Researchers at Arizona State University have developed a way to improve upon cryogenic electron microscopy (cryo-EM), that they report is particularly useful for ferreting out subtleties of protein structure, which are often missed through conventional modeling strategies.
In a paper published in Matter recently, the team describes CryoFold, a method for producing more accurate structures that uses a the maximum entropy statistical approach. This method, they say, is ideally suited to the refinement of cryo-EM data, producing an unbiased structural model of a biological sample.
“Complex biomolecules actually exist in an ensemble of states, and you can take a kind of snapshot of these molecules in different conformations,” says Abhishek Singharoy, corresponding author of the new study. Some of these conformations may persist in time, but others are extremely ephemeral, coming and going on time scales measured in billionths of a second.
CryoFold allows researchers to model these transitory structures, which can play a vital role in biological processes but are often missed using traditional cryo-EM techniques.
The new study presents six examples of elaborately folded proteins of various sizes, including large membrane and multi-domain systems. The results emphasize the ability of CryoFold to discover molecular ensembles, including rare low-probability structures that have been experimentally validated and recognized as functionally relevant.
The maximum entropy technique can be used in conjunction with existing methods of data fitting in an iterative process capable of turning low resolution data into high resolution, 3D structures with a high degree of confidence. Such advances are helping cryo-EM reach its full potential by characterizing the entire conformational landscape of proteins and other important biomolecules.
“This work integrates multiple physics-based approaches to refine protein structures from cryo-EM data, providing not a single, static image of the protein, but rather a collection of structures, which is more representative of the true, dynamic nature of proteins,” said Chitrak Gupta, co-author.