SuperMetal
Predict zinc-binding sites from an uploaded protein structure.
Overview
Predict zinc-ion binding locations in protein structures with SuperMetal's diffusion and confidence models.
Run SuperMetal on Neurosnap
The SuperMetal online webserver allows anybody with a Neurosnap account to run and access SuperMetal, 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
- Predicts zinc-ion binding locations directly from protein structure without requiring the expected number of sites.
- Uses an SE(3)-equivariant score-based diffusion model to generate candidate metal coordinates.
- Applies an optional learned confidence model and spatial clustering to produce final zinc-site predictions.
- Returns visualization-ready protein-plus-zinc and zinc-only PDB files with human-readable CSV summaries.
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.
| Statistic | Value |
|---|---|
| Credit Usage Rate | loading... |
| Estimated Total Cost | loading... |
| Runtime Mean | loading... |
| Runtime Median | loading... |
| Runtime Standard Deviation | loading... |
| Runtime 90th Percentile | loading... |
| Runtime Longest | loading... |
API Request
Access SuperMetal 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.
Similar Tools
Explore tools with similar features, categories, and use cases.
Citations
Please cite the original work when using SuperMetal in publications or research outputs.
|
Lin X, Su Z, Liu Y, Liu J, Kuang X, Cummings PT, Spencer-Smith J, Meiler J. SuperMetal: a generative AI framework for rapid and precise metal ion location prediction in proteins. Journal of Cheminformatics. 2025;17:107. https://doi.org/10.1186/s13321-025-01038-9 |
|
Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |