Posted on by Dr. Francis Collins
Researchers recently showed that a computer could “learn” from many examples of protein folding to predict the 3D structure of proteins with great speed and precision. Now a recent study in the journal Science shows that a computer also can predict the 3D shapes of RNA molecules . This includes the mRNA that codes for proteins and the non-coding RNA that performs a range of cellular functions.
This work marks an important basic science advance. RNA therapeutics—from COVID-19 vaccines to cancer drugs—have already benefited millions of people and will help many more in the future. Now, the ability to predict RNA shapes quickly and accurately on a computer will help to accelerate understanding these critical molecules and expand their healthcare uses.
Like proteins, the shapes of single-stranded RNA molecules are important for their ability to function properly inside cells. Yet far less is known about these RNA structures and the rules that determine their precise shapes. The RNA elements (bases) can form internal hydrogen-bonded pairs, but the number of possible combinations of pairings is almost astronomical for any RNA molecule with more than a few dozen bases.
In hopes of moving the field forward, a team led by Stephan Eismann and Raphael Townshend in the lab of Ron Dror, Stanford University, Palo Alto, CA, looked to a machine learning approach known as deep learning. It is inspired by how our own brain’s neural networks process information, learning to focus on some details but not others.
In deep learning, computers look for patterns in data. As they begin to “see” complex relationships, some connections in the network are strengthened while others are weakened.
One of the things that makes deep learning so powerful is it doesn’t rely on any preconceived notions. It also can pick up on important features and patterns that humans can’t possibly detect. But, as successful as this approach has been in solving many different kinds of problems, it has primarily been applied to areas of biology, such as protein folding, in which lots of data were available for researchers to train the computers.
That’s not the case with RNA molecules. To work around this problem, Dror’s team designed a neural network they call ARES. (No, it’s not the Greek god of war. It’s short for Atomic Rotationally Equivariant Scorer.)
To start, the researchers trained ARES on just 18 small RNA molecules for which structures had been experimentally determined. They gave ARES these structural models specified only by their atomic structure and chemical elements.
The next test was to see if ARES could determine from this small training set the best structural model for RNA sequences it had never seen before. The researchers put it to the test with RNA molecules whose structures had been determined more recently.
ARES, however, doesn’t come up with the structures itself. Instead, the researchers give ARES a sequence and at least 1,500 possible 3D structures it might take, all generated using another computer program. Based on patterns in the training set, ARES scores each of the possible structures to find the one it predicts is closest to the actual structure. Remarkably, it does this without being provided any prior information about features important for determining RNA shapes, such as nucleotides, steric constraints, and hydrogen bonds.
It turns out that ARES consistently outperforms humans and all other previous methods to produce the best results. In fact, it outperformed at least nine other methods to come out on top in a community-wide RNA-puzzles contest. It also can make predictions about RNA molecules that are significantly larger and more complex than those upon which it was trained.
The success of ARES and this deep learning approach will help to elucidate RNA molecules with potentially important implications for health and disease. It’s another compelling example of how deep learning promises to solve many other problems in structural biology, chemistry, and the material sciences when—at the outset—very little is known.
 Geometric deep learning of RNA structure. Townshend RJL, Eismann S, Watkins AM, Rangan R, Karelina M, Das R, Dror RO. Science. 2021 Aug 27;373(6558):1047-1051.
Structural Biology (National Institute of General Medical Sciences/NIH)
The Structures of Life (National Institute of General Medical Sciences/NIH)
RNA Biology (NIH)
Dror Lab (Stanford University, Palo Alto, CA)
NIH Support: National Cancer Institute; National Institute of General Medical Sciences
Posted on by Dr. Francis Collins
When you think of the causes of infectious diseases, what first comes to mind are probably viruses and bacteria. But parasites are another important source of devastating infection, especially in the developing world. Now, NIH researchers and their collaborators have discovered a new kind of treatment that holds promise for fighting parasitic roundworms. A bonus of this result is that this same treatment might work also for certain deadly kinds of bacteria.
The researchers identified the potential new therapeutic after testing more than a trillion small protein fragments, called cyclic peptides, to find one that could disable a vital enzyme in the disease-causing organisms, but leave similar enzymes in humans unscathed. Not only does this discovery raise hope for better treatments for many parasitic and bacterial diseases, it highlights the value of screening peptides in the search for ways to treat conditions that do not respond well—or have stopped responding—to more traditional chemical drug compounds.
Posted on by Dr. Francis Collins
The purple pods that you see in this scanning electron micrograph are the H5N2 avian flu virus, a costly threat to the poultry and egg industry and, in very rare instances, a health risk for humans. However, these particular pods are unlikely to infect anything because they are trapped in a gray mesh of carbon nanotubes. Made by linking carbon atoms into a cylindrical pattern, such nanotubes are about 10,000 times smaller than width of a human hair.
The nanotubes above have been carefully aligned on a special type of silicon chip called a carbon-nanotube size-tunable-enrichment-microdevice (CNT-STEM). As described recently in Science Advances, this ultrasensitive device is designed to capture viruses rapidly based on their size, not their molecular characteristics . This unique feature enables researchers to detect completely unknown viruses, even when they are present in extremely low numbers. In proof-of-principle studies, CNT-STEM made it possible to collect and detect viruses in a sample at concentrations 100 times lower than with other methods, suggesting the device and its new approach will be helpful in the ongoing hunt for new and emerging viruses, including those that infect people.
Posted on by Dr. Francis Collins
Matthew Disney grew up in a large family in Baltimore in the 1980s. While his mother worked nights, Disney and his younger brother often tagged along with their father in these pre-Internet days on calls to fix the microfilm machines used to view important records at hospitals, banks, and other places of business. Watching his father take apart the machines made Disney want to work with his hands one day. Seeing his father work tirelessly for the sake of his family also made him want to help others.
Disney found a profession that satisfied both requirements when he fell in love with chemistry as an undergraduate at the University of Maryland, College Park. Now a chemistry professor at The Scripps Research Institute, Jupiter, FL, Disney is applying his hands and brains to develop a treatment strategy that aims to control the progression of a long list of devastating disorders that includes Huntington’s disease, amyotrophic lateral sclerosis (ALS), and various forms of muscular dystrophy.
The 30 or so health conditions on Disney’s list have something in common. They are caused by genetic glitches in which repetitive DNA letters (CAGCAGCAG, for example) in transcribed regions of the genome cause some of the body’s cells and tissues to produce unwieldy messenger RNA molecules that interfere with normal cellular activities, either by binding other intracellular components or serving as templates for the production of toxic proteins.
The diseases on Disney’s list also have often been considered “undruggable,” in part because the compounds capable of disabling the lengthy, disease-causing RNA molecules are generally too large to cross cell membranes. Disney has found an ingenious way around that problem . Instead of delivering the finished drug, he delivers smaller building blocks. He then uses the cell and its own machinery, including the very aberrant RNA molecules he aims to target, as his drug factory to produce those larger compounds.
Disney has received an NIH Director’s 2015 Pioneer Award to develop this innovative drug-delivery strategy further. He will apply his investigational approach initially to treat a common form of muscular dystrophy, first using human cells in culture and then in animal models. Once he gets that working well, he’ll move on to other conditions including ALS.
What’s appealing about Disney’s approach is that it makes it possible to treat disease-affected cells without affecting healthy cells. That’s because his drugs can only be assembled into their active forms in cells after they are templated by those aberrant RNA molecules.
Interestingly, Disney never intended to study human diseases. His lab was set up to study the structure and function of RNA molecules and their interactions with other small molecules. In the process, he stumbled across a small molecule that targets an RNA implicated in a rare form of muscular dystrophy. His niece also has a rare incurable disease, and Disney saw a chance to make a difference for others like her. It’s a healthy reminder that the pursuit of basic scientific questions often can lead to new and unexpectedly important medical discoveries that have the potential to touch the lives of many.
 A toxic RNA catalyzes the in cellulo synthesis of its own inhibitor. Rzuczek SG, Park H, Disney MD. Angew Chem Int Ed Engl. 2014 Oct 6;53(41):10956-10959.
Disney Lab (The Scripps Research Institute, Jupiter, FL)
Disney NIH Project Information (NIH RePORTER)
NIH Support: Common Fund; National Institute of Neurological Disorders and Stroke