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
Many of us think of soil as lifeless dirt. But, in fact, soil is teeming with a rich array of life: microbial life. And some of those tiny, dirt-dwelling microorganisms—bacteria that produce antibiotic compounds that are highly toxic to other bacteria—may provide us with valuable leads for developing the new drugs we so urgently need to fight antibiotic-resistant infections.
Recently, NIH-funded researchers discovered a new class of antibiotics, called malacidins, by analyzing the DNA of the bacteria living in more than 2,000 soil samples, including many sent by citizen scientists living all across the United States . While more work is needed before malacidins can be tried in humans, the compounds successfully killed several types of multidrug-resistant bacteria in laboratory tests. Most impressive was the ability of malacadins to wipe out methicillin-resistant Staphylococcus aureus (MRSA) skin infections in rats. Often referred to as a “super bug,” MRSA threatens the lives of tens of thousands of Americans each year .
Posted on by Dr. Francis Collins
The term “freeze-dried” may bring to mind those handy MREs (Meals Ready to Eat) consumed by legions of soldiers, astronauts, and outdoor adventurers. But if one young innovator has his way, a test that features freeze-dried biosensors may soon be a key ally in our nation’s ongoing campaign against the very serious threat of antibiotic-resistant bacterial infections.
Each year, antibiotic-resistant infections account for more than 23,000 deaths in the United States. To help tackle this challenge, Ahmad (Mo) Khalil, a researcher at Boston University, recently received an NIH Director’s New Innovator Award to develop a system that can more quickly determine whether a patient’s bacterial infection will respond best to antibiotic X or antibiotic Y—or, if the infection is actually viral rather than bacterial, no antibiotics are needed at all.
Posted on by Dr. Francis Collins
Microbes that live in dirt often engage in their own deadly turf wars, producing a toxic mix of chemical compounds (also called “small molecules”) that can be a source of new antibiotics. When he started out in science more than a decade ago, Michael Fischbach studied these soil-dwelling microbes to look for genes involved in making these compounds.
Eventually, Fischbach, who is now at the University of California, San Francisco, came to a career-altering realization: maybe he didn’t need to dig in dirt! He hypothesized an even better way to improve human health might be found in the genes of the trillions of microorganisms that dwell in and on our bodies, known collectively as the human microbiome.