DockQ
Assess the quality of protein-protein docking models using the native and predicted structure.
Overview
Assess the quality of protein-protein docking models using the native and predicted structure through a combination of Fnat, LRMS, and iRMS.
Run DockQ on Neurosnap
The DockQ online webserver allows anybody with a Neurosnap account to run and access DockQ, 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
- Quantitavely assess predicted protein-protein docking models.
- Combine state-of-the-art metrics to validate docked models in Fnat, LRMS, and iRMS
- Only requires the predicted model and the native structure.
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 DockQ 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 DockQ in publications or research outputs.
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Wallner, Björn et al. "DockQ: A Quality Measure for Protein-Protein Docking Models", https://research.google/, 25 August 2016, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4999177/. |
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Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |