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    Exploring the cellular neighborhood

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    Cells rely on complex molecular machines composed of protein assemblies to perform essential functions such as energy production, gene expression, and protein synthesis. To better understand how these machines work, scientists capture snapshots of them by isolating proteins from cells and using various methods to determine their structures. However, isolating proteins from cells also removes them from the context of their native environment, including protein interaction partners and cellular location.

    Recently, cryogenic electron tomography (cryo-ET) has emerged as a way to observe proteins in their native environment by imaging frozen cells at different angles to obtain three-dimensional structural information. This approach is exciting because it allows researchers to directly observe how and where proteins associate with each other, revealing the cellular neighborhood of those interactions within the cell.

    With the technology available to image proteins in their native environment, MIT graduate student Barrett Powell wondered if he could take it one step further: What if molecular machines could be observed in action? In a paper published March 8 in Nature Methods, Powell describes the method he developed, called tomoDRGN, for modeling structural differences of proteins in cryo-ET data that arise from protein motions or proteins binding to different interaction partners. These variations are known as structural heterogeneity. 

    Although Powell had joined the lab of MIT associate professor of biology Joey Davis as an experimental scientist, he recognized the potential impact of computational approaches in understanding structural heterogeneity within a cell. Previously, the Davis Lab developed a related methodology named cryoDRGN to understand structural heterogeneity in purified samples. As Powell and Davis saw cryo-ET rising in prominence in the field, Powell took on the challenge of re-imagining this framework to work in cells.

    When solving structures with purified samples, each particle is imaged only once. By contrast, cryo-ET data is collected by imaging each particle more than 40 times from different angles. That meant tomoDRGN needed to be able to merge the information from more than 40 images, which was where the project hit a roadblock: the amount of data led to an information overload.

    To address this, Powell successfully rebuilt the cryoDRGN model to prioritize only the highest-quality data. When imaging the same particle multiple times, radiation damage occurs. The images acquired earlier, therefore, tend to be of higher quality because the particles are less damaged.

    “By excluding some of the lower-quality data, the results were actually better than using all of the data — and the computational performance was substantially faster,” Powell says.

    Just as Powell was beginning work on testing his model, he had a stroke of luck: The authors of a groundbreaking new study that visualized, for the first time, ribosomes inside cells at near-atomic resolution, shared their raw data on the Electric Microscopy Public Image Archive (EMPIAR). This dataset was an exemplary test case for Powell, through which he demonstrated that tomoDRGN could uncover structural heterogeneity within cryo-ET data. 

    According to Powell, one exciting result is what tomoDRGN found surrounding a subset of ribosomes in the EMPIAR dataset. Some of the ribosomal particles were associated with a bacterial cell membrane and engaged in a process called cotranslational translocation. This occurs when a protein is being simultaneously synthesized and transported across a membrane. Researchers can use this result to make new hypotheses about how the ribosome functions with other protein machinery integral to transporting proteins outside of the cell, now guided by a structure of the complex in its native environment. 

    After seeing that tomoDRGN could resolve structural heterogeneity from a structurally diverse dataset, Powell was curious: How small of a population could tomoDRGN identify? For that test, he chose a protein named apoferritin, which is a commonly used benchmark for cryo-ET and is often treated as structurally homogeneous. Ferritin is a protein used for iron storage and is referred to as apoferritin when it lacks iron.

    Surprisingly, in addition to the expected particles, tomoDRGN revealed a minor population of ferritin particles — with iron bound — making up just 2 percent of the dataset, that was not previously reported. This result further demonstrated tomoDRGN’s ability to identify structural states that occur so infrequently that they would be averaged out of a 3D reconstruction. 

    Powell and other members of the Davis Lab are excited to see how tomoDRGN can be applied to further ribosomal studies and to other systems. Davis works on understanding how cells assemble, regulate, and degrade molecular machines, so the next steps include exploring ribosome biogenesis within cells in greater detail using this new tool.

    “What are the possible states that we may be losing during purification?” Davis asks. “Perhaps more excitingly, we can look at how they localize within the cell and what partners and protein complexes they may be interacting with.” More

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      in Data Management & Statistics

      Probing how proteins pair up inside cells

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      Despite its minute size, a single cell contains billions of molecules that bustle around and bind to one another, carrying out vital functions. The human genome encodes about 20,000 proteins, most of which interact with partner proteins to mediate upwards of 400,000 distinct interactions. These partners don’t just latch onto one another haphazardly; they only bind to very specific companions that they must recognize inside the crowded cell. If they create the wrong pairings — or even the right pairings at the wrong place or wrong time — cancer or other diseases can ensue. Scientists are hard at work investigating these protein-protein relationships, in order to understand how they work, and potentially create drugs that disrupt or mimic them to treat disease.

      The average human protein is composed of approximately 400 building blocks called amino acids, which are strung together and folded into a complex 3D structure. Within this long string of building blocks, some proteins contain stretches of four to six amino acids called short linear motifs (SLiMs), which mediate protein-protein interactions. Despite their simplicity and small size, SLiMs and their binding partners facilitate key cellular processes. However, it’s been historically difficult to devise experiments to probe how SLiMs recognize their specific binding partners.

      To address this problem, a group led by Theresa Hwang PhD ’21 designed a screening method to understand how SLiMs selectively bind to certain proteins, and even distinguish between those with similar structures. Using the detailed information they gleaned from studying these interactions, the researchers created their own synthetic molecule capable of binding extremely tightly to a protein called ENAH, which is implicated in cancer metastasis. The team shared their findings in a pair of eLife studies, one published on Dec. 2, 2021, and the other published Jan. 25.

      “The ability to test hundreds of thousands of potential SLiMs for binding provides a powerful tool to explore why proteins prefer specific SLiM partners over others,” says Amy Keating, professor of biology and biological engineering and the senior author on both studies. “As we gain an understanding of the tricks that a protein uses to select its partners, we can apply these in protein design to make our own binders to modulate protein function for research or therapeutic purposes.”

      Most existing screens for SLiMs simply select for short, tight binders, while neglecting SLiMs that don’t grip their partner proteins quite as strongly. To survey SLiMs with a wide range of binding affinities, Keating, Hwang, and their colleagues developed their own screen called MassTitr.

      The researchers also suspected that the amino acids on either side of the SLiM’s core four-to-six amino acid sequence might play an underappreciated role in binding. To test their theory, they used MassTitr to screen the human proteome in longer chunks comprised of 36 amino acids, in order to see which “extended” SLiMs would associate with the protein ENAH.

      ENAH, sometimes referred to as Mena, helps cells to move. This ability to migrate is critical for healthy cells, but cancer cells can co-opt it to spread. Scientists have found that reducing the amount of ENAH decreases the cancer cell’s ability to invade other tissues — suggesting that formulating drugs to disrupt this protein and its interactions could treat cancer.

      Thanks to MassTitr, the team identified 33 SLiM-containing proteins that bound to ENAH — 19 of which are potentially novel binding partners. They also discovered three distinct patterns of amino acids flanking core SLiM sequences that helped the SLiMs bind even tighter to ENAH. Of these extended SLiMs, one found in a protein called PCARE bound to ENAH with the highest known affinity of any SLiM to date.

      Next, the researchers combined a computer program called dTERMen with X-ray crystallography in order understand how and why PCARE binds to ENAH over ENAH’s two nearly identical sister proteins (VASP and EVL). Hwang and her colleagues saw that the amino acids flanking PCARE’s core SliM caused ENAH to change shape slightly when the two made contact, allowing the binding sites to latch onto one another. VASP and EVL, by contrast, could not undergo this structural change, so the PCARE SliM did not bind to either of them as tightly.

      Inspired by this unique interaction, Hwang designed her own protein that bound to ENAH with unprecedented affinity and specificity. “It was exciting that we were able to come up with such a specific binder,” she says. “This work lays the foundation for designing synthetic molecules with the potential to disrupt protein-protein interactions that cause disease — or to help scientists learn more about ENAH and other SLiM-binding proteins.”  

      Ylva Ivarsson, a professor of biochemistry at Uppsala University who was not involved with the study, says that understanding how proteins find their binding partners is a question of fundamental importance to cell function and regulation. The two eLife studies, she explains, show that extended SLiMs play an underappreciated role in determining the affinity and specificity of these binding interactions.

      “The studies shed light on the idea that context matters, and provide a screening strategy for a variety of context-dependent binding interactions,” she says. “Hwang and co-authors have created valuable tools for dissecting the cellular function of proteins and their binding partners. Their approach could even inspire ENAH-specific inhibitors for therapeutic purposes.”

      Hwang’s biggest takeaway from the project is that things are not always as they seem: even short, simple protein segments can play complex roles in the cell. As she puts it: “We should really appreciate SLiMs more.” More