is 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.
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 (Germany), where innovative cryo-EM image processing approaches tailored for this specific project were developed, and the Istituto Italiano di Tecnologia (IIT; Genoa, Italy), 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,” shared Shekhar Jadhav, former predoctoral Fellow at EMBL Grenoble, now a postdoc at Uppsala University. “Persistent efforts and extensive screening on electron microscopes ultimately led us to visualize 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 catalyze a chemical reaction, essential to the ribozyme’s function.
Cryo-EM captures the protein that recruits its own bodyguards
Research captures how the GRP94 protein shields itself to make sure it forms properly and suggests a way to target it for future disease treatments.
Capturing the hidden takes
By analyzing 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,” explained Maya Topf, Group Leader at CSSB and a collaborator on the study. “This is a great example of how computational innovation and high-quality cryo-EM data can reveal the hidden conformations of molecular machines.”
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,” commented Marco De Vivo, Head of Molecular Modeling and Drug Discovery Lab and Associate Director for Computation at IIT, 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 a 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.
“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’,” concluded 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.
This article has been republished from the following materials. Material may have been edited for length and house style. For further information, please contact the cited source. Our press release publishing policy can be accessed here.
