{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T12:20:18Z","timestamp":1784290818466,"version":"3.55.0"},"reference-count":55,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T00:00:00Z","timestamp":1620259200000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31670724"],"award-info":[{"award-number":["31670724"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072199"],"award-info":[{"award-number":["62072199"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003397","name":"Huazhong University of Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003397","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Cryo-electron microscopy (cryo-EM) has become one of important experimental methods in structure determination. However, despite the rapid growth in the number of deposited cryo-EM maps motivated by advances in microscopy instruments and image processing algorithms, building accurate structure models for cryo-EM maps remains a challenge. Protein secondary structure information, which can be extracted from EM maps, is beneficial for cryo-EM structure modeling. Here, we present a novel secondary structure annotation framework for cryo-EM maps at both intermediate and high resolutions, named EMNUSS. EMNUSS adopts a three-dimensional (3D) nested U-net architecture to assign secondary structures for EM maps. Tested on three diverse datasets including simulated maps, middle resolution experimental maps, and high-resolution experimental maps, EMNUSS demonstrated its accuracy and robustness in identifying the secondary structures for cyro-EM maps of various resolutions. The EMNUSS program is freely available at http:\/\/huanglab.phys.hust.edu.cn\/EMNUSS.<\/jats:p>","DOI":"10.1093\/bib\/bbab156","type":"journal-article","created":{"date-parts":[[2021,4,6]],"date-time":"2021-04-06T19:14:18Z","timestamp":1617736458000},"source":"Crossref","is-referenced-by-count":35,"title":["EMNUSS: a deep learning framework for secondary structure annotation in cryo-EM maps"],"prefix":"10.1093","volume":"22","author":[{"given":"Jiahua","family":"He","sequence":"first","affiliation":[{"name":"School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng-You","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. 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