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- W3186339118 abstract "1 Abstract The cryo-EM resolution revolution enables the development of algorithms for direct de-novo modelling of protein structures from given cryo-EM density maps. Deep Learning tools have been applied to locate structure patterns, such as rotamers, secondary structures and C α atoms. We present a deep neural network (nicknamed SegmA) for the residue type segmentation of a cryo-EM density map. The network labels voxels in a cryo-EM map by the residue type (amino acid type or nucleic acid) of the sampled macromolecular structure. It also provides a visual representation of the density map by coloring the different types of voxels by their assgned colors. SegmA’s algorithm combines a rotation equivariant group convolutional network with a traditional U-net network in a cascade. In addition SegmA estimates the labeling accuracy and reports only labels assigned with high confidence. At resolution of 3 Å SegmAs accuracy is 80% for nucleotides. Amino acids which can be seen by eye, such as LEU, ARG and PHE, are detected by Segma with about 70% accuracy. A web server of the application is under development at https://dev.dcsh7cbr3o89e.amplifyapp.com . The SegmA open code is available at https://github.com/Mark-Rozanov/SegmA_3A/tree/master" @default.
- W3186339118 created "2021-08-02" @default.
- W3186339118 creator A5030975599 @default.
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- W3186339118 date "2021-07-26" @default.
- W3186339118 modified "2023-09-27" @default.
- W3186339118 title "SegmA: Residue Segmentation of cryo-EM density maps" @default.
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- W3186339118 doi "https://doi.org/10.1101/2021.07.25.453685" @default.
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