Teaching a computer to read rna’s hidden blueprint.

Every living thing relies on tiny molecular machines called ribosomes to build proteins — the workhorses of our cells. Normally, a ribosome latches onto a strand of RNA and starts reading it like a recipe. But some viruses, including the picornaviruses (the family behind the common cold and polio), have evolved a clever workaround. Instead of using the standard starting signal, they use special RNA structures called Internal Ribosome Entry Sites, or IRESes, to hijack our ribosomes and force them to produce viral proteins.

The problem is that these IRES structures are enormous, wildly varied between viruses, and notoriously difficult to map. Scientists have only been able to determine the detailed structures of a handful of them.

A new study tackles this challenge by training an RNA “language model” — a type of artificial intelligence similar to the models behind modern chatbots, but designed to read the genetic language of RNA. The model, named Albatross, learns only from RNA sequences, not structures, yet it can predict how these molecules fold into functional shapes with remarkable accuracy.

To test it, the team gathered real-world chemical probing data from 96 full-length IRESes taken from a diverse range of viruses. Albatross dramatically outperformed existing prediction tools, achieving 80% precision compared to 47%. The researchers then used Albatross to analyze a massive collection of 75,000 IRES structures, uncovering a previously unknown structural category (dubbed “Type II”) that they confirmed in the lab.

But what surprised the researchers most wasn’t the accuracy — it was what the model taught itself along the way. Albatross was never told anything about thermodynamics, the physical rules that govern how RNA folds. It was given only sequences. Yet it spontaneously learned to recognize alternative structures of riboswitches — RNA elements that change shape to control gene activity — and even critical loop-to-loop contacts that hold RNA molecules in precise three-dimensional arrangements.

“This is totally brain shattering,” said Dr. Silvi Rouskin, the study’s advising author (Harvard Medical School). “An LLM that was never told what thermodynamics is, was just given sequences, picks up alternative structures of riboswitches and critical tertiary loop-loop contacts.”

In other words, the model appears to have rediscovered the physics of RNA from raw data alone — much as a language model seems to pick up grammar without ever being taught linguistics. The team is now applying it to human biology, with early hints of previously unknown human riboswitches on the horizon.

The takeaway: this work introduces a scalable, AI-driven framework for discovering RNA structures at a pace and scale previously impossible. Beyond solving a long-standing virology puzzle, it opens new doors for understanding RNA biology and for designing antiviral drugs that target these critical viral structures.

#ArtificialIntelligence #LLM #RNAstructure #picornavirus #physics #Thermodynamics

Millions of RNA sequences are readily available, but the structures that determine their function are not. Picornaviruses initiate translation through Internal Ribosome Entry Sites (IRESes), RNA elements that recruit ribosomes independent of the 5‘ cap. These elements are large and highly divergent, and structural understanding remains limited to a handful of cases. We address this bottleneck by introducing an RNA language model (Albatross), trained purely on sequence, that predicts high-quality IRES structures at scale. We collect in cellulo chemical probing data for 96 full-length IRESes from divergent viruses and show that Albatross achieves far higher precision (0.80) than state-of-the-art predictions (0.47). Analyzing 75,000 IRES structures, we discover a novel Type II structural subclass and validate it experimentally. We demonstrate the pipeline broadly generalizes to identify functional structures, including tertiary contacts and alternative riboswitch structures. These findings establish Albatross as a scalable framework that accelerates RNA structure discovery and antiviral targeting.

The authors have declared no competing interest.