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The structural information of these macromolecular complexes forms the foundation for understanding the molecular mechanism including many human diseases. However, the model building of large macromolecular complexes is often difficult and time-consuming. We recently developed DeepTracer-2.0, an artificial-intelligence-based pipeline that can build amino acid and nucleic acid backbones from a single cryo-EM map, and even predict the best-fitting residues according to the density of side chains. The experiments showed improved accuracy and efficiency when benchmarking the performance on independent experimental maps of protein-DNA\/RNA complexes and demonstrated the promising future of macromolecular modeling from cryo-EM maps. Our method and pipeline could benefit researchers worldwide who work in molecular biomedicine and drug discovery, and substantially increase the throughput of the cryo-EM model building. The pipeline has been integrated into the web portal https:\/\/deeptracer.uw.edu\/.<\/jats:p>","DOI":"10.1093\/bib\/bbac632","type":"journal-article","created":{"date-parts":[[2023,1,22]],"date-time":"2023-01-22T09:10:07Z","timestamp":1674378607000},"source":"Crossref","is-referenced-by-count":27,"title":["Fast and automated protein-DNA\/RNA macromolecular complex modeling from cryo-EM maps"],"prefix":"10.1093","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3253-5312","authenticated-orcid":false,"given":"Andrew","family":"Nakamura","sequence":"first","affiliation":[{"name":"Division of Computing and Software Systems, University of Washington Bothell , Bothell, WA 98011 , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanze","family":"Meng","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Duke University , Durham, NC 27708 , 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