drug development
The Chemistry Clicked: Two NIH-Supported Researchers Win 2022 Nobel Prize in Chemistry
Posted on by Lawrence Tabak, D.D.S., Ph.D.
Through the years, NIH has supported a total of 169 researchers who have received or shared 101 Nobel Prizes. That’s quite a testament to the world-leading science that NIH pursues and its continued impact on improving human health and well-being.
Those numbers include the news late last week that the 2022 Nobel Prize in Chemistry was shared by two long-time grantees for their work on a transformative scientific approach known as “click chemistry.” This form of chemistry has made it possible for researchers to snap together, like LEGO pieces, molecular building blocks to form hybrid biomolecules, often with easy-to-track imaging agents attached. Not only has click chemistry expanded our ability to explore the molecular underpinnings of a wide range of biological processes, but it has provided us with new tools for developing drugs, diagnostics, and a wide array of “smart” materials.
For K. Barry Sharpless, Scripps Research, La Jolla, CA, October 5, 2022 marked the second time that he’s received an early-morning congratulatory call from The Royal Swedish Academy of Sciences. The first such call came in 2001, when Sharpless got the news that he was a co-winner of the Nobel Prize in Chemistry for his discovery of asymmetric catalytic reactions.
This time around, Sharpless was recognized for his groundbreaking studies in the mid-1990s with click chemistry, a term that he coined himself. His initial work established click chemistry as a fast-and-reliable way to attach molecules of interest in the lab [1]. He and co-recipient Morten Meldal, University of Copenhagen, Denmark, who is not funded by NIH, then independently introduced a copper-catalyzed click that further refined the chemistry and helped popularize it across biology and the material sciences [2,3].
For Carolyn R. Bertozzi of Stanford University, Palo Alto, CA, it is her first Nobel. Bertozzi was recognized for expanding the use of click chemistry with so-called bioorthogonal chemistry, which is a copper-free version of the approach that can be used inside living cells without the risk of metal-associated toxicities [4,5].
Bertozzi’s work has been especially interesting to me because of her focus on glycans, which I’ve studied throughout my career. Glycans are the carbohydrate molecules that coat the surfaces of our cells and most secreted proteins. They are essential to life, and, in higher organisms, play fundamental roles in basic processes such as metabolism, immunity, and cellular communication.
Glycans also remain poorly understood, largely because, until recently, they have been so difficult for basic scientists to study with traditional techniques. That has changed with development of new tools to study glycans and the enzymes that assemble them. My long-time collaborator, Kelly Ten Hagen, a senior investigator at NIH’s National Institute of Dental and Craniofacial Research, and I collaborated with Carolyn on identifying small molecules that inhibit the enzyme responsible for the first step in mucin-type O-glycosylation [6]
In the early 2000s, Bertozzi and her team introduced bioorthogonal chemistry, which enabled researchers to label glycans and visualize them in a range of cells and living organisms. Her team’s pioneering approach quickly became an essential tool in basic science labs around the world that study glycans, leading to a number of stunning discoveries that would have otherwise been difficult or impossible.
For clinical researchers, click chemistry has emerged as a workhorse in drug discovery and the improved targeting of cancer chemotherapies and other small-molecule drugs. The approach also is being used to improve delivery of antibody-based therapies and to create new biomaterials. Meanwhile, in the material sciences, click chemistry has been used to solve a number of problems in working with polymers and to expand their industrial uses.
Click chemistry is an excellent example of how advances in basic science can build the foundation for a wide range of practical applications, including those aimed at improving human health. It also highlights the value of strong, sustained public funding for fundamental research, and NIH is proud to have supported Sharpless continuously since 1975 and Bertozzi since 1999. I send my sincere congratulations to both of these most-deserving scientists.
References:
[1] Click Chemistry: Diverse chemical function from a few good reactions. Kolb, HC, Finn, MG, Sharpless, KB. Angew. Chem. Int. Ed. 2001, 40 (11), 2004–2021
[2] A stepwise huisgen cycloaddition process: Copper(I)-catalyzed regioselective “Llgation” of azides and terminal alkynes. Rostovtsev VV, Green LG, Fokin VV, Sharpless KB. Angew. Chem. Int. Ed. 2002, 41 (14), 2596–2599.
[3] Peptidotriazoles on solid phase: [1,2,3]-Triazoles by regiospecific copper(I)-catalyzed 1,3-dipolar cycloadditions of terminal alkynes to azides. Tornøe CW, Sengeløv H, Meldal M. J. Org. Chem. 2002, 67 (9), 3057–3064.
[4] A strain-promoted [3 + 2] azide−alkyne cycloaddition for covalent modification of biomolecules in living systems. Agard NJ, Prescher JA, Bertozzi CR. J. Am. Chem. Soc. 2004, 126 (46), 15046–15047
[5] In vivo imaging of membrane associated glycans in developing zebrafish. Laughlin ST, Baskin JM, Amacher SL, Bertozzi CR. Science 2008, 320 (5876), 664–667.
[6] Small molecule inhibitors of mucin-type O-glycosylation from a uridine-based library. Hang, HC, Yu, C, Ten Hagen, KG, Tian, E, Winans, KA, Tabak, LA, Bertozzi, Chem Biol. 2004 Jul;11(7):1009-1016.
Links:
The Nobel Prize in Chemistry 2022 (The Royal Swedish Academy of Sciences, Stockholm)
Video: Announcement of the 2022 Nobel Prize in Chemistry (YouTube)
Click Chemistry and Bioorthogonal Chemistry (The Royal Swedish Academy of Sciences)
Sharpless Lab (Scripps Research, La Jolla, CA)
Bertozzi Group (Stanford University, Palo Alto, CA)
NIH Support:
K. Barry Sharpless: National Institute of General Medical Sciences
Carolyn R. Bertozzi: National Cancer Institute; National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences
Wearable Sensor Promises More Efficient Early Cancer Drug Development
Posted on by Lawrence Tabak, D.D.S., Ph.D.
Wearable electronic sensors hold tremendous promise for improving human health and wellness. That promise already runs the gamut from real-time monitoring of blood pressure and abnormal heart rhythms to measuring alcohol consumption and even administering vaccines.
Now a new study published in the journal Science Advances [1] demonstrates the promise of wearables also extends to the laboratory. A team of engineers has developed a flexible, adhesive strip that, at first glance, looks like a Band-Aid. But this “bandage” actually contains an ultra-sensitive, battery-operated sensor that’s activated when placed on the skin of mouse models used to study possible new cancer drugs.
This sensor is so sensitive that it can detect, in real time, changes in the size of a tumor down to one-hundredth of a millimeter. That’s about the thickness of the plastic cling wrap you likely have in your kitchen! The device beams those measures to a smartphone app, capturing changes in tumor growth minute by minute over time.
The goal is to determine much sooner—and with greater automation and precision—which potential drug candidates undergoing early testing in the lab best inhibit tumor growth and, consequently, should be studied further. In their studies in mouse models of cancer, researchers found the new sensor could detect differences between tumors treated with an active drug and those treated with a placebo within five hours. Those quick results also were validated using more traditional methods to confirm their accuracy.
The device is the work of a team led by Alex Abramson, a former post-doc with Zhenan Bao, Stanford University’s School of Engineering, Palo Alto, CA. Abramson has since launched his own lab at the Georgia Institute of Technology, Atlanta.
The Stanford team began looking for a technological solution after realizing the early testing of potential cancer drugs typically requires researchers to make tricky measurements using pincer-like calipers by hand. Not only is the process tedious and slow, it’s less than an ideal way to capture changes in soft tissues with the desired precision. The imprecision can also lead to false leads that won’t pan out further along in the drug development pipeline, at great time and expense to their developers.
To refine the process, the NIH-supported team turned to wearable technology and recent advances in flexible electronic materials. They developed a device dubbed FAST (short for Flexible Autonomous Sensor measuring Tumors). Its sensor, embedded in a skin patch, is composed of a flexible and stretchable, skin-like polymer with embedded gold circuitry.
Here’s how FAST works: Coated on top of the polymer skin patch is a layer of gold. When stretched, it forms small cracks that change the material’s electrical conductivity. As the material stretches, even slightly, the number of cracks increases, causing the electronic resistance in the sensor to increase as well. As the material contracts, any cracks come back together, and conductivity improves.
By picking up on those changes in conductivity, the device measures precisely the strain on the polymer membrane—an indication of whether the tumor underneath is stable, growing, or shrinking—and transmits that data to a smartphone. Based on that information, potential therapies that are linked to rapid tumor shrinkage can be fast-tracked for further study while those that allow a tumor to continue growing can be cast aside.
The researchers are continuing to test their sensor in more cancer models and with more therapies to extend these initial findings. Already, they have identified at least three significant advantages of their device in early cancer drug testing:
• FAST is non-invasive and captures precise measurements on its own.
• It can provide continuous monitoring, for weeks, months, or over the course of study.
• The flexible sensor fully surrounds the tumor and can therefore detect 3D changes in shape that would be hard to pick up otherwise in real-time with existing technologies.
By now, you are probably asking yourself: Could FAST also be applied as a wearable for cancer patients to monitor in real-time whether an approved chemotherapy regimen is working? It is too early to say. So far, FAST has not been tested in people. But, as highlighted in this paper, FAST is off to, well, a fast start and points to the vast potential of wearables in human health, wellness, and also in the lab.
Reference:
[1] A flexible electronic strain sensor for the real-time monitoring of tumor regression. Abramson A, Chan CT, Khan Y, Mermin-Bunnell A, Matsuhisa N, Fong R, Shad R, Hiesinger W, Mallick P, Gambhir SS, Bao Z. Sci Adv. 2022 Sep 16;8(37):eabn6550.
Links:
Stanford Wearable Electronics Initiative (Stanford University, Palo Alto, CA)
Bao Group (Stanford University)
Abramson Lab (Georgia Institute of Technology, Atlanta)
NIH Support: National Institute of Biomedical Imaging and Bioengineering
‘Decoy’ Protein Works Against Multiple Coronavirus Variants in Early Study
Posted on by Lawrence Tabak, D.D.S., Ph.D.
The NIH continues to support the development of some very innovative therapies to control SARS-CoV-2, the coronavirus that causes COVID-19. One innovative idea involves a molecular decoy to thwart the coronavirus.
How’s that? The decoy is a specially engineered protein particle that mimics the 3D structure of the ACE2 receptor, a protein on the surface of our cells that the virus’s spike proteins bind to as the first step in causing an infection.
The idea is when these ACE2 decoys are administered therapeutically, they will stick to the spike proteins that crown the coronavirus (see image above). With its spikes covered tightly in decoy, SARS-CoV-2 has a more-limited ability to attach to the real ACE2 and infect our cells.
Recently, the researchers published their initial results in the journal Nature Chemical Biology, and the early data look promising [1]. They found in mouse models of severe COVID-19 that intravenous infusion of an engineered ACE2 decoy prevented lung damage and death. Though more study is needed, the researchers say the decoy therapy could potentially be delivered directly to the lungs through an inhaler and used alone or in combination with other COVID-19 treatments.
The findings come from a research team at the University of Illinois Chicago team, led by Asrar Malik and Jalees Rehman, working in close collaboration with their colleagues at the University of Illinois Urbana-Champaign. The researchers had been intrigued by an earlier clinical trial testing the ACE2 decoy strategy [2]. However, in this earlier attempt, the clinical trial found no reduction in mortality. The ACE2 drug candidate, which is soluble and degrades in the body, also proved ineffective in neutralizing the virus.
Rather than give up on the idea, the UIC team decided to give it a try. They engineered a new soluble version of ACE2 that structurally might work better as a decoy than the original one. Their version of ACE2, which includes three changes in the protein’s amino acid building blocks, binds the SARS-CoV-2 spike protein much more tightly. In the lab, it also appeared to neutralize the virus as well as monoclonal antibodies used to treat COVID-19.
To put it to the test, they conducted studies in mice. Normal mice don’t get sick from SARS-CoV-2 because the viral spike can’t bind well to the mouse version of the ACE2 receptor. So, the researchers did their studies in a mouse that carries the human ACE2 and develops a severe acute respiratory syndrome somewhat similar to that seen in humans with severe COVID-19.
In their studies, using both the original viral isolate from Washington State and the Gamma variant (P.1) first detected in Brazil, they found that infected mice infused with their therapeutic ACE2 protein had much lower mortality and showed few signs of severe acute respiratory syndrome. While the protein worked against both versions of the virus, infection with the more aggressive Gamma variant required earlier treatment. The treated mice also regained their appetite and weight, suggesting that they were making a recovery.
Further studies showed that the decoy bound to spike proteins from every variant tested, including Alpha, Beta, Delta and Epsilon. (Omicron wasn’t yet available at the time of the study.) In fact, the decoy bound just as well, if not better, to new variants compared to the original virus.
The researchers will continue their preclinical work. If all goes well, they hope to move their ACE2 decoy into a clinical trial. What’s especially promising about this approach is it could be used in combination with treatments that work in other ways, such as by preventing virus that’s already infected cells from growing or limiting an excessive and damaging immune response to the infection.
Last week, more than 17,500 people in the United States were hospitalized with severe COVID-19. We’ve got to continue to do all we can to save lives, and it will take lots of innovative ideas, like this ACE2 decoy, to put us in a better position to beat this virus once and for all.
References:
[1] Engineered ACE2 decoy mitigates lung injury and death induced by SARS-CoV-2 variants.
Zhang L, Dutta S, Xiong S, Chan M, Chan KK, Fan TM, Bailey KL, Lindeblad M, Cooper LM, Rong L, Gugliuzza AF, Shukla D, Procko E, Rehman J, Malik AB. Nat Chem Biol. 2022 Jan 19.
[2] Recombinant human angiotensin-converting enzyme 2 (rhACE2) as a treatment for patients with COVID-19 (APN01-COVID-19). ClinicalTrials.gov.
Links:
COVID-19 Research (NIH)
Accelerating COVID-19 Therapeutic Interventions and Vaccines (NIH)
Asrar Malik (University of Illinois Chicago)
Jalees Rehman (University of Illinois Chicago)
NIH Support: National Heart, Lung, and Blood Institute; National Institute of Allergy and Infectious Diseases
Early Data Suggest Pfizer Pill May Prevent Severe COVID-19
Posted on by Dr. Francis Collins
Over the course of this pandemic, significant progress has been made in treating COVID-19 and helping to save lives. That progress includes the development of life-preserving monoclonal antibody infusions and repurposing existing drugs, to which NIH’s Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) public-private partnership has made a major contribution.
But for many months we’ve had hopes that a safe and effective oral medicine could be developed that would reduce the risk of severe illness for individuals just diagnosed with COVID-19. The first indication that those hopes might be realized came from the announcement just a month ago of a 50 percent reduction in hospitalizations from the Merck and Ridgeback drug molnupiravir (originally developed with an NIH grant to Emory University, Atlanta). Now comes word of a second drug with potentially even higher efficacy: an antiviral pill from Pfizer Inc. that targets a different step in the life cycle of SARS-CoV-2, the novel coronavirus that causes COVID-19.
The most recent exciting news started to roll out earlier this month when a Pfizer research team published in the journal Science some promising initial data involving the antiviral pill and its active compound [1]. Then came even bigger news a few days later when Pfizer announced interim results from a large phase 2/3 clinical trial. It found that, when taken within three days of developing symptoms of COVID-19, the pill reduced by 89 percent the risk of hospitalization or death in adults at high risk of progressing to severe illness [2].
At the recommendation of the clinical trial’s independent data monitoring committee and in consultation with the U.S. Food and Drug Administration (FDA), Pfizer has now halted the study based on the strength of the interim findings. Pfizer plans to submit the data to the FDA for Emergency Use Authorization (EUA) very soon.
Pfizer’s antiviral pill is a protease inhibitor, originally called PF-07321332, or just 332 for short. A protease is an enzyme that cleaves a protein at a specific series of amino acids. The SARS-CoV-2 virus encodes its own protease to help process a large virally-encoded polyprotein into smaller segments that it needs for its life cycle; a protease inhibitor drug can stop that from happening. If the term protease inhibitor rings a bell, that’s because drugs that work in this way already are in use to treat other viruses, including human immunodeficiency virus (HIV) and hepatitis C virus.
In the case of 332, it targets a protease called Mpro, also called the 3CL protease, coded for by SARS-CoV-2. The virus uses this enzyme to snip some longer viral proteins into shorter segments for use in replication. With Mpro out of action, the coronavirus can’t make more of itself to infect other cells.
What’s nice about this therapeutic approach is that mutations to SARS-CoV-2’s surface structures, such as the spike protein, should not affect a protease inhibitor’s effectiveness. The drug targets a highly conserved, but essential, viral enzyme. In fact, Pfizer originally synthesized and pre-clinically evaluated protease inhibitors years ago as a potential treatment for severe acute respiratory syndrome (SARS), which is caused by a coronavirus closely related to SARS-CoV-2. This drug might even have efficacy against other coronaviruses that cause the common cold.
In the study published earlier this month in Science [1], the Pfizer team led by Dafydd Owen, Pfizer Worldwide Research, Cambridge, MA, reported that the latest version of their Mpro inhibitor showed potent antiviral activity in laboratory tests against not just SARS-CoV-2, but all of the coronaviruses they tested that are known to infect people. Further study in human cells and mouse models of SARS-CoV-2 infection suggested that the treatment might work to limit infection and reduce damage to lung tissue.
In the paper in Science, Owen and colleagues also reported the results of a phase 1 clinical trial with six healthy people. They found that their protease inhibitor, when taken orally, was safe and could reach concentrations in the bloodstream that should be sufficient to help combat the virus.
But would it work to treat COVID-19 in an infected person? So far, the preliminary results from the larger clinical trial of the drug candidate, now known as PAXLOVID™, certainly look encouraging. PAXLOVID™ is a formulation that combines the new protease inhibitor with a low dose of an existing drug called ritonavir, which slows the metabolism of some protease inhibitors and thereby keeps them active in the body for longer periods of time.
The phase 2/3 clinical trial included about 1,200 adults from the United States and around the world who had enrolled in the clinical trial. To be eligible, study participants had to have a confirmed diagnosis of COVID-19 within a five-day period along with mild-to-moderate symptoms of illness. They also required at least one characteristic or condition associated with an increased risk for developing severe illness from COVID-19. Each individual in the study was randomly selected to receive either the experimental antiviral or a placebo every 12 hours for five days.
In people treated within three days of developing COVID-19 symptoms, the Pfizer announcement reports that 0.8 percent (3 of 389) of those who received PAXLOVID™ were hospitalized within 28 days compared to 7 percent (27 of 385) of those who got the placebo. Similarly encouraging results were observed in those who got the treatment within five days of developing symptoms. One percent (6 of 607) on the antiviral were hospitalized versus 6.7 percent (41 of 612) in the placebo group. Overall, there were no deaths among people taking PAXLOVID™; 10 people in the placebo group (1.6 percent) subsequently died.
If all goes well with the FDA review, the hope is that PAXLOVID™ could be prescribed as an at-home treatment to prevent severe illness, hospitalization, and deaths. Pfizer also has launched two additional trials of the same drug candidate: one in people with COVID-19 who are at standard risk for developing severe illness and another evaluating its ability to prevent infection in adults exposed to the coronavirus by a household member.
Meanwhile, Britain recently approved the other recently developed antiviral molnupiravir, which slows viral replication in a different way by blocking its ability to copy its RNA genome accurately. The FDA will meet on November 30 to discuss Merck and Ridgeback’s request for an EUA for molnupiravir to treat mild-to-moderate COVID-19 in infected adults at high risk for severe illness [3]. With Thanksgiving and the winter holidays fast approaching, these two promising antiviral drugs are certainly more reasons to be grateful this year.
References:
[1] An oral SARS-CoV-2 M(pro) inhibitor clinical candidate for the treatment of COVID-19.
Owen DR, Allerton CMN, Anderson AS, Wei L, Yang Q, Zhu Y, et al. Science. 2021 Nov 2: eabl4784.
[2] 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. November 5, 2021.
[3] FDA to hold advisory committee meeting to Discuss Merck and Ridgeback’s EUA Application for COVID-19 oral treatment. Food and Drug Administration. October 14, 2021.
Links:
COVID-19 Research (NIH)
Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) (NIH)
A Study of PF-07321332/Ritonavir in Nonhospitalized Low-Risk Adult Participants With COVID-19 (ClinicalTrials.gov)
A Post-Exposure Prophylaxis Study of PF-07321332/Ritonavir in Adult Household Contacts of an Individual With Symptomatic COVID-19 (ClinicalTrials.gov)
Artificial Intelligence Accurately Predicts Protein Folding
Posted on by Dr. Francis Collins
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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