Research Area: Biochemistry, Biophysics, and Structural Biology

Can bacteria be used to clean up oil spills?

The Drennan Lab is working on insights into how nature performs challenging chemistry in oxygen-free environments, with potential applications for remediation, such as cleaning up oil spills, in situations where traditional approaches are ineffective.

Produced by Lillian Eden | Department of Biology
August 28, 2025

Can bacteria clean up oil spills? The short answer: no. Or, at least, not yet.

The Drennan Lab is working to understand how bacteria perform incredible, radical chemistry on inert compounds. Inert compounds, like those that make up crude oil, are challenging to break down because they contain very stable chains of carbon and hydrogen (hydrocarbons). Some microbes have special enzymes that attach another compound to these long, hydrocarbon chains, which makes it possible for the previously inert compound to be degraded. 

Using cryo-electron microscopy, the Drennan Lab recently determined the three-dimensional structure of a glycyl radical enzyme that catalyzes the formation of carbon-carbon bonds, outlined in a recent paper published in PNAS.

This work provides insights into how nature performs challenging chemistry in oxygen-free environments and has potential applications for remediation, such as cleaning up oil spills, in situations where traditional approaches are ineffective. 

This research was led by former postdoc Mary C. Andorfer, who will continue to explore the power of anaerobic microbes as an Assistant Professor at Michigan State University. This work was funded by the National Institutes of Health. Catherine Drennan is a Professor of Biology and Chemistry at MIT and a Howard Hughes Medical Institute Investigator. 

Mapping cells in time and space: a new tool reveals a detailed history of tumor growth

Weissman and colleagues have developed an advanced lineage tracing tool that not only captures an accurate family tree of cell divisions, but also combines that with spatial information: identifying where each cell ends up within a tissue.

Greta Friar | Whitehead Institute
July 24, 2025

All life is connected in a vast family tree. Every organism exists in relationship to its ancestors, descendants, and cousins, and the path between any two individuals can be traced. The same is true of cells within organisms—each of the trillions of cells in the human body is produced through successive divisions from a fertilized egg, and can all be related to one another through a cellular family tree. In simpler organisms such as the worm C. elegans, this cellular family tree has been fully mapped, but the cellular family tree of a human is many times larger and more complex.

In the past, Whitehead Institute Member Jonathan Weissman and other researchers have developed lineage tracing methods to track and reconstruct the family trees of cell divisions in model organisms in order to understand more about the relationships between cells and how they assemble into tissues, organs, and—in some cases—tumors. These methods could help to answer many questions about how organisms develop and diseases like cancer are initiated and progress.

Now, Weissman and colleagues have developed an advanced lineage tracing tool that not only captures an accurate family tree of cell divisions, but also combines that with spatial information: identifying where each cell ends up within a tissue. The researchers used their tool, PEtracer, to observe the growth of metastatic tumors in mice. Combining lineage tracing and spatial data provided the researchers with a detailed view of how elements intrinsic to the cancer cells and from their environments influenced tumor growth, as Weissman and postdocs in his lab Luke Koblan, Kathryn Yost, and Pu Zheng, and graduate student William Colgan share in a paper published in the journal Science on July 24.

“Developing this tool required combining diverse skillsets through the sort of ambitious interdisciplinary collaboration that’s only possible at a place like Whitehead Institute,” says Weissman, who is also a professor of biology at the Massachusetts Institute of Technology and an HHMI Investigator. “Luke came in with an expertise in genetic engineering, Pu in imaging, Katie in cancer biology, and William in computation but the real key to their success was their ability to work together to build PEtracer.”

“Understanding how cells move in time and space is an important way to look at biology, and here we were able to see both of those things in high resolution. The idea is that by understanding both a cell’s past and where it ends up, you can see how different factors throughout its life influenced its behaviors. In this study we use these approaches to look at tumor growth, though in principle we can now begin to apply these tools to study other biology of interest like embryonic development,” Koblan says.

Designing a tool to track cells in space and time

PEtracer tracks cells’ lineages by repeatedly adding short, predetermined codes to the DNA of cells over time. Each piece of code, called a lineage tracing mark, is made up of 5 bases, the building blocks of DNA. These marks are inserted using a gene editing technology called prime editing, which directly rewrites stretches of DNA with minimal undesired byproducts. Over time, each cell acquires more lineage tracing marks, while also maintaining the marks of its ancestors. The researchers can then compare cells’ combinations of marks to figure out relationships and reconstruct the family tree.

“We used computational modeling to design the tool from first principles, to make sure that it was highly accurate, and compatible with imaging technology. We ran many simulations to land on the optimal parameters for a new lineage tracing tool, and then engineered our system to fit those parameters,” Colgan says.

When the tissue—in this case, a tumor growing in the lung of a mouse—had sufficiently grown, the researchers collected these tissues and used advanced imaging approaches to look at each cell’s lineage relationship to other cells via the lineage tracing marks, along with its spatial position within the imaged tissue and its identity (as determined by the levels of different RNAs expressed in each cell). PEtracer is compatible with both imaging approaches and sequencing methods that capture genetic information from single cells.

“Making it possible to collect and analyze all of this data from the imaging was a large challenge,” Zheng says. “What’s particularly exciting to me is not just that we were able to collect terabytes of data, but that we designed the project to collect data that we knew we could use to answer important questions and drive biological discovery.”

Reconstructing the history of a tumor

Combining the lineage tracing, gene expression, and spatial data let the researchers understand how the tumor grew. They could tell how closely related neighboring cells are and compare their traits. Using this approach, the researchers found that the tumors they were analyzing were made up of four distinct modules, or neighborhoods, of cells.

The tumor cells closest to the lung, the most nutrient-dense region, were the most fit, meaning their lineage history indicated the highest rate of cell division over time. Fitness in cancer cells tends to correlate to how aggressively tumors will grow.

The cells at the “leading edge” of the tumor, the far side from the lung, were more diverse and not as fit. Below the leading edge was a low-oxygen neighborhood of cells that might once have been leading edge cells, now trapped in a less desirable spot. Between these cells and the lung-adjacent cells was the tumor core, a region with both living and dead cells as well as cellular debris.

The researchers found that cancer cells across the family tree were equally likely to end up in most of the regions, with the exception of the lung adjacent region, where a few branches of the family tree dominated. This suggests that the cancer cells’ differing traits were heavily influenced by their environments, or the conditions in their local neighborhoods, rather than their family history. Further evidence of this point was that expression of certain fitness-related genes, such as Fgf1/Fgfbp1, correlated to a cell’s location rather than its ancestry. However, lung adjacent cells also had inherited traits that gave them an edge, including expression of the fitness-related gene Cldn4­—showing that family history influenced outcomes as well.

These findings demonstrate how cancer growth is influenced both by factors intrinsic to certain lineages of cancer cells and by environmental factors that shape the behavior of cancer cells exposed to them.

“By looking at so many dimensions of the tumor in concert, we could gain insights that would not have been possible with a more limited view,” Yost says. “Being able to characterize different populations of cells within a tumor will enable researchers to develop therapies that target the most aggressive populations more effectively.”

“Now that we’ve done the hard work of designing the tool, we’re excited to apply it to look at all sorts of questions in health and disease, in embryonic development, and across other model species, with an eye toward understanding important problems in human health,” Koblan says. “The data we collect will also be useful for training AI models of cellular behavior. We’re excited to share this technology with other researchers and see what we all can discover.”

Luke W. Koblan, Kathryn E. Yost, Pu Zheng, William N. Colgan, Matthew G. Jones, Dian Yang, Arhan Kumar, Jaspreet Sandhu, Alexandra Schnell, Dawei Sun, Can Ergen, Reuben A. Saunders, Xiaowei Zhuang, William E. Allen, Nir Yosef, Jonathan S. Weissman. “High-resolution spatial mapping of cell state and lineage dynamics in vivo with PEtracer.” Science, online July 24, 2025. https://doi.org/10.1126/science.adx3800

Yunha Hwang

Education 

  • PhD, 2024, Evolutionary and Organismic Biology, Harvard University
  • MS, 2018, Earth Systems, Stanford University
  • B.Sc, 2018, Computer Science, Stanford University

Research Summary

Microbial genomes encode the largest molecular, biochemical, and functional diversity on Earth. We focus on developing machine learning models and experimental approaches to discover and design novel biological functions. We integrate computation with expertise in evolution, ecology, and biochemistry to characterize and harness the functional potential of microbes.

Putting liver cells in context: new method combines imaging and sequencing to study gene function in living tissue

Researchers in the Weissman Lab have developed a powerful approach that simultaneously measures how genetic changes such as turning off individual genes affect both gene expression and cell structure in intact liver tissue, with the goal of discovering how genes control organ function and disease.

Whitehead Institute
June 12, 2025

 

However, capturing both the “visuals and sound” of biological data, such as gene expression and cell structure data, from the same cells requires researchers to develop new approaches. They also have to make sure that the data they capture accurately reflects what happens in living organisms, including how cells interact with each other and their environments.

Whitehead Institute and Harvard University researchers have taken on these challenges and developed Perturb-Multimodal (Perturb-Multi), a powerful new approach that simultaneously measures how genetic changes such as turning off individual genes affect both gene expression and cell structure in intact liver tissue. The method, described in Cell on June 12, aims to accelerate discovery of how genes control organ function and disease.

The research team, led by Whitehead Institute Member Jonathan Weissman and then-graduate student in his lab Reuben Saunders, along with Xiaowei Zhuang, the David B. Arnold Professor of Science at Harvard University, and then-postdoc in her lab Will Allen, created a system that can test hundreds of different genetic modifications within a single mouse liver while capturing multiple types of data from the same cells.

“Understanding how our organs work requires looking at many different aspects of cell biology at once,” Saunders says. “With Perturb-Multi, we can see how turning off specific genes changes not just what other genes are active, but also how proteins are distributed within cells, how cellular structures are organized, and where cells are located in the tissue. It’s like having multiple specialized microscopes all focused on the same experiment.”

“This approach accelerates discovery by both allowing us to test the functions of many different genes at once, and then for each gene, allowing us to measure many different functional outputs or cell properties at once—and we do that in intact tissue from animals,” says Zhuang, who is also an HHMI Investigator.

A more efficient approach to genetic studies

Traditional genetic studies in mice often turn off one gene in an animal, and then observe what changes in that gene’s absence to learn about what the gene does. The researchers designed their approach to turn off hundreds of different genes across a single liver, while still only turning off one gene per cell—using what is known as a mosaic approach. This allowed them to study the roles of hundreds of individual genes at once in a single animal. The researchers then collected diverse types of data from cells across the same liver to get a full picture of the consequences of turning off the genes.

“Each cell serves as its own experiment, and because all the cells are in the same animal, we eliminate the variability that comes from comparing different mice,” Saunders says. “Every cell experiences the same physiological conditions, diet, and environment, making our comparisons much more precise.”

“The challenge we faced was that tissues, to perform their functions, rely on thousands of genes, expressed in many different cells, working together. Each gene, in turn, can control many aspects of a cell’s function. Testing these hundreds of genes in mice using current methods would be extremely slow and expensive—near impossible in practice,” Allen says.

Revealing new biology through combined measurements

The team applied Perturb-Multi to study genetic controls of liver physiology and function. Their study led to discoveries in three important aspects of liver biology: fat accumulation in liver cells—a precursor to liver disease; stress responses; and hepatocyte zonation (how liver cells specialize, assuming different traits and functions, based on their location within the liver).

“Overcoming the inherent complexity of biology in living animals required developing new tools that bridge multiple disciplines – including, in this case, genomics, imaging, and AI,” Allen says.

One striking finding emerged from studying genes that, when disrupted, cause fat accumulation in liver cells. The imaging data revealed that four different genes all led to similar fat droplet accumulation, but the sequencing data showed they did so through three completely different mechanisms.

“Without combining imaging and sequencing, we would have missed this complexity entirely,” Saunders says. “The imaging told us which genes affect fat accumulation, while the sequencing revealed whether this was due to increased fat production, cellular stress, or other pathways. This kind of mechanistic insight could be crucial for developing targeted therapies for fatty liver disease.”

The researchers also discovered new regulators of liver cell zonation. Unexpectedly, the newly discovered regulators include genes involved in modifying the extracellular matrix—the scaffolding between cells. “We found that cells can change their specialized functions without physically moving to a different zone,” Saunders says. “This suggests that liver cell identity is more flexible than previously thought.”

Technical innovation enables new science

Developing Perturb-Multi required solving several technical challenges. The team created new methods for preserving the content of interest in cells—RNA and proteins—during tissue processing, for collecting many types of imaging data and single-cell gene expression data from tissue samples that have been fixed with a preservative, and for integrating multiple types of data from the same cells.

“Overcoming the inherent complexity of biology in living animals required developing new tools that bridge multiple disciplines – including, in this case, genomics, imaging, and AI,” Allen says.

The two components of Perturb-Multi—the imaging and sequencing assays—together, applied to the same tissue, provide insights that are unattainable through either assay alone.

“Each component had to work perfectly while not interfering with the others,” says Weissman, who is also a professor of biology at the Massachusetts Institute of Technology and an HHMI Investigator. “The technical development took considerable effort, but the payoff is a system that can reveal biology we simply couldn’t see before.”

Expanding to new organs and other contexts

The researchers plan to expand Perturb-Multi to other organs, including the brain, and to study how genetic changes affect organ function under different conditions like disease states or dietary changes.

“Without combining imaging and sequencing, we would have missed this complexity entirely,” Saunders says.

“We’re also excited about using the data we generate to train machine learning models,” adds Saunders. “With enough examples of how genetic changes affect cells, we could eventually predict the effects of mutations without having to test them experimentally—a ‘virtual cell’ that could accelerate both research and drug development.”

“Perturbation data are critical for training such AI models and the paucity of existing perturbation data represents a major hindrance in such ‘virtual cell’ efforts,” Zhuang says. “We hope Perturb-Multi will fill this gap by accelerating the collection of perturbation data.”

The approach is designed to be scalable, with the potential for genome-wide studies that test thousands of genes simultaneously. As sequencing and imaging technologies continue to improve, the researchers anticipate that Perturb-Multi will become even more powerful and accessible to the broader research community.

“Our goal is to keep scaling up. We plan to do genome-wide perturbations, study different physiological conditions, and look at different organs,” says Weissman. “That we can now collect so many types of data from so many cells, at speed, is going to be critical for building AI models like virtual cells, and I think it’s going to help us answer previously unsolvable questions about health and disease.”

Notes

Reuben A. Saunders, William E. Allen, Xingjie Pan, Jaspreet Sandhu, Jiaqi Lu, Thomas K. Lau, Karina Smolyar, Zuri A. Sullivan, Catherine Dulac, Jonathan S. Weissman, Xiaowei Zhuang. “Perturb-Multimodal: a Platform for Pooled Genetic Screens with Sequencing and Imaging in Intact Mammalian Tissue.” Cell, June 12, 2025. DOI: 10.1016/j.cell.2025.05.022.

MIT Down syndrome researchers work on ways to ensure a healthy lifespan

An Alana Down Syndrome Center webinar, co-sponsored by the Massachusetts Down Syndrome Congress, presented numerous MIT studies that all share the goal of improving health throughout life for people with trisomy 21.

David Orenstein | The Picower Institute for Learning and Memory
April 24, 2025

In recent decades the life expectancy of people with Down syndrome has surged past 60 years, so the focus of research at the Alana Down Syndrome Center at MIT has been to make sure people can enjoy the best health during that increasing timeframe.

“A person with Down syndrome can live a long and happy life,” said Rosalind Mott Firenze, scientific director of the center founded at MIT in 2019 with a gift from the Alana Foundation. “So the question is now how do we improve health and maximize ability through the years? It’s no longer about lifespan, but about healthspan.”

Firenze and three of the center’s Alana Fellows scientists spoke during a webinar, hosted on April 17th, where they described the center’s work toward that goal. An audience of 99 people signed up to hear the webinar titled “Building a Better Tomorrow for Down Syndrome Through Research and Technology,” with many viewers hailing from the Massachusetts Down Syndrome Congress, which co-sponsored the event.

The research they presented covered ways to potentially improve health from stages before birth to adulthood in areas such as brain function, heart development, and sleep quality.

Boosting brain waves

One of the center’s most important areas of research involves testing whether boosting the power of a particular frequency of brain activity—“gamma” brain waves of 40Hz—can improve brain development and function. The lab of the center’s Director Li-Huei Tsai, Picower Professor in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences, uses light that flickers and sound that clicks 40 times a second to increase that rhythm in the brain. In early studies of people with Alzheimer’s disease, which is a major health risk for people with Down syndrome, the non-invasive approach has proved safe, and appears to improve memory while preventing brain cells from dying. The reason it works appears to be because it promotes a healthy response among many types of brain cells.

Working with mice that genetically model Down syndrome, Alana Fellow Dong Shin Park has been using the sensory stimulation technology to study whether the healthy cellular response can affect brain development in a fetus while a mother is pregnant. In ongoing research, he said, he’s finding that exposing pregnant mice to the light and sound appears to improve fetal brain development and brain function in the pups after they are born.

In his research, Postdoctoral Associate Md. Rezaul Islam worked with 40Hz sensory stimulation and Down syndrome model mice at a much later stage in life—when they are adult aged. Together with former Tsai Lab member Brennan Jackson, he found that when the mice were exposed to the light and sound, their memory improved. The underlying reason seemed to be an increase not only in new connections among their brain cells, but also an increase in the generation of new ones. The research, currently online as a preprint, is set to publish in a peer-reviewed journal very soon.

Firenze said the Tsai lab has also begun to test the sensory stimulation in human adults with Down syndrome. In that testing, which is led by Dr. Diane Chan, it is proving safe and well tolerated, so the lab is hoping to do a year-long study with volunteers to see if the stimulation can delay or prevent the onset of Alzheimer’s disease.

Studying cells

Many Alana Center researchers are studying other aspects of the biology of cells in Down syndrome to improve healthspan. Leah Borden, an Alana Fellow in the lab of Biology Professor Laurie Boyer, is studying differences in heart development. Using advanced cultures of human heart tissues grown from trisomy 21 donors, she is finding that tissue tends to be stiffer than in cultures made from people without the third chromosome copy. The stiffness, she hypothesizes, might affect cellular function and migration during development, contributing to some of the heart defects that are common in the Down syndrome population.

Firenze pointed to several other advanced cell biology studies going on in the center. Researchers in the lab of Computer Science Professor Manolis Kellis, for instance, have used machine learning and single cell RNA sequencing to map the gene expression of more than 130,000 cells in the brains of people with or without Down syndrome to understand differences in their biology.

Researchers the lab of Y. Eva Tan Professor Edward Boyden, meanwhile, are using advanced tissue imaging techniques to look into the anatomy of cells in mice, Firenze said. They are finding differences in the structures of key organelles called mitochondria that provide cells with energy.

And in 2022, Firenze recalled, Tsai’s lab published a study showing that brain cells in Down syndrome mice exhibited a genome-wide disruption in how genes are expressed, leading them to take on a more senescent, or aged-like, state.

Striving for better sleep

One other theme of the Alana Center’s research that Firenze highlighted focuses on ways to understand and improve sleep for people with Down syndrome. In mouse studies in Tsai’s lab, they’ve begun to measure sleep differences between model and neurotypical mice to understand more about the nature of sleep disruptions.

“Sleep is different and we need to address this because it’s a key factor in your health,” Firenze said.

Firenze also highlighted how the Alana Center has collaborated with MIT’s Desphande Center for Technological Innovation to help advance a new device for treating sleep apnea in people with Down syndrome. Led by Mechanical Engineering Associate Professor Ellen Roche, the ZzAlign device improves on current technology by creating a custom-fit oral prosthesis accompanied by just a small tube to provide the needed air pressure to stabilize mouth muscles and prevent obstruction of the airway.

Through many examples of research projects aimed at improving brain and heart health and enhancing sleep, the webinar presented how MIT’s Alana Down Syndrome Center is working to advance the healthspan of people with Down syndrome.

 

MIT biologists discover a new type of control over RNA splicing

They identified proteins that influence splicing of about half of all human introns, allowing for more complex types of gene regulation.

Anne Trafton | MIT News
February 20, 2025

RNA splicing is a cellular process that is critical for gene expression. After genes are copied from DNA into messenger RNA, portions of the RNA that don’t code for proteins, called introns, are cut out and the coding portions are spliced back together.

This process is controlled by a large protein-RNA complex called the spliceosome. MIT biologists have now discovered a new layer of regulation that helps to determine which sites on the messenger RNA molecule the spliceosome will target.

The research team discovered that this type of regulation, which appears to influence the expression of about half of all human genes, is found throughout the animal kingdom, as well as in plants. The findings suggest that the control of RNA splicing, a process that is fundamental to gene expression, is more complex than previously known.

“Splicing in more complex organisms, like humans, is more complicated than it is in some model organisms like yeast, even though it’s a very conserved molecular process. There are bells and whistles on the human spliceosome that allow it to process specific introns more efficiently. One of the advantages of a system like this may be that it allows more complex types of gene regulation,” says Connor Kenny, an MIT graduate student and the lead author of the study.

Christopher Burge, the Uncas and Helen Whitaker Professor of Biology at MIT, is the senior author of the study, which appears today in Nature Communications.

Building proteins

RNA splicing, a process discovered in the late 1970s, allows cells to precisely control the content of the mRNA transcripts that carry the instructions for building proteins.

Each mRNA transcript contains coding regions, known as exons, and noncoding regions, known as introns. They also include sites that act as signals for where splicing should occur, allowing the cell to assemble the correct sequence for a desired protein. This process enables a single gene to produce multiple proteins; over evolutionary timescales, splicing can also change the size and content of genes and proteins, when different exons become included or excluded.

The spliceosome, which forms on introns, is composed of proteins and noncoding RNAs called small nuclear RNAs (snRNAs). In the first step of spliceosome assembly, an snRNA molecule known as U1 snRNA binds to the 5’ splice site at the beginning of the intron. Until now, it had been thought that the binding strength between the 5’ splice site and the U1 snRNA was the most important determinant of whether an intron would be spliced out of the mRNA transcript.

In the new study, the MIT team discovered that a family of proteins called LUC7 also helps to determine whether splicing will occur, but only for a subset of introns — in human cells, up to 50 percent.

Before this study, it was known that LUC7 proteins associate with U1 snRNA, but the exact function wasn’t clear. There are three different LUC7 proteins in human cells, and Kenny’s experiments revealed that two of these proteins interact specifically with one type of 5’ splice site, which the researchers called “right-handed.” A third human LUC7 protein interacts with a different type, which the researchers call “left-handed.”

The researchers found that about half of human introns contain a right- or left-handed site, while the other half do not appear to be controlled by interaction with LUC7 proteins. This type of control appears to add another layer of regulation that helps remove specific introns more efficiently, the researchers say.

“The paper shows that these two different 5’ splice site subclasses exist and can be regulated independently of one another,” Kenny says. “Some of these core splicing processes are actually more complex than we previously appreciated, which warrants more careful examination of what we believe to be true about these highly conserved molecular processes.”

“Complex splicing machinery”

Previous work has shown that mutation or deletion of one of the LUC7 proteins that bind to right-handed splice sites is linked to blood cancers, including about 10 percent of acute myeloid leukemias (AMLs). In this study, the researchers found that AMLs that lost a copy of the LUC7L2 gene have inefficient splicing of right-handed splice sites. These cancers also developed the same type of altered metabolism seen in earlier work.

“Understanding how the loss of this LUC7 protein in some AMLs alters splicing could help in the design of therapies that exploit these splicing differences to treat AML,” Burge says. “There are also small molecule drugs for other diseases such as spinal muscular atrophy that stabilize the interaction between U1 snRNA and specific 5’ splice sites. So the knowledge that particular LUC7 proteins influence these interactions at specific splice sites could aid in improving the specificity of this class of small molecules.”

Working with a lab led by Sascha Laubinger, a professor at Martin Luther University Halle-Wittenberg, the researchers found that introns in plants also have right- and left-handed 5’ splice sites that are regulated by Luc7 proteins.

The researchers’ analysis suggests that this type of splicing arose in a common ancestor of plants, animals, and fungi, but it was lost from fungi soon after they diverged from plants and animals.

“A lot what we know about how splicing works and what are the core components actually comes from relatively old yeast genetics work,” Kenny says. “What we see is that humans and plants tend to have more complex splicing machinery, with additional components that can regulate different introns independently.”

The researchers now plan to further analyze the structures formed by the interactions of Luc7 proteins with mRNA and the rest of the spliceosome, which could help them figure out in more detail how different forms of Luc7 bind to different 5’ splice sites.

The research was funded by the U.S. National Institutes of Health and the German Research Foundation.

A sum of their parts

Researchers in the Department of Biology at MIT use an AI-driven approach to computationally predict short amino acid sequences that can bind to or inhibit a target, with a potential for great impact on fundamental biological research and therapeutic applications.

Lillian Eden | Department of Biology
February 6, 2025

All biological function is dependent on how different proteins interact with each other. Protein-protein interactions facilitate everything from transcribing DNA and controlling cell division to higher-level functions in complex organisms.

Much remains unclear about how these functions are orchestrated on the molecular level, however, and how proteins interact with each other — either with other proteins or with copies of themselves. 

Recent findings have revealed that small protein fragments have a lot of functional potential. Even though they are incomplete pieces, short stretches of amino acids can still bind to interfaces of a target protein, recapitulating native interactions. Through this process, they can alter that protein’s function or disrupt its interactions with other proteins. 

Protein fragments could therefore empower both basic research on protein interactions and cellular processes and could potentially have therapeutic applications. 

Recently published in Proceedings of the National Academy of Sciences, a new computational method developed in the Department of Biology at MIT builds on existing AI models to computationally predict protein fragments that can bind to and inhibit full-length proteins in E. coli. Theoretically, this tool could lead to genetically encodable inhibitors against any protein. 

The work was done in the lab of Associate Professor of Biology and HHMI Investigator Gene-Wei Li in collaboration with the lab of Jay A. Stein (1968) Professor of Biology, Professor of Biological Engineering and Department Head Amy Keating.

Leveraging Machine Learning

The program, called FragFold, leverages AlphaFold, an AI model that has led to phenomenal advancements in biology in recent years due to its ability to predict protein folding and protein interactions. 

The goal of the project was to predict fragment inhibitors, which is a novel application of AlphaFold. The researchers on this project confirmed experimentally that more than half of FragFold’s predictions for binding or inhibition were accurate, even when researchers had no previous structural data on the mechanisms of those interactions. 

“Our results suggest that this is a generalizable approach to find binding modes that are likely to inhibit protein function, including for novel protein targets, and you can use these predictions as a starting point for further experiments,” says co-first and corresponding author Andrew Savinov, a postdoc in the Li Lab. “We can really apply this to proteins without known functions, without known interactions, without even known structures, and we can put some credence in these models we’re developing.”

One example is FtsZ, a protein that is key for cell division. It is well-studied but contains a region that is intrinsically disordered and, therefore, especially challenging to study. Disordered proteins are dynamic, and their functional interactions are very likely fleeting — occurring so briefly that current structural biology tools can’t capture a single structure or interaction. 

The researchers leveraged FragFold to explore the activity of fragments of FtsZ, including fragments of the intrinsically disordered region, to identify several new binding interactions with various proteins. This leap in understanding confirms and expands upon previous experiments measuring FtsZ’s biological activity. 

This progress is significant in part because it was made without solving the disordered region’s structure, and because it exhibits the potential power of FragFold.

“This is one example of how AlphaFold is fundamentally changing how we can study molecular and cell biology,” Keating says. “Creative applications of AI methods, such as our work on FragFold, open up unexpected capabilities and new research directions.”

Inhibition, and beyond

The researchers accomplished these predictions by computationally fragmenting each protein and then modeling how those fragments would bind to interaction partners they thought were relevant.

They compared the maps of predicted binding across the entire sequence to the effects of those same fragments in living cells, determined using high-throughput experimental measurements in which millions of cells each produce one type of protein fragment. 

AlphaFold uses co-evolutionary information to predict folding, and typically evaluates the evolutionary history of proteins using something called multiple sequence alignments for every single prediction run. The MSAs are critical, but are a bottleneck for large-scale predictions — they can take a prohibitive amount of time and computational power. 

For FragFold, the researchers instead pre-calculated the MSA for a full-length protein once and used that result to guide the predictions for each fragment of that full-length protein. 

Savinov, together with Keating Lab alum Sebastian Swanson, PhD ‘23, predicted inhibitory fragments of a diverse set of proteins in addition to FtsZ. Among the interactions they explored was a complex between lipopolysaccharide transport proteins LptF and LptG. A protein fragment of LptG inhibited this interaction, presumably disrupting the delivery of lipopolysaccharide, which is a crucial component of the E. coli outer cell membrane essential for cellular fitness.

“The big surprise was that we can predict binding with such high accuracy and, in fact, often predict binding that corresponds to inhibition,” Savinov says. “For every protein we’ve looked at, we’ve been able to find inhibitors.”

The researchers initially focused on protein fragments as inhibitors because whether a fragment could block an essential function in cells is a relatively simple outcome to measure systematically. Looking forward, Savinov is also interested in exploring fragment function outside inhibition, such as fragments that can stabilize the protein they bind to, enhance or alter its function, or trigger protein degradation. 

Design, in principle 

This research is a starting point for developing a systemic understanding of cellular design principles, and what elements deep-learning models may be drawing on to make accurate predictions. 

“There’s a broader, further-reaching goal that we’re building towards,” Savinov says. “Now that we can predict them, can we use the data we have from predictions and experiments to pull out the salient features to figure out what AlphaFold has actually learned about what makes a good inhibitor?” 

Savinov and collaborators also delved further into how protein fragments bind, exploring other protein interactions and mutating specific residues to see how those interactions change how the fragment interacts with its target. 

Experimentally examining the behavior of thousands of mutated fragments within cells, an approach known as deep mutational scanning, revealed key amino acids that are responsible for inhibition. In some cases, the mutated fragments were even more potent inhibitors than their natural, full-length sequences. 

“Unlike previous methods, we are not limited to identifying fragments in experimental structural data,” says Swanson. “The core strength of this work is the interplay between high-throughput experimental inhibition data and the predicted structural models: the experimental data guides us towards the fragments that are particularly interesting, while the structural models predicted by FragFold provide a specific, testable hypothesis for how the fragments function on a molecular level.”

Savinov is excited about the future of this approach and its myriad applications.

“By creating compact, genetically encodable binders, FragFold opens a wide range of possibilities to manipulate protein function,” Li agrees. “We can imagine delivering functionalized fragments that can modify native proteins, change their subcellular localization, and even reprogram them to create new tools for studying cell biology and treating diseases.” 

Kingdoms collide as bacteria and cells form captivating connections

Studying the pathogen R. parkeri, researchers discovered the first evidence of extensive and stable interkingdom contacts between a pathogen and a eukaryotic organelle.

Lillian Eden | Department of Biology
January 24, 2025

In biology textbooks, the endoplasmic reticulum is often portrayed as a distinct, compact organelle near the nucleus, and is commonly known to be responsible for protein trafficking and secretion. In reality, the ER is vast and dynamic, spread throughout the cell and able to establish contact and communication with and between other organelles. These membrane contacts regulate processes as diverse as fat metabolism, sugar metabolism, and immune responses.

Exploring how pathogens manipulate and hijack essential processes to promote their own life cycles can reveal much about fundamental cellular functions and provide insight into viable treatment options for understudied pathogens.

New research from the Lamason Lab in the Department of Biology at MIT recently published in the Journal of Cell Biology has shown that Rickettsia parkeri, a bacterial pathogen that lives freely in the cytosol, can interact in an extensive and stable way with the rough endoplasmic reticulum, forming previously unseen contacts with the organelle.

It’s the first known example of a direct interkingdom contact site between an intracellular bacterial pathogen and a eukaryotic membrane.

The Lamason Lab studies R. parkeri as a model for infection of the more virulent Rickettsia rickettsii. R. rickettsii, carried and transmitted by ticks, causes Rocky Mountain Spotted Fever. Left untreated, the infection can cause symptoms as severe as organ failure and death.

Rickettsia is difficult to study because it is an obligate pathogen, meaning it can only live and reproduce inside living cells, much like a virus. Researchers must get creative to parse out fundamental questions and molecular players in the R. parkeri life cycle, and much remains unclear about how R. parkeri spreads.

Detour to the junction

First author Yamilex Acevedo-Sánchez, a BSG-MSRP-Bio program alum and a graduate student at the time, stumbled across the ER and R. parkeri interactions while trying to observe Rickettsia reaching a cell junction.

The current model for Rickettsia infection involves R. parkeri spreading cell to cell by traveling to the specialized contact sites between cells and being engulfed by the neighboring cell in order to spread. Listeria monocytogenes, which the Lamason Lab also studies, uses actin tails to forcefully propel itself into a neighboring cell. By contrast, R. parkeri can form an actin tail, but loses it before reaching the cell junction. Somehow, R. parkeri is still able to spread to neighboring cells.

After an MIT seminar about the ER’s lesser-known functions, Acevedo-Sánchez developed a cell line to observe whether Rickettsia might be spreading to neighboring cells by hitching a ride on the ER to reach the cell junction.

Instead, she saw an unexpectedly high percentage of R. parkeri surrounded and enveloped by the ER, at a distance of about 55 nanometers. This distance is significant because membrane contacts for interorganelle communication in eukaryotic cells form connections from 10-80 nanometers wide. The researchers ruled out that what they saw was not an immune response, and the sections of the ER interacting with the R. parkeri were still connected to the wider network of the ER.

“I’m of the mind that if you want to learn new biology, just look at cells,” Acevedo-Sánchez says. “Manipulating the organelle that establishes contact with other organelles could be a great way for a pathogen to gain control during infection.”

The stable connections were unexpected because the ER is constantly breaking and reforming connections, lasting seconds or minutes. It was surprising to see the ER stably associating around the bacteria. As a cytosolic pathogen that exists freely in the cytosol of the cells it infects, it was also unexpected to see R. parkeri surrounded by a membrane at all.

Small margins

Acevedo-Sánchez collaborated with the Center for Nanoscale Systems at Harvard University to view her initial observations at higher resolution using focused ion beam scanning electron microscopy. FIB-SEM involves taking a sample of cells and blasting them with a focused ion beam in order to shave off a section of the block of cells. With each layer, a high-resolution image is taken. The result of this process is a stack of images.

From there, Acevedo-Sánchez marked what different areas of the images were — such as the mitochondria, Rickettsia, or the ER — and a program called ORS Dragonfly, a machine learning program, sorted through the thousand or so images to identify those categories. That information was then used to create 3D models of the samples.

Acevedo-Sánchez noted that less than 5 percent of R. parkeri formed connections with the ER — but small quantities of certain characteristics are known to be critical for R. parkeri infection. R. parkeri can exist in two states: motile, with an actin tail, and nonmotile, without it. In mutants unable to form actin tails, R. parkeri are unable to progress to adjacent cells — but in nonmutants, the percentage of R. parkeri that have tails starts at about 2 percent in early infection and never exceeds 15 percent at the height of it.

The ER only interacts with nonmotile R. parkeri, and those interactions increased 25-fold in mutants that couldn’t form tails.

Creating connections

Co-authors Acevedo-Sánchez, Patrick Woida, and Caroline Anderson also investigated possible ways the connections with the ER are mediated. VAP proteins, which mediate ER interactions with other organelles, are known to be co-opted by other pathogens during infection.

During infection by R. parkeri, VAP proteins were recruited to the bacteria; when VAP proteins were knocked out, the frequency of interactions between R. parkeri and the ER decreased, indicating R. parkeri may be taking advantage of these cellular mechanisms for its own purposes during infection.

Although Acevedo-Sánchez now works as a senior scientist at AbbVie, the Lamason Lab is continuing the work of exploring the molecular players that may be involved, how these interactions are mediated, and whether the contacts affect the host or bacteria’s life cycle.

Senior author and associate professor of biology Rebecca Lamason noted that these potential interactions are particularly interesting because bacteria and mitochondria are thought to have evolved from a common ancestor. The Lamason Lab has been exploring whether R. parkeri could form the same membrane contacts that mitochondria do, although they haven’t proven that yet. So far, R. parkeri is the only cytosolic pathogen that has been observed behaving this way.

“It’s not just bacteria accidentally bumping into the ER. These interactions are extremely stable. The ER is clearly extensively wrapping around the bacterium, and is still connected to the ER network,” Lamason says. “It seems like it has a purpose — what that purpose is remains a mystery.”

Imperiali Lab News Brief: combining bioinformatics and biochemistry

Parsing endless possibilities

Lillian Eden | Department of Biology
December 11, 2024

New research from the Imperiali Lab in the Department of Biology at MIT combines bioinformatics and biochemistry to reveal critical players in assembling glycans, the large sugar molecules on bacterial cell surfaces responsible for behaviors such as evading immune responses and causing infections.

In most cases, single-celled organisms such as bacteria interact with their environment through complex chains of sugars known as glycans bound to lipids on their outer membranes. Glycans orchestrate biological responses and interactions, such as evading immune responses and causing infections. 

The first step in assembling most bacterial glycans is the addition of a sugar-phosphate group onto a lipid, which is catalyzed by phosphoglycosyl transferases (PGTs) on the inner membrane. This first sugar is then further built upon by other enzymes in subsequent steps in an assembly-line-like pathway. These critical biochemical processes are challenging to explore because the proteins involved in these processes are embedded in membranes, which makes them difficult to isolate and study. 

Although glycans are found in all living organisms, the sugar molecules that compose glycans are especially diverse in bacteria. There are over 30,000 known bacterial PGTs, and hundreds of sugars for them to act upon. 

Research recently published in PNAS from the Imperiali Lab in the Department of Biology at MIT uses a combination of bioinformatics and biochemistry to predict clusters of “like-minded” PGTs and verify which sugars they will use in the first step of glycan assembly. 

Defining the biochemical machinery for these assembly pathways could reveal new strategies for tackling antibiotic-resistant strains of bacteria. This comprehensive approach could also be used to develop and test inhibitors, halting the assembly pathway at this critical first step. 

Exploring Sequence Similarity

First author Theo Durand, an undergraduate student from Imperial College London who studied at MIT for a year, worked in the Imperiali Lab as part of a research placement. Durand was first tasked with determining which sugars some PGTs would use in the first step of glycan assembly, known as the sugar substrates of the PGTs. When initially those substrate-testing experiments didn’t work, Durand turned to the power of bioinformatics to develop predictive tools. 

Strategically exploring the sugar substrates for PGTs is challenging due to the sheer number of PGTs and the diversity of bacteria, each with its own assorted set of glycans and glycoconjugates. To tackle this problem, Durand deployed a tool called a Sequence Similarity Network (SSN), part of a computational toolkit developed by the Enzyme Function Initiative. 

According to senior author Barbara Imperiali, Class of 1922 Professor of Biology and Chemistry, an SSN provides a powerful way to analyze protein sequences through comparisons of the sequences of tens of thousands of proteins. In an optimized SSN, similar proteins cluster together, and, in the case of PGTs, proteins in the same cluster are likely to share the same sugar substrate. 

For example, a previously uncharacterized PGT that appears in a cluster of PGTs whose first sugar substrate is FucNAc4N would also be predicted to use FucNAc4N. The researchers could then test that prediction to verify the accuracy of the SSN. 

FucNAc4N is the sugar substrate for the PGT of Fusobacterium nucleatum (F. nucleatum), a bacterium that is normally only present in the oral cavity but is correlated with certain cancers and endometriosis, and Streptococcus pneumoniae, a bacterium that causes pneumonia. 

Adjusting the assay

The critical biochemical process of assembling glycans has historically been challenging to define, mainly because assembly is anchored to the interior side of the inner membrane of the bacterium. The purification process itself can be difficult, and the purified proteins don’t necessarily behave in the same manner once outside their native membrane environment.

To address this, the researchers modified a commercially available test to work with proteins still embedded in the membrane of the bacterium, thus saving them weeks of work to purify the proteins. They could then determine the substrate for the PGT by measuring whether there was activity. This first step in glycan assembly is chemically unique, and the test measures one of the reaction products. 

For PGTs whose substrate was unknown, Durand did a deep dive into the literature to find new substrates to test. FucNAc4N, the first sugar substrate for F. nucleatum, was, in fact, Durand’s favorite sugar – he found it in the literature and reached out to a former Imperiali Lab postdoc for the instructions and materials to make it. 

“I ended up down a rabbit hole where I was excited every time I found a new, weird sugar,” Durand recalls with a laugh. “These bacteria are doing a bunch of really complicated things and any tools to help us understand what is actually happening is useful.” 

Exploring inhibitors

Imperiali noted that this research both represents a huge step forward in our understanding of bacterial PGTs and their substrates and presents a pipeline for further exploration. She’s hoping to create a searchable database where other researchers can seed their own sequences into the SSN for their organisms of interest. 

This pipeline could also reveal antibiotic targets in bacteria. For example, she says, the team is using this approach to explore inhibitor development. 

The Imperiali lab worked with Karen Allen, a professor of Chemistry at Boston University, and graduate student Roxanne Siuda to test inhibitors, including ones for F. nucleatum, the bacterium correlated with certain cancers and endometriosis whose first sugar substrate is FucNAc4N. They are also hoping to obtain structures of inhibitors bound to the PGT to enable structure-guided optimization.

“We were able to, using the network, discover the substrate for a PGT, verify the substrate, use it in a screen, and test an inhibitor,” Imperiali says. “This is bioinformatics, biochemistry, and probe development all bundled together, and represents the best of functional genomics.”

PNAS Profile: Catherine Drennan

Structural intuitions lead to structural insights

Jennifer Viegas | PNAS
November 8, 2024

HHMI Investigator and Professor of Biology and Chemistry Catherine Drennan has spent a distinguished career addressing challenging and wide-ranging structural biology problems.

Catherine Drennan, a Howard Hughes Medical Institute investigator and professor and a professor of biology and chemistry at the Massachusetts Institute of Technology (MIT), has spent a distinguished career addressing challenging and wide-ranging structural biology problems. These include her discovery, while she was a graduate student, of the structure of vitamin B12 bound to protein and her recent determination at atomic resolution of the structure of an active ribonucleotide reductase (RNR) with water molecules, findings reported in her Inaugural Article (IA) (1).

Drennan, who was elected to the National Academy of Sciences in 2023, has uncovered the form and function of metalloenzymes that use metal cofactors to catalyze chemical reactions involving free radicals. Metalloenzymes are of broad human health and environmental interest; some are promising antibiotic and cancer drug targets, whereas others hold the potential for bioremediation efforts, such as converting carbon dioxide into biofuels.

Family of Accomplished Scientists

Drennan was raised in New York City by her father, an obstetrician–gynecologist, and her mother, an anthropologist. Her father was born in Germany and attended medical school at the University of Hamburg. Harboring antifascist leanings, he fled Germany in 1933. He completed medical training in Geneva, Switzerland, before obtaining political asylum in 1940 in the United States, where he became one of the first doctors to practice the Lamaze Method of natural childbirth.
Drennan’s mother attended Antioch College, where she was a student of civil engineer Arthur Ernest Morgan, who was appointed in 1948 to India’s first University Education Commission. She accompanied Morgan to India and served as his administrative assistant before earning her doctorate in anthropology at Cornell University.

“Both my parents were endlessly curious,” says Drennan. “My father was fascinated by the molecular basis of medicine, and my mother was fascinated by people and instilled in me her love for storytelling, teaching, and mentoring.”

Diagnosed with Dyslexia

Although she was an attentive student, Drennan did not learn to read until her second time through sixth grade. “When I finally learned how to read, it was by memorizing the shapes of words,” says Drennan, who was diagnosed with dyslexia when she was in first grade. “Over time I became very good at shape recognition. I am not disabled; I am differently abled. The skill set that I developed to compensate for my dyslexia has made me a world-class structural biologist. We all have strengths and weaknesses, and my ‘weakness’ is also my superpower.”
She was accepted to Vassar College, where she earned a bachelor’s degree in chemistry in 1985. “Miriam Rossi was my undergraduate chemistry research advisor, and she believed in me before I believed in me,” Drennan says. Upon Rossi’s advice, Drennan pursued a doctorate, but not before teaching high-school science and drama at Scattergood Friends School in Iowa.
Following three years of high-school teaching, Drennan pursued graduate studies at the University of Michigan, Ann Arbor, where she earned a PhD in 1995, served as a research fellow from 1995 to 1996, and was mentored by biochemists Martha Ludwig and Rowena Matthews. “They treated me as a colleague, allowing me to see myself as a scientist of value,” Drennan says. “I learned so much from these two incredible scientists. They are, and always will be, my heroes.”

Structure of Vitamin B12 Bound to Protein

With Ludwig et al., Drennan determined the structure of cobalamin (vitamin B12) bound to protein (2). This crystal structure revealed how the protein modulates the reactivity of the B12 cofactor to enable its critical roles in metabolism.
From 1996 to 1999, Drennan did a postdoctoral stint at the California Institute of Technology, under the mentorship of structural biologist Douglas Rees. “Doug taught by example that one does not have to be cutthroat to succeed in the competitive area of structural biology,” she says. “He has continued to mentor me throughout my career, helping me through challenging times.”
Another important mentor was chemist JoAnne Stubbe, a leader in the study of RNRs who recruited Drennan to MIT in 1999 as an assistant professor of chemistry and has been her collaborator for the past 25 years. Drennan says, “Her passion for scientific discovery is unmatched and has inspired me to keep digging to try to understand, at the most fundamental level, how ribonucleotide reductase works.” Drennan advanced to an associate professorship at MIT in 2004 and a full professorship in 2006.

Revealing Metalloenzyme Form and Function

Drennan’s group continues to study B12 and has provided numerous snapshots of cobalamin-dependent proteins and protein complexes. The findings have changed what is known about B12 functions and mechanisms. Using X-ray crystallography, the researchers unveiled a protein complex capable of methyl transfer from folate to B12 (3). They obtained snapshots of the biological process involved in loading B12 into an enzyme (4) and provided structural data on how B12 can be repurposed from enzyme cofactor to light sensor (5).
Drennan has also worked on uncovering the structures of enzymes containing radical S-adenosylmethionine (SAM) cofactors. Drennan and colleagues revealed an X-ray structure of a radical SAM enzyme (6), helping to establish the “core” fold for an enzyme superfamily that has over 100,000 members. Her group further elucidated structures of SAM family members with functions including posttranslational modification (7), antibiotic and antiviral compound biosynthesis (8, 9), and vitamin biosynthesis (6, 10).
Mononuclear nonheme iron enzymes are also of interest to Drennan. The cofactor is simple, but the reactions catalyzed are complex. Her group reported the structure of a nonheme iron halogenase, showing that the halogen binds directly to the catalytic iron (11). Drennan says, “This was a complete surprise that required new mechanistic proposals to be written.”

“Oceanic Methane Paradox”

Early in her independent career, Drennan determined one of the first structures of a nickel-iron-sulfur-dependent carbon monoxide dehydrogenase (CODH), which plays an important role in the global carbon cycle (12). The structure, along with that of an associated enzyme complex (13), provided a series of snapshots of the multiple metal ion centers underlying the ability of certain microbes to live off hydrogen gas and carbon dioxide in a process known as acetogenesis. More recently, they investigated the molecular basis by which the activity of CODH enzymes can be restored after oxygen exposure (14), a discovery with implications for the industrial use of CODHs.
Drennan and her team have also studied the organic compound methylphosphonate that was proposed as a source of methane from the aerobic upper ocean; the biological source was long a mystery. When a methylphosphonate synthase was discovered by chemical biologist Wilfred van der Donk and coworkers, part of the mystery was solved but the gene for this enzyme did not appear to be widespread. When Drennan and colleagues solved the structure of a methylphosphonate synthase; however, they discovered a sequence motif showing that the gene was, in fact, abundant in microbes that inhabit the upper ocean (15). This seminal finding is credited with resolving the oceanic methane paradox.

Radical-Based Chemistry in Ribonucleotide Reductases

Human RNR is an established chemotherapeutic target, and bacterial RNRs hold promise as antibiotic targets. So Drennan and her team have a longstanding interest in uncovering the mechanisms of RNRs. In 2002, her lab determined the structure of a B12-dependent RNR, which showed how cobalamin could be used to initiate radical chemistry (16). Nearly a decade later, Drennan’s team revealed how high levels of the nucleotide deoxyadenosine triphosphate (dATP) down-regulate RNR activity (17, 18). They subsequently provided structures showing the molecular basis of allosteric specificity, which maintains the proper ratios of RNA to DNA building blocks (19), and demonstrated the importance of RNR activity regulation (20).
An atomic-resolution structure of any RNR in an active state had been elusive for many years. Drennan and her team achieved the feat in 2020 when they trapped the active state of Escherichia coli RNR and determined its structure by cryoelectron microscopy (21). However, the resolution of the structure was too low for the visualization of water molecules believed to be critical in the radical transfer pathway.
In her IA, Drennan (1) describes how her team resolved the problem, presenting the structure of an active RNR at atomic resolution allowing for the visualization of water molecules. She explains, “This time, instead of using unnatural amino acids to trap the structure, we used a mechanism-based inhibitor. It was a very long road to get to these data, but it was worth the wait.”

“Superheroes of the Cell”

For her achievements, Drennan has received MIT’s Everett Moore Baker Memorial Award for Excellence in Undergraduate Teaching (2005, 2024), the Dorothy Crowfoot Hodgkin Award from The Protein Society (2020), and the William C. Rose Award from the American Society for Biochemistry and Molecular Biology (2023), among other honors. She has mentored nearly 100 undergraduates and dozens of graduate students and postdoctoral associates, many of whom are from underrepresented minority groups or disadvantaged backgrounds. She considers her students extended family members and takes pride in their accomplishments.
She and her team continue to work on RNR using the tools of structural biology. She says, “We want to obtain a deeper level of understanding of the human RNR, which is a cancer drug target. We also want to identify differences between the human enzyme and bacterial RNRs, differences that could be exploited in the development of new antibiotics.”
Beyond these efforts, Drennan’s overall goal is to understand how enzymes control radical species to enable challenging chemical reactions without damaging themselves or their cellular environment. “Radical enzymes are like the Avengers, powerful but with a high potential for collateral damage,” she explains. “I am fascinated by how nature catalyzes the most challenging of chemical reactions. The enzymes that do this work are the superheroes of the cell and I want to know their secrets.”
1.
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12.
C. L. Drennan et al., Life on carbon monoxide: X-ray structure of Rhodospirillum rubrum Ni-Fe-S carbon monoxide dehydrogenase. Proc. Natl. Acad. Sci. U.S.A. 98, 11973–11978 (2001). Crossref. PubMed.
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