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10.1038/s42003-021-02399-1 http://scihub22266oqcxt.onion/10.1038/s42003-021-02399-1 34267316!8282847!34267316     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Commun+Biol 2021 ; 4 (1): 874 Nephropedia Template TP {{tp|p=34267316|t=2021. DeepEMhancer: a deep learning solution for cryo-EM volume post-processing |pdf=|usr=}}{{34267316}} gab.com Text DeepEMhancer: a deep learning solution for cryo-EM volume post-processing JO: Commun Biol http://www.kidney.de/pget.php?&tw=1&pmid=34267316 #FOAvip Cryo-EM maps are valuable sources of information for protein structure modeling. However, due to the loss of contrast at high frequencies, they generally need to be post-processed to improve their interpretability. Most popular approaches, based on global B-factor correction, suffer from limitations. For instance, they ignore the heterogeneity in the map local quality that reconstructions tend to exhibit. Aiming to overcome these problems, we present DeepEMhancer, a deep learning approach designed to perform automatic post-processing of cryo-EM maps. Trained on a dataset of pairs of experimental maps and maps sharpened using their respective atomic models, DeepEMhancer has learned how to post-process experimental maps performing masking-like and sharpening-like operations in a single step. DeepEMhancer was evaluated on a testing set of 20 different experimental maps, showing its ability to reduce noise levels and obtain more detailed versions of the experimental maps. Additionally, we illustrated the benefits of DeepEMhancer on the structure of the SARS-CoV-2 RNA polymerase. Twit Text FOAVip DeepEMhancer: a deep learning solution for cryo-EM volume post-processing ????Commun Biol http://www.kidney.de/pget.php?&tw=1&pmid=34267316 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34267316 #FOAvip English Wikipedia <ref>{{Cite pmid|34267316}}</ref> DeepEMhancer: a deep learning solution for cryo-EM volume post-processing #MMPMID34267316 Sanchez-Garcia R; Gomez-Blanco J; Cuervo A; Carazo JM; Sorzano COS; Vargas J Commun Biol 2021[Jul]; 4 (1): 874 PMID34267316show ga Cryo-EM maps are valuable sources of information for protein structure modeling. However, due to the loss of contrast at high frequencies, they generally need to be post-processed to improve their interpretability. Most popular approaches, based on global B-factor correction, suffer from limitations. For instance, they ignore the heterogeneity in the map local quality that reconstructions tend to exhibit. Aiming to overcome these problems, we present DeepEMhancer, a deep learning approach designed to perform automatic post-processing of cryo-EM maps. Trained on a dataset of pairs of experimental maps and maps sharpened using their respective atomic models, DeepEMhancer has learned how to post-process experimental maps performing masking-like and sharpening-like operations in a single step. DeepEMhancer was evaluated on a testing set of 20 different experimental maps, showing its ability to reduce noise levels and obtain more detailed versions of the experimental maps. Additionally, we illustrated the benefits of DeepEMhancer on the structure of the SARS-CoV-2 RNA polymerase. |*Deep Learning[MESH] |Cryoelectron Microscopy/*instrumentation[MESH] |DNA-Directed RNA Polymerases/*ultrastructure[MESH] |SARS-CoV-2/*ultrastructure[MESH]DeepEMhancer: a deep learning solution for cryo-EM volume post-process(epmc).pdf DeepDyve Pubget Overpricing