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Artificial Intelligence Getting Smarter! Innovations from the Vision Field

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AI. Photograph of retina

One of many health risks premature infants face is retinopathy of prematurity (ROP), a leading cause of childhood blindness worldwide. ROP causes abnormal blood vessel growth in the light-sensing eye tissue called the retina. Left untreated, ROP can lead to lead to scarring, retinal detachment, and blindness. It’s the disease that caused singer and songwriter Stevie Wonder to lose his vision.

Now, effective treatments are available—if the disease is diagnosed early and accurately. Advancements in neonatal care have led to the survival of extremely premature infants, who are at highest risk for severe ROP. Despite major advancements in diagnosis and treatment, tragically, about 600 infants in the U.S. still go blind each year from ROP. This disease is difficult to diagnose and manage, even for the most experienced ophthalmologists. And the challenges are much worse in remote corners of the world that have limited access to ophthalmic and neonatal care.

Caption: Image of a neonatal retina prior to AI processing. Left: Image of a premature infant retina showing signs of severe ROP with large, twisted blood vessels; Right: Normal neonatal retina by comparison. Credit: Casey Eye Institute, Oregon Health and Science University, Portland, and National Eye Institute, NIH

Artificial intelligence (AI) is helping bridge these gaps. Prior to my tenure as National Eye Institute (NEI) director, I helped develop a system called i-ROP Deep Learning (i-ROP DL), which automates the identification of ROP. In essence, we trained a computer to identify subtle abnormalities in retinal blood vessels from thousands of images of premature infant retinas. Strikingly, the i-ROP DL artificial intelligence system outperformed even international ROP experts [1]. This has enormous potential to improve the quality and delivery of eye care to premature infants worldwide.

Of course, the promise of medical artificial intelligence extends far beyond ROP. In 2018, the FDA approved the first autonomous AI-based diagnostic tool in any field of medicine [2]. Called IDx-DR, the system streamlines screening for diabetic retinopathy (DR), and its results require no interpretation by a doctor. DR occurs when blood vessels in the retina grow irregularly, bleed, and potentially cause blindness. About 34 million people in the U.S. have diabetes, and each is at risk for DR.

As with ROP, early diagnosis and intervention is crucial to preventing vision loss to DR. The American Diabetes Association recommends people with diabetes see an eye care provider annually to have their retinas examined for signs of DR. Yet fewer than 50 percent of Americans with diabetes receive these annual eye exams.

The IDx-DR system was conceived by Michael Abramoff, an ophthalmologist and AI expert at the University of Iowa, Iowa City. With NEI funding, Abramoff used deep learning to design a system for use in a primary-care medical setting. A technician with minimal ophthalmology training can use the IDx-DR system to scan a patient’s retinas and get results indicating whether a patient should be sent to an eye specialist for follow-up evaluation or to return for another scan in 12 months.

Caption: The IDx-DR is the first FDA-approved system for diagnostic screening of diabetic retinopathy. It’s designed to be used in a primary care setting. Results determine whether a patient needs immediate follow-up. Credit: Digital Diagnostics, Coralville, IA.

Many other methodological innovations in AI have occurred in ophthalmology. That’s because imaging is so crucial to disease diagnosis and clinical outcome data are so readily available. As a result, AI-based diagnostic systems are in development for many other eye diseases, including cataract, age-related macular degeneration (AMD), and glaucoma.

Rapid advances in AI are occurring in other medical fields, such as radiology, cardiology, and dermatology. But disease diagnosis is just one of many applications for AI. Neurobiologists are using AI to answer questions about retinal and brain circuitry, disease modeling, microsurgical devices, and drug discovery.

If it sounds too good to be true, it may be. There’s a lot of work that remains to be done. Significant challenges to AI utilization in science and medicine persist. For example, researchers from the University of Washington, Seattle, last year tested seven AI-based screening algorithms that were designed to detect DR. They found under real-world conditions that only one outperformed human screeners [3]. A key problem is these AI algorithms need to be trained with more diverse images and data, including a wider range of races, ethnicities, and populations—as well as different types of cameras.

How do we address these gaps in knowledge? We’ll need larger datasets, a collaborative culture of sharing data and software libraries, broader validation studies, and algorithms to address health inequities and to avoid bias. The NIH Common Fund’s Bridge to Artificial Intelligence (Bridge2AI) project and NIH’s Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Program project will be major steps toward addressing those gaps.

So, yes—AI is getting smarter. But harnessing its full power will rely on scientists and clinicians getting smarter, too.

References:

[1] Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. Brown JM, Campbell JP, Beers A, Chang K, Ostmo S, Chan RVP, Dy J, Erdogmus D, Ioannidis S, Kalpathy-Cramer J, Chiang MF; Imaging and Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium. JAMA Ophthalmol. 2018 Jul 1;136(7):803-810.

[2] FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems. Food and Drug Administration. April 11, 2018.

[3] Multicenter, head-to-head, real-world validation study of seven automated artificial intelligence diabetic retinopathy screening systems. Lee AY, Yanagihara RT, Lee CS, Blazes M, Jung HC, Chee YE, Gencarella MD, Gee H, Maa AY, Cockerham GC, Lynch M, Boyko EJ. Diabetes Care. 2021 May;44(5):1168-1175.

Links:

Retinopathy of Prematurity (National Eye Institute/NIH)

Diabetic Eye Disease (NEI)

NEI Research News

Michael Abramoff (University of Iowa, Iowa City)

Bridge to Artificial Intelligence (Common Fund/NIH)

Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Program (NIH)

[Note: Acting NIH Director Lawrence Tabak has asked the heads of NIH’s institutes and centers to contribute occasional guest posts to the blog as a way to highlight some of the cool science that they support and conduct. This is the second in the series of NIH institute and center guest posts that will run until a new permanent NIH director is in place.]


Biomedical Research Leads Science’s 2021 Breakthroughs

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Artificial Antibody Therapies, AI-Powered Predictions of Protein Structures, Antiviral Pills for COVID-19, and CRISPR Fixes Genes Inside the Body

Hi everyone, I’m Larry Tabak. I’ve served as NIH’s Principal Deputy Director for over 11 years, and I will be the acting NIH director until a new permanent director is named. In my new role, my day-to-day responsibilities will certainly increase, but I promise to carve out time to blog about some of the latest research progress on COVID-19 and any other areas of science that catch my eye.

I’ve also invited the directors of NIH’s Institutes and Centers (ICs) to join me in the blogosphere and write about some of the cool science in their research portfolios. I will publish a couple of posts to start, then turn the blog over to our first IC director. From there, I envision alternating between posts from me and from various IC directors. That way, we’ll cover a broad array of NIH science and the tremendous opportunities now being pursued in biomedical research.

Since I’m up first, let’s start where the NIH Director’s Blog usually begins each year: by taking a look back at Science’s Breakthroughs of 2021. The breakthroughs were formally announced in December near the height of the holiday bustle. In case you missed the announcement, the biomedical sciences accounted for six of the journal Science’s 10 breakthroughs. Here, I’ll focus on four biomedical breakthroughs, the ones that NIH has played some role in advancing, starting with Science’s editorial and People’s Choice top-prize winner:

Breakthrough of the Year: AI-Powered Predictions of Protein Structure

The biochemist Christian Anfinsen, who had a distinguished career at NIH, shared the 1972 Nobel Prize in Chemistry, for work suggesting that the biochemical interactions among the amino acid building blocks of proteins were responsible for pulling them into the final shapes that are essential to their functions. In his Nobel acceptance speech, Anfinsen also made a bold prediction: one day it would be possible to determine the three-dimensional structure of any protein based on its amino acid sequence alone. Now, with advances in applying artificial intelligence to solve biological problems—Anfinsen’s bold prediction has been realized.

But getting there wasn’t easy. Every two years since 1994, research teams from around the world have gathered to compete against each other in developing computational methods for predicting protein structures from sequences alone. A score of 90 or above means that a predicted structure is extremely close to what’s known from more time-consuming work in the lab. In the early days, teams more often finished under 60.

In 2020, a London-based company called DeepMind made a leap with their entry called AlphaFold. Their deep learning approach—which took advantage of 170,000 proteins with known structures—most often scored above 90, meaning it could solve most protein structures about as well as more time-consuming and costly experimental protein-mapping techniques. (AlphaFold was one of Science’s runner-up breakthroughs last year.)

This year, the NIH-funded lab of David Baker and Minkyung Baek, University of Washington, Seattle, Institute for Protein Design, published that their artificial intelligence approach, dubbed RoseTTAFold, could accurately predict 3D protein structures from amino acid sequences with only a fraction of the computational processing power and time that AlphaFold required [1]. They immediately applied it to solve hundreds of new protein structures, including many poorly known human proteins with important implications for human health.

The DeepMind and RoseTTAFold scientists continue to solve more and more proteins [1,2], both alone and in complex with other proteins. The code is now freely available for use by researchers anywhere in the world. In one timely example, AlphaFold helped to predict the structural changes in spike proteins of SARS-CoV-2 variants Delta and Omicron [3]. This ability to predict protein structures, first envisioned all those years ago, now promises to speed fundamental new discoveries and the development of new ways to treat and prevent any number of diseases, making it this year’s Breakthrough of the Year.

Anti-Viral Pills for COVID-19

The development of the first vaccines to protect against COVID-19 topped Science’s 2020 breakthroughs. This year, we’ve also seen important progress in treating COVID-19, including the development of anti-viral pills.

First, there was the announcement in October of interim data from Merck, Kenilworth, NJ, and Ridgeback Biotherapeutics, Miami, FL, of a significant reduction in hospitalizations for those taking the anti-viral drug molnupiravir [4] (originally developed with an NIH grant to Emory University, Atlanta). Soon after came reports of a Pfizer anti-viral pill that might target SARS-CoV-2, the novel coronavirus that causes COVID-19, even more effectively. Trial results show that, when taken within three days of developing COVID-19 symptoms, the pill reduced the risk of hospitalization or death in adults at high risk of progressing to severe illness by 89 percent [5].

On December 22, the Food and Drug Administration (FDA) granted Emergency Use Authorization (EUA) for Pfizer’s Paxlovid to treat mild-to-moderate COVID-19 in people age 12 and up at high risk for progressing to severe illness, making it the first available pill to treat COVID-19 [6]. The following day, the FDA granted an EUA for Merck’s molnupiravir to treat mild-to-moderate COVID-19 in unvaccinated, high-risk adults for whom other treatment options aren’t accessible or recommended, based on a final analysis showing a 30 percent reduction in hospitalization or death [7].

Additional promising anti-viral pills for COVID-19 are currently in development. For example, a recent NIH-funded preclinical study suggests that a drug related to molnupiravir, known as 4’-fluorouridine, might serve as a broad spectrum anti-viral with potential to treat infections with SARS-CoV-2 as well as respiratory syncytial virus (RSV) [8].

Artificial Antibody Therapies

Before anti-viral pills came on the scene, there’d been progress in treating COVID-19, including the development of monoclonal antibody infusions. Three monoclonal antibodies now have received an EUA for treating mild-to-moderate COVID-19, though not all are effective against the Omicron variant [9]. This is also an area in which NIH’s Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) public-private partnership has made big contributions.

Monoclonal antibodies are artificially produced versions of the most powerful antibodies found in animal or human immune systems, made in large quantities for therapeutic use in the lab. Until recently, this approach had primarily been put to work in the fight against conditions including cancer, asthma, and autoimmune diseases. That changed in 2021 with success using monoclonal antibodies against infections with SARS-CoV-2 as well as respiratory syncytial virus (RSV), human immunodeficiency virus (HIV), and other infectious diseases. This earned them a prominent spot among Science’s breakthroughs of 2021.

Monoclonal antibodies delivered via intravenous infusions continue to play an important role in saving lives during the pandemic. But, there’s still room for improvement, including new formulations highlighted on the blog last year that might be much easier to deliver.

CRISPR Fixes Genes Inside the Body

One of the most promising areas of research in recent years has been gene editing, including CRISPR/Cas9, for fixing misspellings in genes to treat or even cure many conditions. This year has certainly been no exception.

CRISPR is a highly precise gene-editing system that uses guide RNA molecules to direct a scissor-like Cas9 enzyme to just the right spot in the genome to cut out or correct disease-causing misspellings. Science highlights a small study reported in The New England Journal of Medicine by researchers at Intellia Therapeutics, Cambridge, MA, and Regeneron Pharmaceuticals, Tarrytown, NY, in which six people with hereditary transthyretin (TTR) amyloidosis, a condition in which TTR proteins build up and damage the heart and nerves, received an infusion of guide RNA and CRISPR RNA encased in tiny balls of fat [10]. The goal was for the liver to take them up, allowing Cas9 to cut and disable the TTR gene. Four weeks later, blood levels of TTR had dropped by at least half.

In another study not yet published, researchers at Editas Medicine, Cambridge, MA, injected a benign virus carrying a CRISPR gene-editing system into the eyes of six people with an inherited vision disorder called Leber congenital amaurosis 10. The goal was to remove extra DNA responsible for disrupting a critical gene expressed in the eye. A few months later, two of the six patients could sense more light, enabling one of them to navigate a dimly lit obstacle course [11]. This work builds on earlier gene transfer studies begun more than a decade ago at NIH’s National Eye Institute.

Last year, in a research collaboration that included former NIH Director Francis Collins’s lab at the National Human Genome Research Institute (NHGRI), we also saw encouraging early evidence in mice that another type of gene editing, called DNA base editing, might one day correct Hutchinson-Gilford Progeria Syndrome, a rare genetic condition that causes rapid premature aging. Preclinical work has even suggested that gene-editing tools might help deliver long-lasting pain relief. The technology keeps getting better, too. This isn’t the first time that gene-editing advances have landed on Science’s annual Breakthrough of the Year list, and it surely won’t be the last.

The year 2021 was a difficult one as the pandemic continued in the U.S. and across the globe, taking far too many lives far too soon. But through it all, science has been relentless in seeking and finding life-saving answers, from the rapid development of highly effective COVID-19 vaccines to the breakthroughs highlighted above.

As this list also attests, the search for answers has progressed impressively in other research areas during these difficult times. These groundbreaking discoveries are something in which we can all take pride—even as they encourage us to look forward to even bigger breakthroughs in 2022. Happy New Year!

References:

[1] Accurate prediction of protein structures and interactions using a three-track neural network. Baek M, DiMaio F, Anishchenko I, Dauparas J, Grishin NV, Adams PD, Read RJ, Baker D., et al. Science. 2021 Jul 15:eabj8754.

[2] Highly accurate protein structure prediction with AlphaFold. Jumper J, Evans R, Pritzel A, Green T, Senior AW, Kavukcuoglu K, Kohli P, Hassabis D. et al. Nature. 2021 Jul 15.

[3] Structural insights of SARS-CoV-2 spike protein from Delta and Omicron variants. Sadek A, Zaha D, Ahmed MS. preprint bioRxiv. 2021 Dec 9.

[4] Merck and Ridgeback’s investigational oral antiviral molnupiravir reduced the risk of hospitalization or death by approximately 50 Percent compared to placebo for patients with mild or moderate COVID-19 in positive interim analysis of phase 3 study. Merck. 1 Oct 2021.

[5] Pfizer’s novel COVID-19 oral antiviral treatment candidate reduced risk of hospitalization or death by 89% in interim analysis of phase 2/3 EPIC-HR Study. Pfizer. 5 November 52021.

[6] Coronavirus (COVID-19) Update: FDA authorizes first oral antiviral for treatment of COVID-19. Food and Drug Administration. 22 Dec 2021.

[7] Coronavirus (COVID-19) Update: FDA authorizes additional oral antiviral for treatment of COVID-19 in certain adults. Food and Drug Administration. 23 Dec 2021.

[8] 4′-Fluorouridine is an oral antiviral that blocks respiratory syncytial virus and SARS-CoV-2 replication. Sourimant J, Lieber CM, Aggarwal M, Cox RM, Wolf JD, Yoon JJ, Toots M, Ye C, Sticher Z, Kolykhalov AA, Martinez-Sobrido L, Bluemling GR, Natchus MG, Painter GR, Plemper RK. Science. 2021 Dec 2.

[9] Anti-SARS-CoV-2 monoclonal antibodies. NIH COVID-19 Treatment Guidelines. 16 Dec 2021.

[10] CRISPR-Cas9 in vivo gene editing for transthyretin amyloidosis. Gillmore JD, Gane E, Taubel J, Kao J, Fontana M, Maitland ML, Seitzer J, O’Connell D, Walsh KR, Wood K, Phillips J, Xu Y, Amaral A, Boyd AP, Cehelsky JE, McKee MD, Schiermeier A, Harari O, Murphy A, Kyratsous CA, Zambrowicz B, Soltys R, Gutstein DE, Leonard J, Sepp-Lorenzino L, Lebwohl D. N Engl J Med. 2021 Aug 5;385(6):493-502.

[11] Editas Medicine announces positive initial clinical data from ongoing phase 1/2 BRILLIANCE clinical trial of EDIT-101 For LCA10. Editas Medicine. 29 Sept 2021.

Links:

Structural Biology (National Institute of General Medical Sciences/NIH)

The Structures of Life (NIGMS)

COVID-19 Research (NIH)

2021 Science Breakthrough of the Year (American Association for the Advancement of Science, Washington, D.C)


Artificial Intelligence Accurately Predicts RNA Structures, Too

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A mechanical claw grabs molecular models
Credit: Camille L.L. Townshend

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 [1]. 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.

Reference:

[1] 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.

Links:

Structural Biology (National Institute of General Medical Sciences/NIH)

The Structures of Life (National Institute of General Medical Sciences/NIH)

RNA Biology (NIH)

RNA Puzzles

Dror Lab (Stanford University, Palo Alto, CA)

NIH Support: National Cancer Institute; National Institute of General Medical Sciences


Decoding Heart-Brain Talk to Prevent Sudden Cardiac Deaths

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Deeptankar DeMazundar in a white doctor's coat
Credit: Colleen Kelley/UC Creative + Brand

As a cardiac electrophysiologist, Deeptankar DeMazumder has worked for years with people at risk for sudden cardiac arrest (SCA). Despite the latest medical advances, less than 10 percent of individuals stricken with an SCA will survive this highly dangerous condition in which irregular heart rhythms, or arrhythmias, cause the heart suddenly to stop beating.

In his role as a physician, DeMazumder keeps a tight focus on the electrical activity in their hearts, doing his best to prevent this potentially fatal event. In his other role, as a scientist at the University of Cincinnati College of Medicine, DeMazumder is also driven by a life-saving aspiration: finding ways to identify at-risk individuals with much greater accuracy than currently possible—and to develop better ways of protecting them from SCAs. He recently received a 2020 NIH Director’s New Innovator Award to pursue one of his promising ideas.

SCAs happen without warning and can cause death within minutes. Poor heart function and abnormal heart rhythms are important risk factors, but it’s not possible today to predict reliably who will have an SCA. However, doctors already routinely capture a wealth of information in electrical signals from the heart using electrocardiograms (ECGs). They also frequently use electroencephalograms (EEGs) to capture electrical activity in the brain.

DeMazumder’s innovative leap is to look at these heart and brain signals jointly, as well as in new ways, during sleep. According to the physician-scientist, sleep is a good time to search for SCA signatures in the electrical crosstalk between the heart and the brain because many other aspects of brain activity quiet down. He also thinks it’s important to pay special attention to what happens to the body’s electrical signals during sleep because most sudden cardiac deaths happen early in the waking hours, for reasons that aren’t well understood.

He has promising preliminary evidence from both animal models and humans suggesting that signatures within heart and brain signals are unique predictors of sudden death, even in people who appear healthy [1]. DeMazumder has already begun developing a set of artificial intelligence algorithms for jointly deciphering waveform signals from the heart, brain, and other body signals [2,3]. These new algorithms associate the waveform signals with a wealth of information available in electronic health records to improve upon the algorithm’s ability to predict catastrophic illness.

DeMazumder credits his curiosity about what he calls the “art and science of healing” to his early childhood experiences and his family’s dedication to community service in India. It taught him to appreciate the human condition, and he has integrated this life-long awareness into his Western medical training and his growing interest in computer science.

For centuries, humans have talked about how true flourishing needs both head and heart. In DeMazumder’s view, science is just beginning to understand the central role of heart-brain conversations in our health. As he continues to capture and interpret these conversations through his NIH-supported work, he hopes to find ways to identify individuals who don’t appear to have serious heart disease but may nevertheless be at high risk for SCA. In the meantime, he will continue to do all he can for the patients in his care.

References:

[1] Mitochondrial ROS drive sudden cardiac death and chronic proteome remodeling in heart failure. Dey S, DeMazumder D, Sidor A, Foster DB, O’Rourke B. Circ Res. 2018;123(3):356-371.

[2] Entropy of cardiac repolarization predicts ventricular arrhythmias and mortality in patients receiving an implantable cardioverter-defibrillator for primary prevention of sudden death. DeMazumder D, Limpitikul WB, Dorante M, et al. Europace. 2016;18(12):1818-1828.

[3] Dynamic analysis of cardiac rhythms for discriminating atrial fibrillation from lethal ventricular arrhythmias. DeMazumder D, Lake DE, Cheng A, et al. Circ Arrhythm Electrophysiol. 2013;6(3):555-561.

Links:

Sudden Cardiac Arrest (National Heart, Lung, and Blood Institute/NIH)

Deeptankar DeMazumder (University of Cincinnati College of Medicine)

DeMazumder Project Information (NIH RePORTER)

NIH Director’s New Innovator Award (Common Fund)

NIH Support: National Heart, Lung, and Blood Institute; Common Fund


Artificial Intelligence Accurately Predicts Protein Folding

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Caption: Researchers used artificial intelligence to map hundreds of new protein structures, including this 3D view of human interleukin-12 (blue) bound to its receptor (purple). Credit: Ian Haydon, University of Washington Institute for Protein Design, Seattle

Proteins are the workhorses of the cell. Mapping the precise shapes of the most important of these workhorses helps to unlock their life-supporting functions or, in the case of disease, potential for dysfunction. While the amino acid sequence of a protein provides the basis for its 3D structure, deducing the atom-by-atom map from principles of quantum mechanics has been beyond the ability of computer programs—until now. 

In a recent study in the journal Science, researchers reported they have developed artificial intelligence approaches for predicting the three-dimensional structure of proteins in record time, based solely on their one-dimensional amino acid sequences [1]. This groundbreaking approach will not only aid researchers in the lab, but guide drug developers in coming up with safer and more effective ways to treat and prevent disease.

This new NIH-supported advance is now freely available to scientists around the world. In fact, it has already helped to solve especially challenging protein structures in cases where experimental data were lacking and other modeling methods hadn’t been enough to get a final answer. It also can now provide key structural information about proteins for which more time-consuming and costly imaging data are not yet available.

The new work comes from a group led by David Baker and Minkyung Baek, University of Washington, Seattle, Institute for Protein Design. Over the course of the pandemic, Baker’s team has been working hard to design promising COVID-19 therapeutics. They’ve also been working to design proteins that might offer promising new ways to treat cancer and other conditions. As part of this effort, they’ve developed new computational approaches for determining precisely how a chain of amino acids, which are the building blocks of proteins, will fold up in space to form a finished protein.

But the ability to predict a protein’s precise structure or shape from its sequence alone had proven to be a difficult problem to solve despite decades of effort. In search of a solution, research teams from around the world have come together every two years since 1994 at the Critical Assessment of Structure Prediction (CASP) meetings. At these gatherings, teams compete against each other with the goal of developing computational methods and software capable of predicting any of nature’s 200 million or more protein structures from sequences alone with the greatest accuracy.

Last year, a London-based company called DeepMind shook up the structural biology world with their entry into CASP called AlphaFold. (AlphaFold was one of Science’s 2020 Breakthroughs of the Year.) They showed that their artificial intelligence approach—which took advantage of the 170,000 proteins with known structures in a reiterative process called deep learning—could predict protein structure with amazing accuracy. In fact, it could predict most protein structures almost as accurately as other high-resolution protein mapping techniques, including today’s go-to strategies of X-ray crystallography and cryo-EM.

The DeepMind performance showed what was possible, but because the advances were made by a world-leading deep learning company, the details on how it worked weren’t made publicly available at the time. The findings left Baker, Baek, and others eager to learn more and to see if they could replicate the impressive predictive ability of AlphaFold outside of such a well-resourced company.

In the new work, Baker and Baek’s team has made stunning progress—using only a fraction of the computational processing power and time required by AlphaFold. The new software, called RoseTTAFold, also relies on a deep learning approach. In deep learning, computers look for patterns in large collections of data. As they begin to recognize complex relationships, some connections in the network are strengthened while others are weakened. The finished network is typically composed of multiple information-processing layers, which operate on the data to return a result—in this case, a protein structure.

Given the complexity of the problem, instead of using a single neural network, RoseTTAFold relies on three. The three-track neural network integrates and simultaneously processes one-dimensional protein sequence information, two-dimensional information about the distance between amino acids, and three-dimensional atomic structure all at once. Information from these separate tracks flows back and forth to generate accurate models of proteins rapidly from sequence information alone, including structures in complex with other proteins.

As soon as the researchers had what they thought was a reasonable working approach to solve protein structures, they began sharing it with their structural biologist colleagues. In many cases, it became immediately clear that RoseTTAFold worked remarkably well. What’s more, it has been put to work to solve challenging structural biology problems that had vexed scientists for many years with earlier methods.

RoseTTAFold already has solved hundreds of new protein structures, many of which represent poorly understood human proteins. The 3D rendering of a complex showing a human protein called interleukin-12 in complex with its receptor (above image) is just one example. The researchers have generated other structures directly relevant to human health, including some that are related to lipid metabolism, inflammatory conditions, and cancer. The program is now available on the web and has been downloaded by dozens of research teams around the world.

Cryo-EM and other experimental mapping methods will remain essential to solve protein structures in the lab. But with the artificial intelligence advances demonstrated by RoseTTAFold and AlphaFold, which has now also been released in an open-source version and reported in the journal Nature [2], researchers now can make the critical protein structure predictions at their desktops. This newfound ability will be a boon to basic science studies and has great potential to speed life-saving therapeutic advances.

References:

[1] Accurate prediction of protein structures and interactions using a three-track neural network. Baek M, DiMaio F, Anishchenko I, Dauparas J, Grishin NV, Adams PD, Read RJ, Baker D., et al. Science. 2021 Jul 15:eabj8754.

[2] Highly accurate protein structure prediction with AlphaFold. Jumper J, Evans R, Pritzel A, Green T, Senior AW, Kavukcuoglu K, Kohli P, Hassabis D. et al. Nature. 2021 Jul 15.

Links:

Structural Biology (National Institute of General Medical Sciences/NIH)

The Structures of Life (NIGMS)

Baker Lab (University of Washington, Seattle)

CASP 14 (University of California, Davis)

NIH Support: National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences


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