E. coli
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
Some ‘Hospital-Acquired’ Infections Traced to Patient’s Own Microbiome
Posted on by Dr. Francis Collins
Caption: New computational tool determines whether a gut microbe is the source of a hospital-acquired bloodstream infection
Credit: Fiona Tamburini, Stanford University, Palo Alto, CA
While being cared for in the hospital, a disturbingly large number of people develop potentially life-threatening bloodstream infections. It’s been thought that most of the blame lies with microbes lurking on medical equipment, health-care professionals, or other patients and visitors. And certainly that is often true. But now an NIH-funded team has discovered that a significant fraction of these “hospital-acquired” infections may actually stem from a quite different source: the patient’s own body.
In a study of 30 bone-marrow transplant patients suffering from bloodstream infections, researchers used a newly developed computational tool called StrainSifter to match microbial DNA from close to one-third of the infections to bugs already living in the patients’ large intestines [1]. In contrast, the researchers found little DNA evidence to support the notion that such microbes were being passed around among patients.
A Lean, Mean DNA Packaging Machine
Posted on by Dr. Francis Collins
Credit: Victor Padilla-Sanchez, The Catholic University of America, Washington, D.C.
All plants and animals are susceptible to viral infections. But did you know that’s also true for bacteria? They get nailed by viruses called bacteriophages, and there are thousands of them in nature including this one that resembles a lunar lander: bacteriophage T4 (left panel). It’s a popular model organism that researchers have studied for nearly a century, helping them over the years to learn more about biochemistry, genetics, and molecular biology [1].
The bacteriophage T4 infects the bacterium Escherichia coli, which normally inhabits the gastrointestinal tract of humans. T4’s invasion starts by touching down on the bacterial cell wall and injecting viral DNA through its tube-like tail (purple) into the cell. A DNA “packaging machine” (middle and right panels) between the bacteriophage’s “head” and “tail” (green, yellow, blue spikes) keeps the double-stranded DNA (middle panel, red) at the ready. All the vivid colors you see in the images help to distinguish between the various proteins or protein subunits that make up the intricate structure of the bacteriophage and its DNA packaging machine.
Adding Letters to the DNA Alphabet
Posted on by Dr. Francis Collins
Credit: William B. Kiosses
The recipes for life, going back billions of years to the earliest single-celled organisms, are encoded in a DNA alphabet of just four letters. But is four as high as the DNA code can go? Or, as researchers have long wondered, is it chemically and biologically possible to expand the DNA code by a couple of letters?
A team of NIH-funded researchers is now answering these provocative questions. The researchers recently engineered a semi-synthetic bacterium containing DNA with six letters, including two extra nucleotides [1, 2]. Now, in a report published in Nature, they’ve taken the next critical step [3]. They show that bacteria, like those in the photo, are not only capable of reliably passing on to the next generation a DNA code of six letters, they can use that expanded genetic information to produce novel proteins unlike any found in nature.
Creative Minds: Giving Bacteria Needles to Fight Intestinal Disease
Posted on by Dr. Francis Collins
Cammie Lesser
For Salmonella and many other disease-causing bacteria that find their way into our bodies, infection begins with a poke. That’s because these bad bugs are equipped with a needle-like protein filament that punctures the outer membrane of human cells and then, like a syringe, injects dozens of toxic proteins that help them replicate.
Cammie Lesser at Massachusetts General Hospital and Harvard Medical School, Cambridge, and her colleagues are now on a mission to bioengineer strains of bacteria that don’t cause disease to make these same syringes, called type III secretion systems. The goal is to use such “good” bacteria to deliver therapeutic molecules, rather than toxins, to human cells. Their first target is the gastrointestinal tract, where they hope to knock out hard-to-beat bacterial infections or to relieve the chronic inflammation that comes with inflammatory bowel disease (IBD).
DNA-Encoded Movie Points Way to ‘Molecular Recorder’
Posted on by Dr. Francis Collins
Credit: Seth Shipman, Harvard Medical School, Boston
There’s a reason why our cells store all of their genetic information as DNA. This remarkable molecule is unsurpassed for storing lots of data in an exceedingly small space. In fact, some have speculated that, if encoded in DNA, all of the data ever generated by humans could fit in a room about the size of a two-car garage and, if that room happens to be climate controlled, the data would remain intact for hundreds of thousands of years! [1]
Scientists have already explored whether synthetic DNA molecules on a chip might prove useful for archiving vast amounts of digital information. Now, an NIH-funded team of researchers is taking DNA’s information storage capabilities in another intriguing direction. They’ve devised their own code to record information not on a DNA chip, but in the DNA of living cells. Already, the team has used bacterial cells to store the data needed to outline the shape of a human hand, as well the data necessary to reproduce five frames from a famous vintage film of a horse galloping (see above).
But the researchers’ ultimate goal isn’t to make drawings or movies. They envision one day using DNA as a type of “molecular recorder” that will continuously monitor events taking place within a cell, providing potentially unprecedented looks at how cells function in both health and disease.
Snapshots of Life: Fighting Urinary Tract Infections
Posted on by Dr. Francis Collins
Source: Valerie O’Brien, Matthew Joens, Scott J. Hultgren, James A.J. Fitzpatrick, Washington University, St. Louis
For patients who’ve succeeded in knocking out a bad urinary tract infection (UTI) with antibiotic treatment, it’s frustrating to have that uncomfortable burning sensation flare back up. Researchers are hopeful that this striking work of science and art can help them better understand why severe UTIs leave people at greater risk of subsequent infection, as well as find ways to stop the vicious cycle.
Here you see the bladder (blue) of a laboratory mouse that was re-infected 24 hours earlier with the bacterium Escherichia coli (pink), a common cause of UTIs. White blood cells (yellow) reach out with what appear to be stringy extracellular traps to immobilize and kill the bacteria.
Cool Videos: Making Multicolored Waves in Cell Biology
Posted on by Dr. Francis Collins
Bacteria are single-cell organisms that reproduce by dividing in half. Proteins within these cells organize themselves in a number of fascinating ways during this process, including a recently discovered mechanism that makes the mesmerizing pattern of waves, or oscillations, you see in this video. Produced when the protein MinE chases the protein MinD from one end of the cell to the other, such oscillations are thought to center the cell’s division machinery so that its two new “daughter cells” will be the same size.
To study these dynamic patterns in greater detail, Anthony Vecchiarelli purified MinD and MinE proteins from the bacterium Escherichia coli. Vecchiarelli, who at the time was a postdoc in Kiyoshi Mizuuchi’s intramural lab at NIH’s National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), labeled the proteins with fluorescent markers and placed them on a synthetic membrane, where their movements were then visualized by total internal reflection fluorescence microscopy. The proteins self-organized and generated dynamic spirals of waves: MinD (blue, left); MinE (red, right); and both MinD and MinE (purple, center) [1].
Creative Minds: Searching for Solutions to Chronic Infection
Posted on by Dr. Francis Collins
Kyle R. Allison
If you or a loved one has ever struggled with a bacterial infection that seemed to have gone away with antibiotic treatment, but then came back again, you’ll probably be interested to learn about the work of Kyle Allison. What sometimes happens when a person has an infection—for instance, a staph infection of the skin—is that antibiotics kill off the vast majority of bacteria, but a small fraction remain alive. After antibiotic treatment ends, those lurking bacterial “persisters” begin to multiply, and the person develops a chronic infection that may be very difficult and costly to eliminate.
Unlike antibiotic-resistant superbugs, bacterial persisters don’t possess any specific genetic mutations that protect them against the killing power of one particular medication or another. Rather, the survival of these bacteria depends upon their ability to enter a dormant state that allows them to hang on in the face of antibiotic treatment. It isn’t clear exactly how the bugs do it, and that’s where Kyle’s work comes in.
