Very grateful for the work, support, and guidance of all authors: Airlie, Tom, @randyjread.bsky.social, @hekstralab.bsky.social, and @moalquraishi.bsky.social. It’s a privilege to work with such a great team. 14/14

ROCKET subsamples MSAs (inspired by www.nature.com/articles/s41...) to generate diverse starting models, selects the best-fit conformation, then refines further with gradient descent. Note: we cannot use pLDDT alone to succeed! 9/14

Gradient-based refinement struggles when AF2’s initial model is too far from experiment. E.g. AF2 predicts the serpin PAI-1 in a metastable active state, but experimental data shows it in a hyperstable “latent” state with a 40 Å loop shift. 8/14

The low-res challenge is key for emerging cryo-ET data.
🔹 ROCKET extracts two conformations from a 9.6Å GroEL map. Its modeling matches humans here and even surpasses them in tough regions, boosting fit to data from CC=0.2 to 0.5 in a flexible domain (see ⭐) 7/14

Model building below 3–4 Å is tough – even for experts.
🔹 ROCKET refines low-res (3.82 Å) HAI-1 X-ray data, improving backbone accuracy beyond AF2. It smartly preserves ambiguous regions & corrects a possibly misregistered helix (310–330), without adding geometric artifacts 6/14

✔️ Peptide flips (e.g. PTP-1B)
✔️ Domain shifts (e.g. GroEL)

5/14

ROCKET samples barrier-crossing conformations that standard refinement methods often fail to reach:
✔️ Ligand-induced loop rearrangements (e.g. c-Abl kinase and PTP1B)

4/14

ROCKET integrates experimental likelihood targets within OpenFold’s differentiable prediction pipeline to optimize MSA profile features. Structure refinement becomes a search in evolutionary space instead of Cartesian space. What does this unlock? 3/14

AF-based methods encode rich structural priors but lack a general mechanism for integrating arbitrary data modalities. ROCKET tackles this by optimizing latent representations to fit experimental data at inference time, without retraining! 2/14

Structural biology is in an era of dynamics & assemblies but turning raw experimental data into atomic models at scale remains challenging. @minhuanli.bsky.social and I present ROCKET🚀: an AlphaFold augmentation that integrates crystallographic and cryoEM/ET data with room for more! 1/14.