Difference between revisions of "2023Terashi DeepMainMast"
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== Citation == | == Citation == | ||
| − | + | Terashi, Genki / Wang, Xiao / Prasad, Devashish / Nakamura, Tsukasa / Kihara, Daisuke. DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction. 2023. Nature Methods, p. 1-10 | |
== Abstract == | == Abstract == | ||
| − | + | 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 | |
| − | the | + | 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. | |
== Keywords == | == Keywords == | ||
Latest revision as of 08:12, 10 January 2024
Citation
Terashi, Genki / Wang, Xiao / Prasad, Devashish / Nakamura, Tsukasa / Kihara, Daisuke. DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction. 2023. Nature Methods, p. 1-10
Abstract
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.
Keywords
Links
https://www.nature.com/articles/s41592-023-02099-0
