[ExI] Deep Learning Is Tackling Another Core Biology Mystery: RNA Structure

[John Grigg]

"Deep learning is solving biology’s deepest secrets at breathtaking speed."

"Just a month ago, DeepMind cracked a 50-year-old grand challenge: protein
folding
<https://singularityhub.com/2021/07/20/new-protein-folding-ai-just-made-a-once-in-a-generation-advance-in-biology/>.
A week later, they produced a totally transformative database of more than
350,000 protein structures, including over 98 percent of known human
proteins. Structure is at the heart of biological functions. The data dump,
set to explode to 130 million
<https://www.nature.com/articles/d41586-021-02025-4> structures by the end
of the year, allows scientists to foray into previous “dark
matter”—proteins unseen and untested—of the human body’s makeup.

The end result is nothing short of revolutionary. From basic life science
research to developing new medications to fight our toughest disease foes
like cancer, deep learning gave us a golden key to unlock
<https://singularityhub.com/2020/12/15/deepminds-alphafold-is-close-to-solving-one-of-biologys-greatest-challenges/>
new
biological mechanisms—either natural or synthetic—that were previously
unattainable.

Now, the AI darling is set to do the same for RNA.

As the middle child of the “DNA to RNA to protein” central dogma, RNA
didn’t get much press until its Covid-19 vaccine contribution
<https://singularityhub.com/2021/08/20/modernas-mrna-vaccine-for-hiv-is-starting-human-trials-this-week/>.
But the molecule is a double hero: it both carries genetic information,
and—depending on its structure—can catalyze biological functions, regulate
which genes are turned on, tweak your immune system, and even crazier,
potentially pass down “memories”
<https://www.cell.com/trends/neurosciences/references/S0166-2236(14)00220-3>
through
generations.

It’s also frustratingly difficult to understand.

Similar to proteins, RNA also folds into complicated 3D structures. The
difference, according to Drs. Rhiju Das and Ron Dror at Stanford
University, is that we comparatively know little about these molecules.
There are 30 times as many types of RNA as there are proteins, but the
number of deciphered RNA structures is less than one percent compared to
proteins.

The Stanford team decided to bridge that gap. In a paper
<https://science.sciencemag.org/content/373/6558/1047> published last week
in *Science*, they described a deep learning algorithm called ARES (Atomic
Rotationally Equivalent Scorer) that efficiently solves RNA structures,
blasting previous attempts out of the water.

The authors “have achieved notable progress in a field that has proven
recalcitrant to transformative advances,” said Dr. Kevin Weeks at the
University of North Carolina, who was not involved in the study.

Even more impressive, ARES was trained on only 18 RNA structures, yet was
able to extract substantial “building block” rules for RNA folding that’ll
be further tested in experimental labs. ARES is also input agnostic, in
that it isn’t specifically tailored to RNA.

“This approach is applicable to diverse problems in structural biology,
chemistry, materials science, and beyond,” the authors said."
https://singularityhub.com/2021/08/31/deep-learning-is-tackling-another-core-biology-mystery-rna-structure/
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