Haruspex: A Neural Network for the Automatic Identification of Oligonucleotides and Protein Secondary Structure in Cryo-Electron Microscopy Maps.
Angew Chem Int Ed Engl
; 59(35): 14788-14795, 2020 08 24.
Article
en En
| MEDLINE
| ID: mdl-32187813
In recent years, three-dimensional density maps reconstructed from single particle images obtained by electron cryo-microscopy (cryo-EM) have reached unprecedented resolution. However, map interpretation can be challenging, in particular if the constituting structures require de-novo model building or are very mobile. Herein, we demonstrate the potential of convolutional neural networks for the annotation of cryo-EM maps: our network Haruspex has been trained on a carefully curated set of 293â
experimentally derived reconstruction maps to automatically annotate RNA/DNA as well as protein secondary structure elements. It can be straightforwardly applied to newly reconstructed maps in order to support domain placement or as a starting point for main-chain placement. Due to its high recall and precision rates of 95.1 % and 80.3 %, respectively, on an independent test set of 122â
maps, it can also be used for validation during model building. The trained network will be available as part of the CCP-EM suite.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Oligonucleótidos
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ADN
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ARN
/
Redes Neurales de la Computación
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Microscopía por Crioelectrón
Tipo de estudio:
Diagnostic_studies
Límite:
Humans
Idioma:
En
Revista:
Angew Chem Int Ed Engl
Año:
2020
Tipo del documento:
Article
País de afiliación:
Alemania