PT  - JOURNAL ARTICLE
AU  - Terashi, Genki
AU  - Wang, Xiao
AU  - Prasad, Devashish
AU  - Nakamura, Tsukasa
AU  - Zhu, Han
AU  - Kihara, Daisuke
TI  - Integrated Protocol of Protein Structure Modeling for Cryo-EM with Deep Learning and Structure Prediction
AID  - 10.1101/2023.10.19.563151
DP  - 2023 Jan 01
TA  - bioRxiv
PG  - 2023.10.19.563151
4099  - http://biorxiv.org/content/early/2023/11/21/2023.10.19.563151.short
4100  - http://biorxiv.org/content/early/2023/11/21/2023.10.19.563151.full
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.