Improved automated model building for cryo-EM maps using CryFold

Constructing atomic models from cryogenic electron microscopy (cryo-EM) density maps is essential for interpreting molecular mechanisms. In this study, we present CryFold, an approach for de novo model building in cryo-EM that leverages recent advancements in AlphaFold2 1 to improve the state-of-the-art method ModelAngelo 2 . To incorporate the cryo-EM map information, CryFold replaces the global attention mechanism in AlphaFold2 to local attention, further enhanced by a novel 3D rotary position embedding. It produces more complete models, accelerates the modeling, and reduces the resolution requirement. Applying CryFold to new maps results in accurate differentiation between paralog sequences in noisy regions, detection of previously uncharacterized proteins with unknown functions, precise compartmentalisation of the map for isolation of non-protein components, and improved modeling of conformational changes. In a particular case, a 104-protein complex was modeled within only 5.6 hours, and a minor conformational change of a single protein domain was detected at the periphery when models from two different maps were compared. CryFold stands as an accurate method currently available for model building of proteins in cryo-EM structure determination. CryFold is open-source software available at https://github.com/SBQ-1999/CryFold .