Cryogenic electron microscopy (CryoEM) plays a crucial role in advancing our understanding and utilization of protein structures, and in generating data that differentiate Generate:Biomedicines in the fast-evolving field of generative AI-led drug discovery.
CryoEM is a powerful imaging technique that captures high-resolution images of flash-frozen biological samples using an electron microscope, enabling visualization of proteins in their native states. We have deployed four room-sized electron microscopes at our 70,000 sq ft site in Andover, Massachusetts to reveal detailed 3D protein structures as an essential tool in our efforts to advance drug discovery and generate novel protein therapeutics beyond traditional trial-and-error methods.
Ed Brignole joined Generate in December 2022 as the Senior Director of CryoEM, bringing over two decades of expertise in structural biology and cutting-edge imaging techniques. Before joining Generate, he built and led the CryoEM Facility at MIT, enabling advanced visualization of molecular structures for researchers at MIT and beyond. Ed also held positions at Johns Hopkins University and the Scripps Research Institute, one of the first institutes to automate CryoEM technology. With his deep knowledge of and leadership in this field, Ed is a pioneer of CryoEM, helping us find new opportunities for this technology and push its capabilities to new heights.
Read on for insights from Ed about his perspective on CryoEM, and the future of the technology that helps us better see and understand our molecules.
Can you tell me a little about CryoEM technology? Why is it so important for our work?
Ed Brignole: Our CryoEM capabilities are like the eyes on our design, build, measure, learn cycle. Without CryoEM, we’re essentially building molecules blindfolded. This technology allows us to visualize our molecules and gives us the capacity to look at far more molecules than anyone else. That’s why we have four machines at our Andover site, coupled with our ability to make any proteins we can imagine. In the age of AI, the company with the most and best data wins. Our CryoEM capability gives us a wealth of the right kind of data, and a kind of data that few others can match.
What got you interested in CryoEM and how did you get your start in this field?
Ed Brignole: I was drawn to structural biology in general. I remember when I first saw those swirly, ribbon-shaped diagrams of proteins in my biochemistry textbook and realized that the structure of these molecules is what allows biology to work. And I became particularly interested in the protein folding problem*, which seemed insurmountable at the time.
My interest in protein structure led me to a postdoc at the Scripps Research Institute from 2006–2010 using CryoEM to study metabolic enzymes and transcription complexes. Scripps had one of the few CryoEM setups in the world at the time, and we were pioneering automation of this technology. However, none of us could truly imagine where CryoEM would go.
So where did the technology go? What unlocked the power of this critical technology?
Ed Brignole: The key advance came with improved camera technology — the same changes were paralleled in the evolution of cell phone cameras. We went from film cameras to digital cameras to suddenly all having a professional-grade video camera in our pocket. That same technology transformed CryoEM. With better cameras inside the microscopes, we could capture higher-resolution images and even movies, revolutionizing our ability to visualize molecules. Advances in CCD and CMOS camera sensors alone would not have been enough — however, we had Moore’s law** and the exponentially increasing power to compute on terabytes per day of this imaging data!
With the leap in camera technology enabling better imaging, what other challenges face CryoEM technology now?
Ed Brignole: I see two main challenges that CryoEM must address in the future. First, managing and fully using the vast amounts of data generated. We are building computational tools to handle, store, and analyze this data efficiently, aiming for more automation across the CryoEM workflow. Second, next generation specimen preparation techniques will need to evolve to mitigate the impact of freezing on outcomes and will enhance structure throughput.
How are we using this technology differently than it has traditionally been deployed? Is working at Generate very different than, say, your previous role managing MIT’s CryoEM Facility?
Ed Brignole: It’s a total mindset shift. I knew biotech would be different from my time in academia – and that’s a small part of what drew me to Generate – but the way we use CryoEM is a lot more different here than I had ever imagined. CryoEM is traditionally an artisanal method, with individual practitioners, postdocs and grad students pouring years of sweat into one-off structures for their individual projects. With decades of artisanal effort by untold thousands of contributors, scientists have populated the public structure database with unique and challenging structures solved by CryoEM. However, this approach doesn’t scale. It’s not automated, and it’s not very robust.
At Generate, we want to make CryoEM a high-throughput method that prioritizes the ability to scale. I love this technology, and here we have the four best microscopes in the world. That, in and of itself, offers an unprecedented opportunity to engage with this technology and push it in a new direction. Coupling our amazing tech with our ambitious talent, from wet lab to dry lab, informatics to engineering, operations to legal, procurement, and finance, everyone here is contributing to our mission. We are building the future of structure-informed drug development!”.
*The “protein folding problem” refers to the challenge of understanding how a protein’s amino acid sequence dictates its 3D structure. This is complex because the number of possible ways a protein can fold is astronomical. Recent advancements in computational biology, AI, and CryoEM technology have significantly improved protein structure prediction – and Generate is pioneering efforts on these fronts.
**Moore’s Law: the number of transistors on a microchip doubles approximately every two years, leading to exponential growth in computing power.