DeepEMhancer: a deep learning solution for cryo-EM volume post-processing
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Abstract
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.
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The record
- Venue
- Communications Biology
- Topic
- Advanced Electron Microscopy Techniques and Applications
- Field
- Biochemistry, Genetics and Molecular Biology
- Canadian institutions
- McGill University
- Funders
- Ministerio de Ciencia e InnovaciónMinisterio de Educación, Cultura y DeporteEuropean CommissionMinisterio de Economía y CompetitividadAgencia Estatal de InvestigaciónEOSC-LifeComunidad de Madrid
- Keywords
- Volume (thermodynamics)Computer scienceArtificial intelligencePhysicsThermodynamics
- Has abstract in OpenAlex
- yes