Use DeepEMhancer

Official Neurosnap webserver for accessing DeepEMhancer online.

Structure Prediction & Folding Cryo-EM Nucleotides Proteins

Overview

Cryo-EM maps often require post-processing to improve interpretability due to loss of contrast at high frequencies. DeepEMhancer is a deep learning tool designed to automatically post-process these maps. Trained on pairs of experimental maps and maps sharpened with their corresponding atomic models, DeepEMhancer has learned to perform both masking-like and sharpening-like operations in a single step, reducing noise and revealing more detailed features in the experimental maps without requiring an atomic model.

Neurosnap Overview

The DeepEMhancer online webserver allows anybody with a Neurosnap account to run and access DeepEMhancer, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong terms of service and privacy policy.

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Features

  • Performs automatic post-processing of cryo-EM maps using a deep learning approach.
  • Combines masking-like and sharpening-like operations in a single step.
  • Reduces noise levels and enhances map details for improved interpretability.
  • Trained on experimental maps and atomic model-sharpened targets to mimic high-quality results.
  • Does not require an atomic model for post-processing.
  • Improves map similarity to the final atomic model, measured by FSC.

Statistics

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API Request

Access DeepEMhancer using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.

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Job Note

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Configuration & Options

Service Inputs

Allowed Types: map or mrc
Upload the cryo-EM map to be post-processed (.map or .mrc format). Raw maps are recommended as input, as they typically have limitations (low contrast, noise, heterogeneous resolution) that DeepEMhancer is specifically designed to address.

Select the appropriate DeepEMhancer model. The tightTarget model is the default and recommended choice for producing a clean map by removing noise from non-protein elements. If this is too aggressive, the wideTarget model offers a more permissive alternative. For high-resolution maps (better than 4 Ã…), use the highRes model to enhance fine details.

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