structural biology
3D Animation Captures Viral Infection in Action
Posted on by Lawrence Tabak, D.D.S., Ph.D.
With the summer holiday season now in full swing, the blog will also swing into its annual August series. For most of the month, I will share with you just a small sampling of the colorful videos and snapshots of life captured in a select few of the hundreds of NIH-supported research labs around the country.
To get us started, let’s turn to the study of viruses. Researchers now can generate vast amounts of data relatively quickly on a virus of interest. But data are often displayed as numbers or two-dimensional digital images on a computer screen. For most virologists, it’s extremely helpful to see a virus and its data streaming in three dimensions. To do so, they turn to a technological tool that we all know so well: animation.
This research animation features the chikungunya virus, a sometimes debilitating, mosquito-borne pathogen transmitted mainly in developing countries in Africa, Asia and the Americas. The animation illustrates large amounts of research data to show how the chikungunya virus infects our cells and uses its specialized machinery to release its genetic material into the cell and seed future infections. Let’s take a look.
In the opening seconds, you see how receptor binding glycoproteins (light blue), which are proteins with a carbohydrate attached on the viral surface, dock with protein receptors (yellow) on a host cell. At five seconds, the virus is drawn inside the cell. The change in the color of the chikungunya particle shows that it’s coated in a vesicle, which helps the virus make its way unhindered through the cytoplasm.
At 10 seconds, the virus then enters an endosome, ubiquitous bubble-like compartments that transport material from outside the cell into the cytosol, the fluid part of the cytoplasm. Once inside the endosome, the acidic environment makes other glycoproteins (red, blue, yellow) on the viral surface change shape and become more flexible and dynamic. These glycoproteins serve as machinery that enables them to reach out and grab onto the surrounding endosome membrane, which ultimately will be fused with the virus’s own membrane.
As more of those fusion glycoproteins grab on, fold back on themselves, and form into hairpin-like shapes, they pull the membranes together. The animation illustrates not only the changes in protein organization, but the resulting effects on the integrity of the membrane structures as this dynamic process proceeds. At 53 seconds, the viral protein shell, or capsid (green), which contains the virus’ genetic instructions, is released back out into the cell where it will ultimately go on to make more virus.
This remarkable animation comes from Margot Riggi and Janet Iwasa, experts in visualizing biology at the University of Utah’s Animation Lab, Salt Lake City. Their data source was researcher Kelly Lee, University of Washington, Seattle, who collaborated closely with Riggi and Iwasa on this project. The final product was considered so outstanding that it took the top prize for short videos in the 2022 BioArt Awards competition, sponsored by the Federation of American Societies for Experimental Biology (FASEB).
The Lee lab uses various research methods to understand the specific shape-shifting changes that chikungunya and other viruses perform as they invade and infect cells. One of the lab’s key visual tools is cryo-electron microscopy (Cryo-EM), specifically cryo-electron tomography (cryo-ET). Cryto-ET enables complex 3D structures, including the intermediate state of biological reactions, to be captured and imaged in remarkably fine detail.
In a study in the journal Nature Communications [1] last year, Lee’s team used cryo-ET to reveal how the chikungunya virus invades and delivers its genetic cargo into human cells to initiate a new infection. While Lee’s cryo-ET data revealed stages of the virus entry process and fine structural details of changes to the virus as it enters a cell and starts an infection, it still represented a series of snapshots with missing steps in between. So, Lee’s lab teamed up with The Animation Lab to help beautifully fill in the gaps.
Visualizing chikungunya and similar viruses in action not only makes for informative animations, it helps researchers discover better potential targets to intervene in this process. This basic research continues to make progress, and so do ongoing efforts to develop a chikungunya vaccine [2] and specific treatments that would help give millions of people relief from the aches, pains, and rashes associated with this still-untreatable infection.
References:
[1] Visualization of conformational changes and membrane remodeling leading to genome delivery by viral class-II fusion machinery. Mangala Prasad V, Blijleven JS, Smit JM, Lee KK. Nat Commun. 2022 Aug 15;13(1):4772. doi: 10.1038/s41467-022-32431-9. PMID: 35970990; PMCID: PMC9378758.
[2] Experimental chikungunya vaccine is safe and well-tolerated in early trial, National Institute of Allergy and Infectious Diseases news release, April 27, 2020.
Links:
Chikungunya Virus (Centers for Disease Control and Prevention, Atlanta)
Global Arbovirus Initiative (World Health Organization, Geneva, Switzerland)
The Animation Lab (University of Utah, Salt Lake City)
Video: Janet Iwasa (TED Speaker)
Lee Lab (University of Washington, Seattle)
BioArt Awards (Federation of American Societies for Experimental Biology, Rockville, MD)
NIH Support: National Institute of General Medical Sciences; National Institute of Allergy and Infectious Diseases
Cryo-EM Scores Again
Posted on by Lawrence Tabak, D.D.S., Ph.D.
Human neurons are long, spindly structures, but if you could zoom in on their surfaces at super-high resolution, you’d see surprisingly large pores. They act as gated channels that open and close for ions and other essential molecules of life to pass in and out the cell. This rapid exchange of ions and other molecules is how neurons communicate, and why we humans can sense, think, move, and respond to the world around us [1].
Because these gated channels are so essential to neurons, mapping their precise physical structures at high-resolution has profound implications for informing future studies on the brain and nervous system. Good for us in these high-tech times that structural biologists keep getting better at imaging these 3D pores.
In fact, as just published in the journal Nature Communications [2], a team of NIH-supported scientists imaged the molecular structure of a gated pore of major research interest. The pore is called calcium homeostasis modulator 1 (CALHM1). Pictured below, you can view its 3D structure at near atomic resolution [2]. Keep in mind, this relatively large neuronal pore still measures approximately 50,000 times smaller than the width of a hair.
The structure comes from a research team led by Hiro Furukawa, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY. He and his team relied on cryo-electron microscopy (cryo-EM) to produce the first highly precise 3D models of CALHM1.
Cryo-EM involves flash-freezing molecules in liquid ethane and bombarding them with electrons to capture their images with a special camera. When all goes well, cryo-EM can reveal the structure of intricate macromolecular complexes in a matter of weeks.
Furukawa’s team had earlier studied CALHM1 from chickens with cryo-EM [3], and their latest work reveals that the human version is quite similar. Eight copies of the CALHM1 protein assemble to form the circular channel. Each of the protein subunits has a flexible arm that allows it to reach into the central opening, which the researchers now suspect allows the channels to open and close in a highly controlled manner. The researchers have likened the channels’ eight flexible arms to the arms of an octopus.
The researchers also found that fatty molecules called phospholipids play a critical role in stabilizing and regulating the eight-part channel. They used simulations to demonstrate how pockets in the CALHM1 channel binds this phospholipid over cholesterol to shore up the structure and function properly. Interestingly, these phospholipid molecules are abundant in many healthy foods, such as eggs, lean meats, and seafood.
Researchers knew that an inorganic chemical called ruthenium red can block the function of the CALHM1 channel. They’ve now shown precisely how this works. The structural details indicate that ruthenium red physically lodges in and plugs up the channel.
These details also may be useful in future efforts to develop drugs designed to target and modify the function of these channels in helpful ways. For instance, on our tongues, the channel plays a role in our ability to perceive sweet, sour, or umami (savory) flavors. In our brains, studies show the abnormal function of CALHM1 may be implicated in the plaques that accumulate in the brains of people with Alzheimer’s disease.
There are far too many other normal and abnormal functions to mention here in this brief post. Suffice it to say, I’ll look forward to seeing what this enabling research yields in the years ahead.
References:
[1] On the molecular nature of large-pore channels. Syrjanen, J., Michalski, K., Kawate, T., and Furukawa, H. J Mol Biol. 2021 Aug 20;433(17):166994. DOI: 10.1016/j.jmb.2021.166994. Epub 2021 Apr 16. PMID: 33865869; PMCID: PMC8409005.
[2] Structure of human CALHM1 reveals key locations for channel regulation and blockade by ruthenium red. Syrjänen JL, Epstein M, Gómez R, Furukawa H. Nat Commun. 2023 Jun 28;14(1):3821. DOI: 10.1038/s41467-023-39388-3. PMID: 37380652; PMCID: PMC10307800.
[3] Structure and assembly of calcium homeostasis modulator proteins. Syrjanen JL, Michalski K, Chou TH, Grant T, Rao S, Simorowski N, Tucker SJ, Grigorieff N, Furukawa H. Nat Struct Mol Biol. 2020 Feb;27(2):150-159. DOI: 10.1038/s41594-019-0369-9. Epub 2020 Jan 27. PMID: 31988524; PMCID: PMC7015811.
Links:
Brain Basics: The Life and Death of a Neuron (National Institute of Neurological Disorders and Stroke/NIH)
Alzheimer’s Disease (National Institute on Aging/NIH)
Furukawa Lab (Cold Spring Harbor Lab, Cold Spring Harbor, NY)
NIH Support: National Institute of Neurological Disorders and Stroke; National Institute of Mental Health
Using AI to Find New Antibiotics Still a Work in Progress
Posted on by Lawrence Tabak, D.D.S., Ph.D.
Each year, more than 2.8 million people in the United States develop bacterial infections that don’t respond to treatment and sometimes turn life-threatening [1]. Their infections are antibiotic-resistant, meaning the bacteria have changed in ways that allow them to withstand our current widely used arsenal of antibiotics. It’s a serious and growing health-care problem here and around the world. To fight back, doctors desperately need new antibiotics, including novel classes of drugs that bacteria haven’t seen and developed ways to resist.
Developing new antibiotics, however, involves much time, research, and expense. It’s also fraught with false leads. That’s why some researchers have turned to harnessing the predictive power of artificial intelligence (AI) in hopes of selecting the most promising leads faster and with greater precision.
It’s a potentially paradigm-shifting development in drug discovery, and a recent NIH-funded study, published in the journal Molecular Systems Biology, demonstrates AI’s potential to streamline the process of selecting future antibiotics [2]. The results are also a bit sobering. They highlight the current limitations of one promising AI approach, showing that further refinement will still be needed to maximize its predictive capabilities.
These findings come from the lab of James Collins, Massachusetts Institute of Technology (MIT), Cambridge, and his recently launched Antibiotics-AI Project. His audacious goal is to develop seven new classes of antibiotics to treat seven of the world’s deadliest bacterial pathogens in just seven years. What makes this project so bold is that only two new classes of antibiotics have reached the market in the last 50 years!
In the latest study, Collins and his team looked to an AI program called AlphaFold2 [3]. The name might ring a bell. AlphaFold’s AI-powered ability to predict protein structures was a finalist in Science Magazine’s 2020 Breakthrough of the Year. In fact, AlphaFold has been used already to predict the structures of more than 200 million proteins, or almost every known protein on the planet [4].
AlphaFold employs a deep learning approach that can predict most protein structures from their amino acid sequences about as well as more costly and time-consuming protein-mapping techniques.
In the deep learning models used to predict protein structure, computers are “trained” on existing data. As computers “learn” to understand complex relationships within the training material, they develop a model that can then be applied for making predictions of 3D protein structures from linear amino acid sequences without relying on new experiments in the lab.
Collins and his team hoped to combine AlphaFold with computer simulations commonly used in drug discovery as a way to predict interactions between essential bacterial proteins and antibacterial compounds. If it worked, researchers could then conduct virtual rapid screens of millions of new synthetic drug compounds targeting key bacterial proteins that existing antibiotics don’t. It would also enable the rapid development of antibiotics that work in novel ways, exactly what doctors need to treat antibiotic-resistant infections.
To test the strategy, Collins and his team focused first on the predicted structures of 296 essential proteins from the Escherichia coli bacterium as well as 218 antibacterial compounds. Their computer simulations then predicted how strongly any two molecules (essential protein and antibacterial) would bind together based on their shapes and physical properties.
It turned out that screening many antibacterial compounds against many potential targets in E. coli led to inaccurate predictions. For example, when comparing their computational predictions with actual interactions for 12 essential proteins measured in the lab, they found that their simulated model had about a 50:50 chance of being right. In other words, it couldn’t identify true interactions between drugs and proteins any better than random guessing.
They suspect one reason for their model’s poor performance is that the protein structures used to train the computer are fixed, not flexible and shifting physical configurations as happens in real life. To improve their success rate, they ran their predictions through additional machine-learning models that had been trained on data to help them “learn” how proteins and other molecules reconfigure themselves and interact. While this souped-up model got somewhat better results, the researchers report that they still aren’t good enough to identify promising new drugs and their protein targets.
What now? In future studies, the Collins lab will continue to incorporate and train the computers on even more biochemical and biophysical data to help with the predictive process. That’s why this study should be interpreted as an interim progress report on an area of science that will only get better with time.
But it’s also a sobering reminder that the quest to find new classes of antibiotics won’t be easy—even when aided by powerful AI approaches. We certainly aren’t there yet, but I’m confident that we will get there to give doctors new therapeutic weapons and turn back the rise in antibiotic-resistant infections.
References:
[1] 2019 Antibiotic resistance threats report. Centers for Disease Control and Prevention.
[2] Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery. Wong F, Krishnan A, Zheng EJ, Stark H, Manson AL, Earl AM, Jaakkola T, Collins JJ. Molecular Systems Biology. 2022 Sept 6. 18: e11081.
[3] Highly accurate protein structure prediction with AlphaFold. Jumper J, Evans R, Pritzel A, Kavukcuoglu K, Kohli P, Hassabis D., et al. Nature. 2021 Aug;596(7873):583-589.
[4] ‘The entire protein universe’: AI predicts shape of nearly every known protein. Callaway E. Nature. 2022 Aug;608(7921):15-16.
Links:
Antimicrobial (Drug) Resistance (National Institute of Allergy and Infectious Diseases/NIH)
Collins Lab (Massachusetts Institute of Technology, Cambridge)
The Antibiotics-AI Project, The Audacious Project (TED)
AlphaFold (Deep Mind, London, United Kingdom)
NIH Support: National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences
The Amazing Brain: Capturing Neurons in Action
Posted on by Lawrence Tabak, D.D.S., Ph.D.
With today’s powerful imaging tools, neuroscientists can monitor the firing and function of many distinct neurons in our brains, even while we move freely about. They also possess another set of tools to capture remarkable, high-resolution images of the brain’s many thousands of individual neurons, tracing the form of each intricate branch of their tree-like structures.
Most brain imaging approaches don’t capture neural form and function at once. Yet that’s precisely what you’re seeing in this knockout of a movie, another winner in the Show Us Your BRAINs! Photo and Video Contest, supported by NIH’s Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative.
This first-of-its kind look into the mammalian brain produced by Andreas Tolias, Baylor College of Medicine, Houston, and colleagues features about 200 neurons in the visual cortex, which receives and processes visual information. First, you see a colorful, tightly packed network of neurons. Then, those neurons, which were colorized by the researchers in vibrant pinks, reds, blues, and greens, pull apart to reveal their finely detailed patterns and shapes. Throughout the video, you can see neural activity, which appears as flashes of white that resemble lightning bolts.
Making this movie was a multi-step process. First, the Tolias group presented laboratory mice with a series of visual cues, using a functional imaging approach called two-photon calcium imaging to record the electrical activity of individual neurons. While this technique allowed the researchers to pinpoint the precise locations and activity of each individual neuron in the visual cortex, they couldn’t zoom in to see their precise structures.
So, the Baylor team sent the mice to colleagues Nuno da Costa and Clay Reid, Allen Institute for Brain Science, Seattle, who had the needed electron microscopes and technical expertise to zoom in on these structures. Their data allowed collaborator Sebastian Seung’s team, Princeton University, Princeton, NJ, to trace individual neurons in the visual cortex along their circuitous paths. Finally, they used sophisticated machine learning algorithms to carefully align the two imaging datasets and produce this amazing movie.
This research was supported by Intelligence Advanced Research Projects Activity (IARPA), part of the Office of the Director of National Intelligence. The IARPA is one of NIH’s governmental collaborators in the BRAIN Initiative.
Tolias and team already are making use of their imaging data to learn more about the precise ways in which individual neurons and groups of neurons in the mouse visual cortex integrate visual inputs to produce a coherent view of the animals’ surroundings. They’ve also collected an even-larger data set, scaling their approach up to tens of thousands of neurons. Those data are now freely available to other neuroscientists to help advance their work. As researchers make use of these and similar data, this union of neural form and function will surely yield new high-resolution discoveries about the mammalian brain.
Links:
Tolias Lab (Baylor College of Medicine, Houston)
Nuno da Costa (Allen Institute for Brain Science, Seattle)
R. Clay Reid (Allen Institute)
H. Sebastian Seung (Princeton University, Princeton, NJ)
Machine Intelligence from Cortical Networks (MICrONS) Explorer
Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative (NIH)
Show Us Your BRAINs Photo & Video Contest (BRAIN Initiative)
NIH Support: BRAIN Initiative; Common Fund
Biomedical Research Leads Science’s 2021 Breakthroughs
Posted on by Lawrence Tabak, D.D.S., Ph.D.
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.
[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)
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