PT - JOURNAL ARTICLE AU - Wang, Xiao AU - Zhu, Han AU - Terashi, Genki AU - Taluja, Manav AU - Kihara, Daisuke TI - DiffModeler: Large Macromolecular Structure Modeling in Low-Resolution Cryo-EM Maps Using Diffusion Model AID - 10.1101/2024.01.20.576370 DP - 2024 Jan 01 TA - bioRxiv PG - 2024.01.20.576370 4099 - http://biorxiv.org/content/early/2024/01/23/2024.01.20.576370.short 4100 - http://biorxiv.org/content/early/2024/01/23/2024.01.20.576370.full AB - Cryogenic electron microscopy (cryo-EM) has now been widely used for determining multi-chain protein complexes. However, modeling a complex structure is challenging particularly when the map resolution is low, typically in the intermediate resolution range of 5 to 10 Å. Within this resolution range, even accurate structure fitting is difficult, let alone de novo modeling. To address this challenge, here we present DiffModeler, a fully automated method for modeling protein complex structures. DiffModeler employs a diffusion model for backbone tracing and integrates AlphaFold2-predicted single-chain structures for structure fitting. Extensive testing on cryo-EM maps at intermediate resolutions demonstrates the exceptional accuracy of DiffModeler in structure modeling, achieving an average TM-Score of 0.92, surpassing existing methodologies significantly. Notably, DiffModeler successfully modeled a protein complex composed of 47 chains and 13,462 residues, achieving a high TM-Score of 0.94. Further benchmarking at low resolutions (10-20 Å) confirms its versatility, demonstrating plausible performances. Moreover, when coupled with CryoREAD, DiffModeler excels in constructing protein-DNA/RNA complex structures for near-atomic resolution maps (0-5 Å), showcasing state-of-the-art performance with average TM-Scores of 0.88 and 0.91 across two datasets.Competing Interest StatementThe authors have declared no competing interest.