A multi-institutional team of scientists has developed an “enhance” tool for powerful microscopes, improving their resolution and accuracy. These machines are crucial to revealing insights into medicine and biology – so when they improve, we all win.
The team – including researchers from the New Mexico Consortium Los Alamos National Laboratory, Cambridge University, Baylor College of Medicine, and Berkeley Lab – demonstrated how the new computer algorithm (responsible for the enhanced effect) improves 3-D molecular quality structure maps generated with cryo-electron microscopy (cryo-EM).
Cryo-EM maps are made by applying image-processing software to many microscopy images. For decades they’ve been a vital tool for researchers studying how molecules within microbes, viruses, animals, and plants function.
Cryo-EM technology has advanced so much in recent years that it can produce structures with atomic-level resolution. However, sometimes even the most sophisticated cryo-EM methods can’t generate maps with a high enough resolution required to reveal details of complex chemical reactions. But now, this algorithm can be used to clean things up.
Paul Adams, the study co-author and Director of the Molecular Biophysics & Integrated Bioimaging Division at Berkeley Lab, said:
In biology, we gain so much by knowing a molecule’s structure. The improvements we see with this algorithm will make it easier for researchers to determine atomistic structural models from electron cryo-microscopy data. This is particularly consequential for modeling essential biological molecules, such as those involved in transcribing and translating the genetic code, which is often only seen in lower-resolution maps due to their large and complex multi-unit structures.
The algorithm works by filtering the database on knowledge of what molecules look like. It then uses the information to best estimate and removes unwanted and irrelevant data in the microscopy images.
The team began testing by applying the algorithm to a publicly available map of the human protein (apoferritin) and then compared their sharpened version to another publicly available apoferritin reference map. There was an improved correlation between the two.
Next, the researchers tested the approach on 104 molecular map datasets from the Electron Microscopy Data Bank. They found that the algorithm increased the visibility of details and improved the correlation between the experimental map and the known atomic structure for most map sets.
The algorithm is highly beneficial as it reveals essential details in data that would otherwise go unseen, and it’s easy to use. That said, it’ll probably become a standard part of the cryo-EM workflow moving forward. Adams already incorporated the algorithm’s source code into the Phenix software suite (a package for automated macromolecular structure solution) they use.
