DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction

Three-dimensional structure modeling from maps is an indispensable step for studying proteins and their complexes with cryogenic electron microscopy. Although the resolution of determined cryogenic electron microscopy maps has generally improved, there are still many cases where tracing protein main chains is difficult, even in maps determined at a near-atomic resolution. Here we developed a protein structure modeling method, DeepMainmast, which employs deep learning to capture the local map features of amino acids and atoms to assist main-chain tracing. Moreover, we integrated AlphaFold2 with the de novo density tracing protocol to combine their complementary strengths and achieved even higher accuracy than each method alone. Additionally, the protocol is able to accurately assign the chain identity to the structure models of homo-multimers, which is not a trivial task for existing methods. DeepMainmast is a protein structure modeling protocol for cryo-EM that combines the strengths of a deep-learning-based de novo protein main-chain-tracing approach with AlphaFold2-based structure predictions for improved performance.

Automated summary

This study developed a protein structure modeling protocol, DeepMainmast, which combines deep learning and AlphaFold2 to assist main-chain tracing in high-resolution maps, resulting in improved accuracy.