RoseTTAFold All-Atom
Protein folding model that supports proteins, nucleotides, ligands, metal ions, and other small molecules.
Overview
RoseTTAFold All-Atom is a highly accurate neural network made by The Baker Lab and is substantially faster than competing models. RoseTTAFold All-Atom predicts a variety of protein complexes with high accuracy.
Run RoseTTAFold All-Atom on Neurosnap
The RoseTTAFold All-Atom online webserver allows anybody with a Neurosnap account to run and access RoseTTAFold All-Atom, 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
- Predict DNA - protein complexes, protein - protein complexes, and protein - small molecule complexes.
- Quick performance relative to competing models.
- Includes readouts and figures of pLLDT, PAE, and easy to digest structure confidence metrics.
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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| Runtime 90th Percentile | loading... |
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API Request
Access RoseTTAFold All-Atom 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 RoseTTAFold All-Atom in publications or research outputs.
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Krishna, R., et al. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. 07 March 2024, https://www.science.org, https://www.science.org/doi/10.1126/science.adl2528 |
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Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |
Video Tutorial
The following youtube video describes how to use RoseTTAFold All-Atom using Neurosnap's online webserver. If you have any questions or want to suggest improvements for future tutorials please contact us here.