ROCKET User Guide

Documentation, installation instructions, and tutorials for ROCKET

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.

New to ROCKET? Install first. Then follow the setup tutorial for your data type.

More details are in our preprint.

GitHub - rs-station/ROCKET: AI-powered macromolecular refinementGitHub

Start here

Keeping it simple: install once, preprocess datasets, then iterate in rk.refine.

Title

Install ROCKET

Target

Installing ROCKET

Title

Set up with X-ray data

Target

Launch with Your Own X-ray Data

Title

Set up with cryo-EM data

Target

Launch with Your Own Cryo-EM Data

Title

CLI reference

Target

API

Example workflows

Title

MSA subsampling with data-based scoring

Target

Latent State of a Human Serpin with MSA Subsampling

Title

Low-resolution cryo-EM refinement

Target

Low Resolution GroEL Sub-Tomogram Average

Citing

@article{fadini2025alphafold,
  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 Terwilliger, Thomas C and Read, Randy J and Hekstra, Doeke and AlQuraishi, Mohammed},
  journal={bioRxiv},
  year={2025}
}