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10.1038/s41594-025-01723-1 http://scihub22266oqcxt.onion/10.1038/s41594-025-01723-1 41361580!?!41361580 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=41361580&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Nat+Struct+Mol+Biol 2025 ; ? (?): ? Nephropedia Template TP {{tp|p=41361580|t=2025. EMProt improves structure determination from cryo-EM maps |pdf=|usr=}}{{41361580}} gab.com Text EMProt improves structure determination from cryo-EM maps JO: Nat Struct Mol Biol http://www.kidney.de/pget.php?&tw=1&pmid=41361580 #FOAvip Cryo-electron microscopy (cryo-EM) has become the mainstream technique for macromolecular structure determination. However, because of intrinsic resolution heterogeneity, accurate modeling of all-atom structure from cryo-EM maps remains challenging even for maps at near-atomic resolution. Addressing the challenge, we present EMProt, a fully automated method for accurate protein structure determination from cryo-EM maps by efficiently integrating map information and structure prediction with a three-track attention network. EMProt is extensively evaluated on a diverse test set of 177 experimental cryo-EM maps with up to 54 chains in a case at <4-A resolution, and compared to state-of-the-art methods including DeepMainmast, ModelAngelo, phenix.dock_and_rebuild and AlphaFold3. It is shown that EMProt greatly outperforms the existing methods in recovering the protein structure and building the complete structure. In addition, the built models by EMrot exhibit a high accuracy in model-to-map fit and structure validations. Twit Text FOAVip EMProt improves structure determination from cryo-EM maps ????Nat Struct Mol Biol http://www.kidney.de/pget.php?&tw=1&pmid=41361580 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=41361580 #FOAvip English Wikipedia <ref>{{Cite pmid|41361580}}</ref> EMProt improves structure determination from cryo-EM maps #MMPMID41361580 Li T; Chen J; Li H; Cao H; Huang SY Nat Struct Mol Biol 2025[Dec]; ? (?): ? PMID41361580show ga Cryo-electron microscopy (cryo-EM) has become the mainstream technique for macromolecular structure determination. However, because of intrinsic resolution heterogeneity, accurate modeling of all-atom structure from cryo-EM maps remains challenging even for maps at near-atomic resolution. Addressing the challenge, we present EMProt, a fully automated method for accurate protein structure determination from cryo-EM maps by efficiently integrating map information and structure prediction with a three-track attention network. EMProt is extensively evaluated on a diverse test set of 177 experimental cryo-EM maps with up to 54 chains in a case at <4-A resolution, and compared to state-of-the-art methods including DeepMainmast, ModelAngelo, phenix.dock_and_rebuild and AlphaFold3. It is shown that EMProt greatly outperforms the existing methods in recovering the protein structure and building the complete structure. In addition, the built models by EMrot exhibit a high accuracy in model-to-map fit and structure validations. ?EMProt improves structure determination from cryo-EM maps (epmc).pdf DeepDyve Pubget Overpricing