A primer on ML in cryo-electron microscopy (cryo-EM)
Note: thank you to Jason Kaelber, a professor at Rutgers University and director of their cryo-EM facility, for commenting on drafts of this essay! Also thank you to Surge Biswas, the founder of Nabla Bio, for answering questions I had over cryo-EM.
Cryo-electron microscopy (cryo-EM) has been gaining increasing popularity over the past few years. Used as a way to perform macromolecular structure determination for decades, cryo-EM really hit its stride around 2010, when it crossed the resolution thresholds needed to determine protein structures. The technique was so deeply powerful, so able to answer biological questions for which no alternative tool existed, that its creators were awarded the 2017 Nobel Prize in chemistry.
I first came across cryo-EM as a concept via Ellen Zhong (a machine learning professor at Princeton) in 2022. Because she co-wrote what has become one of my favorite papers of all time, I was also interested in what else she had worked on. But very much unlike my favorite paper, which had to do with viral language models, almost all of her work had to do with applying ML to cryo-EM.
This was weird! Cryo-EM wasn’t something I ever saw much. While, admittedly, I was entirely ignorant of the field until 2022, it still felt like it wasn’t a very popular topic. Most people seem to work in small molecule behavior prediction or antibody modeling or something you’d see dozens of papers about at a NeurIPS workshop.
