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 . 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 . 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 . 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 .
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.
 2019 Antibiotic resistance threats report. Centers for Disease Control and Prevention.
 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.
 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.
 ‘The entire protein universe’: AI predicts shape of nearly every known protein. Callaway E. Nature. 2022 Aug;608(7921):15-16.
Antimicrobial (Drug) Resistance (National Institute of Allergy and Infectious Diseases/NIH)
Collins Lab (Massachusetts Institute of Technology, Cambridge)
AlphaFold (Deep Mind, London, United Kingdom)
NIH Support: National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences
Posted on by Dr. Francis Collins
At the close of every year, editors and writers at the journal Science review the progress that’s been made in all fields of science—from anthropology to zoology—to select the biggest advance of the past 12 months. In most cases, this Breakthrough of the Year is as tough to predict as the Oscar for Best Picture. Not in 2020. In a year filled with a multitude of challenges posed by the emergence of the deadly coronavirus disease 2019 (COVID-2019), the breakthrough was the development of the first vaccines to protect against this pandemic that’s already claimed the lives of more than 360,000 Americans.
In keeping with its annual tradition, Science also selected nine runner-up breakthroughs. This impressive list includes at least three areas that involved efforts supported by NIH: therapeutic applications of gene editing, basic research understanding HIV, and scientists speaking up for diversity. Here’s a quick rundown of all the pioneering advances in biomedical research, both NIH and non-NIH funded:
Shots of Hope. A lot of things happened in 2020 that were unprecedented. At the top of the list was the rapid development of COVID-19 vaccines. Public and private researchers accomplished in 10 months what normally takes about 8 years to produce two vaccines for public use, with more on the way in 2021. In my more than 25 years at NIH, I’ve never encountered such a willingness among researchers to set aside their other concerns and gather around the same table to get the job done fast, safely, and efficiently for the world.
It’s also pretty amazing that the first two conditionally approved vaccines from Pfizer and Moderna were found to be more than 90 percent effective at protecting people from infection with SARS-CoV-2, the coronavirus that causes COVID-19. Both are innovative messenger RNA (mRNA) vaccines, a new approach to vaccination.
For this type of vaccine, the centerpiece is a small, non-infectious snippet of mRNA that encodes the instructions to make the spike protein that crowns the outer surface of SARS-CoV-2. When the mRNA is injected into a shoulder muscle, cells there will follow the encoded instructions and temporarily make copies of this signature viral protein. As the immune system detects these copies, it spurs the production of antibodies and helps the body remember how to fend off SARS-CoV-2 should the real thing be encountered.
It also can’t be understated that both mRNA vaccines—one developed by Pfizer and the other by Moderna in conjunction with NIH’s National Institute of Allergy and Infectious Diseases—were rigorously evaluated in clinical trials. Detailed data were posted online and discussed in all-day meetings of an FDA Advisory Committee, open to the public. In fact, given the high stakes, the level of review probably was more scientifically rigorous than ever.
First CRISPR Cures: One of the most promising areas of research now underway involves gene editing. These tools, still relatively new, hold the potential to fix gene misspellings—and potentially cure—a wide range of genetic diseases that were once to be out of reach. Much of the research focus has centered on CRISPR/Cas9. This highly precise gene-editing system relies on guide RNA molecules to direct a scissor-like Cas9 enzyme to just the right spot in the genome to cut out or correct a disease-causing misspelling.
In late 2020, a team of researchers in the United States and Europe succeeded for the first time in using CRISPR to treat 10 people with sickle cell disease and transfusion-dependent beta thalassemia. As published in the New England Journal of Medicine, several months after this non-heritable treatment, all patients no longer needed frequent blood transfusions and are living pain free .
The researchers tested a one-time treatment in which they removed bone marrow from each patient, modified the blood-forming hematopoietic stem cells outside the body using CRISPR, and then reinfused them into the body. To prepare for receiving the corrected cells, patients were given toxic bone marrow ablation therapy, in order to make room for the corrected cells. The result: the modified stem cells were reprogrammed to switch back to making ample amounts of a healthy form of hemoglobin that their bodies produced in the womb. While the treatment is still risky, complex, and prohibitively expensive, this work is an impressive start for more breakthroughs to come using gene editing technologies. NIH, including its Somatic Cell Genome Editing program, continues to push the technology to accelerate progress and make gene editing cures for many disorders simpler and less toxic.
Scientists Speak Up for Diversity: The year 2020 will be remembered not only for COVID-19, but also for the very public and inescapable evidence of the persistence of racial discrimination in the United States. Triggered by the killing of George Floyd and other similar events, Americans were forced to come to grips with the fact that our society does not provide equal opportunity and justice for all. And that applies to the scientific community as well.
Science thrives in safe, diverse, and inclusive research environments. It suffers when racism and bigotry find a home to stifle diversity—and community for all—in the sciences. For the nation’s leading science institutions, there is a place and a calling to encourage diversity in the scientific workplace and provide the resources to let it flourish to everyone’s benefit.
For those of us at NIH, last year’s peaceful protests and hashtags were noticed and taken to heart. That’s one of the many reasons why we will continue to strengthen our commitment to building a culturally diverse, inclusive workplace. For example, we have established the NIH Equity Committee. It allows for the systematic tracking and evaluation of diversity and inclusion metrics for the intramural research program for each NIH institute and center. There is also the recently founded Distinguished Scholars Program, which aims to increase the diversity of tenure track investigators at NIH. Recently, NIH also announced that it will provide support to institutions to recruit diverse groups or “cohorts” of early-stage research faculty and prepare them to thrive as NIH-funded researchers.
AI Disentangles Protein Folding: Proteins, which are the workhorses of the cell, are made up of long, interconnected strings of amino acids that fold into a wide variety of 3D shapes. Understanding the precise shape of a protein facilitates efforts to figure out its function, its potential role in a disease, and even how to target it with therapies. To gain such understanding, researchers often try to predict a protein’s precise 3D chemical structure using basic principles of physics—including quantum mechanics. But while nature does this in real time zillions of times a day, computational approaches have not been able to do this—until now.
Of the roughly 170,000 proteins mapped so far, most have had their structures deciphered using powerful imaging techniques such as x-ray crystallography and cryo–electron microscopy (cryo-EM). But researchers estimate that there are at least 200 million proteins in nature, and, as amazing as these imaging techniques are, they are laborious, and it can take many months or years to solve 3D structure of a single protein. So, a breakthrough certainly was needed!
In 2020, researchers with the company Deep Mind, London, developed an artificial intelligence (AI) program that rapidly predicts most protein structures as accurately as x-ray crystallography and cryo-EM can map them . The AI program, called AlphaFold, predicts a protein’s structure by computationally modeling the amino acid interactions that govern its 3D shape.
Getting there wasn’t easy. While a complete de novo calculation of protein structure still seemed out of reach, investigators reasoned that they could kick start the modeling if known structures were provided as a training set to the AI program. Utilizing a computer network built around 128 machine learning processors, the AlphaFold system was created by first focusing on the 170,000 proteins with known structures in a reiterative process called deep learning. The process, which is inspired by the way neural networks in the human brain process information, enables computers to look for patterns in large collections of data. In this case, AlphaFold learned to predict the underlying physical structure of a protein within a matter of days. This breakthrough has the potential to accelerate the fields of structural biology and protein research, fueling progress throughout the sciences.
How Elite Controllers Keep HIV at Bay: The term “elite controller” might make some people think of video game whizzes. But here, it refers to the less than 1 percent of people living with human immunodeficiency virus (HIV) who’ve somehow stayed healthy for years without taking antiretroviral drugs. In 2020, a team of NIH-supported researchers figured out why this is so.
In a study of 64 elite controllers, published in the journal Nature, the team discovered a link between their good health and where the virus has inserted itself in their genomes . When a cell transcribes a gene where HIV has settled, this so-called “provirus,” can produce more virus to infect other cells. But if it settles in a part of a chromosome that rarely gets transcribed, sometimes called a gene desert, the provirus is stuck with no way to replicate. Although this discovery won’t cure HIV/AIDS, it points to a new direction for developing better treatment strategies.
In closing, 2020 presented more than its share of personal and social challenges. Among those challenges was a flood of misinformation about COVID-19 that confused and divided many communities and even families. That’s why the editors and writers at Science singled out “a second pandemic of misinformation” as its Breakdown of the Year. This divisiveness should concern all of us greatly, as COVID-19 cases continue to soar around the country and our healthcare gets stretched to the breaking point. I hope and pray that we will all find a way to come together, both in science and in society, as we move forward in 2021.
 CRISPR-Cas9 gene editing for sickle cell disease and β-thalassemia. Frangoul H et al. N Engl J Med. 2020 Dec 5.
 ‘The game has changed.’ AI triumphs at protein folding. Service RF. Science. 04 Dec 2020.
 Distinct viral reservoirs in individuals with spontaneous control of HIV-1. Jiang C et al. Nature. 2020 Sep;585(7824):261-267.
COVID-19 Research (NIH)
2020 Science Breakthrough of the Year (American Association for the Advancement of Science, Washington, D.C)