Loading…
Cryo-EM and cryo-ET map enhancement model that sharpens density maps with a Mamba-based dual-branch UNet and local resolution-guided learning.
No providers recorded yet. Browse all providers
EMReady2 is a deep-learning tool for improving the quality and interpretability of cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET) density maps. Developed by the Huang laboratory at Huazhong University of Science and Technology and released as a bioRxiv preprint in September 2025 (updated February 2026), it is the successor to the original EMReady map-enhancement method. The model post-processes a reconstructed map to sharpen features, suppress noise, and make density easier to interpret and to build atomic models into.
The core methodological advance is the use of a Mamba-based architecture — a state space model that scales more efficiently than attention for long sequences — embedded in a dual-branch UNet. This design lets EMReady2 jointly capture local detail and global context across a map. A local resolution-guided learning strategy explicitly accounts for the fact that cryo-EM maps are heterogeneous, with resolution varying substantially from region to region within a single reconstruction.
EMReady2 sits in the post-processing and map-improvement niche of the cryo-EM ecosystem, complementing reconstruction and model-building pipelines rather than replacing them. The software is openly available on GitHub under the MIT license, with pre-trained weights distributed via the lab website.
EMReady2 is built on a Mamba-based dual-branch UNet that processes density maps via overlapping boxes (sliding-window inference) with configurable stride and Gaussian patch blending for aggregation. A local resolution-guided training strategy adapts enhancement to per-region map quality. The method was evaluated on 136 diverse maps spanning roughly 2.0-10.0 Å resolution, with improvements reported in both map quality and downstream interpretability, including for nucleic-acid-containing samples and cryo-ET data. The implementation runs on Linux with CUDA ≥ 11.8 and Python 3.10; two pre-trained checkpoints (for ~0.6 Å and ~1.0 Å voxel sizes) are downloaded separately and selected automatically based on input voxel size.
Structural biologists use EMReady2 to improve cryo-EM and cryo-ET maps prior to atomic model building, refinement, and validation, making weak or heterogeneous density easier to interpret. By raising the effective interpretability of intermediate-resolution maps, it can accelerate model building for challenging targets, including assemblies and complexes containing nucleic acids, and extend useful analysis to tomography data.
EMReady2 brings state-space (Mamba) modeling to cryo-EM map enhancement, a domain previously dominated by convolutional and attention-based networks, and pairs it with resolution-aware training to address map heterogeneity directly. As an open-source, MIT-licensed tool with released weights, it is readily adoptable within existing cryo-EM workflows. Reported performance reflects benchmark evaluation on 136 maps; broader community validation across diverse experimental datasets will further establish where its enhancements are most reliable.
Much of this page is generated or calculated automatically. Flag anything that looks off and we will re-run it.