> For the complete documentation index, see [llms.txt](https://rocket-9.gitbook.io/rocket-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://rocket-9.gitbook.io/rocket-docs/rocket-user-guide.md).

# ROCKET User Guide

ROCKET refines protein structure predictions against **X-ray** and **cryo-EM/ET** data.

It combines OpenFold prediction, Phenix preprocessing, and gradient-based maximum-likelihood refinement.

{% hint style="success" %}
New to ROCKET? Install first. Then follow the setup tutorial for your data type.
{% endhint %}

Read our [paper in *Nature Methods*](https://www.nature.com/articles/s41592-026-03047-4).

{% embed url="<https://github.com/alisiafadini/ROCKET>" %}

### Start here

{% hint style="info" %}
Keeping it simple: install once, preprocess datasets, then iterate in `rk.refine`.
{% endhint %}

<table data-view="cards"><thead><tr><th>Title</th><th data-card-target data-type="content-ref">Target</th></tr></thead><tbody><tr><td>Install ROCKET</td><td><a href="/spaces/k7qcAL69XMMAPrtIDoGr/pages/dogmOsjGr1VNjyBsK51m">/spaces/k7qcAL69XMMAPrtIDoGr/pages/dogmOsjGr1VNjyBsK51m</a></td></tr><tr><td>Set up with X-ray data</td><td><a href="/spaces/k7qcAL69XMMAPrtIDoGr/pages/VRekUTJg5zIwTJeTULS5">/spaces/k7qcAL69XMMAPrtIDoGr/pages/VRekUTJg5zIwTJeTULS5</a></td></tr><tr><td>Set up with cryo-EM data</td><td><a href="/spaces/k7qcAL69XMMAPrtIDoGr/pages/KPRh3sQ8v8vPRaUHAaDq">/spaces/k7qcAL69XMMAPrtIDoGr/pages/KPRh3sQ8v8vPRaUHAaDq</a></td></tr><tr><td>CLI reference</td><td><a href="/spaces/k7qcAL69XMMAPrtIDoGr/pages/Ii0ZO8KBv1fKfhM9pK9y">/spaces/k7qcAL69XMMAPrtIDoGr/pages/Ii0ZO8KBv1fKfhM9pK9y</a></td></tr></tbody></table>

### Example workflows

<table data-view="cards"><thead><tr><th>Title</th><th data-card-target data-type="content-ref">Target</th></tr></thead><tbody><tr><td>MSA subsampling with data-based scoring</td><td><a href="/spaces/k7qcAL69XMMAPrtIDoGr/pages/YPvodjGiln3gaWx0Swyt">/spaces/k7qcAL69XMMAPrtIDoGr/pages/YPvodjGiln3gaWx0Swyt</a></td></tr><tr><td>Low-resolution cryo-EM refinement</td><td><a href="/spaces/k7qcAL69XMMAPrtIDoGr/pages/Am7XlfyWF4JG6rMXA20h">/spaces/k7qcAL69XMMAPrtIDoGr/pages/Am7XlfyWF4JG6rMXA20h</a></td></tr></tbody></table>

## Citing

```bibtex
@article{fadini2026alphafold,
  title={AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network},
  author={Fadini, Alisia and Li, Minhuan and McCoy, Airlie J. and Banjara, Suresh and Okumura, Hiroki and Napier, Eve and Fontana, Pietro and Khan, Amir R. and Jovine, Luca and Terwilliger, Thomas C. and Read, Randy J. and Hekstra, Doeke R. and AlQuraishi, Mohammed},
  journal={Nature Methods},
  volume={23},
  number={4},
  pages={785--795},
  year={2026},
  doi={10.1038/s41592-026-03047-4}
}
```


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