RT Journal Article
SR Electronic
T1 Integrated Protocol of Protein Structure Modeling for Cryo-EM with Deep Learning and Structure Prediction
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2023.10.19.563151
DO 10.1101/2023.10.19.563151
A1 Terashi, Genki
A1 Wang, Xiao
A1 Prasad, Devashish
A1 Nakamura, Tsukasa
A1 Zhu, Han
A1 Kihara, Daisuke
YR 2023
UL http://biorxiv.org/content/early/2023/11/21/2023.10.19.563151.abstract
AB Structure modeling from maps is an indispensable step for studying proteins and their complexes with cryogenic electron microscopy (cryo-EM). Although the resolution of determined cryo-EM 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 have developed a protein structure modeling method, called DeepMainmast, which employs deep learning to capture the local map features of amino acids and atoms to assist main-chain tracing. Moreover, since Alphafold2 demonstrates high accuracy in protein structure prediction, we have integrated complementary strengths of de novo density tracing using deep learning with Alphafold2’s structure modeling to achieve even higher accuracy than each method alone. Additionally, the protocol is able to accurately assign chain identity to the structure models of homo-multimers.Competing Interest StatementThe authors have declared no competing interest.