Multimodal deep learning integration of cryo-EM and AlphaFold3 for high-accuracy protein structure determination.

ABSTRACT: Cryo-electron microscopy (cryo-EM) is a key technology for determining the structures of proteins, particularly large protein complexes. However, automatically building high-accuracy protein structures from cryo-EM density maps remains a crucial challenge. In this work, we introduce MICA, a fully automatic and multimodal deep learning approach combining cryo-EM density maps with AlphaFold3-predicted structures at both input and output levels to improve cryo-EM protein structure modeling. It first uses a multi-task encoder-decoder architecture with a feature pyramid network to predict backbone atoms, Cα atoms and amino acid types from both cryo-EM maps and AlphaFold3-predicted structures, which are used to build an initial backbone model. This model is further refined using AlphaFold3-predi

SUBMITTER: Gyawali R 

PROVIDER: S-EPMC12236472 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature