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
At the close of every year, editors and writers at the journal Science review the progress that’s been made in all fields of science—from anthropology to zoology—to select the biggest advance of the past 12 months. In most cases, this Breakthrough of the Year is as tough to predict as the Oscar for Best Picture. Not in 2020. In a year filled with a multitude of challenges posed by the emergence of the deadly coronavirus disease 2019 (COVID-2019), the breakthrough was the development of the first vaccines to protect against this pandemic that’s already claimed the lives of more than 360,000 Americans.
In keeping with its annual tradition, Science also selected nine runner-up breakthroughs. This impressive list includes at least three areas that involved efforts supported by NIH: therapeutic applications of gene editing, basic research understanding HIV, and scientists speaking up for diversity. Here’s a quick rundown of all the pioneering advances in biomedical research, both NIH and non-NIH funded:
Shots of Hope. A lot of things happened in 2020 that were unprecedented. At the top of the list was the rapid development of COVID-19 vaccines. Public and private researchers accomplished in 10 months what normally takes about 8 years to produce two vaccines for public use, with more on the way in 2021. In my more than 25 years at NIH, I’ve never encountered such a willingness among researchers to set aside their other concerns and gather around the same table to get the job done fast, safely, and efficiently for the world.
It’s also pretty amazing that the first two conditionally approved vaccines from Pfizer and Moderna were found to be more than 90 percent effective at protecting people from infection with SARS-CoV-2, the coronavirus that causes COVID-19. Both are innovative messenger RNA (mRNA) vaccines, a new approach to vaccination.
For this type of vaccine, the centerpiece is a small, non-infectious snippet of mRNA that encodes the instructions to make the spike protein that crowns the outer surface of SARS-CoV-2. When the mRNA is injected into a shoulder muscle, cells there will follow the encoded instructions and temporarily make copies of this signature viral protein. As the immune system detects these copies, it spurs the production of antibodies and helps the body remember how to fend off SARS-CoV-2 should the real thing be encountered.
It also can’t be understated that both mRNA vaccines—one developed by Pfizer and the other by Moderna in conjunction with NIH’s National Institute of Allergy and Infectious Diseases—were rigorously evaluated in clinical trials. Detailed data were posted online and discussed in all-day meetings of an FDA Advisory Committee, open to the public. In fact, given the high stakes, the level of review probably was more scientifically rigorous than ever.
First CRISPR Cures: One of the most promising areas of research now underway involves gene editing. These tools, still relatively new, hold the potential to fix gene misspellings—and potentially cure—a wide range of genetic diseases that were once to be out of reach. Much of the research focus has centered on CRISPR/Cas9. This highly precise gene-editing system relies on guide RNA molecules to direct a scissor-like Cas9 enzyme to just the right spot in the genome to cut out or correct a disease-causing misspelling.
In late 2020, a team of researchers in the United States and Europe succeeded for the first time in using CRISPR to treat 10 people with sickle cell disease and transfusion-dependent beta thalassemia. As published in the New England Journal of Medicine, several months after this non-heritable treatment, all patients no longer needed frequent blood transfusions and are living pain free .
The researchers tested a one-time treatment in which they removed bone marrow from each patient, modified the blood-forming hematopoietic stem cells outside the body using CRISPR, and then reinfused them into the body. To prepare for receiving the corrected cells, patients were given toxic bone marrow ablation therapy, in order to make room for the corrected cells. The result: the modified stem cells were reprogrammed to switch back to making ample amounts of a healthy form of hemoglobin that their bodies produced in the womb. While the treatment is still risky, complex, and prohibitively expensive, this work is an impressive start for more breakthroughs to come using gene editing technologies. NIH, including its Somatic Cell Genome Editing program, continues to push the technology to accelerate progress and make gene editing cures for many disorders simpler and less toxic.
Scientists Speak Up for Diversity: The year 2020 will be remembered not only for COVID-19, but also for the very public and inescapable evidence of the persistence of racial discrimination in the United States. Triggered by the killing of George Floyd and other similar events, Americans were forced to come to grips with the fact that our society does not provide equal opportunity and justice for all. And that applies to the scientific community as well.
Science thrives in safe, diverse, and inclusive research environments. It suffers when racism and bigotry find a home to stifle diversity—and community for all—in the sciences. For the nation’s leading science institutions, there is a place and a calling to encourage diversity in the scientific workplace and provide the resources to let it flourish to everyone’s benefit.
For those of us at NIH, last year’s peaceful protests and hashtags were noticed and taken to heart. That’s one of the many reasons why we will continue to strengthen our commitment to building a culturally diverse, inclusive workplace. For example, we have established the NIH Equity Committee. It allows for the systematic tracking and evaluation of diversity and inclusion metrics for the intramural research program for each NIH institute and center. There is also the recently founded Distinguished Scholars Program, which aims to increase the diversity of tenure track investigators at NIH. Recently, NIH also announced that it will provide support to institutions to recruit diverse groups or “cohorts” of early-stage research faculty and prepare them to thrive as NIH-funded researchers.
AI Disentangles Protein Folding: Proteins, which are the workhorses of the cell, are made up of long, interconnected strings of amino acids that fold into a wide variety of 3D shapes. Understanding the precise shape of a protein facilitates efforts to figure out its function, its potential role in a disease, and even how to target it with therapies. To gain such understanding, researchers often try to predict a protein’s precise 3D chemical structure using basic principles of physics—including quantum mechanics. But while nature does this in real time zillions of times a day, computational approaches have not been able to do this—until now.
Of the roughly 170,000 proteins mapped so far, most have had their structures deciphered using powerful imaging techniques such as x-ray crystallography and cryo–electron microscopy (cryo-EM). But researchers estimate that there are at least 200 million proteins in nature, and, as amazing as these imaging techniques are, they are laborious, and it can take many months or years to solve 3D structure of a single protein. So, a breakthrough certainly was needed!
In 2020, researchers with the company Deep Mind, London, developed an artificial intelligence (AI) program that rapidly predicts most protein structures as accurately as x-ray crystallography and cryo-EM can map them . The AI program, called AlphaFold, predicts a protein’s structure by computationally modeling the amino acid interactions that govern its 3D shape.
Getting there wasn’t easy. While a complete de novo calculation of protein structure still seemed out of reach, investigators reasoned that they could kick start the modeling if known structures were provided as a training set to the AI program. Utilizing a computer network built around 128 machine learning processors, the AlphaFold system was created by first focusing on the 170,000 proteins with known structures in a reiterative process called deep learning. The process, which is inspired by the way neural networks in the human brain process information, enables computers to look for patterns in large collections of data. In this case, AlphaFold learned to predict the underlying physical structure of a protein within a matter of days. This breakthrough has the potential to accelerate the fields of structural biology and protein research, fueling progress throughout the sciences.
How Elite Controllers Keep HIV at Bay: The term “elite controller” might make some people think of video game whizzes. But here, it refers to the less than 1 percent of people living with human immunodeficiency virus (HIV) who’ve somehow stayed healthy for years without taking antiretroviral drugs. In 2020, a team of NIH-supported researchers figured out why this is so.
In a study of 64 elite controllers, published in the journal Nature, the team discovered a link between their good health and where the virus has inserted itself in their genomes . When a cell transcribes a gene where HIV has settled, this so-called “provirus,” can produce more virus to infect other cells. But if it settles in a part of a chromosome that rarely gets transcribed, sometimes called a gene desert, the provirus is stuck with no way to replicate. Although this discovery won’t cure HIV/AIDS, it points to a new direction for developing better treatment strategies.
In closing, 2020 presented more than its share of personal and social challenges. Among those challenges was a flood of misinformation about COVID-19 that confused and divided many communities and even families. That’s why the editors and writers at Science singled out “a second pandemic of misinformation” as its Breakdown of the Year. This divisiveness should concern all of us greatly, as COVID-19 cases continue to soar around the country and our healthcare gets stretched to the breaking point. I hope and pray that we will all find a way to come together, both in science and in society, as we move forward in 2021.
 CRISPR-Cas9 gene editing for sickle cell disease and β-thalassemia. Frangoul H et al. N Engl J Med. 2020 Dec 5.
 ‘The game has changed.’ AI triumphs at protein folding. Service RF. Science. 04 Dec 2020.
 Distinct viral reservoirs in individuals with spontaneous control of HIV-1. Jiang C et al. Nature. 2020 Sep;585(7824):261-267.
COVID-19 Research (NIH)
2020 Science Breakthrough of the Year (American Association for the Advancement of Science, Washington, D.C)
Posted on by Dr. Francis Collins
My last post highlighted the use of artificial intelligence (AI) to create an algorithm capable of detecting 10 different kinds of irregular heart rhythms. But that’s just one of the many potential medical uses of AI. In this post, I’ll tell you how NIH researchers are pairing AI analysis with smartphone cameras to help more women avoid cervical cancer.
In work described in the Journal of the National Cancer Institute , researchers used a high-performance computer to analyze thousands of cervical photographs, obtained more than 20 years ago from volunteers in a cancer screening study. The computer learned to recognize specific patterns associated with pre-cancerous and cancerous changes of the cervix, and that information was used to develop an algorithm for reliably detecting such changes in the collection of images. In fact, the AI-generated algorithm outperformed human expert reviewers and all standard screening tests in detecting pre-cancerous changes.
Nearly all cervical cancers are caused by the human papillomavirus (HPV). Cervical cancer screening—first with Pap smears and now also using HPV testing—have greatly reduced deaths from cervical cancer. But this cancer still claims the lives of more than 4,000 U.S. women each year, with higher frequency among women who are black or older . Around the world, more than a quarter-million women die of this preventable disease, mostly in poor and remote areas .
These troubling numbers have kept researchers on the lookout for low cost, but easy-to-use, tools that could be highly effective at detecting HPV infections most likely to advance to cervical cancer. Such tools would also need to work well in areas with limited resources for sample preparation and lab analysis. That’s what led to this collaboration involving researchers from NIH’s National Cancer Institute (NCI) and Global Good, Bellevue, WA, which is an Intellectual Ventures collaboration with Bill Gates to invent life-changing technologies for the developing world.
Global Good researchers contacted NCI experts hoping to apply AI to a large dataset of cervical images. The NCI experts suggested an 18-year cervical cancer screening study in Costa Rica. The NCI-supported project, completed in the 1990s, generated nearly 60,000 cervical images, later digitized by NIH’s National Library of Medicine and stored away safely.
The researchers agreed that all these images, obtained in a highly standardized way, would serve as perfect training material for a computer to develop a detection algorithm for cervical cancer. This type of AI, called machine learning, involves feeding tens of thousands of images into a computer equipped with one or more high-powered graphics processing units (GPUs), similar to something you’d find in an Xbox or PlayStation. The GPUs allow the computer to crunch large sets of visual data in the images and devise a set of rules, or algorithms, that allow it to learn to “see” physical features.
Here’s how they did it. First, the researchers got the computer to create a convolutional neural network. That’s a fancy way of saying that they trained it to read images, filter out the millions of non-essential bytes, and retain the few hundred bytes in the photo that make it uniquely identifiable. They fed 1.28 million color images covering hundreds of common objects into the computer to create layers of processing ability that, like the human visual system, can distinguish objects and their qualities.
Once the convolutional neural network was formed, the researchers took the next big step: training the system to see the physical properties of a healthy cervix, a cervix with worrisome cellular changes, or a cervix with pre-cancer. That’s where the thousands of cervical images from the Costa Rican screening trial literally entered the picture.
When all these layers of processing ability were formed, the researchers had created the “automated visual evaluation” algorithm. It went on to identify with remarkable accuracy the images associated with the Costa Rican study’s 241 known precancers and 38 known cancers. The algorithm’s few minor hiccups came mainly from suboptimal images with faded colors or slightly blurred focus.
These minor glitches have the researchers now working hard to optimize the process, including determining how health workers can capture good quality photos of the cervix with a smartphone during a routine pelvic exam and how to outfit smartphones with the necessary software to analyze cervical photos quickly in real-world settings. The goal is to enable health workers to use a smartphone or similar device to provide women with cervical screening and treatment during a single visit.
In fact, the researchers are already field testing their AI-inspired approach on smartphones in the United States and abroad. If all goes well, this low-cost, mobile approach could provide a valuable new tool to help reduce the burden of cervical cancer among underserved populations.
The day that cervical cancer no longer steals the lives of hundreds of thousands of women a year worldwide will be a joyful moment for cancer researchers, as well as a major victory for women’s health.
 An observational study of Deep Learning and automated evaluation of cervical images for cancer screening. Hu L, Bell D, Antani S, Xue Z, Yu K, Horning MP, Gachuhi N, Wilson B, Jaiswal MS, Befano B, Long LR, Herrero R, Einstein MH, Burk RD, Demarco M, Gage JC, Rodriguez AC, Wentzensen N, Schiffman M. J Natl Cancer Inst. 2019 Jan 10. [Epub ahead of print]
 “Study: Death Rate from Cervical Cancer Higher Than Thought,” American Cancer Society, Jan. 25, 2017.
 “World Cancer Day,” World Health Organization, Feb. 2, 2017.