CryoAtom Cryo-EM Model Builder
CryoAtom builds atomic models from cryo-EM maps using local attention and 3D rotary position embedding, improving model completeness and speed while lowering resolution requirements.
Overview
CryoAtom is an approach for de novo model building from cryogenic electron microscopy (cryo-EM) density maps. It leverages advancements from AlphaFold2, replacing global attention with a local attention mechanism enhanced by a novel 3D rotary position embedding. This allows CryoAtom to produce more complete atomic models, lower the required map resolution, and significantly accelerate the modeling process compared to existing methods.
Run CryoAtom Cryo-EM Model Builder on Neurosnap
The CryoAtom Cryo-EM Model Builder online webserver allows anybody with a Neurosnap account to run and access CryoAtom Cryo-EM Model Builder, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong Terms of Use and Privacy Policy.
Features
- Leverages a local attention mechanism and 3D rotary position embedding for improved accuracy.
- Produces more complete models and reduces the resolution requirement for cryo-EM maps.
- Accelerates the modeling process, enabling the construction of large, 100+ protein complexes in hours.
- Accurately distinguishes between paralog sequences, even in noisy map regions.
- Detects and models previously uncharacterized proteins and structural extensions.
- Captures minor conformational changes within mega-Dalton complexes.
Statistics
Neurosnap periodically calculates runtime statistics based on job execution data. These estimates provide a general guideline for how long your job may take, but actual runtimes can vary significantly depending on factors like input size or settings used.
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API Request
Access CryoAtom Cryo-EM Model Builder 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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Citations
Please cite the original work when using CryoAtom Cryo-EM Model Builder in publications or research outputs.
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Su B, Huang K, Peng Z, Amunts A, Yang J. Improved model building for cryo-EM maps using local attention and 3D rotary position embedding. bioRxiv. 2024. doi: 10.1101/2024.11.13.623164. |
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Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |